Highlights

Given predictions of increased storm severity with global warming, the number of windthrows is also predicted to increase. This increase is likely to make wind disturbance an important mode of tree mortality, especially if large trees become more vulnerable to snapping and uprooting due to other emerging disturbances.

Windthrows, the uprooting or breaking of trees by winds, in the Amazon are produced by downdrafts associated with strong convective storms. Windthrows are a major natural disturbance that can influence forest structure, carbon balance, and species composition. A team of researchers documented an approximately fourfold increase in large (≥30 hectares) windthrows during the analyzed period from 1985 to 2020. Overall, researchers detected no changes in the size distribution or severity of windthrows over the past 35 years, except for an increase in very large events (>500 ha) since 1990. Thus, the number of events has increased.

Convective storms with strong downdrafts create windthrows that locally alter forest structure, composition, and carbon balance. Using Landsat imagery from consecutive years, a team of researchers documented the temporal and spatial variation of large windthrows (≥30 ha) across the Amazon basin between 1985 and 2020. Over the 33-year period, the team identified 3,179 large windthrows. Windthrow density was highest in central and western Amazon regions, with about 33% of all events occurring within approximately 3% of the monitored area. In these “hotspot” regions, return intervals for large windthrows at the same location span centuries to millennia, while in the rest of the Amazon, return intervals exceed 10,000 years.

Data show a nearly fourfold increase in the number of windthrows and affected area, from 78 events (6,900 ha) in 1985 to 264 events (32,170 ha) in 2020, with an increase in events larger than 500 ha after 1990. These extremely large windthrows (ranging from >500 ha to 2,543 ha) drive interannual variation in median (84 ± 5.2 ha; ±95% CI) and mean (147 ± 13 ha) windthrow area. However, the team found no significant temporal trends in the size distribution of windthrows over time.

Models of the global carbon cycle and climate system are needed to understand how emissions of carbon dioxide will affect climate. Benchmarks of the global carbon cycle such as from radiocarbon allow a constraint on the rates of carbon moving between the atmosphere and biosphere. Because observations suggest plant growth rates are too low in current models, these results will provide better constraints on global models and lead to more accurate climate projections.

Atmospheric bomb testing in the 1960s roughly doubled the amount of radiocarbon in the atmosphere. Agreements to limit testing led to an abrupt stopping of bomb testing, and as a result, the subsequent decline of radiocarbon in the atmosphere provided a global constraint on plant growth, which removed radiocarbon from the atmosphere. A team of researchers compared observations and models of the carbon cycle to show model estimates of plant growth are too low, and carbon plants must be cycling more quickly than current models estimate.

A team of researchers compared observed and modeled radiocarbon accumulation in the global biosphere to understand how observations during the immediate period after widespread aboveground bomb testing could constrain growth rates of plants and associated uptake of radiocarbon in plant tissues. This comparison included models with a dedicated representation of radiocarbon, as well as building emulators for models without explicit treatment of radiocarbon.

Modeled radiocarbon uptake rates were much lower than observations, suggesting a strong bias in modeled carbon cycling. Because models agree well with total biomass observations, this too-low growth also implies a too-low rate of turnover of plant tissues, possibly because plants allocate more carbon to short-lived tissues or fast-growing trees tend to die more quickly than slow-growing trees.

Tropical forests have significant impacts on global water and carbon cycles. Adapting to global climate change will require an understanding of the change to these crucial resources. Researchers show increasing temperature and drying may increase tropical forest mortality due to increased plant water stress. Increased atmospheric carbon levels were thought to potentially mitigate this loss, but the team’s model shows it does not at these levels of warming. These increasing mortality levels could significantly reduce tropical carbon storage. Global climate change will increase the annual productivity of tropical forests but remove more water from them.

A team of researchers found increasing plant water stress from warming temperatures will lead to a future risk of increased tropical forest mortality under climate change. Rising atmospheric carbon levels do not mitigate mortality risk but increase plant productivity. Plant traits are crucial to determining this mortality risk.

A team of researchers used a dynamic vegetation model with plant hydrodynamics (FATES-Hydro) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island in Panama. The team calibrated the model by selecting plant trait assemblages that performed well against Next-Generation Ecosystem Experiments Tropics observations of plant hydrodynamics.

These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086–2100: SSP2-45, SSP5-85) and two carbon dioxide (CO₂) levels (contemporary, anticipated). Simulations show an increase of 5.7% to 10.1–11.3% under future climate scenarios due to increasingly negative leaf water potentials. Gross primary productivity increased 27 to 53% under future climate but decreased (–21% to 8.6%) without rising CO₂. Minimum annual leaf water potential (a measure of plant stress) under contemporary simulations decreased under both future scenarios with anticipated CO₂ and under contemporary CO₂ scenarios (indicating greater plant stress). Trait assemblage members that experienced hydraulic failure had substantially more negative minimum season leaf water potential (–1.376  MPa) than those who did not (–0.815  MPa). Simulation analysis shows plant traits played a more significant role in the risk of hydraulic failure (98%) than climate scenarios or models.

Results suggest higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

While improving model resolution is necessary, it alone is not sufficient to enhance the accuracy of rainfall modeling in the Amazon. Additionally, there is an urgent need for measurements to better understand extreme rainfall events.

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project) represent extreme rainfall events at annual, seasonal, and subdaily time scales. Tropical Rainfall Measuring Mission 3B42 3-hour data were used as observations.

Eleven out of 17 HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the subdaily timing of extreme rainfall, though some reproduced daily totals.

Results suggest resolution is necessary but not sufficient to improve extreme precipitation. This finding is particularly true in cases where convective precipitation is the main contributor to extreme precipitation. Convective precipitation in HighResMIP is parameterized, and these parameterizations are tuned to ensure energy is balanced. However, the parameterizations are not designed to capture precipitation extremes. Measurements and understanding of extreme rainfall events in tropical forests is a research area that deserves further attention.

This research provides valuable insights into the factors controlling R_dark, a critical component of plant metabolism and ecosystem carbon balance. The strong performance of leaf spectroscopy in predicting R_dark offers a rapid and efficient alternative to traditional, time-consuming measurements. This could significantly improve monitoring and modeling plant respiration at larger scales, leading to more accurate predictions of carbon fluxes in terrestrial ecosystems.

Leaf dark respiration (R_dark) is a key process in global carbon cycles. This study measured R_dark, leaf reflectance, and other leaf traits across three forest types in China. Spectroscopy out performed leaf trait–R_dark relationships as a tool for predicting R_dark.

This study investigated how R_dark normalized to 25°C (R_dark25) relates to leaf traits and spectral reflectance across three diverse forest types in China. The research was conducted at a temperate forest in Changbai Mountain, a subtropical forest in Gutian Mountain, and a tropical forest in Xishuangbanna. Researchers found that while R_dark25 differed across the three forest types, the ratio of R_dark25 to maximum carboxylation capacity normalized to 25°C (V_cmax25) also varied significantly. This finding suggests the commonly used constant ratio in some terrestrial biosphere models might not accurately reflect real-world variation and may lead to underestimations of R_dark.

When analyzing the relationship between R_dark25 and leaf traits, the study found magnesium and calcium concentrations were most strongly linked to R_dark25 variation. However, all univariate trait–R_dark25 relationships were weak (r² ≤ 0.13) and forest-type specific, suggesting no single universal scaling relationship exists to predict R_dark25 across broad geographic scales. The study found leaf reflectance spectroscopy provided a more robust and efficient method for predicting R_dark25 across forest types compared to models based on leaf traits. The “cross-site” spectral model, which used data from all three sites, showed the highest accuracy (r² = 0.65), highlighting the importance of including a wide range of spectral and trait variability for building generalizable models. The study identified key spectral bands related to pigments, leaf structure, and water content that contribute to R_dark25 prediction, further indicating the potential of spectroscopy for monitoring multiple leaf traits. The team recommends expanding this research to include a broader range of plant species and ecosystems, investigating seasonal and vertical variations in R_dark25, and exploring the use of imaging spectroscopy for monitoring R_dark at larger scales.

This research provides insight into the complex relationship between plant traits and WUE. It challenges previous work suggesting leaf-level WUE differs between early successional and late successional species and suggests a simpler approach to parameterization is possible for representation of tropical forest WUE in land surface models.

A team of researchers investigated how the density of wood in trees—a proxy for successional stage—related to their water-use efficiency (WUE). Leaf-level WUE is a measure of how much water plants need for photosynthesis. The study focused on trees in an Amazonian rainforest, comparing early successional (low wood density) and late successional (high wood density) species. Researchers measured gas exchange in tree leaves to understand how they regulate water loss and carbon uptake.

This study aimed to determine if wood density influences leaf-level WUE in an Amazonian wet tropical forest. Steady-state gas exchange measurements were performed on top-of-canopy leaves in early and late successional species. Contrary to expectations, the study found stomatal control of transpiration and carbon assimilation was similar. Both types of species had similar dark respiration rates and nitrogen contents. However, early successional species invested more nitrogen in photosynthetic capacity and exhibited higher photosynthetic rates and stomatal conductance. This work provides important information for parameterization of fast-growing, light-demanding, early successional species and slow-growing, shade-tolerant, late successional species in land surface models.

The discovery of an active C₁ photosynthesis pathway in plants may resolve the long-standing controversy about plant methane production and explain the large carbon-13 (¹³C) anomaly observed in C₁ carbon pools over 2 decades ago. Additionally, the study predicts an upregulation of C₁ photosynthesis and nutrient assimilation with elevated CO₂ as a potentially key mechanism facilitating the “CO₂ fertilization” of the biosphere. Given its critical importance for growth, genetic engineering of the photosynthetic C₁ pathway could significantly enhance plant productivity.

This research unveils a highly active “photosynthetic one-carbon (C₁) pathway” in poplar trees—crucial for plant growth and potentially widespread across the plant kingdom, suggesting an evolutionary origin in cyanobacteria. This pathway may be a fundamental mechanism in the “carbon dioxide (CO₂) fertilization” response of the biosphere, evolving alongside oxygenic photosynthesis to boost plant productivity, especially under elevated CO₂ conditions.

In the presence of light, C₁ photosynthesis is driven by RuBisCO—the same enzyme behind the Calvin-Benson cycle. While the Calvin cycle focuses on three-carbon (C₃) intermediates, C₁ photosynthesis goes further, integrating CO₂ with sulfate and ammonia assimilation directly into amino acids. This paper describes the process like a soccer game, where RuBisCO continuously “passes the ball” (carbon atoms) to enzymes that work together to “score goals” by attaching these carbon atoms to vital growth compounds.

Although primarily studied in relation to photorespiration, serine metabolism in chloroplasts may play a key role in plant CO₂ fertilization responses by linking CO₂ assimilation with growth. Here, a team of researchers show the phosphorylated serine pathway is part of a “photosynthetic C₁ pathway” and demonstrate its high activity in foliage of a C₃ tree where it rapidly integrates photosynthesis and C₁ metabolism contributing to new biomass via methyl transfer reactions, imparting a large natural ¹³C-depleted signature.

Using ¹³CO₂-labeling, researchers show leaf serine, the S-methyl group of leaf methionine, pectin methyl esters, and the associated methanol released during cell wall expansion during growth are directly produced from photosynthetically linked C₁ metabolism within minutes of light exposure. The team speculates the photosynthetic C₁ pathway is highly conserved across the photosynthetic tree of life, is responsible for synthesis of the greenhouse gas methane, and may have evolved with oxygenic photosynthesis by providing a mechanism of directly linking carbon and ammonia assimilation with growth.

Although the rise in atmospheric CO₂ inhibits major metabolic pathways like photorespiration, results suggest the photosynthetic C₁ pathway may accelerate and represent a missing link between enhanced photosynthesis and plant growth rates during CO₂ fertilization under a changing climate.

Understanding and quantifying CH₄ production temperature sensitivity is important to improving predictions of how wetland ecosystems will respond to and feed back to climate warming. The sensitivity of CH₄ production to temperature is often described by a Q₁₀ value. This study demonstrates that Q₁₀ values of CH₄ production and emission are regulated by a complex interplay of biological, biochemical, and physical processes. This interaction leads to the aggregated Q₁₀ differing from those of the component processes. Terrestrial ecosystem models relying on a constant Q₁₀ value to characterize temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

Methane (CH₄) is the second most important greenhouse gas after carbon dioxide. Quantifying how CH₄ production changes with temperature is crucial to predicting how wetland ecosystems will respond to climate warming. Temperature sensitivity is often derived from laboratory incubation studies. This study applies observations and a well-tested model to interpret laboratory incubation observations. The findings explain how the inferred temperature sensitivity of CH₄ production is affected by incubation duration, incubation temperatures, storage duration, storage temperature, and sampling time.

Researchers apply observations in a thawing permafrost peatland and a well-tested, process-rich model (ecosys) to interpret incubation observations and investigate controls on inferred CH₄ production temperature sensitivity. Results show dynamic CH₄ production rates are regulated by the interplay between substrates (dissolved organic carbon, acetate, and hydrogen) and activities of methanogens and fermenters. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH₄ production. CH₄ production is higher with less perturbation post-sampling, i.e., shorter storage duration and lower storage temperature. This study reports a wide range of inferred Q₁₀ values (1.2 to 3.5), which is attributed to incubation temperatures, incubation duration, storage duration, and sampling time.

The Field-Storage-Incubation (FSI) framework for simulating incubations provides valuable insights for interpreting incubation observations. Further, this work emphasizes the need to accurately measure important variables such as substrate availability and active microbial biomass during incubation experiments to improve mechanistic understanding of carbon cycling responses to warming.

This work showed Ca is important in organic matter behavior even at sites with relatively low soil Ca concentrations. Knowing Ca is associated with the complexation, cycling, or stabilization of organic matter with a particular chemical composition will allow for better understanding and predictions of how soil organic matter stocks change over time and under changing conditions. The observation that Ca could be associating with plant-like organic matter and interrupting its decomposition pathway is a significant insight into the pathways of and controls on organic matter processes in soils.

Understanding soil organic matter decomposition is crucial for predicting its behavior in a changing climate and land management. This project explored the role of calcium (Ca) in these dynamics. Researchers used synchrotron spectroscopy to study soils from Point Reyes National Seashore in California. Results showed a significant portion of organic matter was closely linked to Ca with distinct properties. Specifically, Ca-associated organic matter had more aromatic and phenolic carbon groups compared to iron-associated carbon. In areas with high Ca association, the spectra resembled lignin, indicating oxidative transformation. Thus, Ca appears to favor organic matter that has undergone some oxidative changes in California’s grassland soils.

Soils store a large amount of organic carbon, but it is not fully understood how quickly this carbon returns to the atmosphere as carbon dioxide (CO₂). It’s been suggested that Ca plays a key role in controlling the decomposition of organic matter. Most research has focused on soils with high Ca levels and significant amounts of calcium carbonate. This study shows Ca is also important in soils with lower calcium concentrations, suggesting its role in many types of soils. Compared to organic matter not linked with Ca, the organic matter associated with Ca was chemically different and more similar to lignin-like plant material. This likely indicates that Ca interrupts the normal decomposition process of plant matter. Identifying this new factor in organic matter cycling enhances the ability to predict how soil organic matter will respond to changes in climate and land management.

Between implementing the pipeline in May 2017 and December 2022, AmeriFlux has received 3,468 data uploads containing 6,195 files of flux-met data from 385 sites. The implemented pipeline enables the management team to keep up with growth, publishing an average of around 48 new sites and 330 new site years annually. As of 2024, there are 3,628 site years of AmeriFlux BASE data from 499 sites, representing the world’s largest data repository for flux-met data. The BASE pipeline facilitates more frequent data uploads and releases and allows data users to access recent-year data.

AmeriFlux is a network of hundreds of research sites established by individual site teams driven by diverse research questions. In 2012, the U.S. Department of Energy established the AmeriFlux Management Project (AMP) at Lawrence Berkeley National Laboratory to support data standardization, quality assurance, and data sharing across the network and the broader AmeriFlux community. AMP presents the new BASE data-processing pipeline, which (1) standardizes the flux and meteorological (flux-met) data formats, (2) ensures data quality, (3) facilitates regular and frequent data submissions and publications, and (4) tracks the data and communications with site teams through the pipeline.

AmeriFlux is a group of research sites that measure carbon, water, and energy exchanges between ecosystems and the atmosphere using a method called eddy covariance. The variety of ecosystems, tools, and data methods in AmeriFlux makes it hard to standardize, assure quality, and share data. Therefore, the AMP created the BASE data-processing system. This system starts with site teams uploading data, followed by (1) AMP’s quality checks, (2) adding site metadata, and (3) publishing the data. By 2022, the BASE system held 3,130 site years of data from 444 sites, making it the largest long-term data repository for flux-met data. This data is used for multisite comparisons, model testing, and data syntheses.

Researchers found neither the basin slope nor the presence of knickzones controls the magnitude of recent surface displacements within the study basin, as may be expected under conceptual models of temperate hillslope evolution. Rather, the highest displacement magnitudes tend to occur at the broad hillslope-channel transition zone. In the study area, this zone is occupied by water tracks. Researchers hypothesize gullying within water tracks will outpace infilling by hillslope processes, resulting in the growth of the channel network under future warming.

To inform understanding of hillslope-channel dynamics under changing climates, researchers examined soil-mantled hillslopes within a roughly 300 km² area of the Seward Peninsula in western Alaska, where discontinuous permafrost is particularly susceptible to thaw and rapid landscape change. In this study, researchers paired high-resolution topographic and satellite data to multi-annual observations of Interferometric Synthetic Aperture Radar (InSAR)-derived surface displacement over a five-year period to quantify spatial variations in topographic change across an upland landscape.

Climate and ecology shape hillslopes and the extent of river networks by controlling how much water is available for erosion. These forces also control whether water can erode soil strengthened by ice or roots. The permeability and stability of permafrost hillslopes change with seasonal and long-term warming because of frozen ground’s impermeability and resistance to erosion. This link between temperature and erosion in permafrost landscapes is thus more direct than most geomorphic models developed at lower latitudes presume. Based on the shape of the hillslopes and valleys, researchers related the locations of satellite topographic change to the geomorphic processes that dominate that part of the landscape. This allowed researchers to determine whether the pattern of disturbance across the landscape is related to geomorphic variables, such as slope, or climate-modulated variables, such as soil saturation. Topographic change primarily occurs in saturated areas at the tips of the river network. At these locations, features called “water tracks” form the transition between hillslopes and river valleys. Changes in climate and vegetation in permafrost landscapes are potentially driving water tracks to transition into true channels, expanding the channel network.

This study provides new insights that challenge how researchers predict patterns of near-surface soil carbon and nitrogen stocks in hilly arctic landscapes. It also brings attention to how unique, smaller-scale processes in permafrost regions can override the effects of larger-scale soil transport processes. Further sampling is needed to fully grasp how and where these competing processes alter expected hillslope patterns.

Understanding of the factors that influence where carbon and nitrogen are stored in arctic soils is incomplete. This information is needed to better predict how release of these elements due to thawing permafrost will impact future climate. In hilly regions, slow transport of soil downhill typically causes carbon and nitrogen stocks to increase at lower elevations. But in the Arctic Foothills of Alaska, researchers found similar carbon and nitrogen stocks at all slope positions within the top meter of soil. Localized freeze-thaw processes and other factors helped produce this unexpected storage pattern.

To better understand how topography affects the amounts and pattern of carbon and nitrogen storage in hilly permafrost soils, a team of researchers studied two broad hillslopes in the Arctic Foothills of Alaska. At each site, the team took multiple samples nested within each of seven slope positions, from hilltop to valley. Unlike other hilly landscapes where downhill soil transport increases carbon and nitrogen stocks at lower elevations, the team found similar carbon and nitrogen stocks at all slope positions within a meter of the surface. At these sites, cryoturbation (freeze-thaw mixing of surface organic materials into deeper soil) was enhanced by a relatively thick and stable mantle of loess (silty windblown deposits). This process and variable ground ice contents disrupted typical, larger-scale carbon and nitrogen transport and accumulation mechanisms. Sampling below the loess mantle was limited by rocky soil conditions except at lower slope positions. These deeper samples revealed large carbon and nitrogen stocks consistent with the expected hillslope patterns missing in the top meter of soil. This suggests the larger-scale influence of topography on carbon and nitrogen distributions may still occur, but only at greater depths in some permafrost landscapes. These findings provide new data and insights into the complexity of measuring and predicting carbon and nitrogen stocks in hilly permafrost terrain. They also underscore the importance of increased, widespread deep-soil sampling to better predict the long-term effects of climate change on these vulnerable arctic ecosystems.

Rapid warming in the Arctic is expected to change the types of plants that grow there and their ability to store carbon, but predicting these changes accurately is still difficult. This research highlights the importance and uncertainty of vegetation demographic dynamics. Vegetation demographic’s interaction with climate change ultimately shapes Arctic vegetation change. Models will likely better predict such change by considering vegetation demography and incorporating more measurements of critical traits. These findings will contribute to future studies that integrate models and data at large scales, as well as efforts to compare different models focused on Arctic ecosystems.

This study simulates vegetation recruitment, growth, competition, and mortality at an Alaskan tundra site under historical and future climates using a dynamic vegetation model. Researchers found multiple plant strategies can lead to similar composition and biomass as seen in the field. However, these strategies produce different trajectories under future climate, with uncertainties twice as large as climate-induced changes. The uncertainties are due to unknown cold tolerance of each plant type, recruitment rate, and how big and tall the canopy can grow at the same stem size. Better quantification of these traits will likely improve model predictions.

This study uses a dynamic vegetation model ELM-FATES (E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator) to explore how plant traits affect Arctic vegetation biomass, composition, and productivity in response to climate change. The model reproduces the observed biomass and composition of the three plant functional types (PFTs) co-existing in the tundra study site.

Researchers identified key traits—such as photosynthetic capacity, carbon allocation, allometry, and phenology—that significantly influence model estimates under historical climate conditions. Notably, various trait configurations can yield similar biomass and composition results. These observations provide a baseline for understanding Arctic vegetation dynamics.

The model predicts that, on average, biomass and net primary productivity will increase with warming and increased carbon dioxide levels. However, different trait configurations lead to varying future projections, with trait-related uncertainties being twice as large as the change caused by climate change. The uncertainty arises from different emerging PFT compositions under novel climate regimes, primarily explained by traits controlling cold-induced mortality, recruitment, and allometry.

To improve predictions of Arctic ecosystem composition and productivity, better estimates of these key traits are essential. Better predictions will also benefit from improved model representations and observations of plant-nutrient interaction, plant dieback mechanisms, and acclimation of Arctic ecosystems.

This study points to a quantitative approach for improving electron transport and photosynthetic productivity modeling under changing climates at regional and global scales. Future work will focus on isolating the temperature-dependent changes between the variables.

The maximum quantum yield of photosystem II (F_PSIImax) is a key parameter of the light reactions that influences the electron transport rate needed for supporting the biochemical reactions of photosynthesis. Carbon cycle models often treat F_PSIImax as a constant, even though plant stress is expected to decrease it. This study aims to understand and quantify temperature’s effect on the efficiency of light conversion to electron transport for photosynthesis across plant functional types (PFTs) and climate. Researchers synthesized F_PSIImax values from pulse-amplitude–modulated fluorometry measurements in response to variable temperatures across the globe. The study found F_PSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions.

Modeling terrestrial gross primary productivity (GPP) is central to predicting the global carbon cycle. Much interest has been focused on the environmentally induced dynamics of photosystem energy partitioning and how improvements in the description of such dynamics assist the prediction of light reactions of photosynthesis and therefore GPP. F_PSIImax is generally treated as a constant in biochemical photosynthetic models even though a constant F_PSIImax is expected only for non-stressed plants. Researchers synthesized reported F_PSIImax values from pulse-amplitude–modulated fluorometry measurements in response to variable temperatures across the globe. They found F_PSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions. To understand the spatiotemporal variability in F_PSIImax, researchers analyzed the temperature effect on F_PSIImax across PFT and habitat climatology. The analysis showed that for plants with broad latitudinal distributions or in regions with extreme temperature variability, temperature’s impact on F_PSIImax is shaped more by climate than by PFT. There is a trade-off between the temperature range within which F_PSIImax remains maximal and the overall rate of decline of F_PSIImax outside the temperature range, such that species cannot be simultaneously tolerant and resilient to extreme temperatures.

Genetically improving photosynthesis is a key strategy to boosting crop production to meet the rising demand for food and fuel and for carbon sequestration. The models developed and the insights generated facilitate the development of sustainable photosynthetic systems for greater crop yields and carbon sequestration potentials.

Evolution selects plant species for reproduction and survivability. The electron transport chain (ETC) between the two photosystems of photosynthesis (PSI and PSII) in chloroplast thylakoid membranes appears to be suboptimal for photosynthesis due to evolutionary constraints.

Photochemical models of photosynthetic electron transport were developed to determine how the structure of the ETC and thylakoid controls the oxidation-reduction (redox) reactions between key protein complexes and electron carriers and therefore the maximal electron transport rate. Models were validated for species across diverse environments and C₃ and C₄ photosynthetic pathways.

Models show the electron transport capacity can be increased while the risk of photooxidative damage can be simultaneously minimized by increasing the abundances of reaction centers, cytochrome b₆f complexes, and mobile electron carriers. The new modeling results describe previously unexplained experimental findings regarding the physiological impacts of the abundances of ETC components on plant productivity.

Genetically improving photosynthesis is a key strategy to boosting crop production to meet the rising demand for food and fuel by a rapidly growing global population in a warming climate. Many components of the photosynthetic apparatus have been targeted for genetic modification for improving photosynthesis. Successful translation of these modifications into increased plant productivity in fluctuating environments will depend on whether the ETC can support the increased electron transport rate without risking overreduction and photodamage. At present atmospheric conditions, the ETC appears suboptimal and will likely need to be modified to support proposed photosynthetic improvements and to maintain energy balance. This study derives photochemical equations to quantify the transport capacity and the corresponding reduction level based on the kinetics of redox reactions along the ETC. Using these theoretical equations and measurements from diverse C₃/C₄ species across environments, the researcher identified several strategies that can simultaneously increase the transport capacity and decrease the reduction level of the ETC. These strategies include increasing the abundance of reaction centers, cytochrome b₆f complexes, and mobile electron carriers; improving their redox kinetics; and decreasing the fraction of secondary quinone–nonreducing PSII reaction centers. This research also shed light on several previously unexplained experimental findings regarding the physiological impacts of the abundances of the cytochrome b₆f complex and plastoquinone. The model developed and the insights generated from it facilitate the development of sustainable photosynthetic systems for greater crop yields.

This research explores how changes in groundwater levels affect underground water chemistry, especially in areas where land meets water like wetlands, and informs how climate change could affect water quality and the environment.

This study is one of the first to use high-resolution data to capture rapid instances of significant redox changes in real time. Data highlights permanent and activated ecosystem control points for modeling TAI ecological functions. Permanent control points show redox states from upland to wetlands, representing landscape variance in a model using nested subgrid areas. Activated control points are temporary, arising from processes and mechanisms that dominate biogeochemical transformations.

The diverse range of redox values can support reactive transport models and validate recent advancements in predicting groundwater redox status. This research allows other scientists to better predict environmental changes and could affect fields like environmental science, hydrology, and climate science.

Global change processes modify the extent and functions of the transitional zones between wetlands and uplands—terrestrial–aquatic interfaces (TAIs)—in both marine and freshwater ecosystems. How fluctuating water levels alter groundwater biogeochemistry remains to be explored at these TAIs. This study found short-term water- level changes can significantly alter the redox state of groundwater, especially in transitional areas. Higher groundwater redox potential was observed in uplands than in wetlands. Further, this study revealed more frequent oxidizing states in transitional areas and wetlands than in uplands. Rapid and infrequent water table fluctuations due to the lower elevation of transitional areas and wetlands caused more temporary increases in redox potential, significantly increasing the overall redox state. The redox-oxygen relationship displayed redox hysteresis, which means the rate at which redox potential increased was more rapid with oxygen inputs than the rate at which it decreased when oxygen was consumed. These findings improve model prediction of how climate change affects groundwater chemistry.

Researchers investigated how short-term fluctuations in groundwater levels influence subsurface redox potential across TAIs from wetlands to uplands. High-temporal-resolution data were collected along wetland-to-upland gradients and during fluctuating water levels in freshwater coastal areas to examine spatiotemporal patterns of groundwater redox potential.

Findings reveal that topography influences groundwater redox, with higher values observed in uplands than wetlands. However, high variability within transitional TAIs complicated the redox zonation. Although declining water levels reduced groundwater redox in most locations, there was an increase in redox variability. This increase was likely due to rare, short-term water-level fluctuations introducing oxygen and other oxidant agents like nitrate. The redox-oxygen relationship displayed redox hysteresis, maintaining more oxidizing states longer than dissolved oxygen. Unexpectedly, the study found more frequent oxidizing conditions in transitional zones and wetlands than uplands.

These observations suggest occasional oxygen inputs at wetland–upland boundaries serve as critical control points for biogeochemical processes. High-resolution data can capture these rare but essential biogeochemical events, contributing to redox-informed models and improving the ability to predict climate change feedback.

Traditional methods of soil investigation, such as soil cores, hand augers, excavation, or sensors, are point measurements lacking spatial resolution. Due to this limitation, measurements may not adequately capture the spatial variabilities necessary to upscale models from site to global scale. This work demonstrated geophysical methods expand understanding of soil architecture and subsurface stratigraphic heterogeneities with higher spatial resolution than point sampling methods. Combining multiple geophysical methods generates more comprehensive maps of stratigraphic structures at land–lake interfaces while also providing more information about soil properties missing information useful for improving models of coastal interfaces.

Geophysical methods have been used to map soil spatiotemporal variabilities on terrestrial ecosystems, but their applicability in soils across land–lake interfaces (also known as terrestrial–aquatic interfaces, or TAIs) is not well understood. This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across TAIs. Researchers demonstrated not only that geophysical methods are useful for understanding soil architecture and subsurface stratigraphic heterogeneities across TAIs, but that resulting datasets capture much higher spatial resolution than point sampling methods. Incorporation of geophysical understanding also revealed the stratigraphy and soil moisture dynamics are key drivers of the observed heterogeneities.

The land–lake interface is an active zone where various geochemical and biological changes occur. The unique characteristics of this interface are not fully understood because subsurface properties vary greatly in time and space, making them difficult to measure with traditional soil sampling methods.

To address these limitations, this study compared data from three geophysical methods with measurements from more traditional techniques, including soil core sampling and in situ sensors. The electrical conductivity maps derived from the geophysical tools matched soil maps from a public database that were used for reference. High-conductivity areas from the geophysical-based data also matched the hydric soil units on the original soil maps, which further demonstrates the tools’ utility and accuracy. Measurements from the geophysical approach also detected additional soil units missed in the reference soil maps.

Results from electrical resistivity and radar methods are consistent with the surficial geology of the study area and revealed variation in the vertical silty clay and till sequence down to 3.5 m depth. These results show electromagnetic induction could be used to characterize soils in sampling-restricted sites where only noninvasive measurements are feasible. Ultimately, the study demonstrates use of multiple geophysical methods can deduce soil properties and map stratigraphic structures at land–lake interfaces to improve representations of coastal interfaces for Earth system models.

Oxygen consumption in soils is a complex process that typically occurs due to a combination of chemical and biological mechanisms. Even within a site, this study found these mechanisms varied by landscape position; there was a stronger influence of biological (i.e., microbial) mechanisms in the upland forest soils, whereas chemical processes were more important in the wetland soils.

By conducting laboratory incubations and model simulations, the team was able to test these systems in a controlled setting and isolate crucial processes and mechanisms. This work highlights the effectiveness of integrating field measurements, laboratory incubations, and model simulations to develop a stronger understanding of soil biogeochemical processes in coastal systems. This understanding is especially relevant in coastal systems that experience flooding and are becoming increasingly vulnerable to events like sea level rise.

Oxygen is an important driver of biogeochemical processes in soils. Coastal systems experience frequent flooding due to tidal cycles, variable rainfall, and storm surge events. These events can result in rapid consumption of oxygen, but the time scale of these processes is unknown. This study investigated oxygen dynamics in flooded soils, specifically testing how quickly oxygen was consumed in different coastal soils (e.g., upland vs. wetland and surface vs. subsurface soils) after a flood. By comparing field measurements with laboratory incubations and model simulations, researchers were able to identify different mechanisms controlling oxygen dynamics in wetland vs. forest soils.

The coastal terrestrial–aquatic interface (TAI; a landscape that spans upland forest through a transitional forest to wetland) is a highly dynamic system characterized by strong physical, chemical, and biological gradients. Changing water levels cause regular changes in soil redox conditions. The consequent consumption of terminal electron acceptors in turn strongly influences carbon availability and transformations across TAIs. However, while redox dynamics are well described, there is limited ability to quantitatively forecast the rates at which the redox conditions change across a TAI within which soils have different characteristics and inundation regimes. This study integrated field measurements, laboratory incubations, and model simulations to improve mechanistic understanding of oxygen consumption dynamics in coastal soils.

Continuous in situ monitoring unexpectedly revealed flooding conditions resulted in temporary spikes of subsurface dissolved oxygen, followed by its rapid consumption in the wetlands. To further investigate oxygen consumption mechanisms in a controlled setting, laboratory incubations were performed using surface and subsurface soils collected from a TAI gradient in Western Lake Erie. Oxygen consumption rates were measured during lab-simulated flood events in these TAI soils. Results showed wetland soils reached anoxia the fastest, in approximately 9 hours on average, whereas upland soils turned anoxic in approximately 18 hours. Subsurface upland soils did not turn anoxic even after 2 weeks of saturation in the lab, and their oxygen consumption patterns suggested carbon and/or nutrient limitation.

These results are consistent with in situ groundwater redox and oxygen measurements in the field, where wetland soils exhibited the highest rates of oxygen consumption along the TAI. Model simulations of oxygen consumption suggested oxygen consumption had stronger abiotic controls in wetland soils but stronger biotic controls in upland soils, providing a useful framework for future incubation experiments. This work also determined microbial activity is a strong driver of oxygen consumption in TAI soils but is constrained by the availability of dissolved carbon in subsurface soils.

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. Understanding how soil microbes break down vast Arctic soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes.

As the Arctic warms, thawing permafrost unlocks carbon, potentially accelerating climate change by releasing greenhouse gases. This research delves into the underlying biogeochemical processes mediated by the soil microbial community in response to wet and anaerobic conditions akin to an Arctic summer thaw.

A team of researchers studied microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following permafrost thawing, fermentative bacteria dominated the microbial composition. The increase in iron and sulfate-reducing microbes significantly limits methane (CH₄) production from thawed permafrost, underscoring the competition within microbial communities. Potential carbon decomposition leading to carbon dioxide (CO₂) via fermentation can limit the substrate pool and cause high CO₂ to CH₄ ratios in Arctic soils under post-thaw anaerobic conditions.

The team explored the growth strategies of microbial communities and found slow growth was the major strategy in both the active layer and permafrost. This study challenges the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, observations indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

The boreal region has already warmed more than twice than the global average, and predictions suggest that some regions could potentially warm by 6°C by 2100 compared to a global mean of about 4°C. Understanding changes in leaf characteristics, photosynthetic capacity, and efficiency with ecosystem warming is critical for correctly representing boreal plant carbon uptake in vegetation models that simulate impacts of future climate change.

Boreal forests represent a key component within the global carbon cycle as, through photosynthesis, they absorb a significant amount of carbon from the atmosphere annually. Accurate representation of boreal forest photosynthesis within terrestrial biosphere models (TBMs) is, therefore, important to reliably predict both current and future global carbon cycling and associated climatic conditions. This study investigated the impact of increased growth temperature and elevated carbon dioxide (CO₂) on photosynthetic capacity in mature trees of two North American boreal conifers, tamarack and black spruce.

There are two primary leaf parameters that represent the underlying biochemical processes of photosynthesis, the maximum rate of Rubisco carboxylation (V_cmax) and the maximum rate of electron transport (J_max). The V_cmaxand J_maxare key parameters in many TBMs that simulate current and future carbon uptake and sequestration. However, many TBMs do not currently incorporate long-term acclimation responses of both V_cmax and J_max to climate change variables such as warming and elevated CO₂, largely due to the lack of data, particularly for the boreal region. This knowledge gap limits models’ ability to reliably forecast the feedback between boreal forest carbon cycling and future climate.

This study investigated the acclimation of photosynthetic capacity (V_cmax and J_max) to warming and elevated CO₂ after 2 years of a whole-ecosystem experimental warming (up to +9°C above ambient temperature) combined with 1 year of elevated CO₂ (+430 to 500 parts per million above ambient atmospheric CO₂) in mature trees of North America’s boreal conifers (i.e., black spruce and tamarack) at their southern range of natural distribution. The photosynthetic capacity of mature trees of North American boreal conifers responded independently to warming and elevated CO₂ when exposed to both environmental factors. Data will be applied to improve representation of boreal tree photosynthesis in TBMs such as the DOE’s Energy Exascale Earth System Land Model.

This research shows northern plants maintain a stable balance between growing and dying back, even as the climate changes. This discovery challenges previous ideas that climate change would disrupt the timing of plant life cycles. Understanding how plants naturally regulate their growth could help scientists predict how ecosystems will respond to future climate shifts. This insight also opens new doors for studying how plants can adapt to environmental changes, which is crucial for managing forests, crops, and natural resources in a warming world.

A team of researchers examined how plants in northern regions split their time between growing and dying back each year, even as the climate warms. Using satellite data, the team found the balance between the two stages—green-up and senescence—has stayed almost the same over the past 20 years. This finding means even though the growing season is getting longer, plants are keeping a steady pace, which suggests plants may have built-in controls to adjust to changing environments.

This study explores how plants in northern ecosystems allocate time between two important stages in their yearly cycle: green-up (growth) and senescence (dying back). As climate change continues to extend the length of the growing season, researchers were unsure whether plants were spending more time growing or if this extended season was spread evenly across their life cycle. Using satellite data collected between 2001 and 2020, the team found the ratio of time plants spend on green-up versus senescence has remained remarkably consistent despite the warming climate. This pattern held true across more than 83% of northern ecosystems.

Researchers tested two possible explanations: plants would adjust their time allocation in response to climate shifts or maintain a stable balance between these stages. Findings strongly support the latter hypothesis, which suggests northern plants have built-in biological mechanisms that regulate their life cycle, regardless of climate-driven changes in growing season length. These insights could help scientists predict how ecosystems will function in a warmer world, contributing to better forest and land management strategies.

This research demonstrates how extreme heat affected water systems in the United States. between 2003 and 2022. Researchers found heatwaves can reduce water storage in the ground and soil, increase runoff, and raise flood risk. These findings are important for managing water resources and preparing for climate change. This knowledge can help farmers, city planners, and governments protect water supplies and prepare for extreme weather events, improving water management in a warming world.

Heatwaves are becoming more frequent and intense, and they have serious impacts on water availability. In this study, a research team examined how heatwaves from 2003 to 2022 affected water in the United States and found during and after heatwaves, less water is stored underground, soil dries out, and less moisture evaporates into the air. At the same time, surface runoff and rainfall often increase, especially in the eastern United States, raising the risk of floods. These changes could make managing water resources more difficult in the future.

This study investigates heatwave effects on hydrological processes in the contiguous United States from 2003 to 2022. Researchers examined how extreme heat influences key water-related factors, such as surface runoff, groundwater storage, soil moisture, and evaporation. Using data from advanced systems like the Global Land Data Assimilation System, the team found heatwaves tend to reduce groundwater and soil moisture while increasing surface runoff and the likelihood of rainfall after heatwaves end. These results indicate heatwaves can lead to heightened flood risks in some regions, especially in the eastern United States, where rainfall often follows extreme heat.

Findings suggest heatwaves can significantly alter the water cycle, with potential impacts on agriculture, water management, and flood prevention. Understanding these changes is critical for developing strategies to adapt to climate change and its effects on water resources. This research provides valuable insights that can help policymakers and planners better manage water supplies and prepare for extreme weather events in a changing climate.

Tropical forests exchange more CO₂ with the atmosphere than any other terrestrial biome, store nearly one-third of global soil carbon stocks, and have the shortest mean residence time for carbon, as short as 6 to 15 years. Climate projections suggest a future that will be both warmer and drier for much of the tropics with increasing drought intensity and dry season length for the Neotropics. These findings imply both warming and drying will exacerbate soil carbon losses in tropical forests, which could have large and relatively rapid consequences for tropical ecosystem carbon balance and carbon-climate feedbacks.

Tropical forests account for over half of the global terrestrial carbon sink, but climate change threatens to alter the carbon balance of these ecosystems. A research team identified changes in soil carbon sources contributing to soil surface carbon dioxide (CO₂) emissions in Panamanian tropical forests subjected to either soil warming or drying. Researchers found both warming and drying increased the average age of carbon in soil CO₂ emissions by 2 to 3 years but for different reasons. Warming accelerated decomposition of older carbon as increased CO₂ emissions depleted newer carbon. Drying suppressed decomposition of newer carbon inputs and decreased soil CO₂ emissions.

The team measured soil CO₂ flux rates and collected CO₂ emitted from the soil surface at two climate manipulation experiments in Panamanian tropical forests. The Soil Warming Experiment in Lowland Tropical Rainforest (SWELTR) experiment uses whole-profile in situ heating of soil by 4°C to achieve soil warming. The Panama Rainforest Changes with Experimental Drying (PARCHED) experiment uses throughfall exclusion structures to remove 50% of precipitation inputs to achieve soil drying. Samples were collected in 2019 during contrasting seasons to assess the impact of experimental treatments on CO₂ flux and the carbon source contributing to soil CO₂ emissions.

Researchers observed increased carbon-14 in CO₂ released by soil with experimental warming and drying corresponding to an increase in the average age of the carbon by the equivalent of approximately 2 to 3 years. Importantly, the mechanisms underlying this shift differed between warming and drying. Warming accelerated decomposition of older carbon, while increased CO₂ emissions depleted newer carbon. Drying suppressed decomposition of newer carbon inputs and decreased soil CO₂ emissions, thereby increasing contributions of older carbon to CO₂ release. These results suggest both climate warming and drying will increase the vulnerability of previously stored soil carbon in tropical forests by stimulating the decomposition and loss of old carbon.

Increasing carbon dioxide in the air is causing climate change. However, carbon dioxide also stimulates plant growth, which removes carbon dioxide from the atmosphere and stores it in leaves, roots, wood, and soil. Removal and storage create a negative feedback loop on carbon dioxide increase in the air. This feedback is slowing the pace of climate change. Quantifying this feedback and understanding how long it will last help researchers predict how fossil fuel use translates into temperature increase. In turn, this understanding allows scientists and policy makers to design scenarios that meet international agreements limiting warming from climate change.

Plants use carbon dioxide in the air to make sugars to grow leaves, wood, and roots. Higher amounts of carbon dioxide in the air can stimulate this sugar production, but other factors also limit plant growth. Insufficient nitrogen in the soil can restrict any stimulation of plant growth by higher carbon dioxide. For 14 years, researchers increased carbon dioxide levels in the air of a loblolly pine plantation in North Carolina. They found despite evidence for nitrogen limitation in the forest, carbon dioxide stimulated growth over the whole time of the experiment.

Scientists ran an experiment that elevated atmospheric carbon dioxide levels in a pine plantation with mixed-in hardwoods for more than 14 years. The forest was selected due to its moderate fertility and expectation of nitrogen limitation of the carbon dioxide effect. Carbon dioxide increased forest growth rates by almost 40%. Despite expectations that nitrogen would increasingly limit forest growth response, there was no evidence of decline, and 40% was maintained for the full duration of the experiment. Nitrogen was added to half of each plot in the final 6 years of the experiment, which increased growth by 10% at both carbon dioxide levels.

The carbon dioxide response in pine trees was a result of higher photosynthesis per leaf and higher amounts of leaves. Growth increased in pine trees’ leaves, stems, and roots. In broadleaf trees, the response was from higher photosynthesis per leaf, and additional growth was restricted to the root system. Forests with high carbon dioxide levels had more biomass at the experiment’s end due to higher growth, a higher proportion of wood growth, and reduced tree mortality.

These simulations are crucial for understanding that river water quality is affected by its interactions with groundwater and the river’s meandering nature, and river bends are important zones where biogeochemical reactions take place. Findings can help predict how nutrients and pollutants such as nitrate are transported and transformed in river corridors. This knowledge is key to modeling interactions of flowing water bodies with the land surface, maintaining healthy river environments, and developing effective water management plans.

This study explores how the sinuosity, or bends, in rivers can influence the mixing of river water and groundwater in shallow riverbed sediments and in turn affect water quality. A team of multi-institutional researchers discovered rivers with high sinuosity can shield the effects of regional groundwater fluxes, leading to persistent local river-groundwater exchange zones (hyporheic zones) where biogeochemical reactions take place.

This research used modeling to examine the role of river sinuosity in driving hyporheic exchange, a process that significantly affects water quality and ecosystem health. The research team found the unique shapes formed by river bends can offset regional groundwater flow effects, creating locally stable zones where important water chemistry changes occur. This finding challenges previous research and offers a fresh look at how river geometry influences water chemistry, particularly nitrate contaminant levels. Findings reveal as the sinuosity of river bends increases, their ability to remove nitrate decreases, constrained by available organic carbon. This work also identified specific conditions under which river bends can either add to or reduce nitrate levels. These insights are vital for modeling interactions of rivers and streams with the land surface, managing river quality, and shaping future river restoration efforts.

Forests become exposed due to road building, urbanization, logging, and other landscape disturbances that enhance fragmentation. Forests edges are exposed to higher sunlight and increased temperatures compared to the interior of forest stands. The predominance and acceleration of forest edge formation supports investigations to understand impacts on carbon cycling and storage. Examining both above- and belowground processes is important to fully understand controls and effects of forest fragmentation.

This study aimed to understand how belowground processes influence carbon and water cycling at an urban-suburban forest edge. A team of researchers measured live and dead fine root traits, as well as soil enzyme activity, chemistry, moisture, and respiration along a 75-m urban-suburban transect from the interior of a forest, across the forest’s edge, and into a meadow at the National Institute of Standards and Technology facility in Gaithersburg, Md. Soil carbon content was similar between transect positions, but the forest edge was drier, had higher dead root biomass and sugar degradation enzyme potential activity, and had greater total soil respiration compared to the meadow and interior forest.

Researchers found soil respiration carbon losses and increased sugar decomposition enzyme activities at the forest edge were possibly balanced by increased plant productivity and concomitant increased inputs to soil. Lower soil moisture may have also inhibited microbial decomposition of organic materials delivered to soils. This study suggests the forest edge experiences accelerated rates of carbon cycling and lower soil moisture levels, which is consistent with observed patterns in temperate deciduous forests but different from tropical and boreal forest edges.

Future microbial model applications can potentially use the same parameters across different soil series but not across plant functional types when implementing models at various sites. Besides heterotrophic respiration and microbial biomass data, soil extracellular enzyme data sets are particularly needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

Incorporating soil microbial processes can improve soil model projections, and achieving a common set of microbial parameters across sites would enable more widespread application. Based on a 2‐year soil incubation data set, this study showed key microbial parameters could be generalized at the soil series level (four distinct soil series from three soil orders) but not land cover type (forest vs. grassland). The common set of parameters includes those processes controlling microbial growth and maintenance as well as extracellular enzyme production and turnover.

The study used the Microbial ENzyme Decomposition (MEND) model for simulations. MEND is one of the earlier soil process models (2013) that explicitly incorporates microbial biomass and enzyme function to simulate soil carbon and nitrogen cycling. MEND has been applied to incubation studies, long-term field studies, and ecosystem demographic and earth system models. Therefore, the findings that microbial parameters can be generalized across different soil series and orders could enable broader application of explicit microbial activities in earth system models. This approach should be tested with other datasets and microbial soil carbon cycling models.

Assimilating LAI and aboveground biomass observations into a land model reduced model bias associated with estimating key model outputs. Data assimilation significantly improved the model’s performance in carbon and hydrological cycles, as well as functional relationships. Implementation of a new parameterization accounting for low temperature sensitivity of photosynthesis further reduced model bias in estimating gross primary productivity (GPP).

Arctic and boreal ecosystems are warming rapidly, impacting regional and global carbon cycles. The Community Land Model (CLM) can be used to project future carbon uptake and storage. However, CLM is biased in estimating leaf area index (LAI) and aboveground biomass, which can significantly affect model projections. A team of researchers forced the model estimates of LAI and aboveground biomass to be consistent with satellite-derived LAI observations and a machine learning product of aboveground biomass. Furthermore, the team enabled two key parameters in photosynthesis to vary with leaf temperature. Assessment using the International Land Model Benchmarking Project (ILAMB) showed significant improvement of model projections.

Model representation of carbon uptake and storage is essential for accurately projecting the arctic-boreal zone’s reponse to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of GPP.

To understand and correct bias of LAI and aboveground biomass in CLM, a team of researchers assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM. The team used an Ensemble Adjustment Kalman Filter in an experimental region including Alaska and western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels.

The ILAMB assessment showed data assimilation significantly improved CLM’s performance in simulating carbon and hydrological cycles and in representing functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, the team re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias–corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in land surface models.

Intensified SLR is expected to trigger SWI into coastal freshwater aquifers more extensively. Results from a new simulation study provide the first suggestion that marsh topographic change affects coastal SWI. The study showed marsh accretion under SLR might significantly reduce surface seawater inflow and prolong surface seawater residence time. Future SWI on the evolved marsh landscape might increase sensitivity to upland groundwater inflows. These insights help improve understanding of coastal freshwater system vulnerability under SLR, marsh landscape dynamics, and changes in upland groundwater resources; these interconnections have not previously been considered.

Coastal saltwater intrusion (SWI), the movement of seawater into freshwater aquifers, is one key factor affecting how coastal ecosystems function. Previous simulations have predicted changes to saltwater concentration in coastal aquifers as sea-levels rise, but have always assumed the current coastal landscape elevation will remain the same. However, coastal landscapes are highly dynamic in response to sea level rise (SLR) due to sediment deposition and erosion. A multi-institutional team of researchers investigated how SWI would change under a dynamic coastal wetland system and found coastal marsh evolution plays an important role in controlling seawater inflow, thereby affecting saltwater distribution under SLR. 

This simulation study investigated the impact of coastal marsh evolution on predictions of SWI under future SLR by using the Advanced Terrestrial Simulator, a process-based coastal hydro-eco-geomorphologic model. Using a representative synthetic coastal marsh landscape, a multi-institutional team of researchers first predicted marsh landscape change with different upland slopes under two SLR scenarios. Results showed the coastal marsh landscape was dynamic, responding strongly to SLR. Marsh accretion was projected to cause a significant reduction of saltwater inflow at the ocean boundary due to the decrease in the hydraulic gradient between the land and ocean. Also, a topographic depression zone prolonged the residence time of surface ponding water, which affected surface saltwater infiltration, thereby increasing subsurface salinity under the depression zone.  

Using a simulated but evolved marsh landscape, the team also tested the impact of different upland groundwater conditions on SWI under SLR, reflecting the impact of future drier and wetter climate conditions and human groundwater extraction on fresh groundwater dynamics. With the future topographic change, SWI was found to be more sensitive to the upland fresh groundwater supply because of the intensified freshwater-saltwater interaction in the depression zone. Thus, this study revealed the importance of protecting upland freshwater resources. 

Carbon cycling by microorganisms in subsurface environments is of particular relevance in the face of global climate change. Riparian floodplain sediments contain high amounts of organic carbon that can be degraded into simple compounds such as methane or methanol, the fate of which depends on the microbial metabolic capabilities present as well as the water saturation and oxygen availability. By studying the microbes present in floodplain sediments, researchers can determine which pathways of carbon cycling may occur at different depths in the floodplain, which is important for improving the accuracy of carbon cycling and climate models.

A multi-institutional team of researchers generated over 1,000 genomes for bacteria and archaea from 0.5- to 1.5-meter-deep sediments from a montane floodplain. Samples from above and below the floodplain water table experienced a range of oxygen availability as the water table fluctuated. Genomes extracted from these samples revealed the presence of microbes capable of producing and consuming methane and other single carbon compounds. The team found microbes with genes for making methane at depths without oxygen and microbes that can consume methane at depths both with and without oxygen.

A multi-institutional team of researchers used high-throughput sequencing of total microbial communities to understand the cycling of single-carbon (C1) compounds, particularly methane-cycling, by microorganisms found in the sediments of a montane riparian floodplain. The team generated 1,233 metagenome-assembled genomes (MAGs) from 0.5- to 1.5-m depth below the floodplain surface, capturing the transition between oxygen-containing, unsaturated sediments and oxygen-depleted, saturated sediments in the Slate River floodplain in Crested Butte, Colo.

Genomes of putative methane producers, methane consumers, and other C1 consumers (using compounds such as methanol and methylamines) were recovered. Methane producers were found only in oxygen-depleted depths and originated from three different groups, each with a different pathway for making methane. Putative methane-consuming microorganisms originated from within the Archaea (Candidatus Methanoperedens) in oxygen-depleted depths and from uncultured bacteria (Candidatus Binatia) in depths with oxygen. The genetic potential for C1 consumption was widespread, with over 10% and 19% of MAGs encoding a methanol dehydrogenase or a substrate-specific methyltransferase, respectively. Overall, genomes from Slate River floodplain sediments reveal potential for methane production and consumption in the system and a robust potential for C1 cycling.

This study challenges understanding of how plants and fungi collaborate in lowland tropical forests and reveals these relationships are more intricate than previously believed. Conventional ideas about nutrient levels and plant partnerships may not always hold true. The study stresses the importance of gaining a deeper understanding of the symbiotic relationships between plants and fungi in tropical regions and cautions against assuming they operate similarly to other areas, like temperate and boreal regions. Overall, the research makes researchers rethink how plants and fungi interact in diverse tropical forests, highlighting the need for more studies to understand these complex partnerships.

This study investigates the distribution of mycorrhizae, plant-fungal partnerships that influence ecosystem function. Researchers traditionally believed climate and decomposition rates determined mycorrhizal distribution, with arbuscular mycorrhizal plants being more prevalent in fertile areas and ectomycorrhizal (EcM) plants in less fertile ones. However, a team of researchers used fine-scale data from lowland tropical forests to challenge this notion, revealing soil fertility is not associated with the distribution of EcM-associated trees. The research underscores the importance of understanding mycorrhizal symbiosis in lowland tropics, refuting assumptions based on temperate and boreal regions, and highlighting historical biogeographies that influence mycorrhizal patterns in tropical forests worldwide.

Mycorrhizae mediate vegetation impacts on ecosystem functioning. Climatic effects on decomposition and soil quality are suggested to drive mycorrhizal distributions, with arbuscular mycorrhizal plants prevailing in low-latitude and high-soil-quality areas and EcM plants in high-latitude and low-soil-quality areas. However, these generalizations, based on coarse-resolution data, obscure finer-scale variations and result in high uncertainties in the predicted distributions of mycorrhizal types and their drivers.

Using data from 31 lowland tropical forests, both at a coarse scale (mean-plot-level data) and fine scale (20 × 20 meters from a subset of 16 sites), the study demonstrates the distribution and abundance of EcM-associated trees are independent of soil quality. Resource exchange differences among mycorrhizal partners, stemming from diverse evolutionary origins of mycorrhizal fungi, may decouple soil fertility from the advantage provided by mycorrhizal associations. Additionally, distinct historical biogeographies and diversification patterns have led to differences in forest composition and nutrient-acquisition strategies across three major tropical regions. Notably, Africa and Asia’s lowland tropical forests have abundant EcM trees, but they are relatively scarce in lowland neotropical forests. A greater understanding of the functional biology of mycorrhizal symbiosis is required, especially in the lowland tropics, to overcome biases from assuming similarity to temperate and boreal regions.

The release of volatile mercury from soils and sediments is a critical process in the global movement of mercury; however, the transformation of mercury(II) to mercury(0) is not well understood. Researchers know bacteria and other microorganisms can transform mercury(II) to mercury(0) under oxygen-limited conditions, as can iron-bearing minerals. However, this study shows manganese, which is commonly found in water-logged soils and oxygen-deficient freshwater and marine sediments, can also cause this transformation under mildly oxic conditions. This insight will help improve models of mercury global transport, thereby advancing efforts to protect human health and the environment.

Mercury is found in the environment due to release from natural and manmade sources. As such, mercury is a common pollutant in soils and sediments and a major environmental concern due to its toxicity to humans and wildlife. Researchers found manganese can transform mercury(II) to volatile mercury(0).

Mercury is found in the environment due to release from volcanoes, mining activity, the burning of forests and fossil fuels, and industrial and consumer use. As such, mercury is a common contaminant in many terrestrial and aquatic environments, and its bioaccumulation in organisms, including humans, is a major environmental concern. Mercury in the environment is present as either mercury(II), which tends to remain in soils and sediments, or mercury(0), which as a gas can escape into the atmosphere and is mobile on a global scale. Thus, the reduction of mercury(II) to mercury(0) in soils and sediments, by either bacteria and other microorganisms or by chemical reactions, is a key component of mercury cycling between atmospheric and aquatic/terrestrial reservoirs and the overall biogeochemical cycling of mercury.

Researchers used X-ray spectroscopic capabilities at the Advanced Photon Source at Argonne National Laboratory to show that manganese(II), which is found in oxygen-deficient soils and sediments, can reduce mercury(II) to mercury(0) and partially reduce mercury(II) to mercury(I) in the presence of high sulfate or chloride, a previously unknown process in mercury’s biogeochemistry. The finding that manganese(II) may play a role in the emission of mercury(0) from soils and sediments at the oxic-anoxic interface can lead to improved models of global mercury cycling and better protection of human health and the environment.

Numerical models play an indispensable role in environmental science. Models such as Earth system models, land surface models, ecosystem models, hydrological models, and watershed models are crucial for understanding and predicting complex environmental processes. Despite significant advancements in model development and the inclusion of increasingly complex processes, these models remain approximations of the systems they represent and inherently require parameterization.

Given the complexity and potential computational expense associated with these models, there have been concerted efforts within the scientific community to develop and refine techniques for parameterization, such as BO. A team of researchers aimed to bridge the gap between nondomain experts and BO by introducing BOA.

A team of researchers developed Bayesian Optimization for Anything (BOA), a new high-level Bayesian optimization model wrapping toolkit addressing common barriers in implementing Bayesian optimization (BO). BOA is language-agnostic and can interface with models written at any coding language.

BOA, a high-level BO framework and model wrapping toolkit, presents a novel approach to simplifying BO with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on increasing ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture. BOA’s features enhance its applicability, allowing for broader application in various fields and to a wider audience.

The study showcases BOA’s application through three examples: a high-dimensional optimization with 184 parameters of the Soil and Water Assessment Tool (SWAT+) watershed model, a highly parallelized optimization of this intrinsically nonparallel model, and a multiobjective optimization of the Finite-difference Ecosystem-scale Tree Crown Hydrodynamics (FETCH) model. These test cases illustrate BOA’s effectiveness in addressing complex optimization challenges in diverse scenarios.

Experts struggle to predict how much water will be available each year. This study identifies key reasons why the Colorado River has been getting less water than expected, which is important because millions of people and sensitive ecosystems rely on the river. By showing less spring rain and warmer, sunnier days are causing plants to use more snowmelt, researchers can help water managers make better predictions. The findings suggest that researchers need to focus more on what happens in the spring to better manage water resources.

The Colorado River relies on melting mountain snow for much of its water. Since 2000, the river’s flow has decreased and often has been less than expected. Researchers found the main cause is less spring rain. The combination of drier conditions and sunnier, warmer springs delivers a dual strike to water resources in the Colorado River. With less rain over the springtime growing season, mountain plants are forced to use more snowmelt to grow. As a result, the lack of rain and increased plant water use leave less water flowing into the river.

With over 40 million people dependent on the Colorado River, the 19% streamflow decrease since 2000 has been worrying, especially because its cause is not well understood. To explain this decrease, a team of researchers focused on changes to spring weather in snow-dominated basins, which contribute over 80% of the river’s water. Results showed spring precipitation decreases since 2000 not only reduced streamflow but also correlated with higher temperatures and evaporation rates and less cloudiness. These impacts combined to intensify streamflow declines in basins with earlier snowmelt. The importance of spring precipitation to Colorado River streamflow underscores the need to improve seasonal precipitation forecasts.

This research reaffirms an increasing contribution of ET to atmospheric moisture for forest regions farther from the Atlantic, with the largest contributions happening during the dry season of the Amazon. The deuterium-based estimates of ET-P have the potential to further investigate the hydrological dynamics that control changes in the carbon and water exchanges within the Amazon.

Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region’s water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). Changes in the drivers of evapotranspiration (ET), such as aboveground biomass, could have a larger impact on soil moisture and humidity in the dry Amazon relative to the wet Amazon. The Atmospheric Infrared Sounder (AIRS) observations are sensitive to spatiotemporal variations of ET-P, enabling investigation of the spatial, seasonal, and interannual variability of ET-P over the Amazon.

Atmospheric humidity and soil moisture in the Amazon are closely linked to the region’s water balance, defined as evapotranspiration minus precipitation (ET-P). However, significant uncertainties in both fluxes complicate the assessment of water balance variations and their dependence on ET or P. By using satellite observations of deuterium content in water vapor, this research finds that the HDO (semi-heavy water)/H₂O ratio is sensitive to changes in ET-P across the Amazon. When calibrated with basin-scale estimates from terrestrial water storage and river discharge, the water vapor deuterium data reveal that rainfall primarily drives water balance variability in the wet Amazon, while ET plays a more crucial role in the dry Amazon. Consequently, changes in factors influencing ET, such as aboveground biomass, could significantly affect soil moisture and humidity in the southern and eastern regions of the Amazon compared to the wet areas.

Observations that resolve the processes that link river hydrology and hyporheic transport to production, oxidation, and flux of greenhouse gasses in river sediments can provide key information needed for improving greenhouse gas models.

Greenhouse gas (GHG) emissions from rivers are a critical missing component of current global GHG models. Their exclusion is mainly due to a lack of measurements in the field and a poor understanding of the dynamics of GHG production and emissions across space and time, which prevents optimal model parametrization. The researchers combined observations of porewater concentrations along different beach positions and depths and surface fluxes of methane and nitrous in a large regulated river during three water stages: rising, falling, and low.

Researchers conducted this study to gain insights into the interactions between hydrological exchanges and GHG emissions and elucidate possible hypotheses that could guide future research on the mechanisms of GHG production, consumption, and transport in the hyporheic zone. Observations of dissolved gas in the porewater throughout the soil column and surface flux allow scientists to determine of the effect of hyporheic transport on methane and N₂O production in the sediments, and estimate the effects of spatial and temporal variation of conductivity of the soil and water column to methane and N₂O transport. This research is the first to report these critical temporal, spatial, and vertical variation patterns of these model parameters. Hyporheic mixing and river stage are important factors in the rate of methane flux from the Columbia River. Researchers found that the river acts as an overall source of methane to the atmosphere. Peak rates were observed at an intermediate depth under low water conditions. The sign of N₂O flux changed with river stage. The relationship between soil profile of dissolved gasses and the flux of these gasses varied between methane and N₂O and among different times with different river stages.

Researchers used natural abundance stable isotopes to document pathways and mechanisms leading to the accumulation and dissipation of nitrate under aerobic and anaerobic conditions in floodplain sediments at a Rifle, Colo., field site. Their findings significantly improve the understanding of global nitrogen cycling.

Alluvial sediments subject to the seasonal rise and fall of groundwater are regions of outsized biogeochemical activity relative to their spatial extent in many floodplain environments. This study documents significant changes in the nitrogen cycle under fluctuating hydrological conditions.

A team of researchers from Lawrence Berkeley National Laboratory and Stanford University characterized subsurface nitrogen biogeochemistry at the Rifle field site where snowmelt-driven fluctuations in water table depth change the saturation profile of vadose zone sediments and hence their redox status. The team collected depth-resolved water samples over a year. They analyzed porewater nitrogen concentrations, nitrous oxide and nitrogen gas, and the natural abundance stable isotopes of nitrate (δ¹⁵N_NO3 and (δ¹⁸O_NO3) to determine the role that abiotic and biological mechanisms play in the fate of nitrate. The study concludes that biological nitrogen cycling in Rifle sediments is predominantly attributable to temporally uncoupled nitrification-denitrification reactions. As the water table rises, these reactions occur sequentially as aerobic conditions that favor nitrification and the accumulation of nitrate give way to anaerobic conditions, which favor denitrification rather than anaerobic ammonium oxidation.

Computer models for carbon, nutrient, and contaminant transport and transformations in river corridors inadequately capture current and emerging understanding of hydrological and biogeochemical processes because of a spatial scale mismatch between those processes and the systems that they impact. A new model for river-groundwater exchanges allows those processes to be represented at the scale at which they are typically studied while remaining tractable at watershed scales, thus establishing a framework for a new generation of river network biogeochemistry models.

In many streams and rivers, water is exchanged between the open channel and adjacent groundwater. This exchange enables biogeochemical reactions in the near-stream sediments to remove or transform carbon, contaminants, and nutrients. Researchers from Oak Ridge National Laboratory developed a new modeling strategy to represent these effects in watershed-scale models. The new model is equivalent to existing multiscale transport representations when there are no reactions, but, unlike those existing models, it accommodates biogeochemical reactions. In contrast to alternative representations based on diffusion, the new model is able to represent the development of sharp gradients in oxygen concentrations in sediments near the river.

While solute transport occurs primarily in flowing stream channels, biogeochemical transformations of carbon, nutrients, and contaminants often occur in highly localized metabolically active regions in the hyporheic zone, the region of saturated sediments adjacent to the stream channel. Representation of stream hyporheic-zone processes is thus a central challenge in extending stream network flow models to include biogeochemistry. Multiscale models with hyporheic-zone processes represented in subgrid models that are coupled to stream flow models provide an alternative to explicit three-dimensional representations, which are not feasible at watershed scales.

A new multiscale representation of stream hyporheic processes associates a one-dimensional subgrid model for transport and reactions with each channel grid cell in a stream network flow model. Each subgrid model represents a collection of streamlines that are diverted into the biogeochemically active hyporheic zone before returning to the flowing channel. The subgrid model is written in travel-time form, with hyporheic age serving as the independent spatial variable. In contrast to previous travel-time representations, the new model accommodates multiple mobile or immobile chemical species and general nonlinear biogeochemical reactions. Unlike alternative formulations based on multirate diffusion, the new multiscale model is able to represent biogeochemically important gradients in redox conditions.

There is little research connecting microbiomes at the genetic level to hydro-biogeochemical modeling. This study uncovers the importance of genetic diversity and dynamics in microbial communities involved in key elemental cycling pathways. For example, under extreme environmental conditions an entire biochemical pathway could be altered or eliminated if a single step in that pathway has low genetic diversity in the microbial population, and its loss could not be replaced.

Researchers investigated the role of microbial genetic diversity in two major subsurface biogeochemical processes: nitrification and denitrification. Results show that across different seasons only a few microbe species, namely Nitrosoarchaeum, carry out nitrification functions—demonstrating high resistance to environmental change. However, denitrification genes, which are more broadly distributed in the community, displayed a variety of diversity patterns and abundance dynamics—demonstrating greater microbial interactions as conditions change.

The Pacific Northwest National Laboratory research team, led by Bill Nelson, found that major environmental processes—specifically nitrification and denitrification—are maintained through a variety of diversity strategies. Historically, the bulk of biogeochemical research has focused on microbial communities at the organismal level. But this research focused on the importance of genetic distribution and diversity.

In their recent PLoS ONE paper, the researchers discuss the roles microbes play in ecological functions, the novelty of the genetic makeup of these microbes, and future research opportunities to determine which organisms are genetically expressing nitrogen cycling functions.

The novelty of this study comes from examining the temporal dynamics of diversity at the genetic level. To evaluate all genes in the nitrification and denitrification pathways, novel Hidden Markov Models (HMMs) were developed that can recognize the broad diversity found in environmental samples. The team found that while different environmental conditions impair microbiome growth and the gene expression of some populations, at the same time, those conditions can stimulate other genes and their associated microbes. High biodiversity at the organism or genetic level creates more resiliency, and the microbiome community can respond more rapidly to environmental changes.

In the future, researchers hope to more fully evaluate how diversity dynamics affect community metabolism function, including the role of metatranscriptomes or metaproteomes. The results of such future studies could help determine which organisms are expressing nitrogen cycling functions and could be incorporated into biogeochemical models of ecosystem function.

This work demonstrates that groundwater-river water exchange could dramatically alter ecosystem carbon uptake and evapotranspiration. Also, to predict the response of terrestrial ecosystems to future Earth system changes, the role of lateral water flow in the groundwater-river continuum must be considered, along with the roles of precipitation and other meteorological variables.

Lateral groundwater-river water exchange could play an important role in determining how ecosystem fluxes will respond to changing hydroclimatic conditions in semiarid regions. However, few studies have collected eddy covariance measurements to quantify the impacts of groundwater-surface water interactions on ecosystem fluxes. Researchers collected a unique dataset demonstrating the critical impact of groundwater-surface water exchange on riparian ecosystem fluxes.

This research used one year of data collected at two newly established eddy covariance sites (AmeriFlux sites US-Hn1 and US-Hn2) to examine the impact of groundwater-surface water exchange on riparian ecosystem fluxes. In an upland ecosystem without groundwater access, during the dry season carbon uptake was strongly constrained due to the lack of available moisture. In contrast, in a riparian ecosystem, lateral groundwater-river water exchange provided an additional water source, which allowed the ecosystem to maintain high carbon uptake during the dry season.

Drylands are the largest source of interannual variability in the global carbon sink. Any changes in dryland ecosystems under future climate scenarios would have large implications for the global carbon cycle. This work improves the understanding of how accelerated dryland expansion impacts the productivity of drylands. Dryland expansion and climate-induced conversions among subhumid, semiarid, arid, and hyperarid subtypes will lead to substantial changes in regional and subtype contributions to global dryland GPP variability.

Drylands, such as grasslands, savannas, and deserts, are expected to expand and become more arid at an accelerating rate over the next century. The effects of this expansion and degradation on their gross primary production (GPP) remain elusive. Using model projections coupled with data from a number of FLUXNET sites, a multi-institutional team of scientists quantified the impact of accelerated expansion of drylands on their productivity through the end of this century. In addition, as different subtypes of drylands expand and convert into other types, large changes will be seen in how regional drylands and subtypes will contribute to GPP.

Drylands, such as grasslands, savannas, and deserts, cover approximately 41% of the Earth’s land surface and support more than 38% of the global population. Global dryland ecosystems with high plant productivity account for approximately 40% of global land net primary production (NPP). They also act as the dominant global land carbon dioxide (CO₂) sink and, over recent decades, have contributed the largest amount of net CO₂ flux, which affects interannual variability.

To study the impact of accelerated dryland expansion and degradation on global dryland GPP, researchers from Washington State University and Pacific Northwest National Laboratory assessed MODIS GPP data from 2000 to 2014 and the 5^th Coupled Model Intercomparison Project (CMIP5) aridity index (AI). Results from this modeling study show a positive relationship between GPP and AI over dryland regions, with total dryland GPP increasing by the end of the 21^st century by 12% ± 3% relative to the 2000–2014 baseline. However, GPP per unit dryland area will decrease with degradation of currently existing drylands, meaning that global GPP may not increase. Changes in the expansion and conversions among different subtypes of drylands will lead to variability in regional and subtype contributions to the global GPP of drylands.

Researchers in this study used a cubic fitting method to find the relationship between dryland GPP and AI data from CMIP5. With long-term GPP data, they analyzed the trend and interannual variability of dryland GPP through the end of the century. To verify the accuracy of projected GPP data, the team compared projected GPP data to GPP data from 15 CMIP5 models. The results showed agreement with the modeling data in eight regions during the same period.

In contaminated sites, using ¹³C-labeled electron donors coupled with the reduction of metal or of organic contaminants is a viable method in estimating the efficiency of biostimulation and the fate of organic electron donors. Our approach may be transferred to other contaminated sites by a variety of metal and organic contaminants.

Soils and groundwater contamination by hexavalent chromium Cr(VI) is common in industrial areas and is a serious threat to water quality and human health. In a field-scale experiment of microbial Cr(VI) reduction, the authors demonstrate the transfer of carbon from the original electron donor to the metabolic products.

Hexavalent chromium Cr(VI) is a common inorganic contaminant in soils and groundwater of industrial areas and represents a serious threat to water quality and human health. Among the various techniques currently available, in situ biostimulation has been recognized as a relatively cost-effective and valuable method for the remediation of contaminated groundwater. To date, the transformation and fate of organic electron donors used to stimulate Cr(VI) reduction in the field has been reported only in limited studies due to analytical and technical challenges. In this work, the authors report field-scale experimental results from in situ microbial Cr(VI) reduction stimulated via injection of ¹³C-labelled lactate. Simultaneously with Cr(VI) reduction the authors used concentrations and carbon isotope ratios of metabolic products to monitor the carbon transfer from the original ¹³C-labelled lactate. The authors also monitored the carbon isotope ratios of phospholipid fatty acids (PLFA) to demonstrate the transfer of carbon from ¹³C-labelled lactate to a portion of the microbial community.

This study is among the very few that have been able to examine the ecosystem effects of multiple disturbances in natural settings. It bridges scientific disciplines by linking changes in soil chemistry, microbiome structure, and biogeochemical function using methods from ecological theory.

Multiple disturbances to an ecosystem that follow in close succession have the potential to compound their independent effects and strongly alter ecosystem structure and function. In this work, a team of scientists examined how back-to-back extreme events in the form of a burned landscape followed by extreme precipitation could affect a forest landscape. They found that a forest fire leaves marks far deeper than the destruction visible on the surface, making the soil more vulnerable to damage from subsequent flooding.

Extreme natural events are often thought to be in isolation from each other—a big wildfire in one season, heavy rains in another. But as climate change makes such disturbances more frequent and intense, ecosystems are likely to face chains of disturbance events in relatively quick succession, with one instance affecting the ability to recover from the next. The compounding effects of multiple disturbances on ecosystem health remain poorly understood, since the unpredictability of natural events makes them challenging to study.

To better understand the issue, a team of researchers repeatedly collected soil samples in Boulder, Colorado’s Four Mile Canyon for over three years after a major wildfire. At the 37-month mark, an extreme precipitation event dropped more than 400 millimeters of rain within a week. Samples were collected from an undisturbed forest landscape and an adjacent fire-disturbed landscape, allowing the researchers to investigate the combined effects of multiple disturbances in comparison to a landscape experiencing only flooding. Researchers assessed the samples’ soil edaphic properties (moisture, pH, percent nitrogen, and percent carbon); bacterial community composition and assembly; and soil enzyme activities. They found that previous fire exposure caused forests to be more strongly affected by a subsequent flooding event than unburned forests. This was driven by increases in pH, shifts in microbiome structure, and increased microbial investment in nitrogen versus carbon cycling.

In the United States, irrigation is estimated to consume about 355 billion gallons of water per day. In semiarid and arid regions across the country where vegetation growth is limited by water availability, production of crops could become difficult, if not impossible, without irrigation. This study demonstrates that a widely used land surface model can be a tool to study and predict how irrigation could influence hydrologic and nutrient dynamics throughout a watershed that contains natural vegetation, crops, urban land, and rivers.

Irrigation affects agricultural ecosystems in more ways than growing crops. Increased soil moisture increases atmospheric processes associated with evaporation. The additional water also accelerates the decomposition of organic matter in the soil. Now a team of researchers, including scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory, have used a model to study how irrigation alters these processes on a watershed scale. Using version 5 of the Community Land Model, which accounts for variation in land use and crop type, they simulated water, carbon, and nitrogen budgets at 1-km resolution in a semiarid watershed in the northwestern United States.

The Community Land Model Version 5 (CLM5) simulates hydrological processes, surface energy fluxes, and biogeochemical processes, including runoff generation, soil moisture hydrology, and carbon and nitrogen allocation. In this work, a multi-institutional team of researchers used CLM5 to study the effects of irrigation on these processes in the Upper Columbia-Priest Rapids watershed in Washington state. This semiarid watershed is dominated by cropland and contains natural vegetation, urban areas, and rivers.

The researchers calibrated and evaluated their model using Moderate Resolution Imaging Spectroradiometer satellite data and measurements of water, energy, and carbon fluxes collected at a flux tower site in the region. Their results show that irrigation fundamentally alters the hydrologic and biogeochemical dynamics of the watershed. The additional water from irrigation increases surface evaporation and runoff. Increased crop productivity in response to irrigation increases carbon storage in the watershed. The additional water also increases the rate of denitrification and mineralization during the growing season.

Approximately 75 percent of sites regulated under the federal Superfund law and the Resource Conservation and Recovery Act are located within half a mile of surface water. Understanding and modeling contaminant fate and transport where groundwater and river water interact are essential to assessing risks and making sound remedial intervention decisions.

A uranium plume persists in a river corridor along the Columbia River at a site where highly dynamic variations in water flow and sediment distribution impact contaminant transport and geochemical fate. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) integrated multi-scale field and laboratory experiments with reactive transport modeling to describe the behavior of this plume.

Experimental results revealed significant spatial variability in uranium adsorption/desorption behavior. Modeling demonstrated that ambient hydrologic and geochemical conditions, as well as variation in sediment physical and chemical properties contributed to complex plume behavior and its persistence. This research underscores the challenges in adequately characterizing this type of site to model reactive transport processes over scales of 10 meters or more.

The behavior of a persistent uranium plume at a well-studied site along the Columbia River has not been adequately simulated by reactive transport models, either at the local scale or at system scale. Yet, understanding the behavior of this plume on a system scale of over hundreds of meters is essential for eventual remediation. The hydrologic complexity at this site, along with the lack of information on the in situ physicochemical properties of the hydrologically connected aquifer and river corridor sediments, make geochemical modeling challenging.

To learn how sediments at this site hold uranium, scientists injected non-reactive tracers and hexavalent uranium in wells near the river and measured transport to the groundwater. Then they simulated 3D groundwater flow and reactive transport processes associated with the injections using the high-performance code PFLOTRAN. They used the non-reactive tracer to refine models of aquifer physical heterogeneity to better capture the change in flow in space and time. The scientists also tested various aspects of the subsequent geochemical modeling to understand how they impact the model’s ability to reproduce observed uranium transport behaviors during the experiments.

The simulation results were sensitive to geochemical boundary conditions, underscoring the importance of long-term, high-resolution monitoring of key aqueous species along with uranium concentrations to better constrain reactive transport models. The scientists concluded that thorough characterization of variations in geochemical conditions, as well as hydrological and geochemical properties such as permeability and sorption site concentrations, are important to inform initial and boundary conditions for these models of highly heterogeneous systems under highly dynamic conditions.

In this study, hourly and daily river stage variations, controlled by upstream dam operations, increased both water exchange rates and nutrient consumption in the river corridor. These results indicate that frequent flow variations along rivers regulated by dams generally increase nutrient cycling in river corridors, especially for fast reactions such as aerobic respiration. A better understanding of the fundamental relationships between river flow, water exchange, and nutrient cycling can inform decisions regarding dam operations to regulate the flow of river water.

Large dam-regulated rivers experience complex flow variations. These variations change the patterns of how river water and groundwater mix, which influences nutrient cycles and thus river water quality. A team of scientists from Pacific Northwest National Laboratory and Vanderbilt University used numerical particle tracking to evaluate how flow variations interact with physical subsurface variations to control the spatial and temporal patterns of groundwater–river-water mixing, and alter nutrient consumption rates within the Hanford Reach of the Columbia River. They found that the complex river stage variations led to complex transit-time distributions because exchange pathways change under different flow conditions. Numerical particle tracking provides an efficient method to capture the substantial variability and dynamics of water exchange in complex river corridors where field experiments are not feasible.

Dams upstream and downstream of the Hanford Reach of the Columbia River induce frequent variations in river stage. To understand how this variation affects hydrological exchange between river water and groundwater and downstream nutrient cycling, researchers first used the extensive site characterization data available for this river corridor to build a baseline groundwater flow and transport model. Then they used a forward particle-tracking method to estimate transit-time distributions for water flowing through the subsurface aquifer during a seven-year simulation window. The researchers then paired the estimated transit-time distributions with the rates of aerobic respiration and denitrification known to occur along this section of the river corridor to quantify rates and amounts of nutrients being processed. Finally, the researchers evaluated the effects of dam operation on transit times and nutrient cycling rates and amounts.

The researchers found that dam-induced high-frequency variations in flow increased hydrologic exchanges between the river water and groundwater. These increases accounted for 44% of nutrient consumption in the river corridor along the Hanford Reach. The numerical particle-tracking approach developed in this study can be extended to other study sites that have robust site characterization data, and this approach can be very useful for extending nutrient cycling models from river reaches to larger-scale watersheds and basins.

More realistic numerical representations of the permeability of subsurface sediments lead to improved predictions of groundwater flow and the concentration of constituents that are transported with the flow. The data assimilation framework can also be applied to estimate other subsurface properties from field measurements or from data from other systems, such as watersheds, as long as they can be categorized into a few discrete representative units.

Researchers developed a new iterative data assimilation framework to more accurately describe the permeability of subsurface sediments in numerical models when using facies; a system that classifies dissimilar sediments into distinct geological units that share important features of interest to modelers. The iterative framework applies data from field observations and experiments to inform the delineation of facies at the start of each model run. Researchers achieved further refinements at each iteration through the application of statistical constraints that maintain geologic continuity among adjacent locations.

Observational data on subsurface permeability is limited for most watersheds because of the impracticality of digging enough boreholes or wells to capture the heterogeneous nature of the subsurface environment. To solve for this limitation, researchers have widely adopted approaches that estimate permeability from field experiments such as a) measuring how water levels at a cluster of wells change when water is pumped at a nearby well, or b) monitoring how quickly a tracer released at one well reaches other wells in the aquifer. The U.S. Department of Energy’s (DOE’s) Hanford 300 Area Integrated Field Research Challenge site, for example, is well characterized from data assimilation methods that were used to understand the long-term persistence of nuclear fuel fabrication wastes disposal from 1943 to 1975.

The use of a facies approach to segment the subsurface reduces complexity in numerical models by grouping heterogeneous sediments into distinct homogenous units defined by hydraulic, physical, and chemical properties. A major difficulty with existing facies-based approaches in numerical models is that each facies is treated as an independent unit. Therefore, these models fail to capture the spatial continuity of subsurface sediments. Researchers developed a framework that maintains continuity between neighboring facies in numerical models. This better reflects true subsurface geology and thereby groundwater movement. The improvements come from an iterative data assimilation approach that incorporates direct and indirect data about subsurface permeability gathered from field observations and experiments at the start of each model run as well as the application of statistical constraints about subsurface geology. The data assimilation and statistical constraint steps are re-imposed for each iteration, leading to refined facies delineation. This framework reduces uncertainty about the spatial distribution of sediment types in the subsurface, which results in more accurate predictions of groundwater flow and constituent transport.

The team evaluated performance of the new framework on a two-dimensional, two-facies model and a three-dimensional, three-facies model of DOE’s well-characterized Hanford 300 Area that were conceptualized from borehole and field tracer experiments. The results of the research shows that the framework can identify facies spatial patterns and reproduce tracer breakthrough curves with much improved accuracy over facies-based approaches that lack spatial continuity constraints. With additional data, the team suggests that the framework can also be used to categorize biogeochemical reactive units in an aquifer.

Researchers have developed a new mechanistic modeling approach for estimating (N₂O production from denitrification and nitrification in water bodies and introduce water residence time as a critical limitation on biological activity.

Nitrous oxide (N₂O) is a key greenhouse gas, but emissions from inland waterways remain a major source of uncertainty in greenhouse gas budgets. The Intergovernmental Panel on Climate Change (IPCC) has proposed emission factors (EFs) of 0.25% and 0.75%, but studies have suggested that both these values are either too high or too low. A new approach to modeling nitrous production concludes that the IPCC EFs are likely overestimated by up to an order of magnitude.

The authors calculate global nitrous oxide (N₂O) emissions from rivers, reservoirs, and estuaries within a range of 10.6 to 19.8 Gmol of nitrogen (N) per year (148 to 277 Gg N per year). This estimate is more than half, and up to an order of magnitude, lower than most studies based on IPCC guidelines. Despite the much-reduced N₂O flux estimates, the research team found that anthropogenic perturbations to river systems have doubled to quadrupled N₂O emissions from inland waters. The researchers suggest that IPCC EFs of 0.25% and 0.75% are too high to be applied across all rivers, estuaries, and reservoirs. Instead, the team estimates the following EF ranges: 0.004% to 0.005% for rivers, 0.17% to 0.44% for reservoirs, and 0.11% to 0.37% for estuaries.

Most N₂O emissions in estuaries and reservoirs originate from nitrification, while denitrification tends to dominate emissions in rivers because of the shorter residence times. Researchers therefore expect worldwide N₂O emissions from inland waters to rise substantially in the coming decades because of the ongoing global boom in dam construction. This construction will nearly double the number of large hydroelectric dams on Earth, increasing water residence within these water bodies.

Riparian zone and deep soil microbial communities are functionally differentiated from shallow hillslope communities based on their metabolic capacity. Researchers anticipate that the drivers of community composition and metabolic potential identified along this representative hillslope-to-floodplain transect will predict patterns across similar transects within mountainous systems.

Within mountainous watersheds, microbial communities affect water chemistry and element fluxes as water from precipitation events discharges through soils and underlying weathered rock. However, there is limited information regarding the structure and function of these communities. Within the East River, Colorado, watershed, a team of researchers conducted a depth-resolved, hillslope-to-riparian zone transect study to identify factors that control how microorganisms and their functionality are distributed. The researchers found that microbial community structure and metabolic potential are strongly affected by distance from the river and proximity to groundwater and underlying weathered shale.

Metagenomic and geochemical analyses indicate that distance from the East River and proximity to groundwater and underlying weathered shale strongly impact microbial community structure and metabolic potential. Riparian zone microbial communities are compositionally distinct from the phylum to species level from all hillslope communities. Bacteria from phyla lacking isolated representatives consistently increase in abundance with increasing depth, but only in the riparian zone saturated sediments did the researchers find Candidate Phyla Radiation bacteria. Riparian zone microbial communities are functionally differentiated from hillslope communities based on their capacities for carbon and nitrogen fixation and sulfate reduction. Selenium reduction is prominent at depth in weathered shale and saturated riparian zone sediments and could impact water quality.

Glacier erosion strips away soil, leaving only bedrock behind. Understanding when bedrock was first exposed by melting ice is needed to determine how quickly bedrock is broken down into soil. In the Colorado River headwaters, glaciers were largest about 18,000 years ago and had mostly melted by 13,000 years ago, indicating all the valley soil has formed since then. Based on how far the ice extended down the valley, computer models indicate temperatures were around 7°F (4°C) cooler 15,000 years ago than today.

The Rocky Mountains were covered by glaciers during the ice age. Glaciers eroded deep valleys and left huge piles of sediment and bedrock boulders behind when the climate warmed. Since the ice melted, boulders have been continuously bombarded by cosmic rays, which are produced by exploding stars and that travel through space before colliding with Earth. Cosmic rays have such high energy that they break atoms apart and form new ones when they crash into rock. By measuring the concentration of these new atoms in boulder samples from the Rocky Mountains, scientists can determine when glaciers last filled valleys with ice.

By measuring tiny quantities of rare atoms, a multi-institutional team of researchers determined the timing of glaciation in the East River watershed near Crested Butte, Colorado. The glacial history is like other valleys in the Rocky Mountains. This research indicates only several degrees of temperature change caused glacier melting as Earth warmed during the last ice age’s transition to a warmer Holocene climate.

The East River watershed is a site of intensive research focused on how water changes as precipitation moves through soil before becoming streamflow. Chemical reactions change over time as the rock left behind by melting glaciers is weathered, changing the soil and bedrock chemical composition. With knowledge of when the glaciers melted, scientists can now determine the rates at which chemical reactions occur and build better models to predict how rock weathering influences water quality.

This research addresses the critical problem of balancing accuracy and computational efficiency in modeling of large or entire watersheds. A team from Oak Ridge National Laboratory (ORNL) developed a meshing technique that significantly reduces computational costs while maintaining accuracy. Resolving the stream corridor with stream-aligned meshing achieves more realistic flow, inundation, and connectivity patterns in the stream network. This advancement unlocks new opportunities for representing river-specific hydrodynamics, biogeochemistry, and management infrastructure in broader, basin-scale hydrology models at a lower computational cost, and it also paves the way for understanding basin-scale watershed behavior emerging from intricate stream hydro-biogeochemistry.

Stream channels are vital regions where water, nutrients, sediments, and energy from hillslopes converge, supporting diverse ecosystems. Large-scale watershed models struggle to accurately represent these narrow, dynamic regions without requiring highly detailed mesh and excessive computational power. A team of researchers introduced a novel stream-aligned meshing technique that effectively models stream channels, resulting in realistic inundation patterns near streams and rivers. This method maintains the accuracy of a highly detailed mesh while reducing computational cost.

A new study from researchers at ORNL addresses the challenge of accurately representing stream corridors in large watershed models, where traditional methods using triangulated or raster-based meshes require extensive refinement and excessive computational effort. The team developed a new meshing technique that aligns long quadrilateral cells with streams, meshes the remainder of the land surface with a coarser triangle-based mesh, and extrudes vertically to form a 3D mesh. This approach maintains the accuracy of highly refined models while drastically reducing computational resources—achieving a 96.4% reduction in mesh size and a 99.7% reduction in computational costs.

Simulations using the Advanced Terrestrial Simulator demonstrate this technique produces more realistic flow, inundation, and connectivity patterns in the stream network. An optional hydrologic conditioning process, tailored specifically for stream corridor cells, eliminates erroneous obstructions and generates more reliable water depth representations. The method is integrated within the Watershed Workflow tool, a Python-based library for watershed simulation, and significantly enhances the capacity to represent stream processes. By lowering computational costs, the method makes stream-specific hydrodynamics and related processes accessible for large-scale hydrological applications.

High-intensity hurricanes defoliate forest canopies, resulting in a large pulse of plant debris to soils and creating gaps that alter soil microclimate and forest structure as the forest recovers from disturbance. This study demonstrates through measurements and modeling that high-intensity hurricanes result in a younger total soil carbon pool with faster mean transit times because hurricane disturbances increase the replacement of older soil carbon with new carbon from plant debris. This finding suggests increasing frequency of intense hurricanes will speed up carbon cycling rates in tropical forests, making soil carbon more sensitive to future tropical forest stressors.

Tropical forests account for over half of the global terrestrial carbon sink, but climate change, including increasing intensity of extreme events, threatens to alter the carbon balance of these ecosystems. A team of researchers quantified changes in soil carbon storage and transit time across a forested watershed over 30 years—a period that included four high-intensity hurricanes. The pulses of carbon inputs associated with defoliation of the forest canopy during these hurricanes and the reduction of litter inputs during the post-hurricane recovery period altered the distribution and accelerated decomposition and cycling rates of soil carbon.

The team sampled soils from the same locations across Bisley Watershed in Luquillo Experimental Forest in Puerto Rico in 1988 and 2018. Researchers quantified and compared over time carbon storage, distribution with depth and across pools that differ in residence time and degree of protection from soil microbes, and soil carbon pool radiocarbon and stable isotope signatures. Carbon increased slightly from 1988 to 2018 in the physically protected organic matter pool, but changes in the particulate and mineral associated pools as well as total soil carbon were not detected. Changes in radiocarbon values of soil carbon pools over time suggest that mean carbon transit times decreased from 1988 to 2018.

A reduced complexity soil carbon and radiocarbon model simulated the plant input pulses associated with hurricanes followed by reduced inputs over a 5-year recovery period post-hurricane. The model was fit to observed data to identify the best structure and initialization parameters. The model showed hurricane disturbances resulted in faster incorporation of carbon from plant debris into the physically protected organic matter pool coupled with higher rates of older soil carbon loss, relative to no-hurricane control conditions. These results suggest that hurricanes’ increasing intensities are amplifying soil carbon cycling, which could make hurricane-impacted ecosystems more vulnerable to future events.

The Colorado River, providing the water supply to 40 million people, is mainly sourced by the snowmelt in the Rocky Mountains. To understand the potential of water availability changes, knowledge about the consequences of changes in snowpack and air temperature on the river’s headwaters is crucial. Data from the past 7 years demonstrate that an increase in the relative contributions from high-elevation snowmelt underlines the critical role mountains play in sustaining the water supply. Because snowpack at lower elevations will be impacted most by climate change, the snowmelt water from snowpack at the highest elevations will become more important to sustain ample water flow throughout the summer.

The isotopic signal of water, a natural tracer, was measured in the snowpack and stream water for over 7 years in the mountainous headwaters of the Colorado River. Measurement data provided insights on the share of the water in the headwaters sourced from the highest elevations during the snowmelt period. In years with relatively little snowfall and warm air temperatures, the share of high-elevation snowmelt contributions to the headwaters was highest. Researchers detected the observed variations of high-elevation snowpack contributions during snowmelt in both small mountainous catchments and large watersheds in the Upper Colorado River.

The Watershed Function Science Focus Area (SFA) measured stable water isotopes in the snowpack and headwater rivers in the Upper Colorado River basin over 7 years. These measurements enabled the multi-institutional team to relate the spatial variation in the snowpack isotope ratio along an elevation gradient with the snowmelt stream discharge and its isotopic composition based on mixing analyses. Results of this tracer-based method highlight the snowpack’s importance in the highest elevations of the Rocky Mountains for streamflow generation.

Connecting the U.S. Department of Energy–funded SFA efforts with the stream/river monitoring led by the U.S. Geological Survey allowed the team to scale up from the intensely measured headwaters to larger watersheds. Results suggest the temporal variation of high-elevation snowmelt contributions is transferrable to other snow-dominated mountainous regions. Changes in the stream water isotope dynamics during the snowmelt period could therefore be used to identify changes in the snow water equivalent (SWE) of the snowpack that would be challenging to observe with ground-based instrumentation or remote sensing.

As Earth’s climate changes, understanding water flow through hillslopes is critical to protect freshwater resources. The knowledge gained from this study helps to better predict how changes in rainfall patterns and snowmelt will affect water resources, both in terms of quantity and quality. By knowing how water travels through hillslopes, it is easier to predict how much water reaches streams and rivers during different seasons. This understanding of water movement also informs how much water may be stored and how it becomes available to plants, which helps preparations for floods during heavy rain or snowmelt and droughts during dry periods. The way water moves through the soil also affects its quality. Understanding these pathways enables prediction of where contaminants might end up and how to manage them.

A multi-institutional team of researchers studied how water moves through a mountainous hillslope during snowmelt and rain. They buried sensors and used geophysical imaging and weather data to track water flow above- and belowground. The studied hillslope had two parts: a steep rocky upper section with tall trees and a gentler lower section with deeper soil mostly covered by meadow plants. The team found water on the steep slope moved mostly sideways through shallow soil layers, except where trees were rooted. These roots and cracks in the rock channeled water down deeper. On the lower, flatter section, water moved mostly up and down, soaking deeper into the soil. This study shows the shape of the land and what is underneath the surface strongly affect how water flows through a hillslope. Even over short distances, these differences created distinct water movement patterns.

Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting the belowground water in hillslopes with streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station, a multi-institutional research team led by Lawrence Berkeley National Laboratory monitored above and belowground water driving the hydrological response of a mountainous hillslope in Colorado during snowmelt and the summer monsoon season. The hillslope transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock and a gentle lower part underlain by colluvium. Conifers are the main vegetation cover on the upper part of the hillslope, with grass and veratrum on the lower part.

Combined with a simplified hydrological model, the team showed the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow interrupted by vertical flow mostly at tree locations. This vertical flow is likely facilitated by water movement along bedrock fractures and the plant roots. Vertical flow and upstream-driven groundwater dynamics prevail at the colluvium, presenting a very different hydrological behavior than the upper part. These results show subsurface structure and features have a strong control over the hydrological response of a hillslope and can create considerably varying hydrological dynamics across small spatial scales.

Mountainous ecosystems are facing a warmer and drier future, which can make these environments more vulnerable to other changes and disturbances. For example, forests could die and be replaced by meadows. This study illustrates how different watershed properties in mountainous ecosystems affect the retention and release of nitrogen into headwater streams. Contrasts such as this can be used to predict what future nitrogen releases and cycles might look like as these ecosystems respond to a warming climate.

Nitrogen is a nutrient critical for ecosystem function. Determining how nitrogen enters, cycles, and disappears from watersheds is integral to predicting how the nitrogen cycle will respond to climate change. Using novel analyses, a multi-institutional team of researchers showed that conifer forest–dominated watersheds hold on to most of their nitrogen. In addition, the nitrogen lost into headwater streams from these watersheds is never assimilated into the ecosystem. By contrast, a watershed with a mixed vegetation type (e.g., conifers, aspen, meadows) cycles nitrogen more frequently throughout the year and releases more to their headwaters.

Within the Upper Colorado River Basin, the East River and Coal Creek drain two landforms that have contrasting vegetative, geologic, and geomorphologic traits. The East River watershed has a diverse vegetation coverage, wide floodplains, and a nitrogen-rich Mancos Shale bedrock. The East River exports 3.5 times as much nitrate (NO_3–) relative to Coal Creek, which has a conifer-dominated watershed. While this is partly explained by the larger size of the East River, the distinct functional traits of the two catchments foster different nitrogen cycling.

A multi-institutional team of researchers showed that physical and biological processes are critical in shaping NO_3– export patterns from the East River. Analysis of NO_3– isotopes (i.e., δ¹⁵N_NO3 and δ¹⁸O_NO3) allowed the team to track nitrogen movement throughout both watersheds and provided data that showed the East River watershed is a strong hotspot for biogeochemical processing of nitrogen. In contrast, the Coal Creek watershed retained nearly all the NO_3– deposited from the atmosphere, and NO_3– export was controlled by hydrological traits. This more conservative nitrogen cycle within the Coal Creek watershed is likely due to the abundance of conifer trees and smaller floodplains, which retain more NO_3– overall and reduce cycling prior to export. This study highlights the value of using isotopic analyses to link watershed traits to mechanisms of watershed element retention and release.

A multi-institutional team of scientists developed a new ML-based approach that provides a systematic way to combine results from watershed simulations to study disturbances in addition to other key environmental factors like snowmelt and soil moisture variability. The approach groups watershed areas with similar environmental characteristics to identify and map zones that capture bedrock-to-canopy properties and identify the most representative hillslopes. This approach highlights the power of ML to extract critical information from multiple types of watershed data, including both simulation and satellite products, leading to more accurate model-guided monitoring design and hypothesis generation.

Hydrological simulations and machine learning (ML) approaches provide a systematic approach to guide placement of watershed monitoring locations, characterization, and experimental research so disturbances associated with climate change, such as droughts, can be monitored to determine their impact on downstream water availability and quality. Advanced computational technology could enable scientists to answer complex questions, such as the best locations for sensor and experimental plot placement or how representative a particular location might be of an entire watershed.

To optimize the selection of sites most representative of specific factors or conditions for a watershed, a multi-institutional research team developed a systematic method using ML that combines simulation and satellite data to identify the most appropriate watershed monitoring locations. The team applied the ML approach to study interactions among snow, soil, and plants using data from the East River watershed in Colorado. Results showed that drought sensitivity is significantly correlated with model-derived soil moisture and snowmelt over space and time. The approach also identified the watershed locations with high or low sensitivity to drought in addition to the most representative locations in the watershed accessible by trail or road in each of these areas. These findings can help scientists select the most suitable sites for monitoring watershed characteristics.

RH is a sensitive parameter, and its variations based on the calculation of SVP with or without an over-ice correction meaningfully impact physically based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions when severe flooding (inundation) and cool air temperatures are present, researchers should carefully evaluate how humidity data is estimated for land surface and earth system modeling. These findings have implications in assessing the data quality of humidity variables such as vapor density/diffusivity and simulation performance of surface-subsurface modeling in many other cold regions worldwide.

Near-surface air humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity (SH), relative humidity (RH), and absolute humidity. These different forms can be interderived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to and in equilibrium with liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges without considering the distinction between liquid water and ice.

These different approaches can result in variations in humidity estimates that may impact understanding of surface-subsurface thermal hydrological dynamics in cold regions. However, the degree to which these approaches affect land surface and Earth system model predictions under a changing climate is unknown. In this study, a team of researchers comprehensively analyzed the variations of the relative humidity from SVP with or without over the ice surface and its impact on the predictions of hydrothermal dynamics at a permafrost site using a physics-rich land surface model.

This study is among the first to use a physically based permafrost column land surface model to comprehensively examine the impact of often ignored humidity variations due to the (1) different SVP calculations on surface-subsurface thermal hydrology and snow processes and (2) potential effects of global warming and variations in precipitation and surface water levels. Simulation results and findings provide implications for the correct interpretation of humidity data based on SVP formulation and can be applied to improve parameterization schemes for land surface and Earth system modeling studies under a warming climate.

Humidity data based on SVP formulations are not always calculated separately for water and ice surfaces. The SVP calculated in equilibrium with an ice surface yields higher RH values (up to 40%) if the air temperature is below freezing. Snow depth and sublimation vary by up to 30% depending on whether SVP is calculated in equilibrium with an ice or water surface. Active layer thickness and the shape of thaw front propagation are also sensitive to RH data and SVP formulations when water is ponded above the ground (inundation). For example, the difference can be up to 0.2 m in simulated annual maximum thaw depths. Hydrological simulations for permafrost environments are most sensitive to the formulation of SVP in wet climate conditions. Therefore, land surface and Earth system modelers should carefully evaluate their meteorological forcing data and report any assumptions made in converting between SH and RH, especially when surface inundation occurs in nontropical and high-latitude regions with cold climates.

Overall, the study provides directions for future work and suggests that humidity data could significantly control snowpack and active layer thickness in permafrost regions, especially in those with limited drainage resulting in a perched near-surface water table. Future efforts to predict water vapor flow and related solute/nutrient (and microbial) dynamics should directly address the impacts of humidity and water vapor content on the thawing permafrost catchment.

Permafrost extent is heterogeneous over spatial scales too fine to be accurately predicted by the available coarse-resolution map products. Researchers developed new high-resolution maps of permafrost extent for areas on the Alaskan Seward Peninsula using machine learning algorithms that incorporated geophysical data and remote sensing. These new maps indicate the potential for using machine learning and high-resolution field and remote sensing data to generate spatial predictions of permafrost at scales relevant to land managers and policy-makers.

Permafrost soils are a key component of Arctic and sub-Arctic ecosystems and the global carbon cycle. As Arctic climates warm, permafrost thaw has the potential to release large quantities of carbon into the atmosphere, further increasing warming. Moreover, permafrost thawing can cause rapid ground surface subsidence, which can severely damage infrastructure.

Current maps of predicted permafrost extent are too coarse to adequately evaluate either the potential contribution of thaw to the atmospheric carbon or infrastructure vulnerability. A team of researchers generated new high-resolution maps of permafrost extent using machine learning. These maps and algorithms provide a future direction for generating policy-relevant maps of permafrost extent.

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging crucial infrastructure for local communities. Regional and hemispherical permafrost maps are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw.

A team of researchers trained machine learning models to generate meter-scale maps of near-surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near-surface permafrost presence from measurements of soil temperature and electrical resistivity.

The team trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k-fold cross-validation, and the modeled extents of near-surface permafrost were compared to the observed extents at each site.

At-a-site near-surface permafrost distributions predicted by the ERTr produced the highest accuracy (70% to 90%). However, the transferability of the ERTr to sites outside of the training dataset was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at-a-site prediction (70% to 83%), yet they had greater accuracy when transferred to the nontraining site (62% to 78%).

These models demonstrate the potential for integrating high-resolution spatial data and machine learning models to develop maps of near-surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

Results showed how future warming at levels consistent with the Intergovernmental Panel on Climate Change projections will result in transformative changes to the winter season in boreal peatlands, with impacts on how these ecosystems function and their impact on the climate system.

Climate change is reducing the amount, duration, and extent of snow across high-latitude ecosystems. But, in landscapes where persistent winter snow cover develops, experimental platforms to specifically investigate interactions between warming and changes in snowpack and impacts on ecosystem processes, have been lacking.

Experimental warming observations at the large-scale Spruce and Peatland Responses Under Changing Environments (SPRUCE) study in northern Minnesota enabled direct quantification of how future climate change will influence snowpack and associated hydrology of important ecosystems.

A team of researchers used the SPRUCE study, an active field warming experiment, to disentangle changes in winter precipitation forms under plausible future winter warming. Even modest levels of warming had severe negative impacts on snow variables. For example, warming of just +2°C was sufficient to reduce the number of winter days with a 5 cm snowpack by about 50%. Reductions in snow cover have feedback effects on local winter climate because shrub‐covered ground reflects less solar energy than snow‐covered ground. Researchers estimated that because of this so‐called “snow‐albedo feedback,” maximum daytime air temperature will be elevated by up to about 1°C above snow‐free ground when compared to snow‐covered ground.

Even small uncertainties surrounding the amount of carbon that may be sequestered or lost from Arctic ecosystems can propagate and significantly limit the ability to make adequate policy decisions. In particular, model structural uncertainty is difficult to quantify and can be related to parameter-based uncertainty. This paper provides a framework for combining model structural and parameter-based uncertainty into one analysis and improves understanding of each model’s strengths and weaknesses. This framework will further help to ensure models are applied appropriately.

Climate change significantly impacts Earth’s ecosystems and carbon budgets. In the Arctic, this may result in a historic shift from a net carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic change demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions.

One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. Researchers identified a mismatch for three TBMs (DVM-DOS-TEM, SIPNET, and ED2) and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parameterization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, researchers developed a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes.

Researchers examined three ecosystem models (DVM-DOS-TEM, SIPNET, and ED2) for parameter-based uncertainty and structural considerations and developed a CLD for the Arctic and boreal ecosystem. The team mapped model parameters to processes in the CLD and assessed parameter vulnerability via the internal network structure. One important substructure, feed-forward loops (FFLs), describes processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty.

Researchers found the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, the team identified key parameters responsible for limited model accuracy that should be prioritized for future data sampling to reduce model uncertainty.

This study addresses one of the biggest challenges of observing vegetation in the Arctic, providing a new capability to understand annual plant growth from species to landscapes. Analysis of PiCAM imagery showed high phenological diversity across Arctic plant species not currently represented in models used to project the fate of the Arctic. Shrub species, like Siberian alder, displayed rapid leaf expansion (completing spring growth within 2 weeks) that has not been captured by traditional field measurements or satellite remote sensing. This research highlights a critical need to characterize Arctic seasonality using on-the-ground tools like PiCAM to improve model representation of Arctic vegetation.

The timing of plant seasonal growth plays an important role in determining annual ecosystem carbon, water, and energy fluxes. However, scientists have had limited options available to accurately characterize plant phenology in the remote Arctic where extreme environments pose serious challenges to the long-term unattended operation of scientific equipment. To address this problem, a team of researchers designed a rugged, low-power camera system (called “PiCAM”) to autonomously collect image observations of plant seasonal growth. Results show PiCAM can effectively cope with the harsh Arctic environments and remain operational for over a year, providing a new means to characterize plant seasonality across Arctic landscapes.

Time-lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth, or plant phenology. These simple, relatively inexpensive systems can provide high-frequency observations of plant leaf development, which are critical datasets needed to characterize plant phenology from species to landscapes. However, in remote regions including the Arctic, deploying time-lapse cameras is often challenging. The remoteness and lack of power and telecommunication infrastructure limit options for the installation, maintenance, and retrieval of data and equipment and make it difficult for cameras to survive in extreme weather (e.g., long cold winters).

A team of researchers addressed these challenges by developing a low-power, compact, lightweight time-lapse camera system called PiCAM. PiCAM was explicitly designed for simple and long-term, unattended operations without a need for external power to address challenges associated with camera survival in harsh Arctic environments. The study describes the design, setup, and technical details of PiCAM and provides a roadmap for how to build and operate these systems.

As proof of concept, the team deployed 26 PiCAMs across three low-Arctic tundra sites on the Alaskan Seward Peninsula in early August 2021. Of the 26 PiCAMs installed, 70% remained active in July 2022 when researchers retrieved the cameras, despite the extreme winter temperatures they experienced (<-30°C, heavy snow cover). The team extracted key plant phenology metrics from the PiCAMs that quantified substantial differences across key Arctic plant species. Results demonstrate the PiCAM could be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground-based understanding of Arctic phenological diversity to better understand plant responses to climate change and validate remote sensing products.

This study underlines the importance of accurately estimating subsurface thermal state for assessing and predicting slope instabilities. Furthermore, this study contributes to a deeper understanding of the intricate mechanisms impacting soil carbon fluxes as the Arctic permafrost thaws and the seasonal thawing dynamic changes.

Understanding controls on soil movements along hillslopes is crucial to improving the assessment and prediction of carbon fluxes and infrastructure hazards in the warming Arctic. A team of researchers established a novel sensor network to monitor soil temperature and deformation at 48 locations spanning adjacent hillslopes in a warm permafrost environment. Data reveals that during the thawing season, movements predominantly occur near the thawing front, commencing as thawing reaches depths ranging from 0.4 to 0.75 meters. Key parameters governing shallow soil movement processes include slope angle and soil thermal state.

Solifluction processes in the Arctic are highly complex, introducing uncertainties in estimating current and future soil carbon storage and fluxes and assessing hillslope and infrastructure stability. This study aims to enhance understanding of triggers and drivers of soil movement along permafrost-affected hillslopes in the Arctic. To achieve this, researchers established an extensive soil deformation and temperature sensor network, covering 48 locations across multiple hillslopes within a 1 km² watershed on the Alaskan Seward Peninsula.

Depth-resolved measurements down to 1.8 m depth have been reported for May to September 2022, a period conducive to soil movement due to deepening thaw layers and frequent rain events. Over this period, researchers showed that movements occur close to the thawing front and are initiated as thawing reaches depths of 0.4 to 0.75 m. The largest movements were observed at the top of the southeast-facing slope, where soil temperatures are cold and slopes are steep.

Three primary factors influenced movements: slope angle, soil thermal conditions, and thaw depth. These factors affect soil properties, which are crucial determinants of slope stability. This underscores the significance of a precise understanding of subsurface thermal conditions, including spatial and temporal variability in soil temperature and thaw depth, when assessing susceptibility of slope instabilities. This study offers novel insights into patterns and triggers of Arctic hillslope movements and provides a venue to evaluate their impact on soil redistribution.

New satellite data and products allow measurement of forest structure and ET across large areas. With these data, researchers can better understand how human activities in tropical forests change the earth’s water and energy cycles. The study found that forest structure influences ET more than climate. In addition, the team found that forest degradation may make Amazon forests limited by water; intact forests in the Amazon are normally limited by energy, not water. These findings have important implications for the global water balance and rainfall patterns.

Forest degradation through fires and logging is common in the Amazon and changes forest structure. However, little is known about degradation’s effects on the way tropical forests transpire water. Researchers assessed seasonal water stress and its relationship with forest structure across intact and disturbed forests in the Amazon using high-resolution remote sensing of forest structure from spaceborne lidar (Global Ecosystem Dynamics Investigation; GEDI) and evapotranspiration (ET) derived from Landsat. They found that forest structure exerts a stronger control on ET in more disturbed/drier forests than in intact or lightly disturbed forests.

Deforestation, timber extraction, and forest fires disturb large areas in the Amazon region. These disturbances alter how forests function. Previous work focused on how deforestation affects the water and energy cycles. This research used satellite-based data to understand how degradation changes water and energy fluxes. The research team analyzed ET, land surface temperature, and forest structure (tree cover and forest height) data over a region in the southern Amazon. This region has a mix of deforested, degraded, and intact forests, allowing researchers to study the effects of forest structure on water and energy cycles.

The research team found that water stress conditions start early into the dry in croplands and pastures. They also found that second-growth and burned forests experience stronger water stress than logged and intact forests. Moreover, they found that forest structure is moderately related to ET and temperature, but only in the most disturbed forests. Results show the importance of intact forests in maintaining water balance in the Amazon region and suggest that disturbed forests may be less able to cope with the changing climate.

Results demonstrate that above- and belowground tundra shrub traits respond differently to local environmental and climatic variation. These differing responses contribute to substantial trait variation at small spatial scales and suggest that above- and belowground traits will respond differently to climate change. This may preclude inferring belowground trait responses from more easily detectable aboveground responses. Additionally, results suggest models should account for trait variation and its drivers to increase the accuracy of climate change predictions.

Plant traits are attributes that can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. Understanding the patterns and factors that lead to trait variation across different spatial scales is important for predicting how biodiversity and ecosystem functioning will change in the future, especially in understudied regions like the Arctic. A team of researchers examined above- and belowground traits from three shrub groups expanding across the Alaskan tundra and evaluated their relationships with local environmental and climatic factors. The research found substantial variation in traits at small spatial scales (within sites) and less variation between sites with different climates and between shrub taxa. Local environmental factors, mainly soil moisture and thaw depth, interacted with climatic water deficit to predict variation in shrub height and leaf traits. In contrast, most root traits responded additively to thaw depth and macroclimate.

A team of scientists examined how patterns of trait variation differ across sites, within and among taxa, and across plots. They also investigated the primary environmental drivers of trait variation across these different spatial scales. Findings suggest that above- and belowground tundra shrub traits respond differently to local environmental and climatic variation. Soil moisture, thaw depth, and climatic water deficit were important predictors of variation in shrub size and leaf traits in the Alaskan tundra. In contrast, root traits were more sensitive to thaw depth.

Earth system models typically do not represent the dynamics between plants, water, and soil with the spatiotemporal resolution needed to fully characterize coastal systems. Pore-scale models have a higher resolution but may not include feedbacks between the system components that regulate climate responses. This intermediate scale–model study shows the importance of incorporating daily cycles to better estimate redox processes and how representation of the connections between plants, microbes, and water can improve predictive capacity.

PFLOTRAN, a reactive transport model, was used to test how elevated temperature and carbon dioxide (CO₂) alter the connections between plants, water, and soil processes in a salt marsh. Daily cycles associated with tides and photosynthesis were included in the model, and the impacts of climate stress were applied directly to the soils as well as indirectly through plant responses. Including daily cycles significantly influenced rate estimates, resulting in a higher or lower gas emission than anticipated depending on the time of day. The indirect effects mediated through plant responses were more important in regulating redox cycling than the direct influence of stress on soils.

Coastal ecosystems have been largely ignored in Earth system models but are crucial zones for carbon and nutrient processing. Interactions between water, microbes, soil, sediments, and vegetation are important for mechanistic representation of coastal processes. To investigate the role of these feedbacks, researchers used PFLOTRAN to simulate coastal processes. PFLOTRAN representation included redox reactions important for coastal ecosystems and a simplified representation of vegetation dynamics. The goal was to incorporate oxygen flux, salinity, pH, sulfur cycling, methane production, and plant-mediated transport of gases and tidal flux. Depth-resolved biogeochemical soil profiles were created for the salt marsh habitat using porewater profiles and incubation data for model calibration and evaluation. The updated representation was used to simulate direct and indirect effects of elevated CO₂ and temperature on subsurface biogeochemical cycling.

Increasing CO₂ temperature or concentration in the model did not fully reproduce observed changes in the porewater profile. However, including plant or microbial responses to these stressors was more accurate in representing porewater concentrations. This result indicates the importance of characterizing tightly coupled vegetation-subsurface processes for developing predictive understanding and the need for measuring plant-soil interactions on the same time scale to understand how hotspots or moments are generated.

Open-access and interoperable coastal biogeochemical datasets are needed to predict how coastal systems will respond to global change. Community-driven programs are one such approach to acquiring these datasets. The EXCHANGE consortium is an open-science, community-driven program spanning traditional research and physical domains to advance synthesis and modeling efforts across coastal interfaces.

Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) is a consortium of scientists interested in improving understanding of the biogeochemical exchange between water and land in coastal systems. In EXCHANGE Campaign 1 (EC1), researchers collected water, soil, and sediment samples at 52 sites in the Great Lakes and Mid-Atlantic regions. This work highlights version one of the key EC1 baseline datasets currently published for open access.

Researchers can use cohesive datasets across geographically distributed sites to examine the transferability of coastal ecosystem biogeochemical processes. The EXCHANGE consortium collaborated on study design for EC1, including how data were collected, to increase the comparability of datasets across sites. The team analyzed soils, sediments, and surface waters from across the coastal terrestrial-aquatic interface for biogeochemical variables, ranging from common water quality and soil physicochemical properties to advanced molecular-level characterizations. All data underwent quality control steps to ensure data quality. The consortium also analyzed the datasets across regions to understand when, where, and why variability existed. Others can use these data for subsequent analyses and deposit their code in an open-source repository, which aids in furthering collective knowledge about coastal interfaces.

Understanding the local spatial distribution of soil temperatures is critical to accurately predicting permafrost environment response to climate change. This work measures high-resolution soil temperature data and builds a linkage between soil temperatures and aboveground properties that can help researchers develop products that use aboveground images to estimate soil temperatures. The study also provides valuable data and knowledge to validate Earth system models.

Soil thermal states in the Arctic region are diverse, complicating the understanding of permafrost systems’ response to climate change. Researchers focused on a small region and measured 1-year soil temperature change at different depths from dense locations. Results show that soil thermal states vary across the region, even with uniform weather conditions. Large differences in winter soil temperatures cause the differences in annual soil temperatures. The main drivers of these differences are diverse plant and snow distribution causing different winter cooling processes.

Soil temperatures in the permafrost regions exhibit strong spatial and temporal variability that cannot be explained by weather forcing only. By acquiring high-resolution temperature data, the study aims to understand the local heterogeneity of soil thermal dynamics and their controlling factors. At 45 discrete locations across a relatively small watershed, researchers measured depth-resolved soil temperature over 1 year at 5- or 10-cm intervals up to 85 cm depth. Results showed spatial variability in winter temperatures controls the spatial variability in mean annual temperatures.

The study demonstrates that mean annual or winter ground surface temperatures are good indicators of mean annual ground temperature at 85 cm. Soils clustered as cold, intermediate, or warm groups closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). The spatial variability in mean annual soil temperature is primarily driven by diversity in snow cover, which induces variable winter insulation and soil thermal conduction. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, the study demonstrates the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in field data and reproduced in a model sensitivity analysis.

Graminoids and short-stature shrubs have historically dominated tundra plant communities, but recent warming has caused tall shrubs to become more prevalent. A team of researchers investigated tall-shrub expansion in low-Arctic tundra by modeling past expansion of tall shrubs and predicting how and where future warming will open suitable habitats for tall shrubs. Analysis suggests that nitrogen-fixing alder will accelerate tall-shrub expansion into newly available habitat areas. Species-specific nutrient interactions are therefore important for predicting vegetation dynamics in warming, low-tundra ecosystems.

Researchers used fine-scale remote sensing to model tall-shrub expansion on Alaska’s Seward Peninsula over the last 68 years. The model predicted past expansion well and demonstrated suitable tall-shrub habitat is currently only one-third occupied and well-constrained by permafrost, climate, and edaphic gradients. The model also predicted increases in tall-shrub habitat driven by permafrost degradation and increased wildfire frequency. Analysis of historic imagery also revealed a positive relationship between willow-birch expansion and alder expansion, suggesting that increased nutrient availability from nitrogen-fixing alders can accelerate the rate at which tall shrubs expand into suitable habitats.

Tall deciduous shrubs are critically important to carbon and nutrient cycling in high-latitude ecosystems. As Arctic regions warm, shrubs expand heterogeneously across their ranges, including within unburned terrain experiencing isometric warming gradients. Improved knowledge of local-to-regional scale patterns, rates, and controls on decadal shrub expansion is required to constrain the effects of widespread shrub expansion in terrestrial and Earth system models.

Using fine-scale remote sensing, researchers modeled the drivers of patch-scale tall-shrub expansion over 68 years across the central Seward Peninsula of Alaska. Models show the heterogeneous patterns of tall-shrub expansion are not only predictable but have an upper limit defined by permafrost, climate, and edaphic gradients, two-thirds of which have yet to be colonized. These observations suggest that increased nitrogen inputs from nitrogen-fixing alders contributed to a positive feedback that advanced overall tall-shrub expansion. These findings will be useful for constraining and projecting vegetation-climate feedbacks in the Arctic.

Arctic rivers differ from those in temperate and tropical regions. They transport large quantities of freshwater and carbon following spring snowmelt. Study results show that thawing permafrost and an accelerating water cycle will shift these flows in several ways. More water will enter Arctic rivers in the far north, where massive amounts of carbon stored in soils are experiencing thaw. In turn, additional carbon and other nutrients will enter rivers. Climate change will alter the amount of land-to-ocean freshwater and materials transports, with impacts to coastal ecosystems, ice dynamics, and ocean biogeochemistry.

The Arctic is defined by the presence of frozen soils called permafrost. The warming climate is thawing permafrost and accelerating the water cycle, which alters flows of water, carbon, and other nutrients and materials by Arctic rivers. A team of investigators used a hydrology model that simulates soil thawing and freezing to explore potential future changes in factors that influence river water exports. The results highlight the need to closely watch the Arctic’s transformation and take steps to mitigate the effects.

Arctic river field sampling has shown that climate warming, an enhanced water cycle, and permafrost thaw are transforming river flows to coastal areas. Researchers have found that warming is thawing ancient frozen carbon stored in permafrost. To understand how climate warming changes Arctic terrestrial hydrology, researchers used a numerical model to project how river flows will change as warming continues. By 2100, Arctic rivers will receive more runoff from northern areas where abundant soil carbon exists. More water will enter them via subsurface pathways, particularly in summer and autumn. Study simulations point to a general increase in land runoff to rivers. Importantly, the proportion of runoff from subsurface pathways is projected to increase by as much as 30%. More water coming into northern areas will mobilize carbon from soils, transfer it to growing channel networks, and transport dissolved and particulate carbon downstream. Each season sees an increase in subsurface runoff. Higher surface runoff is noted in spring only, and summer experiences a decline in total runoff despite increased subsurface flows. These shifts in the far north emphasize the need for more frequent and spatially extensive sampling of smaller rivers that ring the Arctic Ocean.

Earth system models answer questions about current and future environmental conditions. Despite the urgent need for gross biogeochemical flux data to improve model performance, such data is rarely collected. A key technique for collecting gross flux data is stable isotope pool dilution, which first gained prominence in the 1990s but remains underutilized in part due to the calculations’ relative inaccessibility. PoolDilutionR is a user-friendly software package that brings the theory of pool dilution into the 21st century by allowing researchers to process their pool dilution data easily in one of the most popular software languages in the field. This open-source tool will allow wider application of pool dilution and easier generation of critical Earth system data.

Biogeochemical processes, often called fluxes, recycle materials through the Earth system. Underlying productive and consumptive processes control the flux magnitude. Typically, these two components cannot be separated, and only the net flux is measured. Using stable isotope tracers, chemically identical, microscopically “tagged” molecules allow researchers to calculate the two gross components, but the equations are difficult to navigate. This study presents a new R package to address complicated equations in this system.

Despite being a powerful method for quantifying gross biogeochemical transformation rates, isotopic pool dilution is seldom employed. Pacific Northwest National Laboratory offers a user-friendly R package that optimizes rates and fractionation constants using standard pool dilution time series data, featuring comprehensive documentation and examples for seamless integration. The package is easily integrated into analytical pipelines to facilitate broader implementation of pool dilution methods.

Climate change is affecting meteorology, including rainfall, temperature, and evaporation. If climate change leads wetlands to dry out periodically, this can change the amount and type of greenhouse gas emitted to the atmosphere. Alternatively, if wetlands don’t flood, their plants may be unable to pull as much carbon dioxide from the atmosphere. Since wetlands are the largest natural source of methane, it is essential to understand how changing water patterns could affect these gas emissions.

Wetlands are known for being natural methane sources because they have large amounts of organic matter submerged in water. This organic matter gets slowly broken down by microbes, and without oxygen, it produces methane, a greenhouse gas. At the Old Woman Creek National Estuarine Research Reserve, parts of the wetland underwent periods of flooding and then drying. When flooded, plants released more methane into the atmosphere, but plants also removed carbon dioxide from the atmosphere. When the wetland dried, less methane was released, but the wetland released carbon dioxide instead of storing it.

A team of researchers sampled the Old Woman Creek National Estuarine Research Reserve wetland from July to October 2022 and measured methane and carbon dioxide fluxes in three areas with vegetation and three without vegetation from 7 AM to 7 PM once a month. In July, the wetland was completely flooded, but it dried out in August and slowly reflooded in September and October. When flooded in July, less oxygen was present in the water column, which supported more methane emissions. Most methane was emitted from plants since plants transport gas from the sediment to the atmosphere, bypassing the water barrier. However, flooding also allowed plants to take in more carbon dioxide from the atmosphere as the plants were, presumably, not water-limited for photosynthesis. Consequently, the greatest carbon dioxide uptake occurred during the afternoon at the height of photosynthetic activity. The wetland both emitted methane and sequestered carbon dioxide during flooding. After the wetland dried, plants were no longer taking in carbon dioxide at a rate faster than emission, so the wetland turned into a source of carbon dioxide. The methane emission rate also dropped since more oxygen converted methane to carbon dioxide during drier conditions. However, the wetland was still a source of both methane and carbon dioxide when the wetland was dry.

As climate warming changes the Arctic landscape above and below the surface, knowing where and how deep the ground is frozen is crucial to predict how the Arctic will change. A combination of temperature and electrical resistivity measurements can provide reliable estimates of permafrost location and depth. Research also shows a direct link between the state of permafrost and its location on hillslopes. Knowing such relationships can improve estimates of where permafrost exist and help predict Arctic change.

Understanding permafrost distribution in the subsurface will help scientists better understand Arctic change due to climate warming. Although ground temperature can be measured in boreholes, few boreholes exist to do these measurements. Researchers measured the temperature and electrical resistivity of the ground on the Seward Peninsula in Alaska and used machine learning to gather details about the permafrost. By linking permafrost properties with observations aboveground, researchers demonstrated the slope aspect and angle and the vegetation exhibit correlation with permafrost size and temperature.

Assessing the lateral and vertical extent of permafrost is critical to understanding Arctic ecosystems’ fate under climate change. Yet, direct measurements of permafrost distribution and temperature are often limited to few borehole locations. In this study, researchers assessed the use of co-located ground temperature and ground electrical resistivity measurements to estimate at high resolution the distribution of permafrost in 3 watersheds underlain by discontinuous permafrost. Synthetic modeling showed that combining co-located temperature and electrical resistivity tomography using machine learning methods can identify permafrost distribution more accurately than conventional methods. By linking the size of the identified permafrost bodies to surface observations, researchers showed that tall vegetation (>0.5 m) and gentle slopes (<15°) are related to warmer and smaller permafrost bodies and a more frequent occurrence of taliks. In addition, results indicate that talik occurrence is not always associated with tall shrubs, confirming a variety of trajectories in temperature and vegetation dynamics across the landscape.

Blume-Werry et al. found that naturally standing variation in rooting depth distribution in the Arctic greatly affected modeled carbon emissions (cumulative 7.2 to 17.6 Pg C by 2100) via root priming of decomposition. This effect was not explainable with relationships derived from aboveground vegetation mapping units, complicating modelers’ ability to make inferences of belowground dynamics from more easily measured aboveground vegetative cover. Blume-Werry et al. propose a “root profile type” classification for future work, which this commentary expounds upon while proposing a coarse-scale and root-focused PFT framework.

Rooting depth distribution describes the spatial extent of plant control over biogeochemical cycling and thus carbon feedbacks. Because soils in northern biomes store more organic carbon than equatorial biomes, small changes in roots’ depth distribution in these biomes have gross effects on carbon emissions. Current regional scale modeling efforts infer rooting depth distribution from aboveground features, which Blume-Werry et al. (2023) found too coarse to capture variability in modeled emissions from measured variation in rooting depth distribution. This commentary builds upon the work of Blume-Werry et al., proposing a root-focused plant functional type (PFT) framework to better capture rooting depth distribution.

Modeled carbon emissions from Arctic soils can vary drastically depending on how deeply Arctic plants grow the bulk of their roots (Blume-Werry et al. 2023). However, this variation was greater within vegetation mapping units than between, which the authors demonstrated through comparisons of rooting depth distributions estimating for each mapping unit and post hoc clustering of rooting depth distributions into shallow, intermediate, and deep “root profile types.”

In this commentary, researchers expand upon the root profile type concept, outlining a PFT framework predominantly grounded in plant belowground features thought to be relevant in Arctic and boreal ecosystems with carbon-rich soils. This PFT framework is likely suitable to such a task as it is more finely resolved to the scale at which rooting depth distribution varies meaningfully more between groups than within (i.e., mapping units) but not so finely resolved as to become intractable (i.e., species). Additionally, the PFT approach takes the belowground-focused perspective of Blume-Werry et al. but converts their analytical approach from an after-the-fact clustering to a predictive framework.

Species have adapted to persist to fire regimes. For example, plants can regenerate relatively quickly following a wildfire. How will species persist if the pressures of fire amplify under a warmer climate?

To better understand this question, a team of researchers synthesized studies exploring fire as a dynamic ecological and evolutionary force and placed them in a broader context of fire research. The study discusses the importance of incorporating evolutionary concepts and perspectives into future frameworks and provides a list of recommendations to enable the scientific community to answer critical questions on the evolutionary responses to fire under a changing climate.

Plants and animals have co-existed and evolved with fire for millennia. As climate rapidly changes and fire increases worldwide, biodiversity will likely evolve new adaptations. However, fire’s evolutionary pressure on species has received less attention than fire’s ecological impacts on plants and their communities.

This editorial addresses this gap in fire research by synthesizing studies that contribute to the perspective of fire as a dynamic ecological and evolutionary force. Researchers provide a list of recommendations to enable the scientific community to better understand the ecological and evolutionary consequences of fire.

This research explores the impacts of novel fire regimes on forest mortality, new approaches to investigate vegetation-fire feedbacks and resulting plant syndromes (or the propensity of plant biomass to ignite and propagate a fire), fire impacts on plant-fungal interactions, and arthropod community responses to fire. Future frameworks must incorporate evolutionary concepts and perspectives to understand how species will persist given that fire pressures are anticipated to be amplified under a warmer climate.

To better understand the ecological and evolutionary consequences of fire, researchers recommend:

• Developing ecological and evolutionary databases for fire ecology;
• Integrating hierarchical genetic structure or phylogenetic structure;
• Developing new experimental frameworks that limit context-dependent outcomes;
• Increasing sample size and availability of curated datasets;
• Increasing functional trait knowledge; and
• Increasing representation of ecological communities in the literature.

Future studies should establish networks, form interdisciplinary partnerships, unify measurement of fire effects and responses, and incorporate knowledge from diverse communities. 

This study quantitatively evaluates the spatial variability of SPW geochemistry within and between 2 distinct catchments underlain with permafrost and seeks to identify the observed spatial variability’s source. Identifying the dominant controls on solute concentration variability within and across catchments will facilitate better projections of soil pore hydrogeochemistry in permafrost landscapes and improve understanding of how these signatures are related to changing soil moisture and increasing tundra shrub abundance in the Arctic. Changes in hydrogeochemistry in small Arctic catchments not only have larger-scale impacts but also impact the future hydrogeochemistry of larger Arctic rivers.

Permafrost thaw in the Arctic is causing significant changes to landscape structure, hydrology, vegetation, and biogeochemistry. These changes produce carbon fluxes and increased nutrients in Arctic rivers, leading to enhanced nutrient loadings with strong implications for the global carbon cycle. Many recent studies focus on environmental change observed and expected as a result of Arctic warming, but only a limited understanding exists of the key environmental controls on the spatial distribution of soil pore water (SPW) solute concentrations. This study analyzes the primary drivers of these changes.

To address knowledge gaps in understanding biogeochemical cycles in a changing Arctic, this study analyzed data from 2 contrasting hillslope sites on the Seward Peninsula in Alaska. A team of researchers sampled SPW from the upper 30 cm of soil with fiberglass wicks and MacroRhizons across the study sites.

This data was paired with additional observations of vegetation characteristics, soil moisture, and permafrost extent to analyze the dominant environmental controls of solute concentrations within SPWs at the sites. Researchers then conducted thermodynamic modeling with PHREEQC to understand what could control SPW solute concentrations. The approach identified mineral phases that may control solute generation processes through solubility limitations.

Vegetation significantly impacted SPW concentrations and was associated with the localized presence of nitrogen-fixing alders and mineralization and nitrification of leaf litter from tall willow shrubs. Vegetation also had a less significant impact on soil moisture–sensitive constituents.

The redox conditions in both catchments were generally limited by iron reduction, with the most reducing conditions found at sampling locations with the highest soil moisture content. Nonredox-sensitive cations were affected by various water-soil interactions that affect mineral solubility and transport. Topographic differences and lack of well-defined drainage channels were the likely environmental controls causing systematically higher SPW solute concentrations at one study site.

Overall, the study provides directions for future work and suggests that evaporative concentration could be a significant control on SPW solute concentrations in permafrost catchments, particularly in those with limited drainage and therefore a perched near-surface water table. Future efforts to predict SPW solute and nutrient dynamics should directly address evaporative concentration’s impacts on permafrost catchments, especially with future permafrost thaw.

Understanding recent advances in permafrost and its change is vital as permafrost exerts controls on land surface energy, water, and carbon balances across the global climate system. The hydrology of permafrost-affected soils is complex due to seasonal freezing and thawing of the active layer. Snow cover and vegetation exert important controls on energy and water balances of permafrost-affected soils and fluxes. Thawing of ground-ice-rich permafrost leads to subsidence, often with irreversible changes in landscape topography, hydrology, and carbon cycling. As the Arctic warms, permafrost thawing is expected to increase the production and release of carbon dioxide and methane, resulting in potentially deleterious effects worldwide.

Permafrost is an important component of the Earth’s cryosphere. It plays a key role in the global carbon cycle, as well as ecosystems and infrastructure in Arctic and sub-Arctic climate zones. Borehole measurements show Arctic permafrost has been warming during the early 21st century. Earth system model simulations demonstrate that carbon release from degrading permafrost due to climate warming represents an important global feedback on climate change. This chapter describes fundamental and cutting-edge findings on permafrost research for constituents, cryo-pedogenetic processes, hydrology, energy, and water balances, as well as snow-vegetation feedbacks and gas transport in permafrost regions.

Permafrost encompasses ground (soil, sediments, rocks) that remains at or below 0°C for at least 2 consecutive years. This overview of recent science of global permafrost systems considers the constituents of permafrost (minerals, organic matter, water, ice, and gas) and the presence and importance of ice leading to patterned ground formation. The chapter discusses permafrost degradation and its effects on Arctic energy, water, and carbon balances.

Rivers are a major component of the Earth system. The study of river metabolism is key to understanding nutrient dynamics, ecosystem health, and food webs in river ecosystems. Researchers found that metabolism patterns for the Hanford Reach section of the Columbia River differ from those observed in most rivers.

Peak photosynthesis occurred in late summer, as opposed to spring or mid-summer as expected for most other rivers. Photosynthesis rates were primarily influenced by temperature and secondarily influenced by light. Photosynthesis and respiration rates were among the highest measured and the two were strongly connected, indicating little accumulation of algae. Finally, most metabolism occurred in the water column by plankton rather than in sediments.

Conducting more metabolism studies in other large rivers will help determine whether these patterns are typical for large rivers.

Large rivers support complex food webs and provide ecosystem services. Despite their importance, metabolism in large rivers is not well-understood because the existing estimation and determination methods apply only to smaller streams. A team of researchers modified existing methods to estimate metabolism for the Hanford Reach of the Columbia River in Washington state. Columbia River metabolism rates, seasonal patterns, the location of metabolism, and the coupling of photosynthesis and respiration all differed from what is typically observed in smaller rivers.

This study focused on understanding ecosystem metabolism in large rivers, an area that has received limited attention compared to small and medium rivers. Large rivers present unique challenges for depth and gas exchange measurements due to their size and large dams.

A team of researchers estimated reach-scale metabolism for the Hanford Reach of the Columbia River in Washington state, a free-flowing stretch with substantial discharge. Researchers used existing, reach-specific hydrologic models to estimate depth and a combination of semi-empirical models and tracer tests to estimate gas exchange.

Metabolism metrics were comparatively high in the Columbia River, with peak values occurring in late summer or early fall. Strong coupling occurred between photosynthesis and respiration. The river exhibited plankton-dominated metabolism driven primarily by temperature and secondarily by light.

These patterns deviate from those typically observed in small and medium rivers and demonstrate that metabolism patterns from smaller rivers may not accurately scale to large rivers.

Greenhouse and laboratory studies have examined how trees respond to increasing exposure to saltwater, but how trees respond is unclear in the real world with rising sea levels and increasing storms. Study results are consistent with the idea that the stress of chronic salinity exposure changes tree leaf shape and function, likely weakening their physiology and setting in motion processes that lead to forest death. These findings are thus useful for understanding the growing effects of saltwater intrusion into upland forests, as well as parameterizing and testing ecosystem-scale models simulating climate change and storm disturbances in coastal forests.

Sea level rise and increasing storms are stressing coastal forests, but the degree to which saltwater exposure changes tree leaves’ structure and function is poorly understood. This study measured how leaf shape—or specific leaf area (SLA), which is the ratio of leaf area to mass—changed along the natural salinity gradient of a tidal creek. Researchers found that salinity significantly affected SLA changes after accounting for the effect on different species. Trees in the downstream areas of the creek had lower SLA with thicker, smaller leaves, which is consistent with increased stress.

This study took advantage of a temperate forest creek’s natural salinity gradient to study how species differences, canopy position, and salinity exposure were associated with changes in SLA. Trees directly exposed to the tidal creek had lower SLA in higher-salinity plots, which is consistent with greenhouse studies reporting that the stress of chronic salinity changes leaf morphology and tree physiology. The study concludes that incipient ecosystem state shifts at the coastal interface may be predictable by observing changes in leaf-level parameters like SLA, which is a change that typically precedes tree death and the formation of “ghost forests.” Further integrated research using models and larger-scale manipulative field experiments is crucial to fully understanding ongoing structural and functional changes in coastal forests worldwide.

This study summarizes outcomes from a long and continuous dataset of soil CO₂ fluxes from two different high-elevation forests in the western United States in relation to precipitation. Results are important for understanding forest functioning because annual snowfall and rainfall amounts are being altered with climate change, and this research addresses how past, current, and future precipitation changes may influence the amount of carbon returned to the atmosphere.

For nearly a decade, a multi-institutional team of researchers measured the amount of carbon dioxide (CO₂) produced in soil in high-elevation mixed conifer and aspen forests in the western United States. The amount of CO₂ produced during the summer was controlled by prior winter snowfall and current summer rains. Summer rainfall, while making up only 10 to 35% of the total moisture inputs, was particularly important for stimulating soil CO₂ fluxes due to the timing and location of the moisture.

Long-term soil CO₂ emission measurements are necessary for detecting trends and interannual variability in the terrestrial carbon cycle. Such records are becoming increasingly valuable as ecosystems experience altered environmental conditions associated with climate change. From 2013 to 2021, researchers continuously measured soil CO₂ concentrations in two dominant high-elevation forest types, mixed conifer and aspen, in the upper Colorado River basin.

The team quantified soil CO₂ flux during the summer months and found that the mean and total CO₂ flux in both forests was related to the prior winter’s snowfall and current summer’s rainfall, with greater sensitivity to rainfall. A decline occurred in surface soil CO₂ production, which was attributed to warming and a decrease in the amount and frequency of summer rains. Results demonstrated strong precipitation control on soil CO₂ flux in mountainous regions, which has important implications for carbon cycling under future environmental change.

Soil organic matter is an important ecosystem component, providing habitats and food for soil organisms, supplying nutrients for plants, and increasing soil water storage. This study demonstrates a biological mechanism for increases in soil porosity and decreases in soil bulk density often observed with increasing organic matter. Organic matter provides additional resources for roots, microbes, and soil fauna, which in turn alter soil physical structure. This research clarifies biology’s importance in modifying hydrologic and gaseous transport in soils and calls for improved representation of bioturbation in soil models.

Soil pore space constrains soil capacity to store carbon and water. Total pore space increases with increasing organic matter content, but mechanisms leading to soil structure changes are unclear. A team of researchers quantified and compared soil characteristics across 2 contrasting soils to clarify the effects of organic matter content on soil porosity. They found that high organic matter content fosters higher biological activity, including root growth and animal burrowing, which increases soil porosity and decreases bulk density.

The team sampled and compared 2 contrasting highly weathered tropical soils from Brazil to 1 m depth: one with high carbon content and one with low carbon content. Researchers developed soils from similar parent materials, with similar soil texture, and in areas currently under savanna vegetation.

The team first verified that differences in porosity and bulk density attributed to soil carbon differences could not be explained by variation in soil texture, mineral composition, or dilution of soil minerals by lower-density organic matter. Researchers also determined that differences in total porosity could not be explained by variation in pore space inside soil aggregates using X-ray tomography. Instead, they found that high-carbon soils had nearly twice as many roots and burrows as low-carbon soils and soil bulk density decreased with increasing carbon content, carbon: nitrogen ratio, black carbon content, and Δ¹⁴C.

Results suggest that in high-carbon soils, increased plant growth, bioturbation, and vertical transport facilitated by high soil porosity bring fresh plant inputs and charcoal down the soil profile from the surface. The team presents a conceptual model detailing organic matter’s indirect effects on soil structure.

Better managing soils to store more carbon can mitigate some of climate change’s worst effects. Efforts to enhance soil carbon content often focus on stabilization processes (i.e., the formation of long-lived types of carbon tightly bound to clay minerals). However, research shows that formation of “stable” soil carbon does not necessarily increase the total amount of carbon in soil. The processes that enhance carbon stabilization also increase microbial respiration (carbon loss). This information is relevant for land managers seeking to build carbon stocks.

Small changes in the relative rates of soil carbon formation (via interactions with soil minerals) and loss (via microbial respiration, or breathing) affect carbon dioxide concentrations in the atmosphere. Predicting these rates is challenging because they are influenced by so many factors simultaneously—type of mineral in the soil, species of microbes present, and chemistry of new carbon inputs via plant litterfall and root exudation. Researchers constructed artificial root-soil systems to independently manipulate these factors and found that roots control soil carbon content by accelerating respiration and carbon stabilization simultaneously.

A team of researchers constructed artificial root-soil systems to independently manipulate the:

• Reactivity of clay minerals;
• Structure of microbial communities (fungal vs. bacterial dominated);
• Presence or absence of artificial roots; and
• Chemistry of fresh carbon inputs (monosaccharide, disaccharide, or polysaccharide).

These factors have all been identified as important controls on soil carbon cycling but are often highly interlinked in real soils, which makes quantifying their individual effects difficult. The dominant control on the size of the soil carbon pool is an interaction between mineral reactivity and root exudation. Root exudates promote destabilization of mineral-associated carbon in weakly active clays but accelerate the formation of mineral-associated carbon in soils with highly reactive iron oxides. However, higher rates of carbon stabilization do not always result in larger soil carbon pools because the same factors that tend to promote mineral-associated carbon formation also accelerate respiratory carbon losses.

Slow decomposition rates are a key characteristic of peatlands that lead to large terrestrial carbon stocks, but these rates are difficult to measure in situ. This research showed decomposition rates were not significantly altered by elevated temperature over the first 3 study years.

Peatlands are large carbon sinks with primary production outpacing decomposition of organic matter. Results from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) study show net losses of organic matter and increased greenhouse gas production from peatlands in response to whole-ecosystem warming.

Researchers assessed depth-specific rates and mechanisms of peat decomposition across elevated temperatures using a newly adapted “peat decomposition ladder” approach. After the first 3 years of study, warming (up to +9°C) had little effect on peat decomposition or organic matter quality. Low rates of mass loss (~4.5%) were observed across all treatments. Microbial communities, however, showed increases in diversity as well as alteration of patterns within their interaction networks with warming treatments.

Researchers investigated how warming and elevated carbon dioxide (CO₂) impact peat microbial communities and peat soil decomposition rates. The team characterized microbial communities through amplicon sequencing and compositional changes across 4 depth increments.

Soil depth, temperature, and CO₂ treatment significantly impacted microbial diversity and community composition. Bacterial/archaeal α-diversity increased significantly with increasing temperature, and fungal α-diversity was lower under elevated CO₂ treatments. Transdomain microbial networks showed higher complexity of microbial communities in decomposition ladder depths from the warmed enclosures. The number of highly connected hub taxa within the networks was positively correlated with temperature. Methanogenic hubs were identified in the networks constructed from the warmest enclosures, indicating increased importance of methanogenesis in response to warming.

However, microbial community responses were not reflected in measures of peat soil decomposition as warming and elevated CO₂ had no significant short-term effects on soil mass loss or chemical composition. Regardless of treatment, 4.5% of the original soil mass was lost on average after 3 years. Variation between replicates was high, potentially masking treatment effects. Previous results from the SPRUCE experiment have shown warming is accelerating organic-matter decomposition and CO₂ and methane production. Results suggest warming-induced shifts in microbial communities may be driving these changes.

More detailed, mechanistic studies of how rapid erosion in permafrost landscapes is triggered are needed to understand how these disturbances may either propagate or be damped out. If newly formed channels begin to consolidate and grow, new networks of drainage channels may form.

Such networks will dramatically alter hillslope integration with river channels and affect how carbon and water are routed through Arctic watersheds. The pathways and rates that water, carbon, and nutrients move across watersheds strongly influence biogeochemical cycles and control carbon’s release from permafrost to the atmosphere, hydrosphere, and ocean.

Thawing permafrost, melting ground ice, and changing hydrological regimes are all predicted to cause expansion of channel networks and increase hydrological connectivity across Arctic watersheds. However, observed erosion of new channels has been isolated in both space and time and has yet to lead to widespread expansion of new channelization or evolution of Arctic watersheds. The presence of permafrost, ice in the ground, and thermal sensitivity of land-surface processes in the Arctic has inhibited predicting and quantifying how a thawing Arctic landscape will alter fluxes of sediments, carbon, and nutrients into streams and rivers.

Despite increasing observations of erosion and channel formation in permafrost watersheds, researchers lack predictive tools to identify when, where, and how rapidly permafrost landscapes will erode. Detailed studies of new channel formations’ location and timing are needed to link these disturbances to specific drivers. These data will allow researchers to test existing models and develop new models capable of capturing permafrost landscapes’ unique characteristics. Developing an understanding of surface processes and accompanying models will allow incorporation of disturbance processes into regional and pan-Arctic models to quantify coupled system responses to permafrost thaw and shifts in Arctic hydrology driven by climatic change.

Earth system models poorly represent seasonality in tree physiology, particularly concerning the efficiency with which plants acquire carbon at the cost of water loss. To address this, researchers measured photosynthesis, transpiration, and leaf traits of temperate trees throughout a growing season to evaluate the patterns and drivers of photosynthesis and transpiration. Results were incorporated into simple models of forest function to evaluate the impact of this new understanding of seasonality, which increased predictive capacity, particularly in the spring and fall phenological periods. Overall, the updated model approach predicts a 16% higher seasonal transpiration and a 3% higher seasonal carbon assimilation.

Photosynthesis is a necessary process for plant growth. However, photosynthesis requires a considerable release of water vapor via transpiration, and the ratio of photosynthesis to transpiration fluxes may change over the season or life of a given leaf. The dynamics of these fluxes over a growing season are not well characterized in Earth system models. Researchers found that photosynthesis and water use efficiency (WUE) are dynamic over a leaf’s lifetime and may not be synchronized. Photosynthesis increases slowly with leaf age and is driven primarily by changes in leaf biochemistry. In contrast, transpiration increases quickly with leaf age and is driven by changes in leaf anatomy.

Stomatal conductance to water vapor directly affects the potential rates of transpiration and photosynthetic carbon assimilation. Through variation in stomatal behavior, stomata dictate the marginal WUE of a plant. Stomatal behavior is known to vary seasonally and with leaf ontogeny. However, land surface models of vegetation do not currently represent this process. In this study, a team of researchers investigated leaf-level physiological, hydraulic, and anatomical properties as they changed throughout a growing season. Researchers paid particular interest to the stomatal slope parameter, which is inversely proportional to WUE.

Photosynthetic capacity and WUE were both found to be seasonally variable, yet their patterns were not synchronized. Parameters related to photosynthesis tracked seasonal trends in leaf structural and nutritional characteristics, while stomatal parameters lagged and tracked changes in anatomy and photosynthetic potential. Research also showed that when stomatal slope is modeled as a seasonally dynamic parameter, computed seasonal transpiration increases by 16%. Simulations indicate a clear need for models to account for seasonality more explicitly in photosynthetic and stomatal parameters.

Comparison between observed gradients’ photosynthetic traits differed from those hypothesized by the models. These differences affected simulations of photosynthesis and transpiration. Most notably, the ratio of dark respiration (carbon dioxide [CO₂] loss) and carboxylation capacity (a key parameter that largely determines CO₂ uptake) was hypothesized to be constant through a vertical profile. However, observations showed that the ratio decreased with canopy depth. If implemented in climate models, these observed gradients would likely increase the carbon gain by understory vegetation.

A team of researchers measured the photosynthetic properties of leaves from multiple species inside a complex tropical forest canopy in Panama using traditional approaches. Researchers combined that with rapid extensive measurements of leaf reflectance that are a reliable proxy for traditional measurements.

The combination of these two approaches enabled the team to determine data-rich vertical gradients in key photosynthetic parameters. The observed gradients were compared to the hypothesized gradients that are used by climate models. This study evaluated the impact of observed and hypothesized gradients on modeled photosynthesis and transpiration.

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies.

A team of researchers compared TBM representation of key leaf traits’ vertical gradients with measurements made in a tropical forest in Panama. They then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes.

Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the canopy top. Leaf-level water-use efficiency was markedly higher at the canopy top, and the decrease in maximum carboxylation rate from the canopy top to the ground was less than TBM assumptions.

The representation of leaf trait gradients in TBMs is typically derived from measurements made within individual plants or, for some traits, assumed constant due to a lack of experimental data. This research shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

By harnessing the power of ML, this study significantly enhanced scientists’ models of different plant types’ coexistence in tropical forests. Artificial intelligence–enhanced ecosystem models could accurately predict the effects of environmental changes on diverse ecosystems, fostering effective strategies for sustainable development, carbon sequestration, and achieving carbon-neutral and net-zero emissions goals. Moreover, this research highlights the need for advancing vegetation demographic models to refine coexisting plant simulations to capture intricate ecosystem interactions.

Tropical forests are critical components of global carbon, water, and energy cycles with the highest biodiversity on Earth. However, modeling the coexistence of different plant types—a key feature of biodiversity—in these forests remains challenging. Researchers used a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), integrated with the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve modeling plant coexistence. The team employed advanced machine learning (ML) techniques to optimize key trait parameters in FATES, remarkably enhancing plant coexistence simulations. The ML approach also improved the accuracy of FATES simulations of water, energy, and carbon fluxes and aboveground biomass.

A research team employed two approaches to optimize trait parameters in FATES: leveraging field-based plant trait relationships and utilizing ML surrogate models. Ensembles of FATES experiments were conducted on a tropical forest site near Manaus, Brazil, in the Amazon basin. The ML-based surrogate models were used to optimize trait parameters in FATES to improve plant functional type (PFT): plants that have similar environmental responses, ecosystem roles, and coexistence and achieve better model-observation agreements.

Considering only observed trait relationships improved the water, energy, and carbon simulations but degraded PFT coexistence in ELM-FATES simulations. The ML approach significantly enhanced PFT coexistence in the FATES experiments, increasing its occurrence from 21 to 73%. After applying observation constraints to identify small simulation biases, the ML-guided simulations retained 33% of the coexistence experiments, showing a 23.6-fold increase in PFT coexistence compared to the default experiments. The ML approach also improved FATES simulations of water, energy, and carbon fluxes, as well as aboveground biomass. Based on these results, researchers propose a reproducible ML method to improve model fidelity and PFT coexistence in vegetation demography models. This research highlights the potential of using ML in Earth system modeling of ecosystem dynamics and their response and feedback to climate change impacts.

Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p < 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree -mortality across the entire gradient of disturbance. (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2).

Remote sensing estimates of windthrow tree mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field-measured windthrow tree mortality (3 20m x 125m transects and 30 10m x 25m subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI).

Although satellites with high spatial resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree mortality. This study addresses how increasing satellites’ spatial resolution affects plot-to-landscape estimates of windthrow tree mortality. Researchers combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015.

Induced riverbank filtration is an important method for providing sustainable drinking water, but a variety of environmental conditions that affect pathogen removal can influence its effectiveness. By comparing the modeled transport behavior of human pathogenic adenovirus to the indicator species under changing seasonal and other environmental conditions, as well as differences in pumping operations, the team gained insights into the effectiveness of induced riverbank filtration for removing pathogens.

Access to safe drinking water is vital for human survival, yet groundwater resources in many regions are increasingly stressed due to growing water demand. To mitigate this issue, induced riverbank filtration has been successfully implemented as a sustainable method for groundwater resource management. The removal of pathogens from groundwater during riverbank filtration is a complex process depending on various environmental factors such as floods and periods of drought, as well as pumping operations.

In this research, a multi-institutional team of researchers used modeling to investigate the effects of specific environmental conditions on pathogen removal in induced riverbank filtration. The team’s results demonstrated the transport behavior of human pathogenic adenovirus differed significantly from pathogen indicators such as somatic coliphages and coliform bacteria. However, reduced travel time primarily influenced the removal rate of coliforms and somatic coliphages in the aquifer. River level changes in rainy seasons and extraction rates at waterworks during dry periods affected travel time.

Water resource management is key to protecting water resource availability and quality, but pathogens remain a significant contaminant group that can persist after filtration processes. Pumping wells direct surface water through a riverbank where many contaminants are naturally removed as they infiltrate through the soil. However, pathogens such as viruses and bacteria can survive wastewater treatment processes and persist in surface waters. They are difficult to remove and remain a significant concern in induced riverbank filtration.

In this study, a multi-institutional team of researchers analyzed pathogen transport at an induced riverbank filtration site in Germany over 15 months. Water samples were collected from the site every 2 weeks and analyzed for human pathogenic adenoviruses, which cause respiratory, ocular, and genitourinary infections. The team also analyzed samples for pathogenic indicator species such as somatic coliphages, which are viruses that infect bacteria, and coliform bacteria. The research team then developed pathogen transport models to account for natural variations in temperature, oxygen content, river level, pathogen background concentrations, and operational variations in pumping.

The modeling results demonstrated that the transport behavior of human pathogenic adenovirus differed significantly from pathogen indicators. River level variations due to rainfall events were the primary factor controlling pathogen removal, whereas natural variations in temperature and oxygen content had minimal impact. Moreover, riverbed erosion during flood events was identified as a key process that reduced the removal efficiency of bacterial pathogens.

On average, bank erosion rates for rivers with permafrost are 9 times lower than rivers without permafrost. The difference in erosion rates increases with the size of the river, with the largest permafrost rivers eroding riverbanks up to 40 times slower than similar non-permafrost rivers. These results answer a long-standing question regarding the influence of permafrost on riverbank erosion and indicate that bank erosion on large Arctic rivers may accelerate as permafrost areas melt.

Across the Arctic, floodplains frozen continuously for more than 2 years are considered to be part of the permafrost layer. Rivers flowing through permafrost areas can erode these floodplains, releasing gravel, sediment, sand, and carbon into rivers, thereby affecting river biogeochemistry and ultimately, the geomorphology of coastal floodplains. Before ice-bounded sediments can be eroded by flowing water, they must be thawed. Using aerial photographs, satellite imagery, and direct field observations, a recent study found that permafrost slows the rate rivers erode their banks relative to rivers without permafrost. The effect of permafrost, however, varies with the size of the river, and the erosion rates of large rivers are disproportionately slowed by permafrost. As a result, permafrost thaw due to climate change will likely increase erosion rates on large rivers. Although erosion rates on small rivers are likely to be much more limited, little data for small rivers in the Arctic are available.

A multi-institutional team of researchers analyzed thousands of kilometers of riverbank erosion rates across the Arctic using aerial photographs, satellite imagery, and direct field observations, and they also assembled a global database of published erosion rates. Bank erosion rates between permafrost and non-permafrost rivers were compared to assess the impact of permafrost on erosion rates. This research also explored how erosion rates varied with the discharge and steepness of rivers. Alternative hypotheses based on differences in total water yield and erosional efficiency were tested to explain different erosion rates of Arctic hydrology and river sediment loads. Neither of these factors, nor differences in river sediment loads, provided compelling alternative explanations for bank erosion rates. Results showed that permafrost lowers maximum bank erosion rates by about 9 times on average. But on larger rivers, the erosion rate difference increases up to 40 times. While the findings suggest that Arctic warming and hydrologic changes are likely to increase bank erosion rates on larger rivers, the erosion rates on small rivers and streams may be reduced.

Two different turbulence regimes were identified at three heights above the canopy: a weakly stable (WS) and a very stable regime. The threshold wind speeds that mark the transition between turbulence regimes were larger during the dry season and increased as a function of the height above the canopy. Downdrafts occurred only in the WS and favored a fully coupled state of wind flow along the canopy profile.

With focus on the Central Amazon, at the Tropical Silviculture Experimental Station (located about 60 km northwest of Manaus, 2°36′S, 60°12′W, 130 m above sea level), researchers investigated the influence of seasonality and proximity to the forest canopy on nocturnal turbulence regimes in the roughness sublayer. Since convective systems of different scales are common in this region, this study also analyzed the effect of extreme wind gusts (propagated from convective downdrafts) on the organization of the turbulence regimes and their potential to cause the mortality of canopy trees.

Study data include high-frequency winds, temperature, and ozone concentration at different heights during the dry and wet seasons of 2014. In addition, researchers used critical wind-speed data derived from a tree-winching experiment and a modeling study conducted at the same study site. This study provides three novel contributions. The first was the identification of different turbulence regimes and their patterns in terms of seasonality and proximity to the forest canopy in the nocturnal roughness sublayer. The second was the assessment of the effects of near-surface wind gusts (propagated from downdrafts) on the organization of turbulence regimes. Finally, this study provides evidence of the occurrence of extreme wind gusts associated with convective downdrafts, with potential to promote damage and mortality of canopy trees. These aspects highlight the strong interactions between atmospheric and biospheric processes and mechanisms regulating forest structure and dynamics.

Little is understood about the roles of soil bacteria in post-fire carbon cycling, which is important given the current shift in wildfire regimes in the boreal forest towards more frequent, higher severity fires. Researchers developed a traits-based framework of bacterial responses to wildfire that may be useful for understanding the impact of changing wildfire regimes on trajectories of bacterial community recovery and their functioning.

To better understand how wildfires may impact belowground processes in the boreal forest, researchers studied the role of three bacterial traits—fire survival, fast growth, and an affinity for post-fire soil conditions—in driving soil bacterial community composition years following wildfires. Following burning, fast-growing bacteria rapidly dominate soil communities but return to pre-burn levels by 5 years post-fire. While fire survival and affinity for post-fire soil conditions do influence post-fire soil community composition, neither trait is particularly influential. This study also found that post-fire soil respiration is unlikely to be limited by fire-induced changes in bacterial communities.

While ecological predictions can be made based on the genetic features of a given organism or community, the extraordinary diversity of soil bacteria impairs the ability to use taxonomy alone to confidently infer bacterial traits. This study used an uncommon approach to assign traits to bacteria in a high-throughput manner. Researchers first explicitly determined which individual bacterial taxa can survive fires, can grow quickly, and are well-adapted to the post-fire environment. To identify traits, scientists worked with soil cores collected from sites within the boreal forest of northern Canada that had not burned in the previous 30 years or more. Using a series of experiments with simulated burns and subsequent soil incubations, this study identified bacterial taxa with at least one of the following traits—fire survival, fast growth, or an affinity for the post-fire soil environment. These trait assignments were then applied to a field dataset of natural wildfires from the same region 1 and 5 years post-burn to evaluate the importance of each trait in the field. Finally, researchers used respiration data from the incubations of the experimentally burned cores to explore whether changes in microbial communities constrain soil carbon mineralization.

This research explores how extreme storms impact tree loss in tropical forests, especially in the Amazon. These storms, responsible for 50–90% of annual rainfall in the tropics, often result in toppling trees, which disrupts the forest’s ability to store carbon, a crucial ability to fight climate change. These phenomena have been studied separately in the past, but this study connects them. By analyzing satellite data, researchers have uncovered relationships between the characteristics of extreme storms and tree loss sizes. This understanding can improve climate models and provide more accurate predictions about the changing environment.

Fan-shaped dead forest patches were found over the entire Amazonia that cover over 37 hectares, affecting the role Amazon forests play in the global carbon cycle. Scientists found frequent storms happen in these dead forest patches, but how does the process happen? This study explores the three characteristics of storms (passing over time, cloud top temperature, and associated precipitation) to identify their relationship with the size of the dead forests. Results show that long-lived storms with thick and tall clouds result in bigger sizes of dead forest patches. Moreover, forests in western Amazonia are more vulnerable to storms than forests in other parts.

This study delves into the relationship between large-scale storm systems known as mesoscale convective systems (MCSs) and the phenomenon of ‘windthrow’—when storms uproot trees—in the Amazon rainforest. Researchers examined 38 pairs of windthrow and their associated MCS events to identify the specific storm characteristics influencing the extent of windthrow. MCSs with a longer storm duration tended to result in more extensive windthrow. A positive correlation was found between the storm’s duration and the area of forest affected.

The depth of convection clouds within the storm also played a role. Deep convection caused larger windthrow across the entire Amazon. In contrast, shallow convection led to medium-sized windthrows in western Amazonia and smaller ones in central Amazonia. Interestingly, rainfall wasn’t uniformly distributed among forest disturbances of the same size, suggesting the need for more precise precipitation data to establish a clearer relationship with windthrow sizes.

This study offers detailed case studies on windthrows and corresponding MCS features. It reduces the uncertainty of previous research due to data mismatches between MCSs and windthrows, offering fresh insights into how land and atmosphere interact. These findings are important for refining climate models and, ultimately, understanding climate change impacts on the ecosystem.

This research deepens science’s understanding of how ECM fungi influence the decomposition of dead material in forests. This knowledge can help scientists better manage forest ecosystems and improve understanding of the carbon storage processes that are essential for Earth’s health. Studying ECM fungi and their effects on the soil provides valuable insights into how forests work and how they play a crucial role in regulating Earth’s climate.

This study examined ectomycorrhizal (ECM) fungi and their impact on decomposition and carbon storage in forests. Using computer models, researchers explored how ECM fungi compete with other microbes for nutrients, like nitrogen, and affect the breakdown of dead materials in the soil. Findings show that the influence of ECM fungi depends on factors like climate, litter quality, and the type of fungi present. Understanding these interactions is crucial for managing and protecting forests. This research sheds light on how ECM fungi shape soil processes and carbon storage, providing important insights into forest ecology.

In this study, researchers investigated the interactions between ECM fungi and saprotrophic soil microbes, focusing on their competition for nitrogen resources and their effect on forest soil carbon storage. By incorporating mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model, researchers simulated various scenarios to explore how ECM fungi influence decomposition and carbon accumulation in the soil. The competition effect of ECM fungi was found to be context-dependent, being more pronounced when litter inputs lacked nitrogen and were relatively recalcitrant. Furthermore, their capacity to extract nitrogen from recalcitrant soil organic matter and microbial necromass contributed to their impact. Climate and seasonality also played a role, with the competition effect being most significant in cooler climates and during peak litterfall. However, despite a substantial competition effect, the increase in soil organic carbon accumulation was relatively modest, around 10%. These findings offer important insights into the intricate mechanisms governing carbon dynamics in forest ecosystems, providing valuable knowledge for better forest management and conservation.

Large-scale groundwater models configured with lateral groundwater flow were developed a decade ago, but this type of modeling mainly focused on water quantity. Few studies were conducted on water quality and age. Recent studies highlighted that the terrestrial water cycle might have a period much longer than one year when researchers identified water pathways in the annual water balance. This longer period is attributed to the contribution of groundwater to the Earth’s surface processes. Communities of hydrology and Earth surface process modelers lacked a particle tracking tool that could handle cross-scale simulations. By parallelizing the EcoSLIM code, there is now a promising tool for the hydrologic community and ESM developers for scientific exploration.

Increasing evidence shows that groundwater regulates water and energy fluxes in the land-atmosphere system and thus is critical in Earth System Models (ESMs). To fully understand the subsurface hydrologic processes and reasonably upscale them to the scales and resolutions of ESMs, modelers need large-scale particle tracking that account for the groundwater flow paths and their connections with the land-atmosphere system. In a new study, a multi-institutional team of scientists developed and tested a parallel framework on distributed, multi-graphics processing unit (GPU) platforms for the EcoSLIM code, thereby enabling large-scale particle tracking with high spatio-temporal resolutions.

EcoSLIM is a Lagrangian particle tracking code that works seamlessly with the integrated hydrologic model ParFlow-CLM to simulate subsurface advection and diffusion of water parcels. EcoSLIM was developed to calculate water ages (e.g., groundwater, evapotranspiration, and outflow), and diagnose source water composition (e.g., snow, rainfall, and historical groundwater).

The team decomposed the modeling domain into subdomains, considered the particle transfer and load balancing between subdomains, and further accelerated the code on GPUs. Tests (4 NVIDIA A100 GPUs relative to 128 AMD EPYC cores) based on the Little Washita watershed showed a significant speedup of 25.49-fold; 8-fold is the basic requirement. Tests based on the Little Washita watershed in Oklahoma and the North China Plain (NCP) showed excellent parallel scaling. Tests based on the NCP and continental United States demonstrated EcoSLIM’s ability to handle regional- to continental-scale simulations with reasonable wall-clock time. While this study uses EcoSLIM as an example, the parallel framework is portable for other particle tracking models in Earth systems research.

The results from this research provide information for risk assessment of rainfall-related hazards like floods and landslides in vulnerable regions, which is also home to a significant portion of the global population that resides in mountains and their foothills. Results produce valuable insights for developing effective adaptation and mitigation strategies and enable incorporation of projected increases in rainfall extremes into infrastructure design and natural resources management. Furthermore, the findings identify climate model components requiring improvement to reduce uncertainty in projections of rainfall extremes.

Global warming is expected to amplify extreme precipitation events, but the partitioning of rain from snowfall during these events is poorly understood. A new modeling study focuses on changes to rainfall (liquid precipitation) extremes with warming due to its potential impact on flooding, landslides, and erosion. In this study, a multi-institutional team of researchers found a 15% intensity increase per 1°C of warming in mountainous regions, which is twice the previously observed rate for total extreme precipitation. Consequently, high-elevation regions (e.g., Sierra Nevada, Cascades, Rockies, Alps, Himalayas) become vulnerable “hot spots” for future rainfall extremes and are likely to experience amplified risks. This insight enhances understanding of rainfall impacts and associated hazards on specific regions.

In a warmer climate, the intensity of extreme precipitation events is expected to increase, posing significant challenges to water sustainability in natural and built environments. Specifically, rainfall extremes are of great concern due to their immediate impact on runoff, as well as their association with floods, landslides, and soil erosion. However, existing scientific studies on precipitation extremes have not distinguished between rainfall and snowfall. This study, by a multi-institutional team of researchers, addresses this gap and reveals that in high-elevation regions of the northern hemisphere, the increase in rainfall extremes is amplified by an average of 15% per degree Celsius of warming—twice the expected rate from atmospheric water vapor increases alone. The team analyzed observations (climate reanalysis data) and undertook model projection studies and demonstrated that this amplified increase is already occurring and is caused by a shift from snow to rain due to warming air temperatures. Moreover, results showed that changes in the fractions of snow and rain explain a significant portion of the intermodel uncertainty in rainfall extremes projections (coefficient of determination 0.47). These findings highlight high-altitude regions as vulnerable hot spots facing future risks from extreme rainfall-related hazards and suggest the need for robust climate adaptation plans to mitigate potential dangers. Furthermore, these results provide a pathway for reducing model uncertainty in rainfall extremes projections.

UAS data were capable of characterizing species composition in heterogeneous tundra landscapes with overall mapping accuracy greater than 86%. The UAS-derived vegetation maps were also highly effective as training data for upscaling and mapping vegetation composition across watersheds with AVIRIS-NG imagery. This approach produced accurate composition maps (5 m) of 12 PFTs at spatial extents large enough for effectively monitoring tundra vegetation changes and informing modeling efforts focused on improving Earth system predictability. Collectively, this study demonstrates the utility of UAS platforms for providing training data for developing cutting-edge multiscale approaches needed to fill gaps in understanding of Arctic regions.

Widespread changes in vegetation cover and composition in the Arctic tundra impact Arctic ecosystems, people, and climate feedbacks. While tundra landscapes are highly heterogeneous, quantifying the distribution and composition of tundra vegetation over large extents has been challenging. Pixels from traditional satellite observations are too coarse and contain a mixture of plant species that are hard to differentiate. To address this, researchers combined high-resolution unoccupied aerial system (UAS) data and airborne imaging spectroscopy to estimate pixel-wise composition of Arctic plant functional types (PFTs) with NASA’s Airborne Visible/Infrared Imaging Spectrometer–Next Generation (AVIRIS-NG).

Widespread changes in vegetation cover and composition in response to ongoing climate changes in high latitudes are responsible for significant effects on Arctic ecosystem functioning and global climate feedbacks. However, accurately quantifying the composition and distribution of tundra vegetation over large areas is challenging given that commonly used satellite observations are too coarse to differentiate low-lying tundra vegetation types.

To address this challenge, researchers combined airborne observations from very high-resolution (VHR, ~5 cm) UAS platforms and hyperspectral imagery from NASA’s AVIRIS-NG instrument to develop novel multiscale methods to map the fractional composition of 12 key low-Arctic tundra PFTs. Using high-resolution vegetation maps developed with novel UAS imagery as training observations, new statistical partial least squares regression (PLSR) models were developed to predict the continuous fractional cover of each PFT with AVIRIS-NG imagery. The PSLR models’ performance was evaluated using additional UAS data reserved from model training and against other traditional methods used to map vegetation fractional coverage within remote sensing pixels.

These methods showed that: (1) a wide range of Arctic PFTs can be mapped using VHR UAS imagery with an overall accuracy greater than 86%; (2) generated UAS maps can then be used effectively as training data for larger-scale models developed with airborne AVIRIS-NG imagery with a mean absolute error <0.13; and (3) the final AVIRIS-NG PLSR models outperformed traditional linear mixture analysis. These new scaling approaches could likely be transferred to other Arctic regions where similar data is available. These approaches could also potentially transform understanding of fine-scale patterns in tundra vegetation composition, improve long-term monitoring of tundra vegetation dynamics, and improve process-based modeling of Arctic tundra ecosystems.

The ability of ferrihydrite-based colloids to withstand anoxic conditions that are also rich in dissolved Fe(II) highlights the extent to which organic matter-Si coatings can protect Fe(III) from reductive dissolution. This passivating feature may also explain the existence of Fe(II) and sulfur within the colloidal structure. Ultimately, the persistence of the colloids suggests they may transport throughout anoxic zones and reach oxic surface waters. These findings shed light on the composition and dynamics of natural Fe-rich colloids in floodplain systems, with implications for elemental transport and cycling.

Colloids can transport nutrients, contaminants, and organic matter throughout watersheds. Their persistence, reactivity, and heterogeneous compositions render them key contributors to biogeochemical reactions. A multi-institutional team of researchers detected iron (Fe)-rich colloids in anoxic groundwater of a redox-active floodplain of the Slate River, CO. The colloids were characterized by a wide array of advanced techniques and found to be mixed-phase assemblages composed of silicon (Si)-coated and organic matter–enmeshed ferrihydrite nanoparticles. Both Fe(II) and Fe(III) co-existing in the colloids under anoxic conditions illustrates the passivating effects of the Si and organic matter matrix against redox-triggered transformations.

Geochemical interfaces are ubiquitous in floodplain environments and sustain a multitude of biogeochemical processes, including the formation and release of reactive, mobile colloids. Colloids are known vectors of micronutrient, contaminant, and organic matter transport and are suspected to be important export agents from floodplains to stream water. However, few studies have characterized naturally occurring Fe-rich colloids at the molecular scale.

Now, a multi-institutional team of researchers combined advanced characterization techniques to decipher the composition of Fe-rich colloids at a floodplain field site of the Slate River, CO. Cascade filtering revealed the presence of Fe-rich colloids in the riparian anoxic soil water and their abundance and composition varied with season. Cryo-electron microscopy and transmission electron microscopy (TEM)–energy dispersive X-ray spectroscopy imaging showed mono-dispersed and nano-assemblages of spherical colloids in the 10–50 nm range containing Fe, oxygen, Si, carbon, and calcium. TEM selected-area electron diffraction analysis and Mössbauer spectroscopy indicated a poorly crystalline ferrihydrite-like phase. Fe-extended X-ray absorption fine structure spectroscopy further verified ferrihydrite and Fe(II)- and Fe(III)-organic matter interactions, as well as a small contribution from Fe-sulfur bonding. Results indicate that the colloids are primarily composed of nanosized ferrihydrite spheres that are stabilized by organic matter, Si, and bridging cations (e.g., calcium). These Fe(III)-rich colloids existed in primarily anoxic zones, which is striking. The Si-organic matter coating is postulated to serve as a passivating layer against reduction, but its efficiency likely depends on the biogeochemical and hydrological conditions.

Findings have direct implications for blue carbon projects globally. This research demonstrates that allochthonous carbon (i.e., carbon not produced by local vegetation) could be up to 50% of the total marsh soil carbon. Blue carbon policy only counts locally produced carbon in offsetting programs.

Additionally, due to the changing location of carbon in the coastal landscape, perturbations in the system (e.g., storms) could have larger consequences for carbon storage in the coastal zone.

Coasts are resilient to climate change and can continue to store increasing amounts of carbon as sea level rises. To do this, marshes expand into lands that were previously coastal forest and maintain their elevation. However, if sea level rise rates are too great, the marsh is unable to keep pace, and the entire marsh system collapses, resulting in lower coastal carbon storage. This study demonstrates that as sea level rises, the coastal landscape changes where most of its carbon is stored from stable forest trees to more vulnerable marsh soils.

The world’s coasts are responding rapidly to climate change, but most models do not incorporate how adjacent ecosystems interact and how this impacts ecosystem function. In this study, researchers coupled geomorphic processes and carbon dynamics in a numerical model that spans the bay-marsh-forest transect to understand the entire coastal zone’s future. Across the coastal zone, carbon storage increases with sea level rise. As the coast continues to store more carbon, stable carbon is lost from the coastal forest and compensated by gains in marsh soils. While this shift increases carbon sequestration and potential for mitigation of climate change, carbon is placed in a more vulnerable place in the landscape. Once extreme rates of sea level rise are achieved, the coastal system collapses.

Through this innovative modeling framework, researchers also were able to track carbon across landscapes. Results show that connectivity of carbon between coastal ecosystems is critical for maintaining the coastal carbon sink. Up to half of the carbon stored in marshes may be carbon that was produced elsewhere and transported to marshes. Without connectivity, the marsh has limited capacity to keep up with sea level rise and collapses under lower rates of sea level rise.

Twenty-five globally respected urban scientists from 10 countries on five continents, including representatives from three of the U.S. Department of Energy’s (DOE) Urban Integrated Field Laboratories (Southeast Texas, Southwest, Community Research on Climate and Urban Science), articulated a vision for the integration of urban science with urban climate adaptation research to advance global climate action. Urban areas contain many people vulnerable to the consequences of climate change and have significant potential to reduce social vulnerability through enhanced adaptive capacity, innovation, and policy. Urban research must address how to minimize growing vulnerabilities while enabling far-reaching and equitable climate action for a better future.

Actionable research on urban adaptation should incorporate social, ecological, physical, and technological systems while recognizing that cities are social networks embedded in built and natural environments. This study unites many academic disciplines to comprehensively understand urban adaptation to climate change and build knowledge that can inform policymaking and enable action. Cities in the global south are growing at an unprecedented pace and scale; these cities and their informal communities must be central to the study of how urbanization can either facilitate or hinder climate action. The proposed research effort is thus a call for the active co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

Cities are dense social networks embedded in physical built space. Interconnections among urban climate, technology, and governance define the scope and emerging challenges of a convergent global research agenda on urban adaptation. This study highlights diverse perspectives that research on urban adaptation to climate change must bring together and be based upon, which is expressed in the form of eight conceptual tenets. (1) Urban actors are the principal drivers of invention, innovation, and development. (2) Urban settings function as “social reactors,” concentrating and accelerating interactions and their social, economic, and political outcomes in space and time. (3) Cities’ historical trajectories result from technological capabilities and socioeconomic processes. (4) Climate risk exposure and adaptive capacity varies with the scale and heterogeneity of urbanization. (5) Cities’ vulnerabilities should be understood, and adaptive capacities should be developed with careful attention to history. (6) The nexus of climate change, biodiversity, ecosystem services, and urban development must be considered. (7) Urban climates are partly socially constructed. (8) Co-creation of knowledge among public and private sectors as well as citizens, specifically the urban poor and residents of informal settlements, must be part of the new research agenda.

Mycorrhizal fungi could be considered keystone organisms in soil food webs. They connect trees to other organisms living in the soil that break down soil organic matter, releasing nutrients that plants need for growth. Findings suggest that trees are found with different communities of free-living microbes depending on mycorrhizal associations. The importance of mycorrhizal associations for breaking down organic matter means that trees and associated mycorrhizae can affect how organic matter decomposes and how much carbon remains stored in soil.

Trees have different traits that affect soil organic matter and nutrients. This study looked at two main traits—tree leaf habit (either deciduous trees that lose their leaves in the fall or evergreen trees that keep their leaves year-round) and root mycorrhizal association. Trees almost always associate with only one type of mycorrhiza that grow either inside or outside the root tips. Mycorrhizae are key for helping plants get nutrients and water from soil and in turn get sugars from trees. Researchers found that mycorrhizal association was more important than leaf habit in affecting the other free-living fungi in the soil around roots. The other fungi are important in breaking down organic matter due to enzyme production. Much like people have enzymes in their digestive tracts, fungi and other microbes digest outside their bodies and then absorb smaller bits of organic matter. Researchers also found that many enzymes needed to break down plant and fungal tissue were affected more by mycorrhizal association than leaf habit.

Forests in the northeastern United States are experiencing shifts in community composition due to the northward migration of warm-adapted tree species and certain species’ declines (e.g., white ash and eastern hemlock) due to invasive insects. Changes in belowground fungal communities and associated functions will inevitably follow. Therefore, a team of researchers sought to investigate the relative importance of two important tree characteristics—mycorrhizal type [ectomycorrhizal (EcM) or arbuscular mycorrhizal (AM)] and leaf habit (deciduous or evergreen)—on soil fungal community composition and organic matter cycling. Soil was sampled in the organic and mineral horizons beneath two AM-associated (Fraxinus americana and Thuja occidentalis) and two EcM-associated tree species (Betula alleghaniensis and Tsuga canadensis) with an evergreen and deciduous species in each mycorrhizal group. To characterize fungal communities and organic matter decomposition beneath each tree species, researchers sequenced the ITS1 region of fungal DNA and measured the potential activity of carbon- and nitrogen-targeting extracellular enzymes. Each tree species harbored distinct fungal communities, supporting the need to consider both mycorrhizal type and leaf habit. However, between tree characteristics, mycorrhizal type better predicted fungal communities. Across fungal guilds, saprotrophic fungi were the most important in shaping fungal community differences in soils beneath all tree species. The effect of leaf habit on carbon- and nitrogen-targeting hydrolytic enzymes depended on tree mycorrhizal association in the organic horizon, while oxidative enzyme activities were higher beneath EcM-associated trees across both soil horizons and leaf habits.

With the development of the photochemical model of electron transport, it is now possible to couple previously developed photophysical and biochemical models to model the complete system of photosynthesis. A complete photosynthesis model will enable many advances that have not been possible previously. For example, carbon cycle modelers can now use a broad scope of measurements including fluorometry and gas exchange to improve carbon cycle predictions. Bioengineers can quantitatively determine how components of photophysical, photochemical, and biochemical reactions can be modified to improve the overall efficiency of the photosynthetic machinery.

Photosynthesis consists of three stages of reactions—photophysical, photochemical, and biochemical. Photophysical reactions harvest photons in light to generate excitation energy in chlorophyll molecules. Photochemical reactions trap excitation energy via electron transport, and biochemical reactions use products from electron transport to assimilate carbon dioxide. The photophysical, photochemical, and biochemical reactions must work collaboratively to convert photon energy in light to chemical bond energy in sugars. Previously, these different stages of reactions could not be modeled together because a model for the middle stage—photochemical reactions—was lacking. In this study, a team of researchers developed a photochemical model of electron transport to improve understanding of light capture to carbon assimilation.

A photochemical model of photosynthetic electron transport (PET) is needed to integrate photophysics, photochemistry, and biochemistry to determine redox conditions of electron carriers and enzymes for plant stress assessment and mechanistically link sun-induced chlorophyll fluorescence to carbon assimilation for remotely sensing photosynthesis. Toward this goal, a team of researchers derived photochemical equations governing the states and redox reactions of complexes and electron carriers along the PET chain. These equations allow the redox conditions of the mobile plastoquinone pool and the cytochrome b₆f complex (Cyt) to be inferred with typical fluorometry. The equations agreed well with fluorometry measurements from diverse C₃/C₄ species across environments in the relationship between the PET rate and fraction of open photosystem II reaction centers. The team found the oxidation of plastoquinol by Cyt is the bottleneck of PET, and genetically improving the oxidation of plastoquinol by Cyt may enhance the efficiency of PET and photosynthesis across species. Redox reactions and photochemical and biochemical interactions are highly redundant in their complex controls of PET. Although individual reaction rate constants cannot be resolved, they appear in parameter groups which can be collectively inferred with fluorometry measurements for broad applications. The new photochemical model developed enables advances in different fronts of photosynthesis research.

Higher plants have two photosystems (II and I) that must coordinate to pass electrons for photosynthesis. The new theory suggests that grana stacks expand the degree of ultrastructural control on photosynthesis through thylakoid swelling and shrinking in coordination with varying stomatal conductance and turgor of guard cells. This process allows land plants to adapt to dry and high-irradiance environments. This theory not only successfully explains a long-standing mystery but also unifies many well-known phenomena of thylakoid structure and function of higher plants.

Among the photosynthetic eukaryotes, higher plants have the most diverse morphology and physiology. Yet without exception and regardless of photosynthetic pathways, all higher plants share thylakoid architectures characterized by appressed grana stacks and unstacked stroma lamellae. This architecture is lacking in other oxygenic photosynthetic organisms (e.g., cyanobacteria and algae). A new theory suggests that plants swell and shrink grana stacks to control electron transport in tandem with the opening and closing of small holes on leaf surfaces known as stomata to control gas exchange between the leaf’s inside and outside. This process is like how an accordionist plays music by controlling the rhythms of bellows and air buttons.

In higher plants, photosystems II and I are found in grana stacks and unstacked stroma lamellae, respectively. To connect them, electron carriers negotiate tortuous multimedia paths and are subject to macromolecular blocking. Why does evolution select an apparently unnecessary, inefficient bipartition? This study proposes that grana stacks, acting like bellows in accordions, increase the degree of ultrastructural control on photosynthesis through thylakoid swelling and shrinking induced by osmotic water fluxes. This control coordinates with variations in stomatal conductance and the turgor of guard cells, which act like an accordion’s air button. Thylakoid ultrastructural dynamics regulate macromolecular blocking and collision probability, direct diffusional pathlengths, division of function of Cytochrome b₆f complex between linear and cyclic electron transport, luminal pH via osmotic water fluxes, and the separation of pH dynamics between granal and lamellar lumens in response to environmental variations. With the two functionally asymmetrical photosystems located distantly from each other, the ultrastructural control, nonphotochemical quenching, and carbon-reaction feedbacks maximally cooperate to balance electron transport with gas exchange, provide homeostasis in fluctuating light environments, and protect photosystems in drought. Grana stacks represent a dry and high irradiance adaptation of photosynthetic machinery to improve fitness in challenging land environments. This theory unifies many well-known but seemingly unconnected phenomena of thylakoid structure and function in higher plants.

The finding and determination of the ecosystem wilting point provide new insights into how vegetation balances water loss from leaves with water acquisition by roots. Results show linkages between traits of the root system and canopy of leaves. When water supply to leaves no longer matches the demand from the air, the leaves dehydrate. When dehydration is severe enough, the ecosystem wilting response is triggered, which restricts forests’ breathing. As an ecosystem trait, the ecosystem wilting point can be used to test climate models’ ability to simulate drought responses.

Like animals, forests breathe; unlike animals, which breathe in oxygen and breathe out carbon dioxide, forests take in carbon dioxide and release water vapor and oxygen through tiny openings on leaf surfaces called stomata. Forests actively regulate the opening and closing of stomata in response to environmental variations. A team of researchers found that this regulation can only be done up to a threshold—the ecosystem wilting point. When drought is so severe that this threshold is passed, forests lose the ability to control their breathing, which will lead to forest decline if sustained.

The ecosystem wilting point is a property that integrates the drought response of an ecosystem’s plant community across the soil–plant–atmosphere continuum. The ecosystem wilting point defines a threshold below which the capacity of vegetation to extract soil water and the ability of leaves to maintain stomatal function are strongly diminished. A team of researchers combined eddy covariance and leaf water potential measurements to derive the ecosystem wilting point of an oak-hickory forest using an analogy to the pressure-volume technique that is usually used to study leaves or roots. During severe drought, the forest crossed the ecosystem wilting point, became insensitive to changes in weather, and was a net source of carbon dioxide for nearly all of July and August. After soaking rains, the forest showed rapid recovery responses, but a legacy of drought damage limited the recovery of canopy photosynthesis. Long-term records of plant water status suggest that this forest is commonly only 2–4 weeks of intense drought away from reaching the ecosystem wilting point and thus highly reliant on frequent rainfall to replenish the soil water supply.

This research has developed a novel modeling framework that offers a promising approach for enhancing the GPP of land models while leveraging remote-sensing SIF data. Additionally, this study has identified primary drivers behind global photosynthesis changes, thereby improving understanding of the effects of environmental changes on ecosystem photosynthesis and enabling more accurate predictions of such impacts. The ML techniques employed in this research can be refined in the future with additional ground- and satellite-based observations and potentially adapted for use with other land surface models.

Modeling ecosystem productivity is challenging due to uncertainty in photosynthesis parameters. However, solar-induced chlorophyll fluorescence (SIF) is a unique proxy for productivity, and machine learning (ML) can help model the relationship between SIF and productivity. A team of researchers used satellite SIF data and flux tower–based gross primary productivity (GPP) observations to train ML models. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) was fed with ML GPP-SIF models to create global SIF estimates. Using surrogate modeling and optimization techniques, researchers optimized major ELM photosynthesis parameters and produced improved spatial patterns of ELM GPP compared to other estimates.

Accurate parameterization of key photosynthesis parameters is critical for modeling GPP but remains a significant source of uncertainty. One promising way to address this challenge is with SIF, which provides a proxy for GPP by directly capturing the photosynthesis process. ML techniques offer a robust approach for modeling the GPP–SIF relationship. A team of researchers trained boosted regressing tree and random forest ML models using data from the Greenhouse Gases Observing Satellite and in situ GPP observations from 49 eddy-covariance towers. These ML GPP-SIF models were then incorporated into ELM to generate global SIF estimates that were benchmarked against satellite SIF observations using a surrogate modeling approach, which demonstrated good model performance.

Results suggest that ML-based GPP-SIF models provide accurate predictions of spatial and temporal variations in SIF. Sensitivity analysis revealed that the fraction of leaf nitrogen in ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the most sensitive parameter to SIF, followed by the Ball-Berry stomatal conductance slope and maximum carboxylation rate entropy. After benchmarking, posterior uncertainty in simulated GPP was substantially reduced, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Overall, this integrated approach represents a promising new avenue for improving land models and leveraging remote-sensing SIF data. With further refinements using additional ground- and satellite-based observations, this approach could further enhance ecosystem models’ accuracy and predictive power.

This study addressed one of the biggest uncertainties about how carbon-rich regions of the Arctic will respond to warming temperatures: the potential for uneven ground subsidence to accelerate permafrost thaw in a positive feedback loop. Simulation results for a tundra site that represents large swathes of the Alaska North Slope suggest that landscape drying will limit the effect of subsidence and prevent abrupt thaw over large areas. However, subsidence increases landscape runoff, which helps maintain streamflow in the face of increased evapotranspiration but causes drier tundra conditions that could have deleterious effects on sensitive Arctic wetland ecosystems.

Researchers extended a permafrost thermal hydrology model to represent uneven sinking of the ground surface caused by soil ice deposits melting. Uneven sinking has been observed to accelerate permafrost thaw over small areas, but models were unable to evaluate potential impacts over larger scales. In this study, spatially resolved simulations focused on tundra containing wedge-shaped deposits of ice, a common type of landscape in the Arctic. Cryohydrology simulations were informed by data from a representative site. Results suggest that subsidence-induced acceleration of permafrost thaw will be self-limiting on decadal time scales.

The study sought to understand consequences of a potential positive feedback loop between uneven subsidence and permafrost thaw, which could trigger abrupt and large-scale change in the Arctic. Researchers extended the Advanced Terrestrial Simulator, a site-scale permafrost thermal hydrology model, to represent uneven ground subsidence in polygonal tundra, carbon-rich regions of the Arctic where soils are honeycombed by wedges of ice that express in polygonal patterns. Existing data and new measurements of ground ice content informed simulations of a small catchment near Utqiaġvik, Alaska. Simulations agreed well with multiple types of observations.

Projections indicate 63 cm of bulk subsidence from 2006 to 2100 in the strong-warming Representative Concentration Pathway 8.5 climate. Permafrost thaw as measured by the increase in active layer thickness (ALT)—the thickness of the soil layer that thaws each summer—is accelerated by subsidence, but the effect is relatively small. ALT increases from the current-day value of approximately 50 cm to 180 cm by 2100 when subsidence is included compared to 160 cm when it is neglected. The effect on thaw-exposed soil carbon is larger. Specifically, the mass of soil solids thawing each summer increases by approximately 65% by subsidence. Subsidence also increases runoff efficiency, which will help maintain streamflow but lead to significantly drier tundra conditions. Although uneven subsidence is unlikely to trigger abrupt thaw over large areas, the effects on landscape hydrology and tundra carbon stocks may be significant and should be included in Earth system models.

Projecting biosphere function requires a holistic viewpoint. However, models have overlooked fine-root processes since the 1970s. Accelerated empirical advances in the last 2 decades have established functional differences along the hierarchical structure of fine roots and their mycorrhizal fungal partners, highlighting a need to embrace this complexity to bridge the data-model gap. This study builds the case for adopting the TAM structure as a quantitative keystone of the bridge between modelers (e.g., ELM) and empiricists (e.g., FRED). This framework can be used across modeling paradigms to guide empirical research, improve understanding of ecosystem functioning, and improve Earth system model predictive capabilities.

Terrestrial biosphere models project large-scale biological responses to climate change. Historically, leaves have received far more attention in models than fine roots, though roots are critical for plant resource acquisition. This study proposes a generalized model structure that includes short- and long-lived fine roots with differing functions (transport and absorptive fine roots), as well as their mycorrhizal fungal partners (TAM). This approach approximates the hierarchical branching structure of fine-root systems and serves as an explicit but tractable approach to model fine-root system function in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) while leveraging the Fine-Root Ecology Database (FRED).

Accelerated empirical progress over the past 2 decades has revealed fine-root system complexity. However, a bias against fine-root systems lingers in ecosystem modeling across spatial-temporal scales. Dedicated efforts are warranted to explore ways to embrace the complexity. In this study, researchers propose TAM as a structure-based, function-oriented framework to approximate the high-dimensional structural and functional variations within fine-root systems. Originating from a conceptual shift, TAM emerges from theoretical and empirical foundations of balancing fine roots and mycorrhizal fungi and holding high parameterization feasibility as a tradeoff between realism and simplicity. The significance of TAM is quantitatively confirmed for simulating temperate forest ecosystem functioning using a big-leaf land surface model with a conservative and radical case. These analyses suggest that current-generation models homogenizing fine-root systems may overestimate forest productivity and carbon stocks and that capturing fine-root system complexity may contribute to simulating sink-limited growth more accurately. Though uncertainties and challenges remain, the study overall supports TAM as a quantitative keystone of the bridge between empiricists and modelers to embrace fine-root system complexity.

Ground-based biomass stocks and fluxes are widely used to estimate carbon budgets, quantify forest carbon offsets, and calibrate and validate remote sensing products employed to obtain biomass estimates at regional and global scales. This study shows biomass loss from damage to living trees constitutes an important and overlooked component of biomass loss. These results contrast with typically low forest biomass losses estimated only from tree mortality and suggest that forest carbon turnover may be higher than previously thought. Since forest disturbance rates are expected to increase under climate change, biomass loss from damage is likely to become more important.

Damage (e.g., branchfall, trunk breakage, and wood decay) is a ubiquitous feature in forest ecosystems. However, traditional forest inventories assume tree mortality is the only source of biomass losses. While previous studies show damage is an important condition preceding tree death, contribution of nonlethal damage (i.e., from surviving trees) to total forest biomass (and therefore carbon) losses remained unclear. Forest Global Earth Observatory (ForestGEO) scientists combined field-based measurements of tree completeness with vertical volume profile models obtained from terrestrial laser scanning to show 42% (range 12% to 76% across forests) of total aboveground biomass loss is due to damage to living trees across seven tropical forests.

Forest carbon losses constitute a significant source of uncertainty in vegetation models. These estimates are typically calculated based on dead tree biomass without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. In this study, forest ecologists employ multiple annual records of tree survival and structural completeness to compare aboveground biomass (AGB) loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. Researchers find that 42% (3.62 Mg ha^-1 yr^-1; 95% CI 2.36–5.25) of total AGB loss (8.72 Mg ha^-1 yr^-1; CI 5.57–12.86) is due to damage to living trees. They also find that conventional forest inventories: (1) overestimate stand-level AGB stocks by 4% (1 to 17% range across forests) because they assume structurally complete trees; (2) underestimate total AGB loss by 29% (6 to 57%) because they overlook damage-related AGB losses; and (3) overestimate AGB loss via mortality by 22% (7 to 80%) because they assume that trees are undamaged before dying. These results indicate that forest carbon fluxes are higher than previously thought. Damage to living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency of forest disturbances increases.

This study could impact new frontiers in science related to understanding and managing ecosystems, especially those in humid areas with similar forest types. The findings suggest that forest type can affect nutrient cycling and loss, which can have significant impacts on the long-term balance of water and nutrient uptake in the ecosystem. This knowledge can be applied to better manage and protect forest ecosystems and to promote sustainable land use practices. Additionally, the study’s use of advanced sampling and measurement techniques could inform future research on soil health and nutrient cycling in other ecosystems, providing valuable insights for future environmental management efforts.

Different forest types affect soil nutrient amounts in a humid area in Kentucky. Researchers examined two types of oak trees (post oak and cherry bark oak) and measured physical, chemical, and mineral properties of the soil in 12 different locations to see how much phosphorus was being lost from the soil and how different oak trees affected this loss. The oak tree with greater phosphorus demand had more loss of phosphorus from the soil because the tree’s roots took up more water, which caused the soil to expand and shrink and made it harder for the soil to hold onto phosphorus. There was more phosphorus loss in soil under post oak trees than cherry bark oak trees. Results showed that the type of clay in the soil was most likely not the main reason for this difference in phosphorus loss. Overall, this study shows that forest type can affect how much water and nutrients trees take up, which can impact the soil and ecosystem over time.

This study aimed to determine whether different forest types affect long-term cycling of soil phosphorus in subtropical river bottomlands. Researchers selected two forest ecosystems, post oak and cherry bark oak, as they are thought to differ in drought tolerance. Study sites had similar landscape positions, parent material, soil age, and climate. Results suggest a greater loss of phosphorus in soils underlying post oak compared to cherry bark oak. Analysis of tree samples showed similar leaf phosphorus content in both types of oak but significantly more phosphorus in the woody biomass of post oak than in cherry bark oak. The study suggests that post oak prioritizes storing phosphorus in woody biomass for efficient water use during dry periods and that phosphorus may be a limiting nutrient for post oak. This research highlights the importance of considering long-term effects of different tree types on nutrient and water balance in soil.

Riverbed CO₂ production accounts for a significant portion of the carbon cycle of inland waters. Previous regional and global studies that estimated stream and river CO₂ release did not include the effect of CO₂ production in riverbeds. The basin-scale coupled carbon-nitrogen model developed in this study allows researchers to quantify the spatial variation of aerobic and anaerobic respiration across the entire Columbia River Basin. This study offers an option for testing hypotheses related to microbially driven respiration processes in river systems in other biomes and climates and can be used as a tool to design sampling schemes for large-scale experimental studies.

The hyporheic zone (HZ) at the bottom of rivers plays an important role in the overall river ecosystem, accounting for a significant portion of carbon dioxide (CO₂) emissions into the water column. However, HZ respiration modeling studies lack quantification of how the HZ contributes to total CO₂ at the scale of the entire watershed or basin. Previous studies have also incompletely considered the contribution of anaerobic respiration. A new modeling study developed an approach to couple carbon and nitrogen cycles in an entire river corridor to quantify microbially-driven aerobic and anaerobic respiration in the HZ. This new model allowed researchers to determine key factors controlling the spatial variability of microbially-driven respiration within the Columbia River Basin.

Microbes in riverbeds generate high amounts of CO₂, but numerical simulation models have not accurately quantified their contributions to total CO₂ budgets across entire river basins and other large regions. In this study, a multi-institutional team of researchers used a numerical simulation model to estimate CO₂ emissions from riverbeds into the water column in the presence and absence of oxygen. The researchers then identified important variables that explain the spatial variation of riverbed CO₂ emissions within the Columbia River Basin. The study found that CO₂ emissions from riverbeds showed high spatial variability. Within the Columbia River Basin, wetter sub-basins showed higher CO₂ emissions than drier sub-basins. Medium-sized rivers generated the highest CO₂ emissions. Most CO₂ emissions from channels occurred in the presence of oxygen. However, reaches in agricultural areas generated relatively high CO₂ emissions without oxygen. Finally, the team found that the water exchange rate between channels and riverbeds, as opposed to other physical variables, could explain the spatial variation of CO₂ emissions.

This new conceptual model linked to subsurface hydrology makes predictions of bedrock weathering fronts and rates more feasible, and connects to water quality and climate change impacts. The approach can be applied to other settings of a watershed.

For the first time, researcher directly determined subsurface bedrock weathering rates from in-situ measurements. The weathering front coincides with the depth of deepest seasonal water table for sedimentary bedrocks. Carbonates and rock organic matter share the same weathering front depth with pyrite, contrary to models that stratify their weathering fronts.

Although bedrock weathering strongly influences water quality and global carbon and nitrogen budgets, the weathering depths and rates within subsurface are not well understood nor predictable. Determination of both porewater chemistry and subsurface water flow are needed in order to develop more complete understanding and obtain weathering rates. In a long-term field study, researchers applied a multiphase approach along a mountainous watershed hillslope transect underlain by marine shale. Researchers found that the deepest extent of the water table determines the weathering front, and the range of annually water table oscillations determines the thickness of the weathering zone. Below the lowest water table, permanently water-saturated bedrock remains reducing, preventing deeper pyrite oxidation. Researchers also found that carbonate minerals and potentially rock organic matter share the same weathering front depth with pyrite, contrary to models where weathering fronts are stratified. Additionally, the measurements-based weathering rates from subsurface shale are high, amounting to base cation exports of about 70 kmolc ha⁻¹ y⁻¹, which is consistent with weathering of marine shale. By integrating geochemical and hydrological data, researchers presented a new conceptual model that can be applied in other settings to predict weathering and water quality responses to climate change.

This study presents a promising approach to the assessment of ET with a high spatiotemporal resolution over watershed scales and investigates factors controlling ET spatiotemporal variations.

This is one of the first studies that comprehensively investigated the spatiotemporal variations of evapotranspiration (ET) in a mountainous watershed and analyzed the factors that control these variations.

ET is a key component of the water balance, which influences hydrometeorology, water resources, carbon and other biogeochemical cycles, and ecosystem diversity. Researchers conducted a study to investigate the spatiotemporal variations of ET at the East River watershed in Colorado and analyze the factors that control these variations. Simulation results showed that 55% of annual precipitation in the East River is lost to ET, and that 75% of the ET is during the summer months (May to September). Researchers also found that the contribution of transpiration to the total ET was ~50%, which is much larger than that of soil evaporation (32%) and canopy evaporation (18%). Spatial analysis indicated that the ET is higher at elevations of 2950–3200 m and lower along the river valley (<2750 m) and at the high elevations (>3900 m). A correlation analysis of factors affecting ET showed that the land elevation, air temperature, and vegetation are closely correlated, and together they govern the ET spatial variability. The results also suggested that ET in areas with more finely textured soil is slightly larger than regions with coarse-texture soil.

Advancing understanding of how watersheds function is becoming increasingly important as warming climate conditions affect water resources. This study evaluated the performance of a high-resolution, process-based hydrology model in reproducing streamflow and evapotranspiration data from seven diverse catchments. Model performance was good in five catchments using only community data products to define model inputs. In the other two catchments, good model performance was realized after correcting the data products to be consistent with known geology. This study shows that high-resolution process-based hydrology models supported by community data products can improve understanding of water supply threats.

Scientists and engineers use hydrology models to simulate water flow across and beneath the Earth’s surface. Hydrology models have traditionally used simplified representations of the landscape and must be calibrated to match observations made under current conditions, which creates uncertainty when the models are used in new conditions. This study found that a new model version that represents hydrologic processes in greater detail can match streamflow data without significant calibration of the model. Avoiding calibration improves overall confidence in a hydrology model as a tool for understanding how climate and land use change will affect water supply.

A team of researchers from Oak Ridge National Laboratory evaluated the performance of a high-resolution surface/subsurface hydrology model, the Advanced Terrestrial Simulator (ATS), using streamflow and evapotranspiration data from seven diverse catchments. Community data products were used to define model inputs without calibration. ATS performance for evapotranspiration was good in all seven catchments using default data products. ATS with default data products performed reasonably well on streamflow for five catchments. Model performance was significantly improved in the other two catchments by adding local information on subsurface properties below the soil layer. ATS performance was also compared to a semi-distributed model called the Sacramento soil moisture accounting (SAC-SMA) model, which was calibrated for each catchment. Uncalibrated ATS performance was comparable to the calibrated SAC-SMA model in terms of streamflow, but overall was found to be better than the SAC-SMA model at reproducing evapotranspiration. Good performance of ATS without catchment-specific calibration provides new confidence in spatially resolved, process-based models as tools for advancing understanding of the function of watersheds in a changing environment. The community data products needed to support these types of models are widely available, but subsurface properties need to be independently verified.

Fire behavior models have long used general fuels in broad groups. With new types of models and remote sensing measurements of fuels and fires, researchers can capture more realistic fuels and how they change in both their structure and condition, such as live fuel moisture. This information is critical for fire management in conditions of drought and warming. Linking how living plants change through time and across a landscape to how fires might behave will provide information to better support communities in a world with more fire.

The condition of living woody plants can change fire behavior. Plants have different levels of dryness throughout the seasons and in different parts of a landscape based on water and nutrients in the soil. Lower levels of live fuel moisture in plants have been linked to faster fires, changes in the way fires burn, and alter how likely plants are to die after a fire. Linking live fuel moisture measurements from remote sensing tools such as airborne systems to models of fire behavior and effects improves understanding of how fires may change in the future.

Wildfires have been recognized as a global crisis, but current fire models do not capture how living plants change in response to changing climate. With drought and warming temperatures increasing the importance of living plants in changing fire behavior, researchers can capture these complex processes and interactions with new model capabilities. This study provides a renewed focus on capturing live woody plants in fire models. Living plant conditions and properties influence fire combustion and heat transfer and often dictate if a plant will survive. These interactions provide a mechanistic link between living plants and fire behavior and effects that can be included in new models. This study includes a conceptual framework linking remotely sensed estimates of plant condition to models of fire behavior and effects, which could be a crucial first step toward improving models used for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

This method allows for real-time stem CO₂ E_s measurements to evaluate diurnal patterns of growth and respiration in hyperdiverse forests to help resolve major uncertainties surrounding stem respiration. While temperature is assumed to stimulate growth and its associated respiratory processes, preliminary real-time diurnal data collected with the technique suggest that plant hydraulics are also key, with midday water stress in the dry season limiting plant growth and respiratory process. Deployment of the techniques to remote tropical forests in Brazil will link plant hydraulics and carbon metabolism in ecosystem demographics models like the Functionally Assembled Terrestrial Ecosystem Simulator (FATES).

Stem respiration is a quantitatively important but poorly understood component of ecosystem carbon cycling in terrestrial ecosystems. However, a dynamic stem gas exchange system for quantifying real-time stem carbon dioxide (CO₂) efflux (E_s) is not commercially available, resulting in limited observations using the static method. The static method has limited temporal resolution, suffers from condensation issues, requires a leak-free enclosure that is difficult to verify in the field, and requires physically removing or flushing the chamber between measurements. In this study, researchers present a custom system design for real-time off-the-grid monitoring of stem CO₂ E_s from diverse tropical forests.

To improve quantitative understanding of biophysical, physiological, biochemical, and environmental factors that influence diurnal CO₂ E_s patterns, researchers created a custom system for quantifying real-time stem E_s in remote tropical forests. The system is low cost, lightweight, and waterproof with low power requirements (1.2-2.4 W) for real-time monitoring of stem E_s using a 3D-printed dynamic stem chamber and a 12V car battery. The design offers control over the flow rate through the stem chamber and eliminates the need for a pump to introduce air into the chamber and water condensation issues by removing water vapor prior to CO₂ analysis. Following a simple CO₂ infrared gas analyzer calibration and match procedure with a 400-ppm standard, researchers quantified diurnal E_s observations over a 24-hour period during the summer growing season from an ash tree in Fort Collins, Colo. Great success was achieved with this system in the Amazon during the rainy season in 2022. The results are consistent with previous laboratory and field studies that show E_s can be suppressed during the day relative to the night.

Researchers found that their machine learning method distinguishes degraded forests from intact forests in 86% of cases. The machine learning approach occasionally confuses logged forests with intact forests but is very good at identifying burnt areas. The team found that logged forests have almost the same amount of carbon as intact forests. However, forest fires can reduce the amount of carbon by 35%.

Forest degradation through fires and logging is widespread in the Amazon. Though it changes forest structure, forest degradation is difficult to detect from space. A team of researchers used commercial high-resolution satellites and developed a machine learning system to automatically distinguish intact forests from logged or burned forests. They also used aircraft laser sensors to calculate how much carbon degraded forests lose. To get the most precise impact of forest degradation on carbon stocks, the team considered that both their classification and carbon stocks have uncertainties.

Forest degradation from logging and fires impacts large areas of tropical forests. However, the impact of degradation on carbon stocks remains uncertain because degradation is difficult to detect. This research used high-resolution images from PlanetScope and produced a series of metrics that described forest canopy texture. These metrics were then used to train a machine learning classifier to calculate the probability of forests being intact, burned, or logged. The team also used biomass estimates from airborne lidar to calculate the impact of forest degradation on carbon stocks.

The classification approach has an accuracy between 0.69 and 0.93 depending on the site. This study found that changes in carbon stocks due to logging were small but burned forests store 35% less carbon than intact forests. The team expected and found that uncertainty in carbon losses due to degradation increases when they account for uncertainty in classification. However, research showed ignoring classification uncertainty could underestimate the impact of degradation on carbon stocks.

This study demonstrates the importance of accounting for intraspecific trait variation when testing trait-environment relationships. The study suggests tree size is a critical source of variability to be included in mechanistic models aiming to predict forest dynamics. The next steps include quantifying physiological traits, functional rooting depths, and water table dynamics to comprehensively understand trees’ vulnerability to climatic drivers (e.g., droughts) and their implications for forest composition and ecosystem services.

Previous work in Amazon forests has shown significant variation in both tree species distribution and drought-induced tree mortality across small ridges and valleys. In this study, forest ecologists measured 18 branch, leaf, and stomatal traits on 1,077 trees of 72 dominant species to identify underlying functional traits driving such changes across topography while controlling for a highly documented source of trait variability within species—tree size. Researchers found large trait variability across trees within species (i.e., intraspecific) that was related to trees’ topographic location for leaf traits and tree size for branch and stomatal traits.

Tropical forest responses to variation in water availability, which are critical for understanding and predicting climate change effects, depend on trait variation among trees. Forest Global Earth Observatory (ForestGEO) scientists quantified interspecific (among species) and intraspecific (across trees within species) variation in 18 branch, leaf, and stomatal traits for 72 dominant tree species along a local topographic gradient in an aseasonal Amazon terra firme forest. They used these sampling designs to test trait relationships with tree size, elevation, and species’ topographic associations as well as trait correlations. Intraspecific trait variation was substantial and exceeded interspecific variation in 10 of 18 traits. For leaf acquisition traits, intraspecific variation was mainly related to tree topographic elevation, while most branch, leaf, and stomatal trait variation was related to tree size. Interspecific variation showed no clear relationships with species’ habitat association. Although trait correlations and coordination were generally maintained among trees and species, bivariate relationships varied among trees within species, across habitat association classes, and across tree size classes. These results demonstrate the magnitude and importance of intraspecific trait variation in tropical trees, especially as related to tree size. Furthermore, these results indicate that previous findings relating interspecific variation with topographic association in seasonal forests do not necessarily generalize to aseasonal forests.

This study reviews how unoccupied aerial system (UAS) remote sensing can be used to enhance Arctic plant research and better understand the impacts from climate change. A team of researchers provided examples of how integration of different remote sensing technologies with UASs could be used to quantify vegetation patterns and processes at scales appropriate for studying Arctic processes (1–10 cm) and enhance the ability to link ground-based measurements with broader-scale information obtained from airborne and satellite platforms. Researchers also provided recommendations on UAS operation in remote regions, data storage and processing, and data sharing protocols to better enable the use of UASs and UAS syntheses to study Arctic ecology.

The Arctic tundra is a critically understudied biome at the top of the planet that is experiencing the fastest warming on Earth. This warming is impacting the health and distribution of tundra vegetation, which, in turn, impacts biodiversity and the balance of carbon, water, and energy. Tundra landscapes are remote and logistically challenging to study in detail across space and time. These challenges are further complicated by a mismatch between the scale of observation and the scale at which Arctic ecological processes occur, leading to significant uncertainties in understanding and model prediction of the Arctic’s fate.

The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure, and function. The heterogeneous nature of Arctic landscapes creates challenges in monitoring and improving understanding of these ecosystems, as most current efforts rely on traditional ground- and satellite-based observations that are limited in either spatial extent or spatiotemporal resolution. The use of remote sensing instrumentation on UASs has emerged as an important tool to view, describe, and quantify vegetation dynamics at scales that are more appropriate for studying Arctic landscapes (1–10 cm). This review discusses how established and emerging UAS remote sensing technologies can enhance Arctic plant ecology by shedding light on fine-scale drivers of vegetation patterns and processes and enhancing the ability to upscale ground-based measurements to airborne and satellite platforms. Researchers reviewed state-of-the-art remote sensing technologies that have been integrated with small UASs and then provided examples of key UAS applications for remote sensing studies in the Arctic. Finally, the review provides perspectives on the remaining challenges associated with collecting data in remote regions along with necessary next steps to advance the future of UAS remote sensing in the Arctic.

As tolerant as brackish marsh communities are to variable salinities, plant productivity and respiration models must account for drought conditions and subsequent impacts on water salinity to avoid overpredicting brackish tidal wetland carbon sequestration. These data improve the ability to model and forecast carbon flux responses to hydrologic changes at this critical land-sea interface. Meteorological drought in California’s winter months leads to hydrological drought in summer months and concomitant increases in coastal ecosystem salinity. Accounting for water quality leads to better model forecasts of greenhouse gas fluxes and climate mitigation potential of tidal wetlands.

Tidal wetlands have high plant productivity and high soil carbon storage. These characteristics make wetlands naturally helpful for combating climate change; they remove carbon dioxide (CO₂) from the atmosphere and trap it underground. A team of researchers collected 4 years of continuous high-frequency measurements of CO₂ exchange in a brackish tidal marsh and investigated ecosystem responses to wet and dry years. Tidal channel salinity was the best predictor of plant productivity changes from year to year with no measurable impact on ecosystem respiration (CO₂ release to the atmosphere). When salinity levels doubled, net removal of CO₂ decreased by up to 30%.

These research findings highlight the value of continuous data for capturing strong climate drivers at multiple timescales. The Peatland Ecosystem Photosynthesis Respiration and Methane Transport (PEPRMT) model can represent key drivers when data is available to support those calibrations. Model-data fusion occurring within this project across multiple U.S. tidal marshes is identifying necessary key constituents for constraining coastal carbon and methane fluxes to air, water, and soil. Continued measurements of atmospheric fluxes (AmeriFlux site US-Srr), water quality, and hydrologic fluxes at the Rush Ranch National Estuarine Research Reserve make this the longest continuously monitored U.S. tidal wetland for coupled high-frequency carbon fluxes. With accelerated sea level rise and increasing western U.S. drought frequency and intensity, Rush Ranch and other brackish wetlands along the Pacific coast are likely to experience profound increases in salinity over the next decade and through operations that control water flows from land to ocean, compromising their ability to mitigate climate change.

Understanding physiological factors that most strongly contribute to variation in leaf-level WUE is a major roadblock to accurate transpiration representation in climate models. In this study, researchers demonstrate that including leaf age as a primary driver of WUE did not improve or explain variation in modeled transpiration. However, models which accounted for diurnal changes in WUE improved representation of transpiration. These findings provide a roadmap for future investigation into the physiological traits that most strongly influence transpiration over space and time. Future studies need to closely consider model assumptions, like constant WUE, implicit in many models that project the future of tropical forests.

To understand how tropical ecosystems will respond to global change, researchers must correctly represent the relationship between water loss and carbon gain in leaves, known as water use efficiency (WUE). There are still significant uncertainties associated with the dynamics of WUE over different timescales, such as a day to the full lifespan of a leaf. Researchers collected data to assess possible physiological and mechanistic factors that influence WUE dynamics. While WUE does differ between leaves of different phenological stages, the trend was not consistent across species. However, researchers identified a unidirectional increase in WUE of approximately 2.5 times over the course of the day in five of the six species studied.

The relationship between carbon dioxide assimilation and water loss via stomatal conductance is a primary source of uncertainty in terrestrial biosphere model projections of ecosystem-scale carbon uptake and water cycling. In models, this relationship is governed by two terms: the stomatal slope (g₁) and intercept (g₀). Accurate mechanistic representation of how the g₁ and g₀ parameters vary over time is crucial, particularly in wet tropical broadleaf forests where trees have a near consistent annual pattern of leaf production and senescence, and precipitation and humidity are strongly seasonal. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where environmental conditions are modified for a single leaf or (2) a survey approach where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions.

Results show stomatal response curves and survey-style measurements produce statistically different estimations of stomatal parameters, which result in large (between 26% and 125%) differences in simulated fluxes of water. Furthermore, g₁ varies both diurnally and to a lesser degree with leaf age. These results show models using stomatal parameters derived from response curves significantly underestimate canopy level transpiration. While leaf traits do vary among leaf phenological stages, models that only include mature vegetation parameterizations perform similarly to those that explicitly simulate three leaf age stages.

Soils contain more than twice the amount of carbon stored in the atmosphere. Most of this carbon resides in deep soils, where it can be stored for millennia. This study showed that root activity in relatively young soils could result in carbon storage by forming new associations between organic carbon compounds and minerals. In contrast, continued root activity in older soils may disrupt existing associations and cause carbon to be released as climate-active carbon dioxide. The results of this study will help scientists determine which soils can better store carbon at depth and which may be vulnerable to carbon loss.

Land use changes, nutrient depletion, and drought can make plant roots grow deeper into the soil, but scientists question how that growth affects carbon in the soil. More roots reaching deep soil layers could result in more carbon being sequestered, or roots may unlock older carbon in deep soils. By combining advanced imaging techniques, this study examined how root activity impacts organic carbon compounds and their association with minerals in soil. The findings show that the amount of time deep soil has been subjected to root activity dictates whether roots promote the storage or loss of carbon.

Scientists from the University of Massachusetts, University of Arizona, and U.S. Geological Survey teamed with scientists from two U.S. Department of Energy (DOE) Office of Science user facilities, the Stanford Synchrotron Radiation Lightsource (SSRL) and Environmental Molecular Sciences Laboratory (EMSL), to examine deep soils that were 3 to more than 5 feet underground. These soils ranged in age from 65,000 to 226,000 years, and all had portions that had been impacted by the repeated growth of roots. A multi-institutional team of scientists used a suite of solid-phase analyses, including EMSL’s high-resolution Fourier-transform ion cyclotron resonance mass spectrometry and Mössbauer spectroscopy capabilities, other capabilities at SSRL, and scanning transmission X-ray microscopy at the Canadian Light Source. When combined, these techniques gave the team unique insights into the nature of associations between minerals and organic carbon compounds in the soil, including their specificity, particle size, and molecular composition. The patterns of root-driven weathering are in excellent agreement with the conditions found at locations with different soil types, climate, and vegetation. The fundamental processes discovered in this study may therefore be useful for modeling the impact of root activity on carbon storage in soils globally.

This study advanced three key tropical-specific biophysical functions to reduce model structure bias. Model bias from parametric estimates was further reduced using surrogate-assisted Bayesian optimization. This study lowered model uncertainties in simulating carbon cycle processes and budgets in tropical forest peatlands. It also improved understanding of how these ecosystems function and respond to future climate change. This improved representation will increase confidence in projecting biophysical feedbacks associated with tropical forested peatlands.

Tropical peatlands are an important global carbon sink and represent a major biophysical feedback factor in the climate system. Researchers use models with empirical data to represent carbon cycle processes for these complex ecosystems. Unfortunately, the lack of field observations for these ecosystems leads to a substantial knowledge gap when simulating real-world tropical forested peatlands. Incorporating field observations from a newly established peatland site in Iquitos, Peru, allowed researchers to boost a land surface model’s ability to simulate carbon dioxide (CO₂) and methane (CH₄) fluxes and energy balance for tropical forested peatlands by advancing tropical-specific biophysical functions and multiobjective parameter optimization.

In this study, researchers evaluated and improved the performance of the Energy Exascale Earth System Model (E3SM) Land Model (ELM) in simulating CO₂ and CH₄ fluxes and energy balance of an Amazonian palm swamp peatland in Iquitos, Peru. Three algorithms were improved according to site-specific characteristics, and key parameters were optimized using an objective surrogate-assisted Bayesian approach. Modified algorithms included soil water retention curve, water coverage scalar function for CH₄ processes, and seasonally varying leaf carbon-to-nitrogen ratio function. The revised tropics-specific model better simulated diel and seasonal patterns of carbon and energy fluxes of the tropical forested peatland. Global sensitivity analyses indicated that the strong controls on carbon and energy fluxes were mainly attributed to parameters associated with vegetation activities. Parameter relative importance depended on biogeochemical processes and shifted significantly between wet and dry seasons. This study advanced understanding of biotic controls on carbon and energy exchange in Amazonian palm swamp peatlands and highlighted knowledge gaps in simulating tropical peatland carbon cycling.

As frozen soils thaw, carbon within them can be converted to carbon dioxide or methane gas. Because methane has a stronger climate warming effect than carbon dioxide, the relative amounts of the two gases that are produced from decomposition are important to predict the impact of permafrost thaw on climatic warming. Iron is thought to suppress production of methane, but results show that iron could enhance methane production in some soils. This study builds groundwork for improving predictions of Arctic feedbacks to climate change by including iron effects on greenhouse gas production from thawing permafrost.

Emissions of methane, a powerful greenhouse gas, could increase as frozen soils in cold regions thaw. This study used a new computer model to simulate how iron, oxygen, and carbon interact to drive carbon dioxide and methane emissions in waterlogged permafrost soils. Iron-reducing microorganisms used iron to fuel carbon dioxide production, but the effect of iron cycling on methane production depended on availability of easily decomposable carbon. When iron reducers competed with methane producers for a small amount of available carbon, methane production declined. However, when easily decomposed carbon was abundant, iron reduction enhanced methane production by decreasing soil acidity.

Methane production is sensitive to soil acidity. Many Arctic soils are rich in iron, which some soil microorganisms can use instead of oxygen for respiration through iron reduction. This produces carbon dioxide while decreasing soil acidity. Computer models that currently predict greenhouse gas emissions from thawing Arctic soils do not include iron or acidity changes. This study used a chemical reaction network model to simulate interactions of iron reduction, methane production, and organic matter decomposition in permafrost soils. The model was compared to measurements of carbon dioxide and methane production as well as soil acidity from a series of laboratory incubation experiments. The model then simulated cycles of waterlogged and aerated conditions to test how iron affected production of greenhouse gases over multiple cycles. Iron reduction occurred during waterlogged periods, producing carbon dioxide and reducing soil acidity, while iron was recycled during aerated periods. Because methane-producing microorganisms prefer less acidic soil conditions, iron reduction enhanced methane production when there was enough available organic matter to support both processes. When easily decomposed organic matter was more limited, iron reducers competed with methane producers, leading to lower methane production.

Amazon forests play important roles in regulating the global carbon cycle, but variable natural disturbances increase uncertainty of the carbon capacity. Extreme storms are important drivers of tree mortality in the Amazon region. In this study, researchers provide a framework for representing coupling between land surface forest mortality and atmospheric extreme storms. This analysis highlights potential for predicting the rate of future storm-driven tree mortality, which is not currently included in global models and emphasizes the need to improve land-atmosphere relationship in models.

A leading cause of tree mortality in the Amazon is windthrow, i.e., trees broken or uprooted by high winds and heavy rainfall in extreme storms. In this study, researchers built a linkage between extreme storms in the atmosphere and forest mortality on the land surface. As global warming makes extreme storms more intense, projected storms are likely to make tree mortality by windthrow commonplace over about 50% more of the Amazon by the century’s end.

Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, linkage between surface windthrows and convective storms in the atmosphere remains unclear. In addition, current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. In this study, researchers manually map 1,012 large windthrow events encompassing 30 years from 1990–2019 and generate hourly convective available potential energy (CAPE) from ERA5 reanalysis data. An empirical relationship is found that maps CAPE, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, the model projects a 51% ± 20% increase in the area favorable to extreme storms and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.

These findings have direct use in informing wetland management decisions. For example, increasing the presence of deep spots in wetland creation and restoration projects will enhance phosphorus accumulation. This means that phosphorus, an element responsible for algal blooms in Lake Erie, could be captured more efficiently in new or restored wetlands. This study can also inform management decisions at the watershed level and have far-reaching implications. Decreasing the load of fertilizers that reach the main waterway will lead to faster carbon, nitrogen, and phosphorus accumulation. In turn, faster build-up could reduce wetlands’ carbon footprint, easing climate change.

Wetlands help remove pollution from fertilizers in waterways while accumulating sediments and organic matter. In this study, researchers investigated how fertilizer load is linked with accumulation of key elements (carbon, nitrogen, and phosphorus) and the variability of accumulation across locations within the same wetland and at water depths. Results showed that carbon and nitrogen build up faster at deep spots and shallow areas, while phosphorus is faster only at deep sites. This study further established that when nutrients from fertilizers increase, the potential to accumulate carbon, nitrogen, and phosphorus in wetlands decreases.

The comprehensive soil dataset created in this study examined the link between nutrient accumulation in wetlands and nutrient loads from watersheds. 36 soil cores were collected from three locations at the Old Woman Creek freshwater estuary in Lake Erie’s western basin. At each location, cores were extracted from three different water depths: shallow (<70 cm), intermediate (70–80 cm), and deep (>80 cm). Cores were segmented in 1 and 2-cm deep increments. Samples of each core increment were dated with lead-210 and analyzed for carbon, nitrogen, and phosphorus content. Nutrient loads were calculated from available datasets of flow and nutrient concentrations.

This research shows that ecosystems that receive relatively abundant rainfall (such as forests) have the capacity to acclimate to precipitation change more readily than water-limited ones (such as deserts), regardless of whether the region is experiencing increased precipitation or drought conditions. Future research should focus on mechanisms that allow currently adaptable ecosystems to acclimate, which can help increase climate change resilience of different ecosystems.

Climate change is altering precipitation patterns globally, creating drought conditions in some regions and increasing rainfall in others. Rainfall patterns strongly affect the amount of carbon dioxide that escapes soil, known as soil respiration, and therefore strongly control ecosystem feedbacks to climate change. Researchers used data from 80 globally distributed studies that manipulated the amount of precipitation that ecosystems received to determine the effect of precipitation change on soil respiration. Initial responses to increasing and decreasing precipitation are consistent across ecosystems, but long-term effects change based on ecosystem type. Soil respiration in deserts became progressively higher with increased precipitation and progressively lower with drought. In contrast, forests showed the opposite pattern, with initial changes to soil respiration rates becoming smaller over time.

Climate change is altering global rainfall patterns, which can affect the global carbon cycle via changes in carbon dioxide released from soil. Understanding how carbon cycling in different ecosystems will respond to increased or decreased precipitation is important when accounting for soil feedbacks into atmospheric carbon dioxide concentrations. Researchers combined results from 80 separate studies to determine effects of altered rainfall on soil respiration. In addition, they looked at how long changes lasted, as well as how different soil properties and intensity of precipitation changes at each study site affected results. They found that more precipitation resulted in greater amounts of carbon dioxide leaving the soil, and less precipitation resulted in less. However, the changes weakened over time in ecosystems that typically receive plenty of rainfall (e.g., forests), while the changes in ecosystems that typically receive little rainfall (e.g., deserts) strengthened over time. Changes in the amount of carbon dioxide leaving the soil were also affected by the amount of biologically derived carbon in the soil, which affects how much water soil can hold. The results suggest that typically dry ecosystems will experience long-term changes in their carbon cycling whether precipitation increases or decreases.

A popular concept in soil ecology is that mycorrhizal type determines how soil carbon and nutrients are stored. Forests dominated by AM mycorrhizae are expected to have lower C:N ratios and more mineral-associated organic matter than forests dominated by EcM. However, this expected pattern was only seen in forests where EcM trees had low-quality leaf litter, like pines and oaks, and where EcM fungi had hard-to-decompose tissues and the ability to break up complex organic molecules. Thus, this concept needs to be adjusted to account for differences among EcM species.

Tree roots form partnerships with fungi to obtain soil nutrients. In forests, there are two main types of partnerships: arbuscular mycorrhizae (AM) and ectomycorrhizae (EcM). These types differ in how fungi interact with roots and acquire nutrients from soil. A team of researchers investigated how mycorrhizae affected soil organic matter across four sites representing distinct climates and tree communities in the eastern United States. Soil carbon (C)-to-nitrogen (N) ratios and the amount of carbon and nitrogen protected by soil minerals strongly correlated with species composition of trees and EcM fungi.

Scientists have suggested that tree species forming a symbiosis with AM versus EcM fungi is a strong predictor of soil carbon storage, but EcM systems are highly variable. In this study, researchers investigated how mycorrhizal associations and species composition of canopy trees and mycorrhizal fungi relate to the proportion of soil C and N in mineral associations and soil C:N across four sites in the eastern United States broadleaf forest biome. Study sites were in New Hampshire, Wisconsin, Illinois, and Georgia, and researchers identified canopy trees to species in each site and collected soil from the top 10 cm of the mineral horizons.

In two study sites (New Hampshire and Georgia), researchers found the expected relationship of declining mineral-associated C and N and increasing soil C:N ratios as the basal area of EcM-associating trees increased. However, soil properties strongly correlated with canopy tree and fungal species composition across all sites. The expected pattern was observed in sites that were (1) dominated by trees with lower quality litter in the Pinaceae and Fagaceae families and (2) dominated by EcM fungi with medium-distance exploration type hyphae, melanized tissues, and potential to produce peroxidases. This observational study demonstrates that differences in soil organic matter between AM and EcM systems depend on the taxa of trees and EcM fungi involved. Important information is lost when the rich mycorrhizal symbiosis is reduced to two categories.

This study demonstrated that the reactive transport model was not good at predicting the transport of specific groundwater constituents, even though the model was able to reproduce the energy required to move the groundwater through the subsurface. The use of natural chemicals identified a flawed groundwater model. Without using these natural chemicals, this flaw would have been invisible, and the model would have been unable to provide accurate predictions.

Because the geologic structure of the subsurface as well as groundwater levels and characteristics are rarely well defined, simulation results of groundwater movement from a single groundwater transport model are almost guaranteed to be wrong. To produce the most plausible and realistic range of predictions, groundwater transport models must be run tens to hundreds of thousands of times using the most likely configurations of groundwater properties. As groundwater transport models become more complex and accurately simulate real physics, they become more computationally expensive, and the time required to run simulations becomes unreasonable. To overcome this limitation, a team of researchers “taught” an artificial neural network (ANN) to reproduce the results of a physics-based model over a broad range of groundwater system properties. To calculate the correct predictive uncertainty of groundwater transport using the ANN, the ANN outputs were compared with field data from natural tracer concentrations.

Quantifying uncertainty in reactive transport model predictions is extremely hard due to the high computational cost of running thousands of realizations of the model. While the gold standard of Bayesian methods for modeling are sought, they are completely intractable in this case with the use of physics-based reactive transport models. In this study, a team of researchers combined physics-based groundwater reactive transport modeling with machine learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. An ANN was trained on a dataset of groundwater hydraulic heads and tritium (³H) concentrations generated using a high-fidelity, physics-based groundwater reactive transport model. After using the trained ANN as a surrogate model to reproduce the input-output response of the high-fidelity reactive transport model, the team quantified the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov-chain Monte Carlo (MCMC) calibration against field observations of groundwater hydraulic heads and ³H concentrations. The methodology was then demonstrated with a model application that predicted Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated site in Wyoming. Results showed that including ³H observations in the calibration dataset reduced uncertainty in the estimated permeability field and infiltration rates compared to calibration against hydraulic heads alone.

Machine learning methods are shown to incorrectly predict that Alaska is currently a net source of carbon when existing site coverage is used for training. This result mirrors a current mismatch between ecosystem model and machine learning estimates of high-latitude carbon balances and points to insufficient site coverage as a likely cause. This study demonstrates that machine learning methods are unable to predict how ecosystem carbon fluxes will respond to climate change because training data cannot capture important relationship changes. These findings highlight the need for cautious interpretation of machine learning predictions of current and future ecosystem processes.

The high-latitude carbon cycle is an important, complex, and highly uncertain component of the global climate system. A growing number of studies have relied on machine learning methods to create regional estimates of current and future ecosystem properties (e.g., carbon balance) based on a small number of site measurements. Because there are few observational data, machine learning model predictions are rarely tested against independent measurements. In this study, a novel approach is used to uncover large biases in machine learning predictions of current and future high-latitude carbon balance.

In this study, carbon fluxes and environmental data are simulated across Alaska using ecosys, a process-rich terrestrial ecosystem model. Boosted regression tree machine learning algorithms are then applied to different subsets of simulated data that mirror and expand upon existing AmeriFlux eddy-covariance data availability. Machine learning predictions across the entire domain are compared to simulated data to understand how variation in site coverage and climate forcing impacts typical data-driven machine learning upscaling and forecasting approaches.

When current Alaska AmeriFlux data coverage is used for training, machine learning methods incorrectly predict that Alaska is a net carbon source. Machine learning predictions are improved with increased spatial coverage of the training dataset (e.g., bias is halved when 240 modeled sites are used instead of 15). However, even the machine learning model trained with 240 sites does not match the substantial increase in Alaska carbon sink strength simulated by ecosys throughout the 21st century. Convergence cross-mapping is used to show that degradation of machine learning model projections can be ascribed to changes in atmospheric CO₂, litter inputs, and vegetation composition. This study reveals large shortcomings in machine learning techniques commonly used to upscale and forecast ecosystem processes.

This study’s theoretical analysis and observational benchmarks indicate that (1) a thermodynamically consistent description of microbial CUE dynamics requires biological growth to be represented explicitly as a function of intracellular metabolism; (2) popular empirical models are unable to represent the microbial CUE dynamics correctly, especially for its tradeoff for growth and substrate uptake rates; and (3) a consistent mathematical upscaling from single enzymatic chemical reactions to microbial population growth is feasible. This study supports the long-held hypothesis that enzyme kinetics can be upscaled to model microbial growth.

To better model microbial growth, a team of researchers developed a revised dynamic energy budget model (rDEB) that represents reserve dynamics using equilibrium chemistry approximation (ECA) kinetics. The rDEB model is consistent with a single biochemical reaction and growth of microbial populations. The rDEB model also includes several widely used microbial models as special cases. This study shows that only DEB models reasonably capture that the same microbial carbon use efficiency (CUE; i.e., the fraction of carbon retained as biomass per unit carbon uptake) can happen at both high and low growth and substrate uptake rates

Modeling environmental biogeochemistry requires a robust mathematical representation of biological growth. The dynamic energy budget theory provides an opportunity to develop a unified mathematical representation of biomass growth for microbes, plants, and even animals. By partitioning biomass into reserve, kinetic, and structural compartments, researchers developed the rDEB model that links a single enzymatic reaction to microbial population biomass growth. The rDEB model better explains proteomic control of biological growth and includes the standard DEB (sDEB) model and many popular empirical models as special cases. Moreover, the rDEB model identifies limitations of the sDEB model and resolves tradeoffs between microbial CUE and growth and substrate uptake rates. The rDEB model also reveals that soil water stress on microbial growth is exerted primarily through diffusion limitation of substrate uptake, with smaller effects from turgor pressure and intracellular macromolecular crowding. If kinetic biomass is further partitioned, the rDEB model will be able to resolve the dynamic proteomic control of microbial growth. Insights from this study can guide microbial model development to consistently organize trait regulation of microbial dynamics and thus obtain more robust predictions of microbial and climate control of soil carbon and nutrient dynamics.

The study indicates that Mn oxides effectively oxidize organic compounds to release CO₂ but also demonstrate a high capacity to adsorb and immobilize organic compounds. These stabilizing and destabilizing interactions may influence soil C storage and transformation.

Much of the organic carbon (C) stored in soils is associated with soil minerals. Therefore, understanding how soil minerals interact with a variety of organic compounds is essential to anticipating soil C storage and fluxes and their contributions to global climate change. Most studies examining C stabilization by soil minerals have focused on iron and aluminum oxides without considering the importance of less abundant manganese (Mn) oxides that have high sorption capacity and reactivity. This study demonstrates that organic compounds experience varied interactions with Mn oxides that primarily result in degradation but can also lead to organic C stabilization on the mineral surface.

Mn oxides are reactive soil minerals that can bind or oxidize organic compounds, but their role in regulating soil C storage is relatively unexplored. To better understand Mn-C interactions, researchers reacted five small organic compounds with Mn oxides to evaluate the potential for organic C to either bind to and be stabilized on the mineral surface or to be destabilized through oxidation reactions that produce carbon dioxide (CO₂) gas. Mn-C interactions primarily resulted in organic C oxidation coupled to Mn oxide dissolution, although select compounds attached to the mineral surface without transformation. Also, a high proportion of organic C was degraded at low C/Mn ratios while increasing proportions were immobilized in solids at high C/Mn ratios.

Soils contain substantial amounts of carbon that can be stored for hundreds to thousands of years or released as greenhouse gases into the atmosphere. Interactions between plants and soils may influence soil carbon stocks by concentrating manganese (Mn), a micronutrient needed to break down leaf litter at the soil surface, but these relationships are poorly understood. Previous studies were limited to a few biomes, but suggested that high Mn concentrations in leaf litter reduce soil carbon storage in forest ecosystems. This work shows that soil carbon and nitrogen stocks decrease with increasing Mn consistently in soils from a database across the U.S., and that carbon and nitrogen stocks were more strongly correlated with Mn than with climatic variables (i.e., temperature and precipitation). The demonstration of these continental scale linkages will help further our understanding of the mechanisms of soil carbon accumulation.

Soil capacity to store carbon depends on interactions between plant inputs, soil minerals, and microbial communities. The role of micronutrients, such as manganese, in regulating carbon storage in soils or release to the atmosphere is not well understood. Soil fungi can use manganese to break down lignin, a difficult-to-degrade component of plant tissue, and manganese can also form minerals that bind and react with carbon. This study shows that soil carbon stocks decrease with increasing manganese content in surface organic soils across the United States. Additionally, enhanced plant uptake of manganese under moderately acidic soil pH enriches manganese in surface soils and may promote decomposition that decreases carbon stocks.

Manganese is an essential plant nutrient that plays a critical role in litter decomposition by oxidizing and degrading complex organic molecules. Using a continental-scale database from the National Ecological Observatory Network (NEON), researchers found that carbon storage in organic soil horizons decreases with increasing manganese content. This finding implies that manganese may promote breakdown of plant matter into carbon dioxide gas that is released into the atmosphere or into smaller compounds leached into underlying mineral soil. Results also show that plant uptake of dissolved manganese from soil and its release back to the soil through litterfall enriches manganese in surface soils under moderately acidic soil pH. Researchers also found that foliar manganese was strongly correlated with foliar lignin, indicating complex links between leaf chemistry and decomposability.

Mountainous watersheds provide 60–80% of Earth’s freshwater in addition to other life-sustaining ecosystem services, such as air and water quality regulation and carbon sequestration. Analyzing long-term spatial and temporal climate data in these important regions can help scientists understand how these critical ecosystems may respond, or are already responding, to changing climate. Scientists developed a statistical framework to assess changes in climatic conditions in Colorado’s East River Watershed. The assessment indicates considerable changes in climatic conditions with time and space, demonstrating that not only is climate change affecting the watershed, but different zones are responding in different ways. Understanding these changes can help researchers predict and monitor how the ecosystems, in addition to services they provide, may change to better adapt to climate change.

Researchers developed a new statistical framework to assess changes in climatic conditions using data from 1966 to 2021 from 17 meteorological stations across the East River watershed near Crested Butte, Colo., which is a typical watershed in the Upper Colorado River Basin providing freshwater to millions of Americans. Grouping similar watershed areas into zones using hierarchical clustering of site locations for three temporal segments of the Standardized Precipitation-Evapotranspiration Index (SPEI) showed significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns.

Researchers developed a statistical framework to assess long-term temporal and spatial variability of meteorological conditions including temperature, dewpoint, precipitation, relative humidity, and wind speed, as well as time series of potential and actual evapotranspiration, Standardized Precipitation Index, and SPEI. Calculations were conducted from 1966 to 2021 for 17 locations of meteorological stations located within the East River watershed in Colorado. Time series segmentation analysis and zonation demonstrate considerable changes in climatic conditions with a non-uniform response across the watershed. A significant shift in cluster arrangements for the temporal segments indicates that zonation patterns are driven by dynamic climatic processes, which are variable through time and space. Therefore, the watershed climatic zonation requires periodic re-evaluation based on climatic changes with space and time.

Researchers demonstrate that ecosystem feedbacks to climate change, such as expansion of beaver populations, alongside ecosystem management practices, such as legal protections for beavers, can partially reverse detrimental effects of climate change on water quality. By illustrating the interplay between beavers’ ecosystem services, climate change, and water quality, this research informs and supports land and ecosystem policies that aim to address water quality impacts of climate change.

Warming temperatures and frequent drought are degrading riverine water quality in the western United States. Simultaneously, climatic shifts and changes in ecosystem management are expanding the range of American beavers, whose dams are known to improve riverine water quality. By comparing the water quality impacts of a beaver dam and historically low river levels, which likely represent river levels of a future hotter climate, researchers found that the beaver dam increased removal of reactive nitrogen, a freshwater contaminant, by 44% compared to low river levels. The beaver dam pushed an enormous volume of river water and reactive nitrogen into surrounding soils, where microbial processes converted reactive nitrogen to nitrogen gas, eliminating its potential as a freshwater contaminant and rendering it harmless.

Scientists monitored hydrologic and geochemical conditions along a reach of Colorado’s East River over multiple years (2018-2019), which captured a historic drought and construction of a beaver dam at this site. Using these field measurements, they developed a reactive transport model to quantify dissolved oxygen and reactive nitrogen fluxes through riparian soils during the drought and construction of the beaver dam (2018), as well as during unusually wet conditions (2019). The model demonstrated that the beaver dam imposed hydraulic gradients across the riparian subsurface which were more than 10 times greater than gradients imposed by low- and high-water conditions. By imposing a steep hydraulic gradient, the beaver dam increased flux of water and nitrate into riparian soils relative to seasonal extremes, where microbial processes converted nitrate to nitrogen gas through denitrification. The overall nitrate flux increase from the beaver dam led to a 44% increase in nitrate removal compared to seasonal extremes. Finally, researchers evaluated the beaver dam’s nitrate removal under a range of denitrification rates, finding that the dam’s relative effects were largely insensitive to microbial process rates.

Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between emphasis in models characterizing nutrient and water uptake rates and carbon costs versus emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing better prediction of tropical forest-climate feedbacks.

Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, scientists know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, researchers identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils.

In this review, researchers identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. The review also identifies interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions increased colonization rates under water scarcity scenarios in some forests rather than reducing mycorrhizal colonization of fine roots as might be expected.

Overall, these data point to the potential utility of a remote sensing product for assessing belowground properties in tropical ecosystems.

Tropical forests are expected to green up with increasing atmospheric carbon dioxide (CO₂) concentrations, but primary productivity may be limited by soil nutrient availability. However, canopy-scale measurements have rarely been assessed against soil measurements in the tropics. In this study, researchers sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil phosphorus (P) and base cations corresponded to declines in remotely sensed mean annual canopy greenness, controlling for precipitation.

In this study, researchers sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil P and base cations (K, Mg) corresponded to declines in remotely sensed mean annual canopy greenness (r² = 0.77–0.85; p < 0.1), controlling for precipitation. This inverse relationship appears to be because litterfall also increased with increasing soil P and cation availability (r² = 0.88–0.98; p < 0.1), resulting in a decline in greenness with increasing annual litterfall (r² = 0.94; p < 0.1). As such, greater soil nutrient availability corresponded to greater leaf turnover, resulting in decreased greenness. However, these decreases in greenness with increasing soil P and cations were countered by increases in greenness with increasing soil nitrogen (N) (r² = 0.14; p < 0.1), which had no significant relationship with litterfall, likely reflecting a direct effect of soil N on leaf chlorophyll content but not on litterfall rates. In addition, greenness increased with extractable soil aluminum (Al) (r² = 0.97; p < 0.1), but Al had no significant relationship with litterfall, suggesting a physiological adaptation of plants to high levels of toxic metals. Thus, spatial gradients in canopy greenness are not necessarily positive indicators of soil nutrient scarcity. Using a novel remote sensing index of canopy greenness limitation, researchers assessed how observed greenness compares with potential greenness. A strong relationship with only soil N was found (r² = 0.65; p < 0.1), suggesting that tropical canopy greenness in Panama is predominantly limited by soil N, even if plant productivity (e.g., litterfall) responds to rock-derived nutrients. Moreover, greenness limitation was also significantly correlated with fine root biomass and soil carbon stocks (r² = 0.62–0.71; p < 0.1), suggesting a feedback from soil N to canopy greenness to soil carbon storage.

This study shows that changes in tropical forest net primary productivity (NPP) will alter the quantity, biochemistry, and stability of carbon (C) stored in strongly weathered tropical soils. This suggests that climate change induced shifts in plant growth, and primary production will have cascading effects on soil carbon storage in carbon-rich tropical forests.

Tropical forest soil carbon chemistry was sensitive to changed leaf litter biomass inputs, both for litter doubling, and for total litter removal. Soil carbon in stable organo-mineral associations increased with litter addition and declined with litter removal. This is typically thought of as the most stable fraction of soil carbon. Waxes and proteins were the most stable component of organo-mineral associations after a decade of litter removal, and remaining soil carbon was older carbon compared with control sites. Phenolic and aromatic carbon was lost from mineral associations with litter removal.

This study demonstrates that the physical and biochemical nature of soil C stocks are sensitive to changes in tropical forest NPP with global change. Most notably, the relatively stable mineral-associated soil organic carbon (SOC) fraction changed markedly following a decade of litter manipulation. Litter addition promoted the accumulation of C into relatively stable organo-mineral associations (i.e., not leachable as dissolved organic carbon), suggesting that strongly weathered tropical soils have the capacity to store more C if tropical forest NPP increases. The most stable portion of mineral-associated SOC included lipids like waxes (alkyl C) and microbial products like proteins and cell walls (N-alkyl and O-alkyl C). In contrast, plant-derived compounds, characterized by aromatic and phenolic C, formed a more dynamic portion of mineral-associated SOC, demonstrating that these compounds are less important than N-containing compounds for long-term soil C storage in strongly weathered tropical soils. Free-debris SOC accumulated during the dry season, whereas occluded-debris and mineral-associated SOC increased during the wet season, promoting greater bulk soil C content during the wet season. Thus, change in duration or severity of the dry season may interact with changes in tropical forest NPP to alter soil C storage in tropical forests. Overall, findings show that changes in tropical forest NPP will alter the quantity, stability, and biochemical character of soil C stocks.

Overall, nutrient availability regulated soil respiration responses to increased moisture during the wet season, while low soil moisture uniformly suppressed soil respiration across sites during the dry season. Phosphorus availability might therefore regulate feedbacks to climate change among humid tropical forests.

Humid tropical forests contain some of the largest soil carbon (C) stocks on Earth, yet scientists are uncertain about how carbon dioxide (CO₂) fluxes will respond to climate change in this biome. This study revealed a strong seasonal shift in soil respiration from the wet to dry season across 15 distinct tropical forest sites in Panama along rainfall and soil fertility gradients. Soil moisture, air temperature, and rainfall together were the best predictors of instantaneous soil respiration. Somewhat surprisingly, soil phosphorus and base cations were the strongest predictors of spatial variation in the magnitude of season change in soil respiration, which did not follow rainfall trends.

The research sites cover a three-fold range in soil C stock, two-fold range in rainfall, five soil orders, over 25 geological formations, 20-fold range in base cations, and >100-fold range in extractable phosphorus. Thus, research results are robust and likely applicable to a much broader geographic area than the study region.

This study suggests that variation in soil phosphorus (P) and base cation availability are related to the magnitude of soil respiration seasonality across tropical forests. While shifts in soil moisture were an important driver of soil CO₂ flux rates, as expected, variation in soil nutrients appeared to override the influence of natural rainfall gradient. Soil respiration was suppressed in the most infertile sites during the wet versus dry season. These results indicate that accurately predicting how drying will affect tropical soil C losses will require incorporation of P and base cation availability into ecosystem models, as well as explicit microbial and root respiration relationships to moisture.

These data show that large surface root biomass stocks are associated with large subsoil carbon (C) stocks in strongly weathered tropical soils. Further studies are required to evaluate why this occurs and whether changes in surface root biomass, as may occur with global change, could in turn influence soil organic carbon (SOC) storage in tropical forest subsoils.

Root depth distributions in 43 tropical forests were predicted by pH and exchangeable potassium, with more surface roots in acidic, nutrient-poor soils. Similarly, soil carbon stocks in subsoils were greatest in infertile, base cation-poor soils. Root and soil carbon depth distributions were inversely related across sites, such that large stocks of surface root biomass were correlated with large stocks of subsurface soil carbon (deeper than 50 cm).

Overall, results from 43 lowland seasonal tropical forests showed that depth distribution index numbers (Root β and SOC β) were inversely related, suggesting that concentration of fine root biomass in surface soils may be linked to large subsoil C storage (50–100 cm). Soil acidity and nutrient scarcity, in particular lack of potassium, appear to drive proliferation of fine roots in surface soils, while subsoil properties appear to drive retention of SOC in these sites. Further mechanistic studies are needed to elucidate the observed patterns, including measurements of fine root turnover and exudation rates, organic matter in leachate and macropore flow, microbial recycling, contribution of coarse roots to deep SOC, and influence of mineralogy and other physiochemical subsoil properties in retaining C in subsoil. The short- and longer-term sensitivity of subsoil C storage to changes in surface root dynamics could improve prediction of future climate-forest feedbacks for the humid tropics.

Future temperatures were observed to increase salt marsh resilience and carbon storage at moderate amounts of warming, where optimized root productivity increased elevation and belowground biomass, but as rates of decomposition accelerated with increased temperatures, results showed evidence of marsh elevation loss and exacerbated break-up of the marsh surface. Therefore, future temperatures may be temporarily beneficial for marsh resilience and function, but projected end-of-century temperatures are likely detrimental to marshes.

Scientists used a whole-ecosystem warming experiment to increase temperatures in a salt marsh to examine how future warming affects ecosystem and soil quality. By measuring changes in elevation, they were able to estimate if warming is beneficial or detrimental to ecosystem resilience and function as sea levels rise.

As sea levels rise, ecosystems near the coast become increasingly threatened by drowning. Some ecosystems, like salt marshes, are able to survive rising sea levels by increasing their elevation through root growth and sediment capture. However, sea level rise happens simultaneously as global temperatures increase. Therefore, interactions between higher temperatures and the marsh ecosystem could affect a marsh’s ability to survive higher sea levels. From this experiment where both the surface and 1-meter deep soils of the marsh ecosystem were heated, results revealed that a slight amount of warming was beneficial to the marsh because increased root growth elevates the marsh surface. Meanwhile, high amounts of warming were detrimental to the ecosystem because decomposition decreased marsh elevation quicker than root growth increased elevation. Additionally, marsh elevation loss observed at higher temperatures was associated with increased carbon mineralization and increased microtopographic heterogeneity, a potential early warning signal of marsh drowning. Maximized elevation and below-ground carbon accumulation for moderate warming scenarios uniquely suggest linkages between metabolic theory of individuals and landscape-scale ecosystem resilience and function, but this work indicates nonpermanent benefits as global temperatures continue to rise.

Shale, a widespread sedimentary rock, represents a large reservoir of carbon due to its high content of fossil organic matter and carbonate minerals. To better estimate global carbon budgets, scientists developed a modeling approach that accounts for the interplay between microbial respiration and mineral reactions. Furthermore, mineral reactions in the subsurface strongly influence the quality of headwaters. The model can be used to explore the impact of global warming on water delivered by mountains by simulating the chemical composition of streams in future climatic scenarios.

The weathering or breakdown of sedimentary rock is an important component of global carbon, nutrient, and geochemical cycling. Scientists developed a new modeling approach to explore the long-term weathering of shale–a major sedimentary rock that makes up 25% of Earth’s continental rocks. They validated the model using observations from the East River watershed in Colorado and found that aerobic respiration–the consumption of oxygen and organic matter by microbes to make energy–exerts a key control on shale weathering. They showed that aerobic respiration strongly enhances removal of carbonate minerals through production of carbon dioxide and acidification of pore water. Furthermore, oxygen consumption by microbes limits the oxidation of sulfide minerals at depth.

The interface between the Earth’s surface and the atmosphere typically involves complex interactions between hydrological, biogeochemical, and physical processes. Due to this complexity, understanding the mechanisms of shale weathering remains challenging. Scientists implemented a simulator that describes the long-term chemical weathering of Mancos shale (starting from the last glaciation period 15,000 years ago) at the East River study site in Colorado. The model accounts for gas exchange between the atmosphere and subsurface, percolation of water from precipitation, mass transfer between the gas and aqueous (solutes in water) phases under partially saturated conditions, and decomposition of soil and shale organic matter, water-rock interactions. The model was validated on mineral concentration profiles, solid organic carbon content, and carbon dioxide gaseous emissions measured in three monitoring wells. The researchers demonstrate that aerobic respiration of organic matter from plant litter is a key control for the development of the saprolite horizon in shale. This microbially mediated process limits oxygen, which largely prevents the dissolution of pyrite. In contrast, it releases carbon dioxide that drives the removal of carbonate minerals through the acidification of pore water.

Stable isotopes of water are used as tracers to better understand how water moves through ecosystems. In mountain systems dependent on snow, it is difficult to obtain adequate data to understand how snow accumulation and melt affect isotopic inputs. Using a large stable water isotope dataset across a large mountain basin, this study found that elevation is the dominant control on snowmelt isotopic inputs. Elevation controls snow presence and absence, change in precipitation’s isotopic signature with altitude, and precipitation phase changes from snow to rain. Transformations to snowpack isotopic signature due to melt-freeze cycles and vapor loss were found significant at lower elevations where temperatures are warmer and snowpack accumulation is shallow.

Isotopes are elements with a different number of neutrons than protons, which allows them to be used as tracers to understand how different materials move throughout an environment. Scientists collected stable water isotopic information over five years in a large Colorado watershed, with data spanning different elevations, vegetation types, and seasonal climates. The data was combined with a land-surface model for daily estimates of snowfall and climate at sample locations. The study showed how landscape position and annual climate control snow water isotopic inputs across the watershed.

Stable water isotopes are used as natural tracers to assess water sourcing to vegetation water use, groundwater replenishment, and stream water export. Mountainous watersheds have strong variability in snowpack accumulation and snowmelt, which may affect the accuracy of using water isotopes as tracers. However, studies that assess how water isotopes vary in the snowpack and snowmelt are limited in mountain environments. Over a five-year period, researchers collected the largest known snow water isotope dataset within a mountainous watershed. Isotopic inputs in snowfall adjusted for altitude described most of the snowpack isotopic variability. North- and east-facing slopes act as a secondary control through vapor loss of persistent snowpack in the early winter. Melt-freeze cycles and vapor loss back to the atmosphere altered the isotopic signature of snowpack. This occurred where and when air temperatures were high and snow accumulation was low. Overall, observed data indicate that elevation is the dominant control on snow water isotopic inputs to mountainous basins. Elevation dictates timing of snow accumulation and melt, rate of isotopic change in precipitation with altitude, and effect of vapor loss on snowpack isotopes. Studies in mountain environments will require adjustment for elevational controls to properly understand water sourcing of stable water isotopes from snowmelt.

Watersheds reliant on snow water inputs alter the timing of stable water isotope inputs through snow storage, while fractionation processes, or the partitioning of lighter and heavier isotopes through phase changes between solid, liquid, and vapor in the snowpack, can potentially change the isotopic input signature of snowmelt. Studying snow accumulation, melt, and fractionation can help scientists more accurately use water isotopes to track water movement through an ecosystem.

The study found that, annually, the lightest isotopes occur in the upper subalpine environment, where snow accumulation is high and rainfall is low. Results indicated incoming spring precipitation during the snowmelt period was most important to snowmelt isotopic evolution over time, while fractionation processes in the snowpack accounted for <25% of snowmelt enrichment in heavy isotopes. Enrichment by vapor loss was least important in the subalpine where the snowpack is deep, shaded from sun by conifer forests, and can be ignored. Vapor loss changes to isotopic inputs in open areas with less snowpack are more important and should be considered. Given the East River is largely energy-limited, wet water years reduce the effect of snowpack vapor loss on isotopic inputs across the basin. The exception was at the lowest elevations where snow-limited conditions are influenced by added snowfall to increase the effect of vapor losses on snowmelt isotopic inputs.

Researchers combined a hydrologic and snowpack stable water isotope model constrained with a comprehensive isotopic dataset for the East River in Colorado, a large, snow-dominated mountain basin. The approach accounted for snow storage, snowmelt timing, rain-on-snow, and fractionation processes in the snowpack due to melt-freeze cycles and vapor loss. Scientists assessed the relative importance of climate inputs, snow dynamics, and landscape position on stable water isotope inputs across the basin.

Stable water isotopes are used in hydrology to track how water moves through an environment. Snow storage and melt alter the timing of water inputs in watersheds, which can influence the timing of isotopic inputs. In addition, changes to isotope ratios in snowpack due to melt-freeze cycles and vapor loss of the snowpack can also occur.

Researchers combined a hydrologic and snowpack stable water isotope model to understand how landscape position and climate affect isotopic water inputs in a large mountain basin. The lightest isotopes occur in the upper subalpine where snow accumulation is highest and rain inputs are low. The temporal change of isotopes in snowmelt is largely controlled by elevation and its influence on the amount, phase (rain or snow), and isotopic mass of spring precipitation occurring with the snowmelt period. Snowpack alterations account for <25% of total snowmelt enrichment in heavy isotopes. Changes to the snowpack isotopic signature by vapor loss are most important where vegetation does not shade snow, moderate snowfall occurs, and evaporation potential is relatively high. Changes are highest above tree line and in areas with meadows and aspen forests. Vapor loss effects on snowpack are lowest in deep snow found in conifer forests and snow-limited lower elevations. These findings can help scientists more accurately use water isotopes to track water movement through mountain environments.

The Colorado River provides water for more than 40 million people, highlighting the urgent need to study and understand how climate change may impact the watershed’s water quality and quantity. This study’s results show that changing rainfall and early snowmelt in the Upper Colorado River Basin affect both water volume and mineral reactions, impacting water quality observed downstream. The 3D model makes it possible to understand how the watershed’s topography, stream water flow, and groundwater interact in time and space. The model demonstrates that north- and south-facing slopes of the river valley contribute differently to observed effluent concentrations. The effects are relatively small in this study but could become enhanced with larger climate variability.

Climate change significantly impacts freshwater quantity and quality–especially in mountainous watersheds like the Upper Colorado River Basin that are key for water supply in downstream regions of the western United States. Researchers used a mathematical model to quantify the movement of water and chemicals under changing weather and climate conditions. This first-of-its-kind numerical model simulates hydrology and chemical transport processes at high resolution in a mountainous watershed.

The researchers studied how changing environmental factors that determine surface and subsurface water flow affect chemical transport in a watershed ecosystem. The team analyzed the relationship between the water flow volume and concentration of chemicals, or Concentration-Discharge relationship, to develop a predictive understanding of exports from the watershed to the larger river basin. The developed model simulates integrated hydrological transport and reaction processes in both surface and subsurface water. Simulation results also show that the model can resolve changes in snowmelt and infiltration associated with spatial variability throughout the watershed. Additionally, the model captures annual changes in the Concentration-Discharge relationship between wet and dry years and demonstrates how changing infiltration in time and space affects mineral weathering, which contributes to the observed effluent concentrations. Overall, this newly developed model can account for spatial variability that impacts water availability and increase understanding of how the volume of flowing water and concentration of chemicals impact water quality and quantity.

HSHMs can largely impact environmental processes and natural resource quality. Studying and quantifying HSHMs can help address natural resource management issues such as groundwater contamination, heavy metal transport, and toxic algal blooms by identifying dominant times and regions that control carbon, nutrient, water, and energy exchanges. To better understand the Critical Zone and HSHMs that largely influence these ecosystem processes, researchers have provided a description of the HSHMs concept, example applications, and a path forward using numerical modeling. Incorporating HSHMs into critical zone science can help better predict ecosystem function and manage natural resource quality as earth’s climate changes.

The Critical Zone–the environment from fresh bedrock to canopy–involves very different environmental properties and processes. Therefore, scientists need to study this environment at multiple time scales to better predict and understand ecosystem fluxes, exchange rates, and biogeochemical functioning. Hot spots and hot moments (HSHMs) are regions or times in the environment that, when compared to surrounding areas or intervening times, experience high reaction rates and significantly influence environmental processes or natural resource quality. Researchers reviewed models, questions, and recent findings involving HSHMs to better understand how they impact nutrient dynamics, greenhouse gas emissions, and water and energy exchanges in the critical zone.

The Critical Zone encapsulates interacting ecosystem levels from the atmosphere to soil, groundwater, and bedrock. Differences in these environments occur at multiple scales, posing challenges to understanding the zone holistically. However, predicting how this zone functions is critical to protecting natural resources and monitoring environmental processes such as water and element cycling.

HSHMs can significantly impact environmental quality and functioning. For example, spring melt and storm events can result in hot moments that largely contribute to mercury loading into nearby water bodies, having direct consequences for fish spawning and ecosystem health. Because of their substantial environmental influence, quantifying and modeling these moments and areas in the Critical Zone can help scientists better predict and manage ecosystem function and natural resource quality. Scientists’ review of HSHMs shows that incorporating them into modeling can help quantify ecosystem processes such as nutrient dynamics, greenhouse gas emissions, and water and energy exchange in the critical zone.

The availability of groundwater to humans and ecosystems depends on both its quantity and quality. This study documents a cascading environmental mechanism in which change in the circulation of floodplain groundwater causes change in its chemical composition. This study provides a model that can be used as a stepping stone to better predict the impact of climate change on the groundwater resource.

Floodplain groundwater is a critical resource for human activities and ecosystems, though it is increasingly threatened by climate change. While the impact of climate change on groundwater quantity is well documented, its impact on groundwater quality has received far less attention. In this study, researchers showed that changes in the flow rate or chemical composition of groundwater can destabilize sediments rich in organic matter and microorganisms. This process creates zones, or “halos,” of intense microbial activity that further amplify changes in groundwater composition.

The researchers combined laboratory experiments and numerical simulations to investigate how mixing and reaction zones develop in floodplain aquifers. They built a series of 30 cm-long flow-through column experiments. The columns contained lenses of fine-grained, organic matter-rich sediments embedded inside coarser-grained, organic matter-poor aquifer sand. Both types of sediments were collected from the same floodplain in Wyoming. The arrangement inside the columns mimicked observed depositional patterns. Oxygen-rich artificial groundwater was continuously injected at the columns’ inlets.

The fine-grained lenses released large amounts of particulate organic matter, likely including live microorganisms, that were transported and redeposited in the surrounding aquifer material. These transfers of organic matter sustained the development of secondary zones, or “halos,” of intense microbial activity. The cumulative microbial activity in these halos could exceed the activity inside the lenses by several orders of magnitude due to their larger volume as well as their access to fresh pools of reactants. The impact of these halos on groundwater quality was both immediate (e.g., decrease in oxygen concentration, increase in iron concentration) and long term, with the accumulation of an inventory of mineral reaction products that could be easily remobilized by subsequent environmental changes.

Applying ELM-ParFlow-FATES at BCI, researchers show water table depth (WTD) can play a large role in governing AGB when drought-induced tree mortality is triggered by hydraulic failure, which is when plants cannot move water from roots to leaves. Spatial variations of simulated AGB and WTD can be well explained by topographic attributes, including surface elevation, slope, and convexity. Contrary to simulations, observed AGB in the well-drained 50-hectare forest census plot within BCI cannot be well explained by topographic attributes or observed soil water, suggesting factors like nutrient status, heterogeneity in soil property, and plant traits may have a greater influence on observed AGB. While highlighting the important topographic control on water availability and tree growth, the disagreement between the model and observation shown in this study indicates the need to consider interactions of nutrient, water, and soil properties in future studies.

Topographic variability and lateral subsurface flow on hillslopes may have outsized impacts on tropical forests through their influence on water available to plants. However, these interactions between vegetation dynamics and finer-scale hydrologic processes are not currently well-represented in Earth system models. This study integrated the Energy Exascale Earth System Model (E3SM) land model (ELM) that includes the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) with a 3D hydrology model (ParFlow) to understand how hillslope-scale hydrologic processes influence tropical forest aboveground biomass (AGB) along water availability gradients at Barro Colorado Island (BCI), Panama.

The team developed a coupled model that integrates a 3D hydrology model into the Energy Exascale Earth System Model (E3SM). E3SM includes the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for vegetation dynamics. The new coupled model is a useful tool for understanding the diverse impact of local heterogeneity on vegetation dynamics and plant-water interactions. The model explicitly resolved hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. The team applied the model at BCI to simulate AGB variability. They found AGB is higher in wet areas than dry areas in the domain-wide simulations, and AGB decreases nonlinearly with increasing WTD when WTD is less than 10 m. The degree of AGB variability differs depending on how hydraulic-failure induced mortality is represented. Unlike the modeled AGB, the team was not able to find similar relationships between topography and WTD with the observed AGB. To support the findings, this study calls for more data collection, e.g., soil moisture, WTD, AGB, and plant traits such as wood density, across the hydrologic gradient. This study points to opportunities for improving understanding of hydrological and ecological processes using the newly developed coupled model combined with observations.

Currently, there are large differences between observationally derived and numerical model estimates of future high-latitude C stocks. While it is clear that climate warming and wildfire can cause rapid soil C losses, it is unclear how increases in vegetation growth may offset these losses and over what time frame that may happen. Researchers found that wildfire will increase net C losses to the atmosphere and thus feedbacks to climate warming, but this transition will take around two centuries. Therefore, on this time scale, wildfire C losses from combustion may reverse the historical C sink of northern ecosystems.

In this study, researchers evaluated and applied a mechanistic ecosystem model, Ecosys, to disentangle the direct and indirect effects of wildfire on ecosystem and soil organic carbon (SOC) stocks across the tundra and boreal ecosystems of Alaska. The researchers hypothesized that climate warming and increasing atmospheric carbon dioxide (CO₂) will enhance plant carbon (C) uptake, plant biomass, and thereby litter C inputs to the soil. However, they found that, in the long term, accelerated SOC decomposition and combustion losses from wildfire will result in net SOC losses.

Arctic and boreal permafrost SOC decomposition has been slower than C inputs from plant growth since the last glaciation. Recent climate warming has increased SOC decomposition and altered wildfire regimes in a trend that is expected to continue. Researchers first demonstrated that their model accurately represented observed plant biomass and C emissions from wildfires in Alaskan ecosystems. They then found that future warming and increased atmospheric CO₂ will result in plant biomass gains and higher litterfall. However, increased C losses from (a) wildfire and (b) rapid SOC decomposition driven by the increased plant C inputs to the soil and deepening active layer will lead to about 4.4 PgC of SOC losses, mostly in the top 1 m of soil. These SOC losses offset plant C gains by about 2200, resulting in the ecosystem becoming a net C source to the atmosphere. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. These results show that projected wildfire and warming will accelerate high-latitude soil C losses, resulting in a positive feedback to climate change.

This study provides a novel approach to infer depth-resolved estimates of soil thermal diffusivity at numerous locations across a watershed. Improving depth-resolved estimates of soil thermal properties is critical as they are strongly associated with the fraction of soil components (including water, organic, mineral, and air) that are key for improving the predictive understanding of water and carbon cycling. Also, the thermal properties modulate soil heat transfer and thus can, for example, accelerate or delay climate change effect on permafrost distribution and associated carbon storage in the Arctic. This study also shows promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity and potentially in bulk density or water content, which both are critical to understand changes in soil, water, and carbon resources. Overall, this research identifies under which environmental conditions and acquisition strategy soil thermal diffusivity can be reliably inferred from temperature time-series, which is critical to guide development of cost-effective methodologies to estimate soil thermal and physical properties at numerous depths and locations.

Improving the quantification of soil thermal properties is key to achieving better prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations with conventional soil sampling is challenging. The increasing availability of vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series.

In this study, researchers develop a parameter estimation approach that combines thermal modeling, Bayesian inference, Markov chain Monte Carlo simulation, and sliding time windows to estimate thermal diffusivity and its uncertainty over time, at numerous locations, and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series.

Researchers first assessed under which environmental conditions, temperature sensor characteristics, and deployment geometries soil thermal diffusivity can be reliably inferred. Synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5°C at 5 cm below the ground surface, temperature gradients > 2°C m^-1, and a sliding time window of at least 4 days, the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10%. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced with accuracy to a few millimeters and with a bias defined by a standard deviation ≤ 0.01°C. Researchers then applied the developed approach to field data acquired on the Seward Peninsula, Alaska. Results indicate significant similarity with independent measurements as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity as needed to potentially capture changes in bulk density or water content. These findings represent a critical step in the development of cost-effective methodologies to estimate soil thermal and physical properties at numerous depths and locations.

By advancing understanding of how landscape variability is created and structured, this research will help scientists monitor and predict ecosystem processes like soil temperatures, shrub growth, and carbon fluxes at larger scales. Identifying tight couplings between water, heat, and carbon cycles will help guide future efforts to understand how these ecosystems will be affected by climate change. Additionally, this work demonstrates that failure to account for small-scale variability in regional and global modeling efforts may lead to inaccurate and biased predictions.

At high latitudes, properties such as soil temperatures, vegetation cover, and carbon fluxes vary considerably across a landscape. For example, a patch of tall shrubs with warm soil temperatures and large carbon uptake is surrounded by low-lying tundra vegetation with cold soil temperatures and small carbon fluxes. Using a model sensitivity analysis, researchers working for the NGEE-Arctic project demonstrated that local changes in snow depth and soil water content create the landscape variability observed in these ecosystems.

Discontinuous permafrost environments are characterized by strong spatial heterogeneity at scales too small to be driven by weather forcing or captured by Earth system models. Using a global sensitivity analysis of ecosys, a process-rich terrestrial ecosystem model, researchers demonstrated that near-surface hydrologic processes create the observed heterogeneity in soil temperatures, vegetation composition, and carbon fluxes. In the sensitivity analysis, soil temperatures are more impacted by snow depth, O-horizon thickness, and near-surface water content, which vary at scales of 1m, than by an air temperature gradient corresponding to a 140 km north-south distance. Tall shrub growth, which is an important indicator of change in the region, is only observed in simulations with perennially unfrozen soils that are neither too wet nor too dry. While simulated net carbon balance was generally low, simulations with a near-surface water table or tall shrub growth had high net carbon uptake. The researchers showed that estimates of net carbon uptake for a watershed are 60% higher when the observed shrub distribution is considered. The results of this study can be used to advocate for higher-resolution measurements and improved model representation of landscape variability.

Recent observations of increasing tree mortality from a variety of disturbances have raised concern over the global resilience of forests to changing climate.  Before this study, scientists did not know the global distribution of forest resilience to disturbance or the change in forest resilience due to climate drivers and lacked the ability to predict these disturbances. This study suggests that a large fraction of the tropical and temperate zones will experience increasing disturbance in the near term with a large impact on terrestrial carbon sink.

Forest resilience to changing climate is suspected to change in many regions globally. However, global trends of forest resilience changes are unknown. In this study, researchers examined global patterns of forest resilience. The study found declining forest resilience in tropical and temperate biomes, while resilience increased in the boreal biome. Forest management did not influence trends, suggesting resilience changes were driven by regional-scale changes in water availability and temperature.

Researchers used remotely sensed estimates of kernel NDVI (canopy greenness) at the global scale to quantify changes in NDVI from 2000-2020.  The response of dTAC was particularly strong over time, with divergent patterns among the tropics and temperate biomes, where there was a decline in resilience, and the boreal zone, where there was an increase in resilience. This study revealed that ~23% of undisturbed forests globally have reached a tipping point by which disturbance is likely imminent without a rapid change in climate. These results are of particular concern because this represents a large amount of carbon uptake and storage globally, and tropical forest loss has a large impact on the global carbon budget.

While the upper 2 m of soil can provide much of the water needed during a dry period, deeper sources of water will be required during drought. Tree hydraulic strategies vary, and those that access and shift to deep water sources may be better able to survive drought. Knowing how different tree species respond to drought and how soil water availability changes with drought is important for modeling the responses of tropical forests to projected changes in precipitation patterns.

Tree water use and soil water extraction patterns were monitored during a month-long dry period in a Central Amazon upland tropical forest. During the 2018 dry period, tree water use increased, remained the same, or decreased, depending on species. Water use was dependent on tree size and the amount of conducting sapwood in the trunk. While most roots were in the upper soil layers, some roots exceeded 2 m depth. As the upper soil dried out, more water was taken up from deeper depths.

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impacts that extensive dry periods may have on tree and ecosystem-level water use and photosynthesis has become increasingly important. This research investigated soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was measured by sap flow sensors installed in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Soil moisture probes were installed near six of those trees and measured water content and soil water extraction within the upper 1 m. To link depth-specific water extraction to patterns to root distribution, fine root biomass was measured through the soil profile to 235 cm. To scale plot-level tree water use, tree diameters were measured for all trees within a 5 m radius around each soil moisture probe. 

The sensitivity of tree water use to reduced rainfall varied by tree, with some increasing and some decreasing water use during the dry period. Tree-level water use ranged from 11-190 liters per day. Stand level water use based on multiple plots encompassing sap flow and adjacent trees varied from ~1.7 to 3.3 mm per day, increasing with tree density. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels, and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2-3 m. Results indicate variation in rates of soil water extraction across the research area and temporally through the soil profile. These results provide key information on tree water use and soil water extraction as water availability changes and can be used in models that project tropical forest response to drought.

Fire has historically been studied from distinct disciplines as an ecological process, human hazard, or engineering challenge. In isolation, connections between human and non-human aspects of fire are lost. Research needs to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches. This shift will support pathways towards mitigating and adapting to the increasingly flammable world, including the utilization of fire for human safety and benefit.

Fires can be both useful to and supportive of human values, safe communities, and ecosystems. However, fires can also threaten lives and livelihoods. Climate change, fire suppression, and living closer to the wildland-urban interface have helped create a global wildfire crisis. Living more sustainably with fire is an urgent and ethical need. Re-envisioning fire science can stimulate discovery that helps communities better navigate the fiery future. This study argues that overcoming institutional silos and accessing knowledge across diverse communities is the only way to effectively undertake research that improves future outcomes.

Fire is a fundamental part of ecosystems globally and has been used to manage landscapes for millennia. Humans change wildfire activity via climate change, fire suppression, land development, and population growth. Altered fire regimes impact health, infrastructure, and ecosystem services. A group of 87 fire experts from many disciplines outlined barriers and opportunities in the next generation of fire science. Understanding, mitigating, and managing the impacts of fire require addressing key challenges to inform environmental and social justice by sustainably living and interacting with fire. A coordinated and integrated proactive approach across fire science, social science, and ecological research is necessary. Knowledge from diverse communities is essential to inform progress to safer and more sustainable communities and ecosystems. Establishing infrastructure and reducing barriers to information will accelerate scientific discovery and advances that promote fire-resilient communities. Fire experts agree that management, including utilization of fire, is essential to supporting safe communities and ecosystems. Inclusion and consideration of human dimensions and values, including where people live and their impacts on the world, are critical to forecasting and anticipating future fire. Supporting a holistic and collective approach is fundamental for science to inform policy and action in the future fiery world.

The ecosystem model accurately simulated observed forest damage from Hurricane Hugo and how fast forests recovered from the hurricane. The study found that damaged forests could accumulate more carbon than undamaged forests because hurricanes killed many small trees, allowing large trees to grow even larger. These results indicate that infrequent hurricanes may have little impact on long-term forest carbon cycling. With this model, researchers can explore other effects on forests resulting from changes in hurricane frequency and strength.

Existing ecosystem models for tropical forests do not account for damage caused by hurricanes, which is problematic as hurricanes are becoming stronger because of climate change. A team of scientists modified an ecosystem model to simulate hurricane damage in tropical forests using data from a forest in Puerto Rico to test and improve the model predictions. Using the improved model, they tested how long it takes for tropical forests to recover from hurricane damage.

To develop the ecosystem model, the research team accounted for three observations. First, more trees die when hurricane winds exceed 90 miles an hour. Second, hurricanes cause more damage to forests that have only a few large trees. Third, palms are more resistant to hurricane damage than trees. The team used data from the Luquillo Experimental Forest in Puerto Rico to validate the model. The model correctly simulated the widespread loss of trees following Hurricane Hugo and forest growth and changes in tree and palm abundances over the following 30 years.

The team used the validated model to study the long-term impacts of hurricane disturbances. The team conducted three simulations: one without any hurricane damage, one with severe damage similar to Hugo, and one with moderate damage similar to Maria. The model predicted large losses of biomass immediately following the hurricane disturbances. However, over 80 years after the hurricane, the damaged forests recovered. Surprisingly, forests damaged by Hurricane Maria showed 5% more biomass than undamaged forests. This result occurred because the hurricane killed small trees, which reduced the competition for light and water and allowed surviving trees to grow larger.

Forests cycle large amounts of water, energy, and carbon with the atmosphere and play an important role in regulating the Earth’s climate. However, forest disturbances that cause crown damage are predicted to become more severe and frequent in the future. Understanding how forests will respond to these disturbances is critical for understanding the long-term role of forests in the biosphere.

Forest trees are exposed to a variety of disturbances such as windstorms and lightning. These disturbances can result in significant damage to their crowns, the part of a tree made up of branches and leaves. Little is known about how tree crown damage influences the growth and survival rates of trees or interactions among different tree species. In this study, researchers introduced a way to represent crown damage in a vegetation model. This new capability allows scientists to test how tree crown damage impacts forest dynamics and the carbon cycle.

A multi-institutional team of Next-Generation Ecosystem Experiments-Tropics (NGEE-Tropics) researchers introduced a crown damage module into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a submodel of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM). Using this new functionality, scientists were able to test how crown damage alters forest dynamics relative to equivalent increases in tree mortality. Simulated growth and survival rates were benchmarked against data from Barro Colorado Island in Panama. Results revealed that the largest impact of crown damage on aboveground biomass and carbon residence time is due to increases in mortality associated with crown damage. However, simulated crown damage caused changes to forest canopy organization and competitive dynamics between plant functional types. Representing crown damage in vegetation models is important to capture the legacy effects of disturbance and the ways that disturbances that overlap in space or time may interact to increase forest mortality.

Recent observations of rising plant transpiration at the global scale are consistent with increasing leaf area due to CO₂ fertilization but at odds with the well-known stomatal closure response. The study provides a testable framework of hypotheses regarding how transpiration responds globally to rising atmospheric CO₂ and stresses the need for empiricists and modelers to unify efforts to better understand and predict transpiration under future conditions.

Plant transpiration is the largest hydrologic flux of water globally after precipitation and therefore plays a large role in driving surface water availability. Transpiration responds to rising atmospheric carbon dioxide (CO₂) at stomatal to whole plant to regional scales, with feedbacks between scales. This study reviewed the biophysical mechanisms by which rising CO₂ impacts global-scale plant transpiration and identified a path forward to improve predictions of transpiration under future conditions.

In this study, researchers review the myriad ways by which rising CO₂ can influence plant transpiration directly and indirectly at the global scale. Many compensating mechanisms and feedbacks make predicting transpiration challenging with rising CO₂. Global changes in plant transpiration in response to rising CO₂ will manifest through droughts, vapor pressure deficit, plant physiological processes including shifting leaf area and phenology, and forest loss (disturbance). The researchers place these mechanisms into a testable framework of hypotheses that outlines a path forward for both empiricists and modelers. The impacts of changing transpiration at large scales are significant for water provision and utilization demands.

This study suggests a progressively critical role of soil moisture under a drier future. This could happen in the Central Amazon and other places that were previously thought to have plenty of water. The soil moisture threshold provides a crucial benchmark to test and improve model simulations of future land-atmosphere feedbacks in the Amazon under climate change.

Transpiration is the process of water moving through a plant from soil to atmosphere. In humid tropical rainforest regions, soil water recharges during the wet season to support dry season transpiration, making transpiration considered light- but not water-limited. Scientists are unsure if tropical rainforests with abundant water will become water limited under extreme climate conditions. To address this uncertainty, a team of researchers from the Next Generation Ecosystem Experiment-Tropics (NGEE-Tropics) used field data to examine dynamics of transpiration, soil water, and meteorological variables during the record-breaking Central Amazon 2015-16 El Niño drought. The researchers found a shift from light- to water-limitation of sap velocity and identified a soil moisture threshold of water limitation in the Central Amazon.

Researchers measured sap velocity, soil water content, and meteorological variables in an old-growth upland forest in the Central Amazon throughout the 2015-16 drought. A rapid decline in sap velocity and temporal variability was found during the drought, accompanied by a marked decline in soil moisture and an increase in temperature and vapor pressure deficit. To understand water or light limitation, researchers examined the covariation of sap velocity with soil water content and net radiation using partial correlation analysis. The study found that sap velocity was largely limited by net radiation during normal dry seasons but was limited by soil water during drought. To identify the timing of this shift, researchers used a moving window approach to conduct partial correlation analysis every 10 days and examined how the coefficient changed during the whole period. Water stress started to occur in late August to early September in 2015. The soil moisture control continued throughout September then became intermittent and disappeared after several rainfall events. During the strong water control period, the light control disappeared. The threshold of soil moisture was identified at 0.33 m³/m³ (around -150 kPa in soil matric potential).

This study by Next-Generation Ecosystem Experiments-Tropics (NGEE-Tropics) researchers is the first to document the pantropical role of soil P as a factor mediating tropical forest responses to cyclones. Litterfall mass and nutrient pulses caused by cyclones both respond and contribute to variability resource availability that can affect species regeneration, growth, and competitive interactions. Additional research can test how plants across pantropical forest ecosystems differ in their resistance and resilience to cyclones to better represent forest response to cyclone disturbance in predictive models.

Changing tropical cyclone regimes may lead to long-lasting effects on tropical forests under climate change. This pantropical meta-analysis investigated the importance of total soil phosphorus (P) in mediating forest litterfall resistance (ability to withstand change) and resilience (ability to return to pre-cyclone condition) during 22 tropical cyclones. Results showed that as soil P increased, litterfall resistance to cyclones decreased.

While the influence of tropical cyclone frequency and intensity on the structure and function of tropical forests has been widely studied, much less attention has been given to the role of resource availability on the functional stability of tropical forests across the globe in the face of cyclone disturbance. Single-site studies in Australia and Hawaii suggest that litterfall on low-P soils is more resistant and less resilient to cyclones. Researchers conducted a meta-analysis to investigate the pantropical importance of total soil P in mediating forest litterfall resistance and resilience to 22 tropical cyclones. The researchers evaluated cyclone-induced and post-cyclone litterfall mass (g/m²/day), and P and nitrogen (N) fluxes (mg/m²/day) and concentrations (mg/g), all indicators of ecosystem function and essential for nutrient cycling.

Across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan, total litterfall mass flux increased from ~2.5 ± 0.3 to 22.5 ± 3 g/m²/day due to cyclones, with large variation among studies. Litterfall P and N fluxes post-cyclone represented ~5% and 10% of the average annual fluxes, respectively. Post-cyclone leaf litterfall N and P concentrations were 21.6 ± 1.2% and 58.6 ± 2.3% higher than pre-cyclone means. Mixed-effects models determined that soil P negatively moderated the pantropical litterfall resistance to cyclones, with a 100 mg P/kg increase in soil P corresponding to a 32% to 38% decrease in resistance. Based on 33% of the resistance case studies, total litterfall mass flux reached pre-disturbance levels within one year post-disturbance. Across pantropical forests observed to date, these results indicate that litterfall resistance and resilience in the face of intensifying cyclones will be partially determined by total soil P. This work will support benchmarking of E3SM Land Model – Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) predictions against pantropical ground data.

Vegetation demographic models represent forest dynamics in the Earth system, providing the opportunity to integrate ecological understanding into predictions of future climate and ecosystems. In this study, researchers identify critical areas where models are not prepared to capture future forest responses to global change variables like changing precipitation and disturbance. This review helps modelers identify necessary improvements and field ecologists understand what data best supports model improvement. Improving models will advance our ability to predict the role that forests will play in sequestering and storing carbon, providing habitat for biodiversity, and provisioning critical natural resources for people.

Forest regeneration processes are generally not well represented in models ecologists use to predict future forests. A team of researchers critically reviewed how regeneration processes are represented within models that strive to predict forest demography in Earth system models. The researchers found a need to improve parameter values and algorithms for reproductive allocation, dispersal, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes.

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality despite its critical role in maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. This critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data collection protocols, and novel model algorithms compatible with global scale simulations. Vegetation demographic models offer the opportunity to integrate ecological understanding more fully into Earth system prediction, including a critical focus on regeneration processes.

The study found that fires cause much more damage to forests than logging and that recently burned forests did less photosynthesis than intact forests. Burned and logged forests were already doing as much photosynthesis as intact forests only 4 years after a disturbance. However, the structure of burned forests remained very different from intact forests even after 14 years, suggesting that each forest characteristic may take a very different time to recover from degradation.

Large areas of the Amazon Forest are being degraded through fires and logging. Using multiple remote sensing data, researchers tested whether degraded forests suffer more water stress than intact forests during the dry season. By comparing datasets of forest structure and photosynthesis, researchers evaluated how long it takes for forests to recover following disturbance.

Humans cause disturbances, such as selective logging and fires, that degrade tropical forests, which alters forest structure and function. These changes also impact the ability of forests to uptake carbon. This study used airborne laser scanning data over the Amazon to investigate how forest structure varies across burned and logged forests of different ages since disturbance. The team also used solar-induced chlorophyll fluorescence (SIF) data from the TROPOspheric Monitoring Instrument (TROPOMI) mission. SIF is a proxy for photosynthesis, and the TROPOMI data provide information on how photosynthesis varies across seasons in degraded and intact forests.

The researchers found that forest fires suffered the largest changes in the vertical distribution of foliage and canopy height compared to logged and intact forests. Moreover, SIF in recently burned forests were significantly lower than in intact forests. In contrast, within 4 years after the disturbance, SIF values were higher in regenerating forests than in intact forests despite their lower leaf area. These findings highlight that degraded forests recover photosynthesis rates faster than they recover forest structure. The results also indicate that degraded forests can accumulate large amounts of carbon during recovery from disturbance.

In grasslands worldwide, trees and shrubs are increasing at unprecedented rates, causing a loss of grassland ecosystems. In any given grassland, the increase of woody plant abundance is typically the result of a few woody species. Understanding the mechanisms that enable these species to survive in the open grassland is critical to understanding the complex phenomenon of woody plant encroachment. This study reveals the growth investment strategy of rough-leaf dogwood to achieve dense canopies, respond positively to periodic grassland disturbance, and ultimately facilitate successful encroachment in grassland ecosystems.

Fire and herbivory restrict survival of most woody plants in grasslands. However, some woody species have strategies to overcome these disturbances. Many shrubs form dense canopies which displace grassland species, resulting in reduced fire intensity. While dense canopies play a key role in the survival of many woody species in grasslands, the mechanisms enabling them to maintain dense canopies are not well understood. In this study, scientists evaluated the vertical canopy structure of rough-leaf dogwood, the predominant encroaching woody shrub in the Kansas tallgrass prairie. The results show that these canopies contain: (1) large vertical variation in leaf morphology and physiology, enabling rough-leaf dogwood to deal with limitations of self-shading to form dense canopies, and (2) temporal variation in leaf traits, allowing rough-leaf dogwood to respond positively to periodic grassland disturbance.

Leaf trait variation enables plants to utilize large gradients of light availability that exist across canopies of high leaf area index (LAI), allowing for greater net carbon gain while reducing light availability for understory competitors. To better understand how mesic woody encroaching shrubs achieve high LAI canopies, researchers investigated vertical distribution of leaf traits and physiology across canopies of Cornus drummondii, or rough-leaf dogwood, the predominant woody encroaching shrub in the Kansas tallgrass prairie. This study revealed that leaf mass per area (LMA) and leaf nitrogen per area (N_a) varied approximately threefold across canopies, exceeding that of most deciduous tree species, and leading to large differences in the physiological functioning of leaves in different light environments.

The vertical allocation of leaf traits in C. drummondii canopies was also modified in response to browsing. This response, along with increased light availability, facilitated greater photosynthesis and resource-use efficiency deeper in browsed canopies compared to control canopies. These results illustrate how C. drummondii facilitates high LAI canopies and a compensatory growth response to browsing—two key factors contributing to the success of C. drummondii and other species responsible for grassland woody encroachment.

Snow controls Arctic and sub-Arctic energy balances, and recent changes have a reverberating effect on regional and global climate. As changes in snow are anticipated in the future under associated climate warming, understanding and characterizing snow patterns is vital to better predict future climate shifts. The characterization of Snow Water Equivalent (SWE) patterns will be used to validate and improve snow distribution modeling in the Department of Energy’s (DOE) Earth system model and for improved understanding of hydrology, topography, and vegetation dynamics in the sub-Arctic and Arctic.

In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations to snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this study, a team of scientists used modeling to characterize snow and better understand the drivers of snow distribution patterns in the high latitude regions of the globe.

Snow spatial distribution plays a vital role in sub-Arctic and Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, the spatial distribution of snow is not well understood and therefore not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, a team of scientists carried out intensive field studies over multiple years (2017–2019) for two small sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (>22,000 data points), researchers developed simple models of SWE spatial distribution using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index) and classification, and a metric for approximating winds. A machine learning model developed for both study sites and all years was the most successful and was able to accurately capture the complexity and variability of snow characteristics. The machine learning model at the study sites accounted for approximately 86% of average SWE distribution. Factors that impacted year-to-year snow distribution included greenness, elevation, and a metric to represent coarse microtopography, while slope, wind, and fine microtopography factors were less important.

Human activity relies on abundant and clean freshwater resources. Therefore, accurately predicting water and solute fluxes from watersheds is critical. Researchers developed a novel mathematical approach for studying water quantity and quality, minimizes assumptions and ensures accurate accounting for all chemical solutes as they move along and between various surface and subsurface pathways. Moreover, this approach makes it possible to apply the model in ways that not only represent entire watersheds but also capture watershed subsystems, such as floodplains or hillslopes. This capability makes the approach uniquely flexible and useful for predictive studies.

To evaluate the natural processes affecting freshwater availability and quality in watersheds, a multi-institutional team of scientists developed a mathematical approach that describes how chemical solutes move and transform in water as they flow over the Earth’s surface (such as in streams or in runoff) and in subsurface soil as groundwater. They implemented their mathematical approach in the Advanced Terrestrial Simulator (ATS) code, which combines existing pieces of software, expedites development, and ensures code quality.

Despite the widespread use of integrated hydrology models in a variety of applications, consideration of solute transport and geochemical reactions is still not common. Now, a multi-institutional team has implemented solute transport and geochemical reactions into the Advanced Terrestrial Simulator (ATS), a software code that provides a flexible multi-physics framework that facilitates their coupling. This coupling uses a novel algorithm to calculate exchange fluxes across the surface-subsurface interface, and a point-by-point solution of the geochemical problem. Geochemical capabilities were added using well-established external codes through a generic interface instead of a custom interface for each code. These new capabilities were demonstrated by simulating tracer transport in a soil column and reactive transport in a hillslope.

To best capture peatland C cycle warming responses, models will need to be reconciled with both observable variations under current climate and experimental warming responses that extend the response surface. This study will help improve understanding of peatland carbon cycle and potential impacts of a warming environment on the storage and release of carbon to the atmosphere.

This research quantifies current inter-annual and seasonal variation in peatland carbon (C) emissions as a function of warming. It is imperative that we understand peatland because they cover only 3% of Earth’s land surface but contain about 20% of the global soil carbon pool. Under current ambient conditions, peatland net C sink capacity changes in sign and magnitude across seasons. Direct comparison of multiyear eddy covariance data sets with DOE’s Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental results shows that ambient variation in net C exchange is small in the context of warming responses under in situ experimental conditions.

Peatlands have acted as net carbon dioxide (CO₂) sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes where peatlands are abundant can disturb their CO₂ sink function. This study shows that sensitivity of peatland net CO₂ exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO₂ sink responses. Multiannual net CO₂ exchange observations from 20 northern peatlands showed that warmer early summers are linked to increased net CO₂ uptake, while warmer late summers lead to decreased net CO₂ uptake. Thus, net CO₂ sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. These results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO₂ uptake over the coming few decades.

A naturally occurring element, sulfur is abundant on Earth and stored primarily in rocks. However, research has shown that climate change may be resulting in high amounts of sulfur in freshwater systems; warmer temperatures may increase weathering, or rock deterioration, which releases sulfur in the process, and water cycle changes may lead to less water available to dilute the element. This study used a holistic approach to better understand how sulfur moves between rocks, soils, and water in an undisturbed ecosystem. A highly sensitive method called X-ray absorption spectroscopy provided new information on how sulfur is released from rocks as well as the exact chemical forms of sulfur found in rocks, soils, and in the sediments next to rivers. This research allows for a deeper understanding of sulfur cycling that can enhance predictions of water quality and watershed responses to climate change.

Climate change is expected to increase the release of sulfur from rocks—the largest pool of sulfur on Earth—into rivers and lakes, which could lead to deteriorating water quality. Researchers identified the major forms of sulfur in different parts of a pristine mountainous watershed, including rocks, soils, and sediments near rivers. Biological conversion of sulfur to organic forms in shallow soils and sediments were found to serve as a limited sink for newly released sulfur, meaning this biological transformation would store, or ‘hold onto’, the element. In near-river sediments, however, sulfur was converted to the mineral mackinawite, which does not dissolve in water. These near-river sediments may hold more sulfur as Earth’s climate changes. This process could partially offset the increased sulfur released from rocks and lower the risk of sulfur contamination in freshwater.

Sulfur is an important component of the Earth’s crust, and its cycling has critical impacts on water quality and human health. Weathering of pyrite, an abundant mineral containing sulfur, is the primary pathway by which sulfur enters surface waters. Although biological cycling of sulfur in watershed ecosystems ultimately mediates the release of sulfur to rivers and the ocean, climate change has led to water cycle alterations that may enhance pyrite weathering rates and therefore the amount of sulfur released from these minerals. In this study, researchers identified the major forms of sulfur across a pristine mountainous watershed, including shale bedrock, hillslope soils, and near-river sediments using a highly sensitive technique called X-ray absorption spectroscopy. When shale weathering occurred, pyrite was transformed into sulfate, with large accumulations of elemental sulfur. Close to the river, researchers observed precipitation of mackinawite, another mineral containing sulfur, in water-saturated sediments. By contrast, shallow, unsaturated soil and sediment contained primarily organic sulfur compounds. The whole-watershed approach, combined with a highly sensitive analytical technique, shows that riverine sulfur exports are controlled by a balance of rock weathering and biological cycling, where sulfur retention in saturated sediments may partially offset the increased release of sulfur from rocks.

Plant representation in mathematical models often involves assuming that observed processes for a single leaf on a branch removed from a plant apply broadly to the full intact plant. Likewise, model representation is also based on the assumption that the processes measured at one point in the day are constant over the entire day. This study showed that in cases where measurements are made early in the day, or a short time after branch removal, estimations of plant functioning are very similar for cut branches and intact branches. However, results also showed that estimated parameters may vary during the day, and that the longer a researcher waits after a branch has been removed, the larger the discrepancy between cut and intact branches. Thus, failure to consider these effects may confound comparisons of results and, in some cases, may lead to incorrect representation of critical photosynthesis and transpiration processes.

Many of the mathematical models scientists use to represent plant-environment interactions depend on the relationship between stomatal conductance (water loss) and photosynthesis (carbon gain) from leaves. However, the scientific community lacks a clear consensus on the best method for empirically measuring this relationship, known as stomatal slope. This research tested one aspect of measurement methodology: whether branch excision (i.e., branch removal from the tree) prior to measurement influences stomatal slope. Results showed that predawn branch excision did not significantly affect stomatal slope when measurements were made within 4 hours of excision. However, measurements made later in the day increased stomatal slope by an average of 55%. This research further demonstrated that when applied to plant function models, this stomatal slope change reduces modeled transpiration by 18% over a day.

Many ecosystem models represent the link between water loss via stomatal conductance and carbon gain via photosynthesis with a linear function. In this framework the relationship between stomatal response and leaf level environmental conditions is linearly scaled by the stomatal slope parameter (g₁), and bounded by a lower intercept parameter (g₀). Researchers tested if estimates of the g₁ and g₀ parameters are impacted by a common aspect of measurement methodology by conducting paired stomatal response curves on intact and excised branches of a hybrid poplar clone.

The study demonstrated that predawn excision, combined with late day measurement, can strongly influence parameter estimates of g₀ and g₁. In the morning the team observed no significant difference in estimated g₀ or g₁. However, in afternoon measurements, cut branches produced g₁ estimates which were 25% lower than an intact control. Over the diurnal course, g₀ decreased by 55% and g₁ increased by 56%, irrespective of treatment. These differences in parameter estimates have the potential to alter modeled daily transpiration rate by up to 18%. These findings suggest that late day measurement of excised branches has the potential to introduce considerable uncertainties into the modeling of plant carbon and water cycling.

This study adds to existing knowledge of large-scale biogeography and ecology of soil microbes, advancing the ability of scientists to predict changes in soil microbial communities in a drier world. Relative to previous work, this study spanned a large geographic domain: 3,500,000 km² across northern China. This scale is important because it indicates that the results are likely to be transferable to other dryland systems across the Earth. In addition, researchers used cutting-edge ecological theory and analytical tools to provide deep insights into processes governing the ecology of soil microbes. The team also developed models that showed good predictive power and could be used to simulate changes in microbial distributions (and the biogeochemical functions they provide) under future climate scenarios.

Soil microbes drive local-to-global cycles of carbon, nutrients, and greenhouse gases, but there is relatively limited understanding of how specific groups of microbes are linked to major changes in environmental conditions. A new study found clear associations from deserts to grasslands, between environmental conditions and the diversity and abundance of two groups of soil microbes—Haloarchaea and ammonia-oxidizing archaea (AOA). The study also found a clear distinction in the ecological processes responsible for the spatial patterns of these two groups.  Haloarchaea were governed primarily by deterministic selection-based processes while AOA were assembled mostly by stochastic (i.e., random) movement.

This work contributes to a previously limited understanding on the large-scale biogeography of Haloarchaea and AOA in drylands. Researchers consider it original and significant, as it reveals strong ecological differentiation between these two dominant topsoil archaeal groups—primarily driven by habitat specialization associated with contrasting ecosystem types (i.e., deserts and grasslands) rather than small-scale microsites (i.e., bare ground and vegetated areas). Moreover, this work also provides new insights into the community assembly processes underpinning the distinct biogeographical patterns of Haloarchaea and AOA. It reveals that the distribution of Haloarchaea is mainly determined by environmental-based processes, while AOA are more influenced by stochastic (i.e., random) spatial-based processes. These observations are important under future climatic scenarios and suggest that topsoil archaeal communities will likely change due to climate forecasts for drylands worldwide.

Large discrepancies between published estimates of global photosynthesis and respiration reflect uncertainties that hamper the scientific community’s capacity to understand and model how the global carbon cycle will evolve in response to climate change. This study documents that more recent estimation methods seem to be closing the gap between estimates of these two dominant land-based, or terrestrial, carbon fluxes. This finding is crucial as accurate estimates of the largest terrestrial carbon fluxes are necessary for correctly determining the land carbon sink, or how strongly human emissions are being taken up by ecosystems worldwide.

How large are the carbon flows in the global carbon cycle? Satellites provide estimates of plant photosynthesis while researchers use ground measurements to understand respiration—the process by which living organisms send carbon dioxide, or CO₂, back into the atmosphere. These two quantities should be linked because photosynthesis is the ultimate source of all respired carbon. A new study calculated photosynthesis rates from respiration data and vice versa. The results show that estimates of these two processes differ widely, raising questions about current scientific understanding of the global carbon cycle.

The terrestrial carbon sink—the balance between photosynthesis and respiration—removes about a quarter of anthropogenic CO₂ emissions. The magnitude of global photosynthesis (GPP) is therefore one of the largest sources of uncertainty in predicting future trajectories of global temperature. Global GPP is roughly balanced by ecosystem-to-atmosphere respiratory fluxes and dominated by soil respiration (R_S). Although GPP and R_S are physiologically linked—since the former is the ultimate source of all respired carbon—no attempts have been made to quantify how consistent GPP and R_S estimates are at the global scale. This study compares these two large carbon fluxes by using published estimates of one flux (either GPP or R_S) to compute the likeliest values of the other. Researchers found inconsistencies in the estimates that raise doubts about how robustly Earth system models can project changes in global carbon cycling. These results emphasize the importance of cross-comparing datasets and models to understand terrestrial carbon cycling as well as future climate change.

The soil water retention curve is a key component of plant and soil hydrological modeling from the ecosystem to global scales. The use of retention curve parameters derived from root free soil (e.g., repacking root free soil for measurements in the lab) will not correctly represent actual soil water movement water retention or water release dynamics in situ.

The presence of roots, or fungal hyphal structures, in the soil can alter key soil hydraulic parameters, such as the relationship between water content and water potential (the soil water retention curve). Roots can reduce the maximum rate of water movement through the soil, likely by clogging larger soil pores. Roots and fungal hyphae can also increase the amount of water stored in the soil and change the size distribution of pores in the soil.

Soil hydraulic properties describe the storage and movement of water in the soil under changing conditions, such as wetting or drying. Knowledge of these properties is critical to accurate hydrological modeling. The soil water retention curve describes how water content changes as the soil dries. The shape of the curve varies based on soil type and reflects the rate and amount of water availability. The curves are often estimated based on laboratory data or generic functions that depend on soil physical properties, but they do not consider potential impacts of soil roots or fungal hyphae. This research reviewed current knowledge of how these soil biotic components affect hydraulic properties. Laboratory experiments were conducted to test if the presence of roots and fungi had an effect on the hydraulic properties of two soils with different amounts of sand or clay. Switchgrass seeds were planted in pots and grown in a greenhouse. Some pots also had the beneficial root fungus added.

After several months hydrological measurements of the soils were collected, and the results were applied to a commonly used soil water retention curve function. In sand, the roots reduced the maximum rate of water flow through the soil. This reduction was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve by increasing water content at saturation expanding the distribution of soil pores sizes. The presence of mycorrhizal fungi added to the root effects. The results indicated that the impact of root and fungal structures on models of soil hydraulic properties must be considered.

With climate warming, soil temperature and snowpack are predicted to change, which could largely impact the global carbon cycle, terrestrial ecosystem functioning, and freshwater resources. Scientists developed a DTP system and demonstrated its potential for measuring soil and snow temperature at varying depths with a newly developed level of detail, high accuracy, and low cost, while also minimizing energy consumption and the effects of installation. Soil and snow temperature data are gathered with a high spatial resolution to capture both changes in snow depth and the thickness of soil freezing and thawing layers. This development can help improve scientists’ ability to predict and understand the heat and water fluxes in snow and soil across watershed scales, which is essential for assessing and managing water resources and forecasting potential soil warming impacts to the global carbon cycle.

Measuring soil and snow temperature at varying depths with high accuracy is critical to better predict and understand water and carbon fluxes. Temperature measurements of layers throughout snow and soil depths help scientists understand temperature fluctuations, heat and water fluxes, frozen and thawed soil depth, and snow thickness – all of which are essential to understand as earth’s temperature changes. However, obtaining these measurements in numerous locations with a high level of detail is difficult due to their total cost, the challenge of obtaining accurate measurements, and the potential disturbance caused by installation. This study presents the development and importance of a novel Distributed Temperature Profiling (DTP) system that makes it possible to measure soil and snow temperature at varying depths in greater detail to address these challenges.

Studying ecosystems on multiple scales is required to better understand the complex behavior of the environment in a changing climate. To study thermal dynamics and temperature distribution in snowpack and soil, scientists have developed a DTP system – an efficient and easy-to-install sampling method that provides detailed and accurate temperature measurements at varying depths with a low cost. 

The system provides depth-profiles of temperature measurements at newly detailed resolutions, and also enables automated data acquisition, management, and wireless transfer to other devices and computers. A novel calibration approach confirms an accuracy of up to +/– 0.015 ºC, which will allow scientists to better understand how temperature varies in the depth of snow and soil, enabling improved predictions of how rising temperatures may influence these resources and, ultimately, ecosystem health and functioning. By using the system in various environments, scientists showed that the DTP system reliably captures temperature dynamics throughout snow depth and the depth of frozen and thawed soil layers. This study advances understanding of how the intensity and timing in surface processes impacts below-ground temperature distribution. The development of the DTP system is an important step toward optimizing environmental data accuracy and modeling at low cost.

Global climate change will result in a hotter, drier, and CO₂-enriched environment. Because stomata are the gatekeepers of carbon and water exchange, representing their function accurately in models is key to improving projections of plant responses to global change. This study showed that both models of stomatal function performed well but differed in their responses in dry air, with their responses to dry soil conditions being the biggest driver of uncertainty.

Stomata control the movement of water and CO₂ between the atmosphere and the leaf balancing water lost through transpiration and carbon dioxide taken up by photosynthesis. The flux of water vapor through the stomata is called stomatal conductance. This study compared and evaluated two model representations of stomatal conductance in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) and demonstrated each model’s efficacy for modeling carbon and water exchanges in a tropical forest.

Stomata play a central role in regulating the exchange of carbon dioxide and water vapor between ecosystems and the atmosphere. Their function is represented in land surface models by stomatal conductance models. The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is a dynamic vegetation demography model that can simulate both detailed plant demographic and physiological dynamics. This study implemented an optimality-based stomatal conductance model—the Medlyn (MED) model and compared with previous FATES default Ball—Woodrow—Berry (BWB) model. To evaluate how the behavior of FATES is affected by stomatal model choice, a model sensitivity analysis was conducted to explore model response of stomatal conductance to climate forcing, including atmospheric CO₂ concentration, air temperature, radiation, and vapor pressure deficit (VPD). Results showed that modeled stomatal conductance values varied greatly between the BWB and MED formulations due to the different default stomatal slope parameters. After harmonizing parameters for both model formulations, this study found that the divergence was limited to conditions when the VPD exceeded 1.5 kPa. Evaluations of model simulation results against measurements from a wet evergreen forest in Panama showed that both model formulations were able to simulate the carbon and water exchange in the tropical forests well, except under dry conditions. Thus, this research suggests that the current model representation of soil water stress effects should be used with caution.

The location and timing of nitrate availability is important because permafrost soils are typically poor in nutrients, especially nitrogen. As arctic ecosystems continue to warm, expansion of alder shrubs across hillslopes will impact the location and timing of nutrient availability for neighboring plants and soil microbial communities. This study presents the first comprehensive characterization of nitrate in soil water collected in and around alders growing in permafrost soils.

The team collected soil porewater from an Alaskan hillslope that is experiencing rapid warming and expansion of a nitrogen-fixing shrub, Alnus viridis spp. fruticosa (alder). Analysis revealed that porewater had the highest levels of nitrate underneath alder shrublands and that this nitrate was flushed downslope following rain events.

In Arctic ecosystems, warming is driving the expansion of shrubs across tundra landscapes. The proliferation of shrubs can change local soil chemistry, especially if the shrub species is capable of fixing “unavailable” nitrogen from the atmosphere into a biologically available form that plants can use. The team investigated nitrate in soil porewater at locations upslope, within, and downslope of shrublands dominated by nitrogen-fixing alder (Alnus viridis spp. fruticosa). Samples were collected during three field campaigns under a variety of weather conditions. Soil pore water from underneath alder shrublands had significantly higher levels of nitrate (4.27±8.02 mg N L-1) compared to areas outside the shrubland (0.23±0.83 mg N L-1; p<0.05). After rain events, elevated nitrate levels were found in samples from tussock tundra located downslope from alder shrublands, indicating that nitrate had been flushed downhill. Since alder shrublands have been expanding at this hillslope site since the 1950’s, these findings highlight how changing climate and vegetation together can alter the spatial and temporal patterns of nitrogen availability across an otherwise infertile arctic landscape.

The designed sensor probe consists of a thin, semi-flexible tube that contains accelerometer and temperature sensors mounted on multiple cascaded boards with novel board-to-board connectors. Experiments performed with probes up to 1.8 m long demonstrated high spatial resolution and accuracy, long-term connector stability, and mechanical flexibility. In contrast to alternative solutions, this approach measures depth-resolved deformation, which can inform about shallow sliding surfaces. This low-cost technology enables scientists to acquire data with an unprecedented resolution through densely distributed sensor networks. It is an essential tool for understanding landslide behavior, as well as various cryospheric, hydro-biogeochemical, and geomorphological processes that impact water and carbon fluxes.

Predictive understandings of soil biogeochemical processes and slope stability are limited partly by the inability to observe subsurface geomechanical dynamics and their drivers at a relevant number of locations. Technological solutions are needed for long-term, multiscale monitoring of soil deformation. However, current instruments are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Here, scientists present a novel sensing solution that uses linear arrays of temperature sensors and accelerometers. From an electromechanical perspective, a novel board-to-board connection method was developed that enables narrow, semi-flexible sensor arrays and a streamlined assembly process.

Scientists developed a novel, low-power, flexible sensor array for monitoring soil deformation and temperature in slopes with shallow instabilities. In contrast with conventional approaches, the presented solution is low-cost, lightweight, robust, and easy to install, enabling multi-scale deployments in densely distributed, wirelessly connected configurations. The electronic design contains a configuration of cascaded temperature sensors and accelerometers, compatible with an existing 2×AA battery-powered data logger. To meet the mechanical requirements of the sensor probe, a novel, solderless board-to-board connection method was developed. This method does not require any components and enables extremely thin, semiflexible probes of adjustable length. An extensive study of the contact resistance demonstrated long-term stability, even under bending (radius up to 200 mm). The entire probe assembly shows significant deformation under small (<1 N) forces, which demonstrates that the probe’s deformation is representative for soil movement. An assessment of the measurement accuracy shows that deformation measurements under a constant temperature have a 95% confidence interval of ±0.73 mm/m. A set of probes in a permafrost environment showed continuous soil displacement at a rate of 2 mm/day, starting from the interface between frozen and unfrozen soil. This represents a first step in quantifying soil movements and their controls in permafrost environments, which is critical to improve our understanding of carbon cycle.

Coastal change research has traditionally focused on seaward environments, such as barrier islands, intertidal wetlands, and subtidal ecosystems, with conflicting results. Consequently, the sensitivity of coastal forest soil carbon to future climate conditions remains largely unknown. Results from this study suggest that disturbance legacies shape coastal forest soil responses to changing salinity and inundation from rising sea levels and storms. In the context of ongoing climate change, manipulative transplant experiments provide a crucial inferential link between purely observational experiments, data synthesis efforts, and large-scale ecosystem manipulations.

Coastal forests are increasingly exposed to climate change and sea level rise, but the impacts to soil stability are poorly understood. This experiment examined how soil might change when transplanted between parts of a tidal creek that differed in salinity. Scientists found that soils with a history of salinity and inundation exposure were more resistant to changing hydrology, suggesting that the soils already learned how to adapt to environmental changes. Differences in the resilience of soil carbon cycling will likely vary across landscapes, explained by soils’ ecology, biogeochemistry, and legacy of prior exposure to disturbance.

Researchers used a natural salinity gradient in an eastern Maryland tidal creek to examine how soil respiration and chemistry may change under novel salinity and inundation disturbance regimes. Soil monoliths were transplanted among plots varying in seawater exposure and elevation above the creek and were monitored for two years. The response of soil respiration—the flow of carbon dioxide from the soil to the atmosphere—was dependent upon the salinity and inundation legacies associated with each study location. Respiration did not change (i.e., high resistance) under new moisture conditions in lowland soils with a history of seawater exposure. Conversely, respiration decreased (i.e., low resistance) in upland soils that had little past exposure to seawater or inundation decreased (i.e., low resistance) and remained suppressed (i.e., low resilience) when those soils were exposed to wetter, saline conditions. 

Additionally, transplantation resulted in greater changes to upland soil chemistry relative to that observed in lowland soils. Together, these results suggest that disturbance legacies shape coastal forest soil responses to changing salinity and inundation disturbance regimes. However, fully understanding the dependence of system responses on disturbance legacies requires future study across a variety of systems and spatial and temporal scales.

Better water quality models can help water managers make optimal decisions on water use and treatment. Artificial intelligence (AI) and machine learning methods can enable faster, more accurate predictions of river water quality that are relevant for decision making. However, there are many considerations for how such models should be designed. Watershed managers need predictions that are both accurate and robust to choices for how the model was built. They also need predictions that are explainable and trustworthy to help those who use the models (stakeholders). This study discusses how to design models that are enabled by AI technologies to serve these purposes.

Growing populations and climate change are stressing water quality in rivers and streams across the world. Water managers need good models to predict river water quality in rivers. However, current models cannot account for the complex factors that influence water quality. This paper discusses how the latest advances in machine learning and artificial intelligence can improve models of water quality in watersheds and river basins.

Watershed managers need to adapt to multiple stressors that include population growth, land use change, global warming, and extreme events. Water managers need good models to predict river water quality in rivers to make optimal decisions on water use and treatment. However, current models cannot account for the complex factors that influence water quality. This paper discusses how artificial intelligence and machine learning can enable more accurate, fast, and scalable models for river water quality. It provides (1) reviews of relevant state-of-the art machine learning applications for water quality and (2) descriptions of opportunities for selecting and designing a new class of models that make use of AI technologies. These models should be designed so that the predictions are accurate and robust to choices for how the model was built. Watershed managers also need predictions that are explainable and trustworthy to help stakeholders who use the information from the models. Successful integration of machine learning into water quality models can help make better water management decisions to plan for an uncertain future.

Future climates are expected to be warmer and drier, with more intense extreme events like floods. Growing populations will also increase the extent of urbanization. These results provide insights into how salt levels of rivers will change due to floods and other factors related to climate and human development. These results will be useful for developing new models that watershed managers can use to plan for an uncertain future.

Salinity (the amount of salts dissolved in water) is an important water quality variable. It directly affects fish and other aquatic life and determines how river water can be used for agricultural and industrial purposes. Scientists studied how floods affect river salinity by analyzing a large dataset from 259 monitoring stations in rivers across the United States. They found that floods mostly decrease salt levels in rivers by dilution. However, salt levels can increase for roughly 6% of flood events. The changes depend strongly on salt levels in the few days prior to the flood. Climate and human development also affect how salts in different rivers change during floods.

Researchers examined how floods affect river salinity by analyzing a large dataset of streamflow and specific conductance (a measure of salt levels) for 259 United States Geological Survey (USGS) monitoring sites. Scientists used a combination of statistical methods and machine learning models to determine how river salt levels change at different sites due to floods. They found that floods mostly decrease salt levels in rivers by dilution. However, salt levels can increase for roughly 6% of flood events. The changes depend strongly on salt levels in the few days prior to the flood. Climate and the extent of human development also affect how salts in different rivers change during floods. Notably, urbanization in temperate climates can increase dilution of salts, and mining in arid climates can increase river salinity during floods.

Declining winter snowpack and impacts to plant roots have direct effects on the diversity and abundance of soil bacteria and fungal communities with important consequences for nitrogen (N) cycling in northern hardwood forests.

Rising winter air temperatures are reducing seasonal snow cover in many temperate ecosystems. Such reductions in snow depth may affect soil bacteria and fungi directly, but also affect soil microbes indirectly through effects of snowpack loss on plant roots. However, the role of plant roots in moderating the impact of snowpack loss on bacterial or fungal communities remains poorly resolved.

Studies have investigated the effects of climate-induced warming on soil biogeochemical processes, with temperature increases affecting snow-dominated systems through reduced snowpack and early onset of melt. To date, few studies have focused on the role that roots play in enhancing or moderating nutrient cycling in soils by bacteria and fungi. To address this knowledge gap, root ingrowth and exclusion cores (216 cores total) were incubated for 29 months at the Hubbard Brook Experimental Forest in central New Hampshire, which has experienced a decline in winter snowpack over the past 50 years. Both a declining winter snowpack and effects of reduced snow on plant roots had a direct effect on the diversity and abundance of soil bacteria and fungal communities and interacted to reduce rates of soil N cycling in this northern hardwood forest. Such results are broadly relevant to other temperate ecosystems where climate change and climate disturbance are affecting snowpack, such as many mountainous regions worldwide.

Researchers found that unsupervised learning methods can reduce the dimensionality of timelapse images effectively. The results identify spatial regions—a group of pixels— that have similar snow-plant dynamics (based on Normalized Difference Vegetation Index) as well as their association with key topographic features and soil moisture. This cluster-based analysis can tractably analyze high-resolution timelapse images to examine plant-soil-snow interactions, guide sampling and sensor placements, and identify areas likely vulnerable to ecological change in the future.

In the headwater catchments of the Rocky Mountain region, plant dynamics are largely influenced by snow accumulation and melting as well as water availability. Key properties such as snow coverage, soil moisture and plant productivity are highly heterogeneous in mountainous terrain. This study identifies the spatiotemporal patterns in co-varied snow, plant, and soil moisture dynamics associated with microtopography based on high-resolution satellite imagery and unsupervised machine learning.

In the headwater catchments of the Rocky Mountain region, plant productivity and its dynamics are largely influenced by water availability. Understanding and quantifying the interactions between snow, plants, and soil moisture has been challenging. These interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. In this study, researchers investigated the relationships among topography, snowmelt, soil moisture, and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope Normalized Difference Vegetation Index (NDVI) images. To make use of a large volume of high-resolution timelapse images, researchers used unsupervised machine learning methods to identify the spatial zones that have characteristic NDVI time series and to reduce the dimensionality of time lapse images into spatial zones. Results show that identified zones are associated with snow-plant dynamics and microtopographic features. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution timelapse images to examine plant-soil-snow interactions, guide sampling and sensor placements, and identify areas likely vulnerable to ecological change in the future.

An advantage of generating multiresolution meshes is that they can often use different criteria for optimal refinement. This approach allows the generation of meshes that are able to accurately represent a broad range of processes, reducing errors, and maintaining efficient use of information.

Multiresolution meshes are generated using a single error-threshold criterion, which are errors in the approximation of topographic slope. This technique reduces the number of free parameters that are typically needed by other approaches. In the Lower Triangle Region of the East River, Colorado, watershed, researchers used two such criteria:  topographic slope and topographic curvature. Simulation results show that using curvature as refinement criteria is preferable in mountainous catchments.

Multiresolution mesh generation usually utilizes a number of free parameters, which are tuned by inputting field-collected data. For this study, researchers used wavelet analysis—a mathematical method for signal analysis—to reduce the number of free parameters to exactly one: the acceptable error threshold. The researchers applied the wavelet analysis on bed slope and bed curvature to generate multiresolution meshes for high-intensity overland flow simulations. They used case studies ranging from laboratory scale experiments to a subcatchment of the East River Watershed, Colorado to compare results obtained on these meshes. For the latter case, computational results indicate that meshes generated by the curvature-based criterion give a more accurate prediction of stream discharge, which implies that in mountainous watersheds, these flow processes are controlled by the curvature of the terrain. The wavelet approach is general enough to be used for a variety of different criteria to drive mesh refinement.

Radiocarbon measurements reveal that 23-34% of OC in East River floodplain sediments is derived from shale, including types of sediment-OC which are considered to be relatively mobile and available for use by microbes. While the contribution of shale-derived OC to CO₂ production and export is currently unknown in this system, the observation of shale-derived OC in carbon pools which is actively cycling suggests that this topic warrants further research. The results demonstrate the importance of shale weathering in the floodplain, particularly under low plant-litter environments, with implications for the global carbon budget and other shale-associated elements, including growth-limiting nutrients (e.g., N) and toxic elements (e.g., As, Se, U).

Shales contain high levels of organic carbon (OC) and represent a large fraction of the earth’s carbon stocks. Recent evidence suggests that shale-derived OC may contribute to the carbon cycle in some riverine systems, however this process is poorly understood and not currently considered in global C models. Through detailed sediment analysis coupled with radiocarbon measurements, and synchrotron carbon spectroscopy, researchers determined the abundance, chemistry, and mobility of shale-derived OC in floodplain sediments of a shale-rich mountainous watershed.

Shales contain high levels of organic carbon (OC) and represent a large fraction of the earth’s total carbon stocks. While recent evidence suggests that shale-derived OC, which is millions of years old, may be actively cycled in riverine systems, this process is poorly understood and not currently considered in global C models. In this study, researchers analyze sediments collected from the floodplain of the East River, Colorado, located in a high-elevation mountainous watershed underlain by shale bedrock, to determine the importance and mobility of shale-derived OC in this environment. OC closely associated with sediment minerals is the largest (84 ± 6%) and oldest OC pool, containing a large, but variable, amount of shale-derived OC. Evidence of shale-derived OC is also observed in other sediment OC pools which are considered to be more mobile and more easily degraded to carbon dioxide by bacteria (e.g., water-soluble). Carbon spectroscopy revealed that floodplain sediments had a higher degree of functionalized aromatic groups and lower carbonate content compared to shale collected nearby, consistent with chemical alteration and mixing with other C sources in the floodplain. This study concludes that there are two primary OC sources in floodplain sediments, plant-litter and shale-derived OC, each with distinct chemical characteristics and reactivity. The authors estimate 23-34% of the sediment OC is derived from shale, demonstrating the important contribution of shale-OC to the carbon cycle at this site, particularly in environments with low plant-litter inputs.

Reducing conditions sustained within fine-grained sediment lenses enhanced the extent and breadth of retained Zn species compared to the coarse sediments of a model alluvial aquifer system. Furthermore, Zn loading, and the distribution of Zn species differed between individual lenses, suggesting that unbalanced multiple driving forces vary in intensity along the flow-path. These results emphasize the complex nature of alluvial aquifer systems and behavior of metal contaminants residing within them, which has direct implications to the quality of the groundwater. Thus, the spatial and compositional heterogeneities of alluvial aquifers must be specifically taken into account when maintaining and managing groundwater systems.

Alluvial aquifers are an essential source of groundwater worldwide, particularly for water storage purposes. Fine-grained lenses of clay and organic matter, enriched in iron and sulfur, are abundant within aquifers and support cycling of nutrients, carbon, and toxic metals by providing chemical-reducing conditions in otherwise oxygenated systems. Changes in redox status may have immense influences on the behavior (dissolved concentration, bioavailability, mobility) of heavy metals within the aquifer and resulting groundwater quality. Therefore, researchers investigated the retention of a simulated zinc (Zn) plume in a model alluvial aquifer system containing fine-grained reduced clayey lenses. The fine-grained lenses were specifically responsible for significantly increased Zn retention (23%) resulting in both spatial and compositional differences in the uptake of Zn.

Understanding the biogeochemical conditions in alluvial aquifers experiencing redox heterogeneities is essential to preserve the quality of the groundwater stored within them. The fate of metal contaminants within these complex systems is challenging to predict. Thus, researchers studied the retention pathways of Zn within a model dual-domain (clayey-sandy) alluvial aquifer. The research team used natural coarse aquifer sediments in columns with or without fine-grained lenses from the Wind River−Little Wind River floodplain near Riverton, Wyoming to examine biogeochemical controls on Zn concentrations, retention mechanisms, and transport. Zn preferentially accumulated within the fine-grained lenses, which enhanced Zn uptake by 23%, despite only comprising 5% of the sediment mass in the model aquifer. The research team found that clay minerals and layered double hydroxides dominated Zn retention in the coarse sediments, whereas zinc sulfide prevailed in the fine-grained lenses, emphasizing distinct differences in Zn species between the domains. Zinc was resistant to solid-phase aqueous extraction, but sensitive to acid extraction, which suggests limited but measurable capacity for re-release and transport unless pH decreases considerably. These findings emphasize the importance of considering differences in sediment composition and the size and distribution of heterogeneities in evaluating potential threats of metal contaminants to aquifer groundwater.

Findings from these studies imply that an additional reactive transport mechanism and more long-lived pool of reducing equivalents controls redox cycling in oxic aquifers, identifying gaps in recent numerical models. The studies show that microbial redox cycling of micronutrients and contaminants that need anoxic conditions can be sustained within nominally oxic aquifers in the vicinity of organic-enriched sediment lenses. This means that a larger volume of the subsurface matrix is redox active. However, the redox conditions in these “reducing halos” in the surrounding sandy environment are far more sensitive to the influx of oxidants than are the lenses. Thus, the mobility of redox-sensitive micronutrients and contaminants can quickly change within this environment.

Groundwater quality is driven by complex biogeochemical processes determined by the chemistry and composition of both the groundwater and the aquifer. Many otherwise sandy aquifers contain abundant organic-enriched, fine-grained, and sulfidic lenses that   are important sources of organic carbon, Fe(II), and sulfur (S).  r i While these lenses are recognized as playing important roles in aquifer biogeochemistry and redox cycling, the specific reactive transport mechanisms by which these reactive species influence biogeochemical function in the surrounding aquifer are poorly understood. Numerical models of these processes generally have microbially driven reduction reactions occurring only inside the actual sediment lenses.  . However, in two experimental studies investigating reactive transport in and around these lenses, researchers showed that in addition to reduced aqueous species (e.g., Fe(II) and HS^–) that were produced by redox reactions inside the lenses, organic carbon is also exported from organic-enriched lenses into the sandy aquifer matrix. This stimulates microbial anaerobic reduction in the surrounding aquifer and creates a microbial redox-active zone around the lenses.

In these studies, researchers used natural floodplain sediments and examined the influence of organic-enriched, fine-grained lenses on redox conditions in surrounding sandy aquifer sediments, and they examined the consequential implications for speciation and mobility of zinc (Zn) (Engel 2021) and arsenic (As) (Kumar 2020). Synchrotron X-ray absorption spectroscopy at the Stanford Synchrotron Radiation Lightsource’s beam lines 4-3 and 7-3 showed that Fe(II) minerals, including FeS and elemental S, were present in the surrounding nominally oxic aquifer in abundances that exceeded what abiotic, aqueous-reduced products could explain. The research team concluded that, when sulfate concentrations in the groundwater are high, the export of reducing capacity (“exported reactivity”) from fine-grained, sulfidic lenses into aquifer sand can promote microbial Fe and sulfate reduction, which in turn leads to FeS precipitation and elemental S formation. Elemental S can then react with As to form thiolated As species, which appear to have a higher solubility and mobility than other As species. In contrast, when Zn(II) is present as a dissolved contaminant, it reacts strongly with dissolved HS^– and precipitates as ZnS, sharply limiting the export of HS^– and Zn (but not impacting Fe and organic matter export) from the organic-enriched lenses. Thus, the combination of high-sulfate groundwater and heterogeneous sediment composition (e.g., fine-grained, organic-rich/coarse interfaces) can locally promote severely elevated As concentrations, even when sediment As concentrations are below the global average. Conversely, Zn attenuation is amplified by the same sediment heterogeneities.

Advancing collaboration and resources in the field of geoscience can close knowledge gaps and break barriers that limit scientific development and progress in addressing global issues. A team of researchers advocated for the development of an international network-of-networks framework that can create meaningful connections with all relevant groups represented and working together as equals. This framework can mobilize the scientific community and serve as a foundation for a more international, collaborative, and open science model underpinned by strong communication channels.

Geoscience fields such as Volcanology, Geochemistry, and Petrology (VGP) are extremely broad, involving applications and research questions ranging from planetary geology to the creation of mountains. For this reason, working across traditional disciplinary VGP boundaries has been largely limited to specific challenges and application areas. This limitation has prevented broad sharing of metadata, standards, protocols, and models as scientists move from one application area to the next, thereby keeping the VGP field in “stamp-collecting” mode. To allow for future innovation in VGP, there is an urgent need to advance collaboration, increase resource efficiency, and create transferable knowledge in VGP through Integrated, Coordinated, Open, and Networked (ICON) science. In this article, scientists described the elements of, challenges to, and path forward in implementing ICON principles within VGP.

This article is part of a recent Earth and Space Science collection of commentaries (Goldman et al. 2021) spanning the geosciences about the state and future of Integrated, Coordinated, Open, and Networked (ICON) science. To implement ICON principles in VGP, researchers advocated for an open, inclusive, collaborative, and evolving model of an international coordinated network. For this team, ICON means collaboration, equitable access to data for the entire scientific community, and forging partnerships that can contribute to more innovative ways of coordinating and sharing research. Establishing ICON in VGP also entails implementing effective measures to enhance access to funding, equipment, resources, and mentors that can optimize equity and advancement in the earth sciences.

Coastal plant mortality is rising globally, leading to negative impacts on ecosystem services valued by society. Knowledge of the mechanisms leading to this mortality is in its infancy, which impairs researchers’ ability to make predictions about future coastal ecosystem loss. For the first time, scientists measured the key metrics of hydraulic failure and carbon starvation in trees that were dying from seawater exposure. The event was an anomaly, but it was indicative of what will likely happen as sea levels continue to rise. Results from the study will pave the way for improved understanding of coastal tree mortality, thus creating a pathway for model development that targets the key processes leading to coastal forest loss. 

Increasing mortality rates of coastal woody plants is a conundrum, and researchers expect the rates to worsen as sea levels continue to rise. This study concentrated on a forested floodplain in the Pacific coast of Washington State that was exposed to seawater after a culvert was breached in November 2014 to allow salmon habitat restoration. The anomalous exposure caused rapid and widespread death of the forest. Hydraulic failure—the plant’s inability to move water from roots to leaves—was the immediate threat to the trees. However, rather than kill them outright, the small degree of hydraulic failure promoted carbon starvation through reductions in photosynthesis, eventually leading to mortality. 

Coastal plant mortality is rising in concert with sea level, yet the mechanisms of mortality in these systems are untested. This lack of research leads to a large knowledge gap that subsequently precludes mechanistic prediction of future coastal ecosystem loss. In this study, scientists examined the key processes expected to kill plants—hydraulic failure and carbon starvation—in trees that experienced novel exposure to seawater. In November 2014, a culvert was breached below a forested floodplain along Beaver Creek in Washington State to increase salmon spawning habitat, resulting in seawater exposure that was unprecedented in the life of this forest. The ability to transport water within the plants was reduced within the first year of the event, leading to carbon starvation via photosynthetic loss. In short, carbon starvation was ultimately the dominant driver of mortality. These results pave the way for improved knowledge and model development for prediction of future coastal forest loss. 

Soil carbon (C) dynamics affect atmospheric CO₂ levels, but these dynamics are uncertain in numerical models used for climate change analyses. This study contends that an important source of that uncertainty is the current lack of mechanistic representation of dominant processes in land models. This study first reviews seven important classes of biogeochemical processes affecting soil C. It goes on to describe the open-source reactive transport solver BeTR-S, which can be used to explore hypotheses of how these processes should be represented. Finally, the study discusses how BeTR-S was applied to a research team’s field warming manipulation at Blodgett, California.

Soils contain Earth’s largest actively cycling carbon (C) stocks and currently store at least several times the amount of carbon (as CO₂) in the atmosphere. Yet, model predictions are highly uncertain. Therefore, improving model structures and availability of open-source platforms is imperative. This study (1) reviews seven dominant classes of soil biogeochemical processes affecting soil organic matter stability, (2) describes the open-source framework called Biogeochemical Transport and Reaction for Soils (BeTR-S) that can be applied to simulate these dynamics, and (3) discusses its application at a field site in Blodgett, California.

Substantial uncertainty exists in site- to global-scale assessments of soil organic matter cycling. Current site- to global-scale land models have very simple representations of soil organic matter cycling, likely contributing to this uncertainty. This study describes seven dominant classes of soil biogeochemical processes that affect soil organic matter dynamics (see figure): (P1) litter input and polymeric SOM degradation; (P2) microbial physiology, microbial population dynamics, and macronutrient controls; (P3) trophic interactions; (P4) mineral–organic interactions; (P5) soil redox and pH chemistry; (P6) rhizosphere-bulk soil interactions; and (P7) soil structure dynamics. It then describes how these processes can be numerically represented and simulated in a vertically-resolved, open-source package called BeTR-S. Finally, this study discusses how BeTR-S was applied to evaluate the effects of warming on soil C at a field site Blodgett, California.

The BASIN-3D software helps environmental researchers who use data from public and private sources address some critical challenges by automating the process of pulling together data from different sources. Thus, it enables users to have access to the latest data available from providers of their choice without having to manually download data and reconcile differences. This software can be used to support data integration for both web-based tools and data analytics. It is also applicable to environmental field and modeling studies requiring data integration.

Earth data include measurements and model results of physical, chemical, and biological processes in ecosystems. The data are diverse and often stored across many databases, with different formats and conventions. A new software tool called Broker for Assimilation, Synthesis, and Integration of eNvironmental Diverse, Distributed Datasets (BASIN-3D) helps reduce the burden on scientists to integrate their research data by acting as a “broker” that retrieves data on demand from different sources and transforms it into a unified view. This study presents two applications of BASIN-3D to integrate time series (data collected at different time intervals). The first is for advanced search and exploration of data on a web portal, and the second is to provide data to machine learning models for water quality predictions.

Earth scientists invest significant effort integrating data from multiple data sources for both modeling and data analyses. This study introduces BASIN-3D as a data brokering approach to reduce the data processing burden on scientists. BASIN-3D can synthesize diverse data from different sources on demand, without the need for additional storage. The software is currently implemented to integrate time series earth observations across a hierarchy of spatial locations commonly used in field measurements (such as river basins, watersheds, sites, plots, and wells). Its framework enables users to map data sources of interest to a common format. The utility of this tool is demonstrated in two applications: (1) a web portal that allows scientific users to explore and access data through features such as an interactive map, graphs, and download; and (2) a Python package that can be embedded in scripts to input data to machine learning models for water quality predictions. Hence, BASIN-3D can be used to support data integration for both web-based tools and data analytics.

Biogeoscience requires multiscale global data and joint international community efforts to tackle environmental challenges. However, several technical, institutional, and cultural hurdles have remained major roadblocks toward scientific progress. ICON science aims to address these challenges and create transferrable knowledge. In this article, researchers combined three related commentaries about the state of ICON science. They discussed the need to reduce geographical bias in data for enhancing scientific progress. The team identified actions people can take to advance biogeosciences, such as engaging local stakeholders across the globe, incentivizing collaborations, and developing training and workshops.

Many environmental challenges such as climate change are global in scope and surpass national boundaries. These challenges involve local-to-global ecosystem processes (e.g., carbon or nitrogen cycling) that require observations across spatial scales. Tackling these grand challenges requires actions that are Integrated, Coordinated, Open, and Networked (ICON). A team of scientists outline several opportunities for ICON science, including organized experimentation and field observation across global sites to advance science and social progress.

Researchers combined three independent commentaries about the state of ICON principles and discussed the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (1) global collaboration, technology transfer, and application, (2) community engagement, community science, education, and stakeholder involvement, and (3) field, experimental, remote sensing, and data research and application. To implement ICON principles in biogeosciences, the team calls for a suite of short and long-term actions, with an approach toward capacity building, cultural shifts, breaking barriers through reduced entry costs, building research networks, and promoting community engagement with open and fair research practices. They also suggest developing methods and instrumentation to confront global challenges and solve key questions in biogeosciences.

This study is the first to document the fate of wood-C ingested and processed by a subterranean termite species, thereby providing new insights into the metabolic pathways and providing needed data for modeling. This work showed that a significant proportion of the consumed dead wood (~40%) was transferred to other pools where it could be processed by other organisms or become part of the soil carbon pool. Also, a significant proportion of the dead wood was returned to the atmosphere, primarily as carbon dioxide, with very little as methane. 

Termites are considered an important agent for decomposing wood; however, little is known about their rate of wood consumption and the fate of wood-carbon (C) that they consume. Yet, this information is fundamental to modeling wood decomposition and understanding how termites may influence soil biogeochemistry (Myer and Forschler 2019).  This study investigated the subterranean termite (Reticulitermes flavipes) to determine the fate of wood-C. This study was made feasible through the use of loblolly pine (Pinus taeda) grown on the Duke Free Air CO2 Enrichment (FACE) site, as wood from this site has a distinct isotopic signature that enables tracking of consumed wood. 

Subterranean termites are ecosystem engineers that consume dead wood, effectively transferring the wood-carbon into soil and atmosphere; however, little is known about the breakdown of those products, which are largely unaccounted for in carbon cycling models. The fate of C from wood utilized by Reticulitermes flavipes (Kollar) was determined in a laboratory study using δ¹³C labeled wood as a tracer. The percentage of wood-based carbon in respiratory gases, tissues, and organic deposits (frass and construction materials) was measured to determine wood-C mass distributed into metabolic and behavioral pathways. Termites emitted 42% of the C from wood as gas (largely as carbon dioxide), returned 40% to the environment as organic deposits (frass and construction materials), and retained 18% in their tissues (whole alimentary tracts and degutted bodies). These findings affirm that termites are a source of greenhouse gases but are also ecosystem engineers that return approximately half the C from dead wood as organic deposits into their surrounding environment.

Stream water temperature is a master water quality variable expected to increase in the future due to climate change. Water managers need accurate stream temperature predictions to make optimal decisions. Researchers found that machine learning could be used to accurately predict monthly stream temperatures, both locally and regionally, in pristine and human-impacted watersheds. Such monthly models can play an important role in near-term seasonal forecasting to plan for and understand future impacts on stream temperatures due to a changing climate and extreme events.

Researchers used classical machine learning models to predict monthly stream temperatures for 78 pristine and human-impacted watersheds in the Mid-Atlantic and Pacific Northwest hydrologic regions with diverse geologies, climate, and land use. The models improved local and regional prediction accuracies by 15 to 48% relative to a baseline statistical model. Results showed that air temperature was the primary factor affecting monthly stream temperature, indicating that the models could be used with a minimal amount of input data on climate variables that are broadly available. These models enable predictions of stream temperature at new sites, such as unmonitored and dammed watersheds.

Stream water temperature is an important water quality variable that affects river ecosystem health and water use. Short and long-term predictions of stream temperatures are needed to make optimal water management decisions that account for a changing climate and extreme events. This study used classical machine learning models (support vector regression, gradient boosted trees) to predict monthly stream temperatures in 78 watersheds of the Pacific Northwest and Mid-Atlantic regions in the United States, which have diverse climate, land use and geologies. The models used input data on climate and stream flow records with basic watershed information (location, elevation, size). The models were used for local, regional, and unmonitored scenarios and improved prediction accuracies by 15 to 48% relative to a baseline statistical model. Results showed that air temperature was the primary factor affecting monthly stream temperature, indicating that the models could be used with a minimal amount of input data on broadly available climate variables. These results will expand the capabilities of models to predict stream temperature at new sites—such as in watersheds with dams, and for watersheds that lack extensive historical data or other information describing their properties (e.g., extent of land cover, number of dams).

An important need for understanding how forests will function under future atmospheric CO₂ levels is how mature and diverse forests will respond to elevated CO₂. Future studies should address how changes in canopy structure can affect how forests will respond to drought and infertile soils. This study engaged many questions about whether a more diverse, mixed species forest would respond similarly to the young, single-species stand. Results showed the likely value of including more detailed descriptions of canopy structure in models.

Trees display their leaves so that the forest canopy can best make use of critical environmental resources, including light, carbon, water, and nitrogen. As a forest grows and develops, the forest’s structure and canopy also change. Researchers observed these changes during a 12-year experiment in a sweetgum forest growing in an atmosphere enriched with CO₂ concentrations that will occur in the future. Although growth in elevated CO₂ can alter the use of other resources, this young forest showed little evidence that elevated CO₂ altered tree and stand development or canopy structure.

Canopy structure—the size and distribution of tree crowns and the spatial and temporal distribution of leaves within them—exerts dominant control over primary productivity, transpiration, and energy exchange. Stand structure—the spatial arrangement of trees in the forest (height, basal area, and spacing)—has a strong influence on forest growth, allocation, and resource use. Forest response to elevated atmospheric CO₂ is likely to be dependent on canopy and stand structure. Scientists investigated elevated CO₂ effects on forest structure of a sweetgum (Liquidambar styraciflua) stand in a free-air CO₂ enrichment (FACE) experiment, considering leaves, tree crowns, forest canopy, and stand structure. During the 12-year experiment, the trees increased in height by 5 m, and basal area increased 37%. Basal area distribution among trees shifted from a relatively narrow distribution to a much broader one, but little evidence emerged regarding an effect of CO₂ on height growth or basal area distribution. The differentiation into crown classes over time led to an increase in the number of unproductive intermediate and suppressed trees and a greater concentration of stand basal area in the largest trees. A whole-tree harvest at the end of the experiment permitted detailed analysis of canopy structure. Results showed little effect of CO₂ enrichment on the relative leaf area distribution within tree crowns and little change from 1998 to 2009. Leaf characteristics (leaf mass per unit area and nitrogen content) varied with crown depth; any effects of elevated CO₂ were much smaller than the variation within the crown and were consistent throughout the crown. This young, even-aged, monoculture plantation forest not only showed little evidence that elevated CO₂ accelerated tree and stand development but also demonstrated remarkably small changes in canopy structure.

Plant mortality is rising globally, leading to negative impacts on ecosystem services of societal value, including economic, aesthetic, and ecological consequences. Plant mortality is rising in concert with increasing droughts, warming, and carbon dioxide, but the mechanisms driving the increased mortality are poorly known. This knowledge gap leads to large challenges for predicting the future of terrestrial ecosystems, including their role in water, carbon, and nutrient cycling. This study integrated the literature on plant mortality and subsequently generated a synthetic and testable hypothesis framework describing the mechanisms underlying plant death in a warming and drying world.

This study reviewed the literature to identify key mechanisms underlying warming-induced woody-plant mortality, including trees and shrubs, and presented a testable framework that yields insight into the drivers of plant death as well as how to better model these processes. Ultimately, mortality under drought, rising temperature, and rising carbon dioxide result from depletion of water and carbon stores, leading to irreversible dehydration and the inability to maintain metabolism. Warming exacerbates these storage declines, while elevated carbon dioxide has mixed impacts. The net result of the increasing rate and severity of warming and drought overwhelms the benefits of elevated carbon.

Increasing rates of woody-plant mortality, including trees and shrubs, presents a large scientific challenge due to an insufficient understanding of the cause of rising plant loss. Plant mortality reduces carbon uptake and increases carbon loss, promoting a decline in terrestrial carbon storage. Despite these consequences, predicting plant mortality is limited by a lack of knowledge of the underlying mechanisms, their response to climate, and their integration into models. Here, scientists reviewed the literature to generate a synthetic hypothesis framework that pinpoints key mechanisms driving mortality under a changing environment. The result of this study is a roadmap for future research, including the provision of a set of testable hypotheses that will rapidly increase understanding and identification of key mechanisms that should be included in process models to enable more accurate representation of the impacts of climate change on plant survival. Carbon and water stores are depleted under changing climate, with some amelioration due to rising carbon dioxide. The decline in these stores not only leads to failure to maintain hydration and metabolism but can also promote death outright or through failure to defend against attacking biotic agents. Acclimation can promote survival to an extent. Determining the net impacts of rising carbon dioxide versus drought and warming remain a major science challenge.

Model predictions from Earth science research are valuable for climate, water, land, and energy resource management. This research provides scientists with data publication guidelines to make their research more visible and valuable. In particular, datasets published with these guidelines will be easier to reuse for a variety of purposes. For example, it would be easier to compare observations with model predictions. It would also be easier to compare models to each other, in what are scientifically referred to as “model intercomparison studies.” Finally, publishing model data with these guidelines will increase research transparency and reproducibility. 

U.S. Department of Energy’s (DOE) researchers use a variety of “terrestrial” models (models of the processes that occur on land and their interactions with climate). However, scientists do not have guidelines for making these data public in a manner that enables their reuse. This study researched (1) the aspects of terrestrial model data considered scientifically useful and (2) the purposes served by publishing the data. Based on the results, guidelines for archiving model data are provided, to include inputs and testing data, model code, and workflow scripts. Easier ways to store and reuse model data are also included. 

Earth science models provide valuable information that can be used to guide resource management and policy. Scientists and other stakeholders can more easily reuse model data if it is made public with adequate information on how to interpret and use the data. However, to date, no practical, established guidelines exist for how modelers should publish their data. In particular, terrestrial models (models of processes on land and their interactions with climate) are very diverse, with several types of models being used at different spatial and temporal scales. This study researched how, what, where, when, and why to publish model data and found that archiving model data for scientific purposes requires publishing different data components, including inputs and testing data, model code, and workflow scripts. A set of guidelines was created not only to offer practical suggestions to scientists seeking to publish their data but also to provide greater visibility to their research, making it easier to discover, access, and reuse the data. These guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, new model spin up, and field observation comparisons. 

By enhancing predictions of tree recruitment using environmentally sensitive processes, the TRS is well-positioned to improve predictions of future forest range boundaries, composition, and function. This advancement is important for predicting the role that forests will play in sequestering and storing carbon, promoting biodiversity, and provisioning critical natural resources. By representing the early stages of tree development, the TRS will allow ecosystem modelers to simulate more complicated interactions between vegetation and changing disturbance regimes, such as the effect of more severe fire on vegetation composition.

Forests will only persist where future trees are able to reproduce, disperse, germinate, and grow into mature trees (i.e., “recruit”). These critical regeneration processes are generally not represented in models ecologists use to predict future forests. The recently developed Tree Recruitment Scheme (TRS) was created specifically to capture how changing environmental conditions will affect future trees’ ability to recruit. The TRS was shown to not only improve predictions of tree recruitment rates in a tropical forest in Panama but also capture how reduced soil moisture and light constrain tree recruitment.

The TRS was developed and evaluated at Barro Colorado Island (BCI), Panama, where ecologists have collected a significant amount of forest demography and meteorological data since the early 1980s. These data allowed researchers from the Next-Generation Ecosystem Experiments-Tropics (NGEE-Tropics) to parameterize TRS algorithms that represent how soil moisture and light affect critical regeneration processes, such as seedling emergence, seedling mortality, and seedling to sapling transition rates. By simulating recruitment under observed meteorological conditions, researchers were able to compare TRS predictions of recruitment to census observations at BCI. Compared to prior models, the TRS made significant improvements in predicting which types of trees recruit and at what rate under the current climate. Additionally, by running the TRS under El Niño, wetter-than-observed, and drier-than-observed precipitation scenarios, researchers found that the TRS predicted recruitment responses to varying soil moisture and light levels that were consistent with ecological expectations.

Peatlands cover less than 3% of the world’s land surface but hold at least one third of global soil carbon in deep deposits of peat. Increases in peat nutrient availability in response to warming could impact plant and microbial community growth and decomposition, and therefore affect peatland carbon storage. However, the magnitude and timing of the observed increases in peat nutrient availability with warming in the SPRUCE experimental plots were not captured in the virtual space of ELM-SPRUCE—a special version of the Energy Exascale Earth System Model (E3SM) land model (ELM) developed for simulating the unique vegetation, hydrology, and soil biogeochemistry in peatland ecosystems. This mismatch pinpoints a need for improved model mechanisms controlling nutrient cycling to predict future peatland climate responses. 

The dynamics and availability of soil nutrients that limit plant and microbial growth underpin ecosystem responses to changing environmental conditions. Researchers investigated climate impacts on peat nutrient availability within the framework of the large-scale Spruce and Peatland Responses Under Changing Environments (SPRUCE) warming and CO₂-enrichment experiment in a nutrient-limited bog at the southern end of the boreal peatland range. Above- and below-ground warming exponentially increased nutrient availability throughout the belowground peat profile, especially in recent years, as the carpet of Sphagnum mosses at the peat surface died in the warmest experimental treatments. However, nutrient dynamics were not yet affected by elevated CO₂. 

Warming is expected to increase the net release of carbon from peatland soils, contributing to additional future warming. This positive feedback may be moderated by the response of peatland vegetation to rising atmospheric CO₂ or to increased soil nutrient availability. Researchers asked (1) whether a gradient of whole-ecosystem warming (from +0°C to +9°C) would increase plant-available nitrogen and phosphorus in an ombrotrophic bog in Northern Minnesota and (2) whether elevated CO₂ would modify the nutrient response. They tracked changes in plant-available nutrients across space and time and compared with other nutrient pools. Afterwards, they assessed whether nutrient warming responses were captured by a point version of the land-surface model, ELM-SPRUCE. They found that warming exponentially increased plant-available ammonium and phosphate, but that nutrient dynamics were unaffected by elevated CO₂. The warming response increased by an order of magnitude between the first and fourth year of the experimental manipulation, perhaps because of dramatic mortality of Sphagnum mosses in the surface peat of the warmest treatments. Neither the magnitude nor the temporal dynamics of the responses were captured by ELM-SPRUCE. Relative increases in plant-available ammonium and phosphate with warming were similar, but the response varied across bog microtopography (raised hummocks and depressed hollows) and with peat depth. Plant-available nutrient dynamics were only loosely correlated with inorganic and organic porewater nutrients, likely representing different processes. Future predictions of peatland nutrient availability under climate change scenarios must account for dynamic changes in nutrient acquisition by plants and microbes, as well as microtopography and peat depth.

Protecting and monitoring groundwater is becoming increasingly critical in light of climate change and prolonged droughts. Understanding how the subsurface affects groundwater flow is crucial not only to predict how this resource may change over time but also to develop management approaches. This research shows that critical subsurface properties can be predicted from observations of the Earth’s surface, which are much easier to measure. Knowing the Earth’s properties will eventually lead to better management of groundwater resources and drought resilience.

Mountainous watersheds are often referred to as the world’s “water towers” because they provide more than half of earth’s freshwater. Climate change can influence watershed function and delivery to communities downstream. To predict the impact of this change, scientists must understand how water flows in the ground and how the earth’s properties affect this flow. However, measuring the earth’s properties is difficult—especially over a large area. Researchers have tested how to use observations from space or from the air to estimate the earth’s properties. The team demonstrated this method at a mountainous watershed close to Crested Butte, CO, one of the best characterized watersheds in the world. Results showed that, although the relationships are complex, the earth’s subsurface properties vary with properties on the earth’s surface, such as the angle of hillslopes, their gradient, elevation, and the vegetation that grows on them. Using these relationships, researchers can predict what the subsurface looks like and map features in the subsurface that are controlling groundwater flow.

Bedrock measurements are critical for predicting the hydrological response of watersheds to climate disturbances. However, estimating how water flows in bedrock over watershed scales is difficult, particularly in areas where bedrock may be cracked. By linking data from subsurface and surface measurements, researchers used machine learning to test the co-variability of above and belowground features throughout an entire watershed. The team studied the relationships between bedrock properties, surface formation features, and vegetation to show that relationships derived from machine learning can estimate most of their co-variability. Using these relationships, the team predicted bedrock properties across the watershed and showed that regions of lower variability provide better estimates. The results emphasize that this integrated approach can be used to derive bedrock characteristics on a smaller scale, allowing for a better understanding of subsurface variations across an entire watershed. Knowing how bedrock may vary with surface properties may be critical to assess the impact of disturbances on freshwater function in these ecosystems.

The effect of freeze-thaw cycles on the physical structure of thawing permafrost soils can influence soil moisture and pore connectivity. Researchers observed a decrease in the relative volume of connected water-filled pores following freeze-thaw cycles, as well as an overall decrease in pore connectivity. Specifically, the frequency of pores connected only to one other pore (instead of multiple pores) increased following freeze-thaw. As a result, the researchers inferred that following a thaw, the initial freeze-thaw cycles will decrease the connectivity of permafrost soils. The finding has implications for water movement, gas flow, and microbial access to carbon in soils. It also highlights how permafrost thaw can result in transformations at the micro-scale, as well as larger landscape changes.

Climate change is increasing Arctic air temperatures, causing permafrost soils to thaw and then subjecting them to new and repeating cycles of freeze-thaw. These cycles change the organization of pore spaces within soils, deforming single pores and the connections between pores (pore throats). This has consequences for the movement of water and solutes through the soil pore network. A new experiment examined the impact of freeze-thaw cycles on the pores of permafrost soil aggregates. Pore throat sizes and pore connectivity within the aggregate changed following five simulated freeze-thaw cycles, notably shifts in pore throat sizes under 100 microns and decreases in pore connectivity. The pore response to freeze-thaw varied across aggregates, indicating the importance of initial pore structures prior to freeze-thaw. The subsequent changes to pore size and connectivity have implications for water holding capacity and microbial access to carbon.

Climate change in Arctic landscapes may increase freeze-thaw frequency within the active layer (soil depths above the permafrost table that undergo seasonal thaw) as well as newly thawed permafrost. Freeze-thaw can deform soil pores and alter the architecture of the soil pore network with varied impacts to water transport and retention, redox conditions, and microbial activity. Researchers measured the impact of freeze-thaw cycles on pore morphology, pore throat diameter, and pore connectivity with X-ray computed tomography using six permafrost aggregates with sizes of 2.5 cm³ from a mineral soil horizon (Toolik, Alaska). Freeze-thaw cycles were performed using a laboratory incubation during which five freeze-thaw cycles (− 10 ˚C to 20 ˚C) were conducted. Spatial connectivity of the pore network decreased across all aggregates. Water-filled pores connected to the pore network decreased in volume, while the overall connected pore volumetric fraction was not affected. Shifts in the pore throat diameter distribution were mostly observed in pore throat ranges of 100 µm or less, with no corresponding changes to the pore shape factor of pore throats. Responses of the pore network to freeze-thaw varied by aggregate, suggesting that initial pore morphology may play a role in driving freeze-thaw response. This research suggests that freeze-thaw cycles alter the microenvironment of permafrost aggregates during the beginning stages of deformation following permafrost thaw, impacting soil properties and function in Arctic landscapes undergoing transition. 

Earth system models represent the exchange of CO₂ and water vapor with models of stomatal conductance. The key parameter in stomatal models describes the water use efficiency of vegetation and, in current models, is the slope of an assumed linear relationship between stomatal conductance and photosynthesis for given set of environmental conditions. This research found that this assumption of linearity was false and developed an improved representation of stomatal conductance. The proposed model accounts for the non-linearity and enables robust parameterization of water use efficiency across a range of environmental conditions.

Stomata play a central role in plants by controlling the exchange of water vapor and carbon dioxide (CO₂) with the atmosphere. Researchers with the Next-Generation Ecosystem Experiments-Tropics (NGEE-Tropics) measured the response of stomatal conductance and photosynthesis in six tropical species at different leaf ages. Contrary to current model assumptions, data from this study showed that the response of stomata to photosynthesis was non-linear and accounting for non-linearity resulted in a notable impact on model simulations of CO₂ and water vapor fluxes.

Measurement of the response of stomatal conductance to changes in photosynthesis are rare, particularly in the tropics. Researchers measured the response of stomatal conductance and photosynthesis to irradiance in six tropical species at different leaf ages. Contrary to current stomatal model assumptions, results showed that the relationship between stomatal conductance and photosynthesis was not linear, challenging the key assumption that water use efficiency for a leaf is constant. Study data showed that increasing photosynthesis resulted in a small increase in stomatal conductance at low irradiance, but a much larger increase at high irradiance. As a result, the research team reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of key model parameters. This modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, this modification revealed previously undetectable relationships between the stomatal slope parameter and other leaf traits. Results also revealed nonlinear behavior between stomatal conductance and photosynthesis in independent data sets that included data collected from plants grown at elevated CO₂ concentration. This study proposes that this empirical modification of the USO model can improve the measurement of stomatal conductance parameters and the estimation of plant and ecosystem-scale CO₂ and water vapor fluxes.

An estimated 65 percent of the human population lives in water-stressed regions. Freshwater resources supporting millions of people are becoming increasingly contaminated, posing a serious problem to developing a water-secure future. In this review, researchers summarized approximately 500 DOE-funded articles published from the late 1990s to present day. The team explored implications of findings ranging from microbiology to large-scale ecosystem nutrient and chemical functioning to recommend future research directions. This review article is the first of its kind, referring to information gained across seven DOE research sites –the Savannah River Site in South Carolina, Oak Ridge Reservation in Tennessee, Hanford in Washington, Nevada National Security in Nevada, Riverton in Wyoming, and Rifle and East River in Colorado – to synthesize the DOE Biological and Environmental Research (BER) Program’s leading contributions to ecosystem sciences. This review also demonstrates how improved understanding of ecosystem functioning – from the subsurface to the atmosphere – has advanced knowledge critical to address issues of water contamination.

Accessible and clean freshwater resources, including groundwater and prominent rivers worldwide, are dwindling because of contaminant and nutrient loads. Understanding how various contaminants move through and affect the environment is key to ensuring water security. For decades the Department of Energy (DOE) has significantly contributed to the progress of environmental sciences and has addressed challenges affecting Earth’s subsurface, such as treating radioactive waste and toxic chemicals in the environment. A review of DOE-supported research conducted over the past two decades reveals insights that can be applied worldwide to examine the fate and effect of various contaminants and nutrients in freshwater systems.

Water security is critical for human health, food and energy production, and economic development. As the Earth’s population reaches nine billion, the demand for freshwater resources has intensified. However, climate change may lead to changes in hydrology and disturbances, such as wildfires, droughts, floods, and land-use changes, that can impact water availability and quality. DOE-funded research has significantly contributed to progressing environmental sciences since the late 1980s. Findings from this research have addressed groundwater quality issues, such as treating radioactive waste and toxic chemicals. These efforts have developed an advanced understanding of ecosystem processes, valuable field monitoring strategies, predictive capabilities, and approaches that consider data at different scales to efficiently tackle the complexity of Earth’s ecosystems. Researchers have synthesized and documented these scientific advancements to generalize and apply them to a range of global water security problems.

Northern permafrost stores almost twice as much carbon as the atmosphere. Increased temperatures will make the extensive carbon stock vulnerable to decomposition and loss back to the atmosphere. This study illustrates the potential for increasing amounts of progressively older carbon to be mobilized with increasing permafrost thaw, which is expected with climate change. These findings also suggest a high potential for this carbon to contribute to greenhouse gas emissions as warming increases permafrost thaw.

Planetary warming is increasing the seasonal thaw of permafrost, making this extensive and old carbon stock vulnerable to loss back to the atmosphere. A research team assessed the age and chemistry of dissolved organic carbon in surface and soil pore waters that were collected between July and September 2013 from drainages in the vicinity of Utqiaġvik in northern Alaska. The amount and age of this carbon increased as the thaw layer deepened over the summer. Indicators of carbon source and lability suggested this carbon was derived from soil organic matter throughout the summer in 2013 and that this carbon may fuel microbial respiration that contributes to carbon emissions.

The team sampled surface, shallow, and deep pore waters from 17 drainages in the Barrow Environmental Observatory near Utqiaġvik, Alaska in July and September 2013 to assess changes in age and chemistry of dissolved organic carbon over the summer. They used radiocarbon (¹⁴C) and assessment of organic matter composition with ultraviolet–visible spectroscopy to identify where and under what conditions old permafrost carbon is mobilized. Dissolved organic carbon age was highly variable, ranging from modern to approximately 7000 yBP. Over the summer, dissolved organic carbon age increased with depth as the active layer deepened, and with increasing drainage size. Dissolved organic carbon quality indicators did not differ with carbon age but reflected a carbon source rich in high molecular-weight and aromatic compounds, characteristics consistent with fresh vegetation that had not undergone extensive decomposition. In deep porewaters, dissolved organic carbon age was also correlated with several biogeochemical indicators (including dissolved methane concentration, δ¹³C, and the apparent fractionation factor), suggesting a coupling between carbon and redox biogeochemistry influencing methane production. In the drained, thawed lake basins included in this study, dissolved organic carbon concentrations and contributions of vegetation-derived organic matter declined with increasing basin age. The weak relationship between dissolved organic carbon age and chemistry and the consistency in chemical indicators over the summer in 2013 suggest a high biolability of old carbon released by thawing permafrost.

Field-based assessments of tree damage are increasingly needed to better estimate biomass losses and drivers of tree mortality. This research provides a set of models that can be used to estimate volume losses in living trees when the living length of the trunk and the proportion of newly broken branches are available.

As the climate changes, monitoring tropical forest health is crucial. Tree-level damage (i.e., branch fall, trunk breakage, and decay caused by wood decomposition in standing trees) is one of the most important factors preceding tropical tree deaths. However, field-based damage assessments are very limited, in part due to the lack of whole-tree (trunk + branches) volume equations in tropical trees. Using terrestrial laser scanning, forest ecologists with the Next Generation Ecosystem Experiment–Tropics (NGEE–Tropics) studied the vertical distribution of trunk and crown (i.e., branches) volumes to provide models to estimate the proportion of volume contained up to any height in tropical trees.

Tree volume models are critical for forest management and for obtaining accurate forest carbon estimates. In this paper, researchers present species-composite cumulative volume profile models that describe the volume contained up to a given height in the trunks and crowns of tropical trees. They used terrestrial laser scanning (TLS) and quantitative structure models to estimate the trunk and crown volume of 177 trees (49 species) in a lowland tropical forest in the Barro Colorado Island in Panamá. The researchers found that (1) the rate at which volume accumulated with height was much higher and variable in the whole tree (trunk + branches) than only in the trunk; (2) the variability in the rate of volume accumulation was three times higher in the trunk and nine times higher in the whole tree across individuals within species than between species; and (3) the parameters describing the rate of volume accumulation significantly depended on the height of attachment of the lowest branch, but not on the tree size.

The mechanisms involved in this apparent suppression of respiration are a hot topic of research because the pattern behaves opposite of expectations when considering only temperature. Mechanisms under investigation include: (1) increased CO₂ transport in the transpiration stream, as well as (2) an actual decrease in cellular respiration rates linked to reduced stem water potentials during warmer daytime periods of high transpiration and inhibited growth.

Current models predict that tree respiration increases with growth rates and temperature. Scientists found that when averaged over the annual timescale, a positive relationship existed between tree stem growth and carbon dioxide (CO₂) emitted from the stem into the atmosphere as a part of growth respiration. However, over a single day, growth and respiration were suppressed during the warmer periods associated with high transpiration and water use.

Tropical forests cycle a large amount of CO₂ between the land and atmosphere, with a substantial portion of the return flux due to tree respiratory processes. However, on-site estimates remain scarce of woody tissue respiratory fluxes and carbon use efficiencies (CUEW) and their dependencies on physiological processes, including stem wood production (Pw) and transpiration in tropical forests. This study synthesized monthly Pw and daytime stem CO₂ efflux (ES) measurements over one year from 80 trees with variable biomass accumulation rates in the central Amazon. On average, carbon flux to woody tissues, expressed in the same stem area normalized units as ES, averaged 0.90 ± 1.2 µmol m^-2 s^-1 for Pw, and 0.55 ± 0.33 µmol m^-2 s^-1 for daytime ES. A positive linear correlation was found between stem growth rates and stem CO₂ efflux, with respiratory carbon loss equivalent to 15 ± 3% of stem carbon accrual. CUEW of stems was non-linearly correlated with growth and was as high as 77 to 87% for a fast-growing tree. Diurnal measurements of stem CO₂ efflux for three individuals showed a daytime reduction of ES by 15 to 50% during periods of high sap flow and transpiration. The results demonstrate that high daytime ES fluxes are associated with high CUEW during fast tree growth, reaching higher values than previously observed in the Amazon Basin (e.g., maximum CUEW up to 77 to 87%, versus 30 to 56%). These observations are consistent with the emerging view that diurnal dynamics of stem water status influences growth processes and associated respiratory metabolism.

Forests play a critical role in regulating the world’s climate by cycling large amounts of carbon, water, and energy with the atmosphere. Yet, forests are threatened by changes to climate and an increase in disturbance frequency and intensity, which are both likely to alter the species composition of forests globally. Therefore, scientists must understand how the species composition of forests relate to demographic rates and forest dynamics. This study highlighted the importance of high survival, large statured species for carbon storage.

Plants take up carbon from the atmosphere through photosynthesis and store it in their tissues. Tree growth and survival determine how much, and how long, carbon is stored by forests. Recent growth and survival rate analysis of thousands of tree species explored (1) how the number of species in a forest plot is related to the range of tree growth and survival rates (demographic diversity) and (2) how that influences carbon cycling dynamics. The study revealed that demographic diversity plateaus as numbers of species increases. Further, presence of species with particular demographic rates, rather than demographic diversity, govern carbon dynamics.

Individual tree growth and survival determine a forest’s physical structure, with important consequences for forest function. This study calculated growth and survival rates of 1,961 tree species from temperate and tropical forests and explored (1) how the range of demographic rates and the presence or absence of distinct demographic strategies differ across forests and (2) how these differences in demography relate to the number of species in the forest and carbon storage. Results showed wide variation in demographic rates across forest plots, which could not be explained by the number of species or climate variables alone. Results showed no evidence that a large range of demographic rates lead to higher carbon storage. Rather, the relative abundance of high-survival, large-statured species predicts both biomass and carbon residence time. Linking the demographic composition of forests to resilience or vulnerability to climate change will improve precision and accuracy of predictions of future forest dynamics.

Most recent tree die-offs (regional-scale mortality events) greatly exceeded expectations regarding their occurrence, speed of onset, and magnitude, indicating a need for improved detection and prediction capabilities. This study provides methods for improved die-off monitoring and model simulations, as well as a road map for future research on the patterns, causes, and future trends in tree mortality.

Researchers summarized the known die-off events through literature analyses as well as personal anecdotes to identify the level of expectedness of regional die-off events. They subsequently synthesized the literature not only on remote sensing of die-offs at the global scale but also on the path forward for prediction of die-offs.

Tree mortality in global forests, particularly in tropical forests, reduces the carbon storage potential of terrestrial ecosystems. Tropical forests are an important terrestrial carbon sink globally but are experiencing increasing rates of tree die-off at regional scales. This study’s discovery of the unexpected nature of mortality events was particularly alarming in the tropics, which were long assumed to be resilient to drought and a changing climate, highlighting the importance of better understanding these events. The study’s identification of paths forward for improved monitoring and prediction of die-offs provides a road map for future research.

Sustainable management of groundwater is becoming urgent as groundwater resources are increasingly withdrawn in response to population increase and climate change. Mapping groundwater flow pathways is crucial for understanding freshwater behavior and movement. This research shows that machine learning can not only help scientists understand how the geology of an area forms groundwater flow pathways, but can also be applied to enhance freshwater resource management. In places affected by drought or contamination, knowing the path of groundwater flow can help conserve water or stop the spread of contaminants.

Groundwater provides about a third of Earth’s freshwater, yet much is still unknown about where and how water moves underground. Geological features affect groundwater movement, but these structures often can’t be seen from Earth’s surface. Understanding how these features may have formed can help enhance knowledge about the broader behavior and structure of watersheds, allowing for better predictions of freshwater movement. A team of scientists developed a method to map underground flow pathways and understand how they formed. The researchers used Bayesian hypothesis testing to compare multiple interpretations, or scenarios, for what created the flow pathways, such as from a crack in earth’s surface or rock-mass movements. These interpretations were ranked by how consistent they are with measured data using machine learning. This method was applied at a fractured bedrock zone—an area of cracked and crushed subsurface rock—in the Elk Mountains of Central Colorado, where water flows much faster through these fractures than in surrounding rock. The method demonstrated that the fractured bedrock was most likely created by a fault or sedimentary layer.

Certain structures in the earth form groundwater “highways,” where water moves faster than normal. Finding these structures is crucial for understanding when and where groundwater moves. When flow pathways are hidden below the surface, they are found by sending electrical, magnetic, and other signals into the ground and measuring how the ground responds. Since different geological formations respond differently to the signals, scientists can use the signals to find places underground that are likely to contain groundwater flow pathways. However, multiple geological structures can have similar responses, which makes it hard to choose the best interpretation of how these structures could have formed. A team of scientists developed a method to test multiple interpretations of these types of signals.

The proposed method has three parts. First, for each proposed interpretation, the signals and measurements are simulated on a computer. Second, the researchers compare the simulated data to the field data for each interpretation. Finally, using machine learning the team ranks each interpretation according to how closely it matches data gathered in the field. The research team applied this method to a zone of fractured rock in the Elk Mountains of Central Colorado. Six interpretations were proposed and ranked according to how closely they match the measurements. The team concluded that the fractured rock was from either a fault or a sedimentary layer.

This study illustrates the importance of deep soil organic carbon to the global carbon cycle. These findings indicate that a fundamental understanding of organic carbon storage and dynamics, including the information needed to anticipate and project responses and feedbacks to climate change, requires the inclusion of deep soil organic matter in experiments. Further quantification of the vulnerability and resilience of deep soil organic carbon to shifts in environmental drivers (such as planetary warming) is needed to appropriately represent this large and important carbon reservoir in Earth System Models.

The spatial distribution of deep soil organic carbon and its vulnerability to climate change is uncertain. Researchers measured the distribution, stability, and chemical composition of soil organic carbon to 10 m depth across a bioclimate gradient in California’s southern Sierra Nevada. They found that deep soils and weathered bedrock can store over 75% of total soil organic carbon. Climate controls soil carbon storage by influencing vegetation and the thickness of soil and weathered bedrock. Deep soil carbon was a mixture of very old and actively cycling carbon, suggesting a portion of this pool may respond to climate change.

Soil organic carbon is the largest terrestrial reservoir that actively exchanges carbon with the atmosphere. Soils can be tens of meters deep, but few studies on soil organic carbon have included soils below 30 cm. Researchers investigated the distribution and chemical composition of soil organic carbon to the depth of hard bedrock (down to 10 m) along a bioclimate gradient in the southern Sierra Nevada in California. These sites are part of the AmeriFlux and Critical Zone Observatory networks, allowing the team to evaluate the relationships between ecosystem-level fluxes of carbon and water to their investigations on soil carbon storage, characterization, and age. They found that deep soil and weathered bedrock play a significant role in carbon budgets across a range of environmental conditions.  

Researchers found that at their study sites, up to 80% of soil organic carbon is stored below 30 cm depth and up to 30% of total soil organic carbon is stored in deep weathered bedrock (between 1.5 and 10 m depth). Carbon storage in deep soils and weathered bedrock were largest at mid-elevations where soil thickness and ecosystem gross primary productivity were greatest. They also found that mean annual air temperature explained more variability in soil carbon stock than other climatic variables (mean annual precipitation and deep-water percolation), indicating that topsoil and subsoil carbon may be vulnerable to planetary warming.  

Using radiocarbon measured at the Center for Accelerator Mass Spectrometry (CAMS) at Lawrence Livermore National Laboratory (LLNL), researchers discovered that organic carbon in deep soil and weathered bedrock ranged in age from 5,000 to 20,000 years old, not only showing that deep soils store carbon for long periods of time but also indicating that relatively young carbon is actively incorporated into some deep layers. In addition, infrared spectroscopy suggested that this deep soil organic carbon is a mixture of organic matter in various stages of decay and transformation by soil biota. These results challenge a long-standing assumption that deep soil carbon pools play a minor role in global carbon cycles and climate by illustrating that carbon in deep soil and weathered bedrock is a larger carbon pool that is potentially more responsive to changes in climate than previously realized. 

Soil contains twice as much carbon as all vegetation on Earth and far more than is currently in the atmosphere as CO₂. Predicting how carbon is stored in soil and released as CO₂ is a critical calculation in understanding future climate dynamics. This study used novel numerical experiments to examine how microbial respiration in soil should be modeled. Results show that simulations must acknowledge the proximity of microbes and substrates within the soil to accurately predict carbon emissions. 

Soils act as a vast carbon storehouse that could also be a huge source of greenhouse gas emissions. Microbes within the soil control carbon emissions through cellular respiration, which feeds on surrounding carbon. Oddly, microbes’ metabolic activities are generally substrate (carbon) limited. This contradiction creates significant challenges in the development of models that predict carbon dioxide (CO₂) emissions from soil. This project used a spatial modeling analysis to demonstrate how distance among diverse soil components impacts microbial access to substrate—its nourishment—and thus respiration rates at micrometer scales. Findings indicate that contrary to previous predictions, less CO₂ emissions are present when models account for substrate distribution. 

The distribution of carbon in soil is highly localized due to the arrangement of soil particles, organic carbon, water, and gas. This diverse makeup influences how microbes access substrates for nourishment, which fuels their respiration and how that respiration also depends on soil moisture. Using a simple diffusion-reaction model and numerical experiments, this study demonstrates that moisture interacts with varying substrate distribution at the micrometer scale to control the dynamic transitions between regimes in which either substrate diffusion rate or microbial metabolic activity limits respiration. Such regime shifts are driven by the nonlinearity that emerges from varying distances between microbes and substrates and the varying saturation behaviors of microbial utilization of substrates. As a result, the “real” spatially resolved rates of microbial respiration are always lower than rates calculated based on homogeneous substrate distribution. The novel formulation of diffusion-limited microbial respiration proposed in this study provides biophysical insights about how microscale nonlinearity between substrate distribution and microbial respiration drives prediction biases at a macroscopic level. 

Arctic soils contain enormous amounts of carbon that is vulnerable to climate change impacts. Predicting the fate of these soil carbon stocks under long-term warming also requires accounting for short-term disturbances, including more frequent wildfires. An urgent need exists for developing models that accurately represent wildfire impacts on tundra ecosystems against a backdrop of climate change. This study shows how increased soil nutrient availability enables quicker recovery of plant communities and soil carbon.

As the Arctic continues to warm and become increasingly dry, severe wildfires outbreaks are becoming more frequent. Wildfire onset leads to the combustion and loss of carbon from soil and vegetation, and the continual export of soil nutrients to waterways. This research used a mathematical ecosystem model to better understand how quickly these ecosystems recover from wildfire and how soil nutrient availability underpins that recovery.

Researchers used a well-tested, process-rich model, ecosys, to simulate the response of the soil carbon and nutrient cycles to acute wildfire onset and chronic changes in climate. The foundation for the model spin-up was the 2007 Anaktuvuk river fire, one of the largest (and most comprehensively sampled) wildfires in high-latitude systems. Model performance was evaluated by comparison to site data and included pre-and post-fire net primary productivity, soil carbon stocks, and physicochemical variables. Once benchmarked, several questions were addressed, including: (1) What are the long-term ramifications of fire disturbance against the backdrop of ongoing climate change across the 21^st century? (2) What role does the belowground microbial community play in enabling the recovery of the aboveground plant community? 

This study shows that over the first 5 years post-fire, fast-growing bacterial heterotrophs colonized regions of the soil previously occupied by slower-growing saprotrophic fungi. The bacterial heterotrophs mineralized organic matter, releasing nutrients into the soil. This pathway outweighed new sources of nitrogen (e.g., nitrogen fixation), reestablished biogeochemical equilibrium, and facilitated the recovery of plant productivity. 

This study reviews current understanding and model representation of GPP in northern latitudes, focusing on three components—vegetation composition, phenology, and physiology—and how they are altered by climate change. This review highlights GPP prediction challenges in the region, but also focuses on unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

The Arctic-Boreal region (ABR) is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models, and reducing this uncertainty is critical for more accurate global carbon cycling estimates and understanding the region’s response to global change. Process representation and parameterization associated with gross primary productivity (GPP) drive a large amount of this model’s uncertainty, particularly within the next 50 years when the existing vegetation’s response to climate change will dominate regional GPP estimates.

The ABR has a large impact on global vegetation–atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of carbon dioxide. The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with GPP drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region.

This paper reviews current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and considers how climate change alters these three components. The paper highlights challenges in the ABR for predicting GPP and focuses on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

This project outlines a comprehensive data analytics workflow to mine community-wide sorption data from the literature. Through the development of a consistently formatted data compilation approach, this work enables traditional surface complexation model development and sets the stage for novel artificial intelligence and machine learning algorithms to process large, community-based sorption data for more flexible and accurate modeling predictions.

Reactions at the solid-water interface, known broadly as sorption, play an important role in nutrient availability and contaminant transport in the environment. While the field of sorption is rapidly growing, few efforts have been made to capitalize on this rich data source. Because various surface complexation models that describe sorption processes carry differing fundamental assumptions, a present-day challenge exists in unifying the plethora of experimental results published over past decades. The Lawrence Livermore National Laboratory Surface Complexation-Ion Exchange (L-SCIE) database presented in this study demonstrates a path forward by compiling community-based experimental sorption data and conducting a series of transformations to unify, filter, and format the data. The outputted .csv dataset of commonly formatted experimental sorption data enables the application of powerful new modeling techniques, such as artificial intelligence and machine learning methods.

This study presents a data-to-model workflow that unifies individual sorption datasets across the research community into a consistently formatted database. Additionally, this project demonstrates the use of a data fitting workflow to efficiently optimize the newly formatted dataset of adsorption reactions. The discussed modeling framework, which performs data digitization and unification, is shown to effectively characterize uranium sorption onto the mineral quartz. The uranium-quartz reaction constants determined from this project captured all sorption data available from the literature. Ultimately, the L-SCIE sorption database presented in this study allows for data pre-processing automation across a wide range of metals and minerals, unlocking an important step towards the implementation of novel machine learning methods in sorption research.

Watershed function can significantly impact energy production, agriculture, and water quality and availability. Now that the frequency and intensity of environmental disturbances, such as drought, wildfires, and floods are in what many have called a “new normal” state, scientists can no longer depend on historical trends to project future watershed behavior, but instead need to develop new approaches for studying watershed response to environmental changes. However, predicting watershed behavior is challenging because watersheds are extremely heterogeneous, including the complex interactions taking place across different Earth compartments from tree canopy to the deep subsurface as well as from one hillslope in a watershed to another. Using machine learning, researchers organized the watershed research site into zones based on similar environmental features and were able to show how different zones process/export nutrients and respond to droughts. By using multiscale spatial data layers to capture different characteristics throughout a watershed, this approach allows for more accurate large-scale predictions of watershed responses to climate change. Understanding these responses is critical for managing and protecting critical freshwater resources as water demand continues to increase.

More than half of Earth’s freshwater comes from mountainous watersheds. Watersheds are “systems of systems,” meaning there are many interacting compartments—such as bedrock, soil, and snow plants—that affect their functioning. Predicting watershed behavior is challenging because there are different environmental processes and characteristics—both at different scales and levels, from bedrock to the atmosphere—that affect watershed function and water quality. To understand how watersheds may respond to droughts as climate changes, researchers used data from the Colorado East River Watershed to develop a watershed zonation approach—a method that uses machine learning to characterize entire watersheds by grouping zones of similar functioning and characteristics, like watershed “zip codes.” The team grouped hillslopes since these features are a functional unit in hydrology. Hillslopes capture waterflow and a range of environmental characteristics like elevation, topography, and vegetation. This method combines data of multiple types and scales of state-of-the-art airborne remote sensing data layers to identify zones with similar bedrock-to-canopy features. The method also shows how these areas respond to disturbances in different ways to advance holistic and large-scale predictions of watershed response to change.

This paper developed a watershed zonation approach—a method that uses unsupervised machine learning—to characterize the heterogeneous bedrock-to-canopy compartments and their impacts of watershed function by identifying the zones of similar functioning and characteristics. The method was demonstrated using the multiple remote sensing and existing spatial data layers collected at the East River Watershed (Crested Butte, Colo.), including snow-on/off LiDAR, airborne electromagnetic surveys, landcover classes, and geology maps. A team of researchers considered a hillslope to be a fundamental unit for watershed hydrology and element cycling, funneling water and elements from the ridge to the river, as well as representing aspect controls on critical zones. For clustering, researchers compared k-means, hierarchical tree, and Gaussian mixture methods, and confirmed that the zones are consistent across different methods. In addition, this study provided a significant understanding of the multi-compartment watershed heterogeneity: (1) it is possible to define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties [such as elevation, net potential annual radiation, and peak snow-water equivalent (SWE)], (2) elevation and aspect are independent controls on plant and snow signatures, and (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution.

Tree mortality is a major control over tropical forest carbon stocks globally, but the strength of associations between abiotic drivers and tree mortality within forested landscapes is poorly understood. Previous studies have shown that mortality rates are important for variation in standing biomass regionally and globally; this study shows that the same is true on a landscape scale for mature tropical forest and identify abiotic variables that control this variation.

Using five years of drone images over Barro Colorado Island, Panama, scientists identified new canopy disturbances resulting from tree mortality and damage. The resulting dataset shows that disturbance rates vary locally depending on soils, topography, and forest age. Disturbances were most strongly associated with certain soil types, and were also higher in older forests, steeper slopes, and local depressions. Additionally, disturbance rates were important for variation in forest height across the landscape.

Repeat drone photogrammetry across 1500 ha of forest in Central Panama during 2015-2020 was used to quantify spatial variation in canopy disturbance rates and its predictors. Researchers identified 11,153 canopy disturbances greater than 25 m² in area, including treefalls, large branchfalls, and standing dead trees, affecting 1.9% of the studied area per year. Soil type, forest age, and topography explained up to 46-67% of disturbance rate variation at spatial grains of 58-64 ha. Further, disturbance rates predicted the proportion of low canopy area across the landscape, and mean canopy height in old growth forests. Thus, abiotic factors drive variation in disturbance rates and thereby forest structure at landscape scales.

SAPFLUXNET will enable scientific understanding of the climatic, ecological, and biological factors driving plant water use across the globe, and open up new frontiers of research into the water cycle. SAPFLUXNET contains 202 globally distributed datasets with sap flux time series for 2,714 plants across 174 species. This dataset provides the first global benchmark of plant water use for model testing, and adds to advance understanding of water resource use and conservation globally.

Despite how critical plant water use is to biology, ecology, and biogeochemistry, its response to global change is currently not well understood. Plant sap flux, a measure of water use, links vegetation with the water, energy, and carbon budgets of terrestrial ecosystems.  Here, scientists introduced the first global compilation of whole-plant water use data from sap flux measurements, combining efforts of 164 scientists to generate a global database of sap flux measurements (SAPFLUXNET). The dataset and associated open-source code are publicly available.

Tree water use is the dominant movement of water in the water cycle and is critical for accurate predictive models of water, carbon, and energy budgets. A large collaboration of scientists brought together a globally distributed set of existing tree water use datasets and assembled 202 sites with sap flux data. All datasets were quality controlled and integrated with additional site-specific datasets of local meteorology and tree physiology. Open-source R-code was generated to enable independent scientists to extract and process data, and all aspects of the dataset were made publicly available. Through provision of this global database, discovery of new controls over vegetation water use can be achieved. Ultimately, this database will rapidly advance scientists’ ability to understand and predict not only the role of vegetation in the water cycle but also the role of water use in plant growth and survival.

This work is important for improving the ability to understand, observe, and predict dry-tropical forest vegetation dynamics and their response to drought. This approach greatly improves the utility of satellite measurements for this purpose from local to regional scales, and these datasets can be used to benchmark predictive model performance.

Scientists investigated the controls over forest mortality in Costa Rica’s dry-tropical region using satellite remote sensing. Estimates of tree mortality using the Enhanced Vegetation Index (EVI). matched well with field measurements. Substantial fine-scale variability in forest mortality was related primarily to the cumulative water deficit during the drought, leaf traits, and topography.

Remote sensing provides a powerful approach to quantify changes in vegetation on the Earth’s surface. The Enhanced Vegetation Index (EVI), an indicator of vegetation function and resilience, was derived from Landsat 30x30m resolution imagery and used to quantify changes in vegetation biomass due to mortality during a severe drought in 2015 in Costa Rica’s dry-tropical forests. After strong validation with in-situ ground inventories of tree mortality, the approach was applied to examine local drivers of mortality. The degree of drought, represented by the cumulative water deficit, played a strong role in localized mortality. Ecosystems with a greater fraction of evergreen tree species experienced greater mortality than those with a greater abundance of deciduous species, demonstrating the influence of plant trait strategies on drought vulnerability. Topographic position also played a significant role, with sun-exposed and steep slopes having the greatest mortality. These findings highlight the potential of high-resolution remote sensing to “fingerprint” forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.

Vegetation water content is a useful measure of forest health and function because plants lose internal water when they are stressed or when they die. The research team identified the need for a satellite observational system to measure water content, which would provide data on day-to-day water fluxes and help identify the earliest signs of water stress in forests.

Hot droughts are becoming more common because of climate change, but scientists still do not know how forests respond to water stress conditions. Field measurements are too sparse, and most satellite measurements cannot detect early signs of water stress in forests. A team of researchers with expertise from field measurements, remote sensing, and numerical models identified opportunities and challenges for using water content from remote sensing to detect water stress. Microwave measurements from space can be used to estimate vegetation water content and detect water stress across all forests on Earth.

This research review describes how extensive and frequent estimates of vegetation water content from microwave remote sensing could improve scientists’ ability to detect signs of water stress and anticipate critical conditions for fire and mortality in forests across the world. Vegetation water content estimates could also allow for inference of belowground soil moisture and root water uptake conditions across large scales, which is challenging otherwise. Additionally, this review identified the need to establish relationships between vegetation water content and ecosystem-scale water potential to be able to detect signs of stress across different forest systems, and to be able to effectively link remote sensing measurements with terrestrial biosphere models. Improving methods will also be critical for distinguishing variations in water content due to changes in surface water (dew and rainfall interception) and changes in water stored inside plants. Moreover, this review points to the need for field campaigns that will help establish the volume-potential relationships at ecosystem scale, which are critical to define thresholds for wilting, mortality, and fire risks in different forests. Finally, the monitoring of forest water stress could greatly benefit from geostationary measurements of vegetation water content, as it would provide information at a sub-daily scale, which could be more directly related to field measurements and improve the quantification of water stress.

How plants adjust their root traits to better obtain nutrients is relevant for understanding their distribution and can help predict their response to future climate scenarios. Most root trait adjustments are either overly generalized or unrepresented in predictive models, and tropical plants are less studied than temperate plants. This study highlights negative relationships between root architectural and physiological/symbiotic traits, and differences between pioneer and non-pioneer tree species in relationship to their root strategies to acquire phosphorus. No change in most root traits after hurricanes shows the stability of nutrient acquisition strategies. These results can help better understand root adjustments of some tropical trees under soils with low phosphorus availability.

This study measured a combination of root traits for acquiring soil phosphorus from five tropical tree species before and after two hurricanes in Puerto Rico. Pioneer tree species had a strategy of high phosphatase activity and fungal colonization, whereas species with a non-pioneer life history strategy relied on high root branching to explore the soil. There was no change in root trait strategies after the hurricanes, but root phosphatase activity decreased.

Trees have the ability to adjust their root traits to better obtain soil phosphorus. For example, they can adjust structural traits like root length or root branching, or physiological and symbiotic traits like root phosphatase activity and mycorrhizal colonization. It is still not clear which combination of adjustments tropical trees might use to better obtain soil phosphorus. This study measured seven root traits of five common tropical trees in Puerto Rico to describe their trait adjustments, as well as their changes after the forest was impacted by two hurricanes. Roots with high colonization of fungi and high phosphatase activity were found to present less branching. This strategy was mostly shown in pioneer trees, while the opposite occurred in non-pioneers. Furthermore, root traits adjustments showed no change before and after the hurricanes, except for root phosphatase activity, which strongly decreased following the hurricanes. These results showed a combination of root trait adjustments for better obtaining soil phosphorus in tropical trees and stability of most root traits adjustments after hurricane disturbances.

This study provides a better understanding of how leaf functional traits and their connections with the carbon cycle and energy metabolism vary in different environmental conditions. These findings highlight the importance of representing light suppression of leaf respiration in dynamic vegetation models aimed at predicting the future of tropical forests under climate change.

Leaf respiration contributes an estimated 50% of total plant respiration. But with few observations in the tropics, there is high uncertainty in the amount of leaf respiration, how it varies across common tree species as a function of height, and how light influences the respiratory process. This study shows that canopy position has an important influence on leaf respiratory rates and the degree of light suppression in tropical forests of the Amazon.

Leaf respiration is a major contributor to plant respiration but is poorly characterized in diverse tropical ecosystems. Light can inhibit this process, but little information is available about the degree to which light suppression impacts leaf respiration or how that impact varies within a tropical forest canopy. Due to the Amazon rainforest’s importance in the global climate context, this study quantified rates of daytime and dark leaf respiration and investigated potential influences of canopy position on variation in leaf respiration rates and light suppression. Measurements were collected from 26 tree individuals of different species distributed in three different canopy positions: canopy, lower canopy, and understory. While rates of leaf respiration increased from the understory upward into the canopy, the influence of light suppression followed an opposite pattern. Canopy trees had significantly higher rates of R_dark and R_day than trees in the understory. However, the difference between R_dark and R_day (the light suppression of respiration) was greatest in the understory (68 ± 9%, 95% CI) decreasing in the lower canopy (49 ± 9%, 95% CI), and reaching the lowest values in the canopy (37 ± 10%, 95% CI). These findings highlight the importance of including representation of the light suppression of leaf respiration in terrestrial biosphere models, as well as accounting for vertical gradients within forest canopies and connections with functional traits for predicting tropical forest function.

High-latitude soils store large amounts of carbon that could be released to the atmosphere, thus making it a region of interest to climate scientists and policymakers. This study predicts that high-latitude ecosystems are carbon sinks that will continue to accumulate carbon throughout the century. Analysis of seasonal dynamics provides support for these predictions. Some of the projected changes to carbon cycle seasonality are unexpected. In particular, the finding that spring uptake will outpace summer uptake by year 2100 merits further investigation. The results of this study are also used to address mismatches between recent model and observation-based studies of high-latitude carbon balance.

At high-latitudes, sunlight and air temperature change dramatically with the seasons. Summer days are warm and very long. Winter days are freezing and very short. As a result, plants and microbes are most active in summer. However, climate change will cause air temperatures to rise higher and faster at high-latitudes than anywhere else. Scientists use mathematical models to simulate how ecosystems will respond to climate change. In this study, the ecosys model was used to predict changes to the seasonality of plant and microbial activity throughout the coming century in Alaska.

ecosys, a well-tested and process-rich mechanistic ecosystem model, was used to explore how climate warming will shift carbon cycle seasonality in Alaska. Model performance was evaluated using site and regional data products, and recently reported large carbon losses during the fall and winter were successfully reproduced. Nevertheless, the model predicted that the system is a carbon sink. This result helps resolve current conflicts between modeled and observation-based estimates of high-latitude carbon balance. 

The results of this study suggest that climate change will result in surprisingly large changes in carbon cycle seasonality at high-latitudes. In particular, spring net carbon uptake is projected to overtake summer net carbon uptake in the coming century. This shift is driven by a large relaxation of spring temperature limitation to plant productivity. Additionally, warmer soil temperatures and increased carbon inputs lead to combined fall and winter carbon losses that are larger than summer net uptake by year 2100. However, this increase in microbial activity leads to more rapid nitrogen cycling and increased plant nitrogen uptake during the fall and winter that supports large increases in spring plant productivity. Taken together, these results suggest that high-latitudes will continue to accumulate carbon throughout this century.

Variability in precipitation recycling ratios has important implications for water availability of plants as well as tracking water movement through the water cycle. Thus, changes in this quantity are important to understand, as they may provide direct indications of health and sustainability of forest ecosystems. The new approach presented in this study can be used to check and improve model performance. Such efforts will be critical to understand and predict how plants and the water cycle will be impacted by climate change at local to regional scales.

Precipitation recycling represents the amount of rainfall whose water originated from local plant transpiration or evaporation to the atmosphere. Heavily forested areas like the Amazon rainforest are known to get one-third to one-half of its water from precipitation recycling. Because field sampling can be challenging over large scales, modeled precipitation recycling estimates are often used, whereas precipitation isotopes are primarily used for local measurements. By accumulating isotopic observations through space and time, this study provided the first global-scale assessment of modeled precipitation recycling estimates over the humid tropics.

The amount of precipitable water derived locally through evaporation from the land surface or transpiration from plants is known as precipitation recycling. By transpiring water that recently fell as rain, plants are effectively recycling water back to the atmosphere, so it can fall as rain again. A multi-international team of scientists comprised of both modelers and experimentalists developed a new approach that uses observed isotopes in the precipitation record (a known proxy of precipitation recycling) to constrain estimates from models, which had largely been used to evaluate these quantities over larger scales. Two types of models were assessed in this study – the mass balance and particle tracking models – the latter of which were only made possible very recently through advancements in computational power required to perform such simulations. This research highlights which of these models tend to perform better over different times of year based on comparisons to the isotopic observations. In addition, the models were assessed over different climate zones of the tropics to see how these might be playing a role in model performance. This new approach can be used to improve future modeled estimates of precipitation recycling so that scientists may better understand its variability and potential impact on plants in response to climate change.

Drought is the major culprit of  tropical tree mortality, which has major implications for the carbon cycle. As droughts are anticipated to become more frequent and severe globally, being able to predict the risk of tree death and associated potential impact to forest carbon storage is critical. These results point to tree rooting depth as a key trait for understanding and predicting future tree responses to drought, and further suggest that tropical forests may be more resilient to drought than previously anticipated.

This study examined how tropical rainforest trees respond to artificially induced drought. A canopy crane located in Queensland, Australia, was used to measure a variety of aboveground plant traits, such as leaf photosynthesis and transpiration. These measurements were used in an optimization model to calculate shifts in tree rooting depth, revealing that trees maintained consistency in aboveground carbon and water cycling by increasing the soil depth in which they foraged for water.

Drought increases tropical tree mortality, with large implications for the global water and carbon cycles. Yet how tropical trees respond to drought, specifically how they can mitigate drought impacts, is not fully understood. Through an experimentally-imposed, multi-year drought, this study discovered that wet-tropical trees can maintain function of aboveground traits, such as photosynthesis and transpiration. The trees achieve this apparent resilience to drought through increasing the soil depth at which they obtain water for transpiration. Drought caused declines in surface soil moisture content, but deeper soils maintained sufficient water to provide the tree’s transpirational requirements, leading to maintenance of canopy photosynthesis and transpiration. These results suggest that tropical trees can withstand a certain degree of drought through shifting their roots to deeper soil depths where water is more plentiful.

Tree mortality in tropical forests has been increasing in some regions, with the primary culprit thought to be drought. Increasing tree mortality results in a decrease in the potential carbon sink of tropical forests, which has major implications for the global carbon budget. This paper provided a novel test of the relationship between mortality, species distributions, and tree hydraulic architecture. The results of this study provide new information on the regulation of plant mortality and distribution in tropical forests, and guide future modeling efforts intended to predict the future tropical carbon budget.

We compiled literature values for hydraulic traits that regulate water stress, species distributions, and species mortality rates for 27 species that live across the moisture gradient formed by the Isthmus of Panama. The hydraulic traits investigated included parameters such as the safety a plant maintains from hydraulic failure during drought, and associated traits that regulate these safety margins. Correlation and cluster analyses were conducted to investigate if any traits were correlated with species distributions or mortality rates.

We discovered that hydraulic safety margins, that is, the risk of exceeding stress thresholds that lead to fatal dehydration, were not correlated with tree mortality rates measured during an El-Nino drought. However, these traits were correlated with species distributions across the moisture gradient, suggesting that long-term acclimation to drought does manifest through avoidance of hydraulic failure.

Over the next 20 years, tropical forests are expected to be greatly affected by global warming, but how these forests and specifically their roots will respond remains unknown. This experiment has provided an unprecedented look at the complex interactive effects of disturbance and climate change on the root component of a tropical forested ecosystem. These findings suggest a decrease in root production in a warmer world and slower root recovery after a hurricane disturbance might have longer term consequences on these forests.

This study measured root responses to experimental soil warming and two hurricane disturbances in a tropical forest in Puerto Rico. Root images were used to measure root production, mortality, and biomass. Root production and biomass decreased with warming. Further, root recovery after the hurricanes was slower in warmed plots compared to controls.

In hurricane-adapted forests of Puerto Rico, recovery from disturbance is critical to ecosystem function. However, human-caused temperature increases could alter recovery processes. The Tropical Responses to Altered Climate Experiment (TRACE) evaluated the response of forest root dynamics to experimental warming before and after being impacted by two consecutive hurricanes. Although warming was halted due to the hurricanes, root measurements continued, creating a unique opportunity to evaluate legacy effects of prior warming on forest recovery following hurricanes. Warming prior to the hurricane disturbance suppressed root production. After the hurricanes, root standing stocks increased overall due to a change in plant composition. This increase was less in previously warmed plots, suggesting that antecedent warming conditions suppressed roots’ capacity to recover following hurricane disturbance. These findings suggest tropical forest responses to disturbance may be dramatically changed as Earth warms.

Identification of priority risk factors for tree mortality can help focus and improve dynamic vegetation model predictability for tropical forests and their ecosystem processes. Future research should focus on the links between damage-related risks, their climatic drivers, and the physiological processes to enable mechanistic predictions of tree mortality.

The rate at which trees are dying is increasing worldwide. Yet, little is known about what kills trees in natural forests. Tree mortality is particularly difficult to study in diverse tropical forests, where species vary widely in their responses to different conditions. Forest ecologists assessed trees of 1,900 species in six tropical forests to provide the first ranking of importance of mortality risk factors. Among 19 mortality risk factors evaluated, researchers found that those related to tree-level damage were the dominant risks associated with tree mortality.

Carbon losses due to tree mortality in tropical forests constitute a significant source of uncertainty in vegetation models. Yet, the relative importance of mortality risk factors remains poorly understood. In this study, researchers recorded data on a broad suite of observations of living trees and monitored their subsequent survival to provide a ranking of importance of tree mortality risk factors in tropical forests. The researchers presented a new framework for quantifying the importance of mortality risk factors and applied it to compare 19 risks on 31,203 trees (1,977 species) in 14 one-year periods in six tropical forests. Factors related to light-limitation and tree-level damage, such as crown or trunk loss, were the most impactful in terms of their contribution to total mortality. Leaning, defoliation, and lower elevation ranked next in importance, whereas other risks expected to be important, such as those associated with lianas, stranglers, trunk deformities, and trunk rot, showed lesser impact in this study. This ranking should inform future investigations to improve predictions of the fate of forests in global dynamic vegetation models.

Moist tropical forests account for 40% of global biomass carbon stocks, and uncertainty regarding the future of these stocks is a major contributor to uncertainty in the future global carbon cycle. A mechanistic understanding of how tropical tree mortality responds to climate variation is needed to predict current and future carbon cycling in tropical forests under climate change. These findings demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers.

Researchers used five years of approximately monthly drone-acquired imagery for 50 ha of tropical forest on Barro Colorado Island, Panama, to quantify temporal variation and climate correlates of treefalls, branchfalls, or collapse of standing dead trees. They found canopy disturbance rates are highly temporally variable and related to extreme rainfall events.

A mechanistic understanding of the controls on woody residence time in tropical forests is urgently needed to predict the future of tropical-forest carbon stocks and biodiversity under global change. Researchers used drone-based imaging of 50 ha of old-growth tropical forest for 5 years to quantify major drops in canopy height such as those created by branchfalls and treefalls, and thus determine the temporal variation of canopy disturbances and climate correlates. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though windspeeds were lower in the wet season. The strongest correlate of monthly variation in canopy disturbance rates was the frequency of extreme rainfall events. Treefalls accounted for 74% of the total area and 52% of the total number of canopy disturbances. These findings suggest  that extremely high rainfall is a good predictor of canopy disturbance because it is an indicator of high wind speeds as well as saturated soils that increase uprooting risk.

These results provide a conceptual model to understand how historical drought impacts how microbes and their environment influence an ecosystem’s response to rewetting. This model is transferable across all environmental systems, providing a new opportunity to link divergent systems together under a common theory. This unification is key to incorporating additional mechanistic detail into ecosystem models.

Seeking to better understand how microbes influence ecosystem function, scientists have proposed conceptual frameworks linking environmental microbiomes with their environment and emergent function. However, these proposed frameworks largely remain untested. Recently, a multi-institutional team modified a current conceptual framework for hyporheic zones that exist within riverbed sediments. They tested the framework with controlled laboratory experiments of wetting-drying transitions using sediment from the hyporheic zone of the Columbia River’s Hanford Reach. Results strongly supported all framework components and provided the most comprehensive evaluation of such a framework to date.

Hyporheic zone ecosystems are areas where groundwater and surface water mix, and they are also hotspots for microbiome activity involving nutrient cycling. Physical moisture variation also modifies chemical reactions in this environment. The resulting biological and chemical dynamics can impact ecosystem function. A multi-institutional team of scientists developed and tested a conceptual framework to describe microbe–environment–ecosystem interactions in hyporheic zone ecosystems, and they evaluated their framework using controlled laboratory experiments. The team exposed hyporheic zone sediment from the Columbia River to wetting–drying transitions. Then they performed molecular analyses to determine key framework characteristics and conditions. Some of these experiments used instruments at the Environmental Molecular Sciences Laboratory, EMSL, a U.S. DOE Office of Science user facility located at Pacific Northwest National Laboratory. Results suggest that sediment drying initiates previously unrecognized internal feedbacks in the microbial community. These responses drive biological and chemical dynamics, and those dynamics influence microbial responses to re-wetting that depend on drying history. These results demonstrate that the impacts of disturbance can be thought of as an external forcing that triggers internal dynamics contingent on the environmental history of the system.

Collecting leaf-level gas exchange data requires specialist training, is time consuming, can involve elaborate logistics, and often utilizes techniques adapted to particular experiments, instruments, and environments. Thus, resulting data products are low volume, have diverse and heterogeneous content, and are not easily shared. Adoption of a common reporting format will make these data more FAIR (Findable, Accessible, Interoperable, and Reusable.) These characteristics facilitate data synthesis, incorporation into models, and scientific discovery. Development of this reporting format has garnered considerable interest beyond the ESS community, with contributions from 80 experts from around the world, including data collectors, modelers, data scientists, and instrument manufacturers.

Leaf-level gas exchange data inform the mechanistic understanding and model representation of plant fluxes of carbon and water in terrestrial biosphere models where parameters derived from gas exchange data also determine how plants will respond to global environmental change. The high value of leaf-level gas exchange data is exemplified by the many publications that reuse and synthesize gas exchange data. However, the previous lack of metadata and data reporting conventions have made full and efficient use of these data difficult. Researchers have proposed a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. The reporting format has been developed for use in the Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) and has received strong support from the global plant physiology community.

The leaf-level gas exchange reporting format provides recommendations on how to prepare these data for sharing in data repositories. The format comprises defined variable names and definitions, and for a number of the most common measurement types, lists the minimum required data variables. A comprehensive metadata description template has been developed to allow unambiguous interpretation of data by future users. The format strongly encourages archive of the original complete instrument output to allow for novel future use of these valuable data.

Autumn phenology was observed in several species of trees for up to 12 years in six FACE experiments. Elevated CO₂ usually had no effect or delayed when leaves turned color and fell to the ground. In two experiments, elevated air temperatures and CO₂ in outdoor chambers delayed autumn senescence in warmer temperatures, in contrast to the lack of response to warming reported by Zani et al. These FACE and outdoor chamber experiments are realistic tests of CO₂ and warming effects on autumn canopy dynamics.

Zani et al. (Science, 27 November 2020, p. 1066) proposed that enhancement of deciduous tree photosynthesis in a warmer, carbon dioxide (CO₂)-enriched atmosphere will lead to earlier autumn senescence. If this premise is true, there would be an important constraint on future growing season length and carbon uptake of trees. This premise, however, is not supported by consistent observations from free-air CO₂ enrichment (FACE) experiments. In most FACE experiments leaf senescence or leaf fall was not altered or was delayed in trees exposed to elevated CO₂.

FACE experiments are controlled experiments of trees exposed in situ for multiple years to future atmospheric CO₂ concentrations under real-world environmental conditions.  The experiments permitted careful observations of the timing of autumn senescence or leaf fall. Leaf fall of Betula pendula trees occurred later in elevated CO₂ compared to control plots in two of four years in the Bangor FACE experiment in Wales and was four to five days later in mature Carpinus betulus and Fagus sylvatica trees in the Web-FACE experiment in Switzerland. The longest record comes from the Oak Ridge National Laboratory (ORNL) FACE experiment with Liquidambar styraciflua trees. The average time of 50% leaf fall over 12 years was day-of-year 283 ± 2.4 in both ambient and elevated CO_2. In 9 of 12 years, there was no effect of CO₂ on the timing of abscission. There was considerable investment of financial and scientific resources in developing and operating large-scale FACE experiments, and FACE results have supported important advances in ecosystem modeling of CO₂ responses and global-scale evaluation of the future trajectory of the terrestrial carbon sink. They provide the best available data for testing hypotheses about ecosystem responses to future atmospheric CO₂ conditions, including future projections of autumn phenology.

This study pairs new data on soil and root phosphatase with fine-root and soil factors. The root and soil factors regulate enzyme activity in the soil profile. The results improve understanding of root-soil interactions that influence phosphorus dynamics. These findings from a tropical forest in Puerto Rico generated predictive relationships that were robust across a wide range of soil conditions. The best equation predicted root phosphatase from specific root length and soil available phosphorus content. These relationships will enable more accurate models of phosphorus control on tropical forest productivity under changing environmental conditions.

In tropical forests, available phosphorus can limit plant growth. Enzymes released by plant roots and soil microbes can increase phosphorus availability throughout the soil profile. Phosphatase enzymes convert phosphorus bound in organic molecules to an inorganic form that is available to plants. Roots of different tree species can have different effects on phosphatase activity. The number of roots and their activities vary with depth in soil. Current models distribute roots through the soil column; new data on how root traits, soil characteristics, and phosphorus availability vary with soil depth will improve how models represent tree growth in tropical forests.

The study’s objective was to determine fine-root traits and soil measurements that influenced soil and root phosphatase activity in the soil profile. Researchers measured soil and root phosphatase to 1 m and 30 cm in soil depth, respectively, including corresponding soil conditions (phosphorus concentrations, soil texture, and bulk density) and fine-root traits (specific root length and fine-root mass density). The team found that soil phosphatase can be predicted by bulk density, organic phosphorus, and fine-root mass density and that variation in root phosphatase can be explained by available phosphorus and specific fine-root length. Thus, both fine-root traits and soil phosphorus measurements are needed to understand mechanisms, like phosphatase, that mediate phosphorus availability in tropical forests. These findings strengthen the link between phosphatase activity and existing root and soil phosphorus parameters in ecosystem models, enabling a more accurate representing of the phosphorus cycle. The study’s data merge phosphatase activity—a root and microbial function important to phosphorus acquisition—with fine-root traits and soil data, informing the understanding of phosphorus acquisition throughout the soil profile and the potential feedbacks to tropical forest growth.

Coastal wetlands are important carbon sinks but are missing from many models used for global-scale climate prediction. This work represents initial steps in incorporating coastal wetlands in global models by simulating tidal marsh plants, soils, and tides. The model was tested by comparing results to field data to pinpoint areas for future data collection. Targeted data collection can be used to improve model simulations and provide more accurate estimates of carbon cycling.

Using the Energy Exascale Earth System Model (E3SM) Land Model as a base framework, researchers added plants, soil, and water flow to represent a coastal salt marsh. Once updated, they used the salt marsh model to simulate elevated carbon dioxide (CO₂) and temperature treatments from the SMARTX experiment (https://serc.si.edu/gcrew/warming). The researchers were more successful at predicting aboveground than belowground responses. Simulations of C₃ species were more successful than those of C₄ species. Similar to field data, simulations showed that CO₂ increased plant growth for C₃ plants and had little effect on C₄ species, and that temperature responses for both plant functional types were nonlinear.

E3SM simulates the connections between plants, soil, and water and their interactions with climate. However, E3SM does not include systems at the terrestrial-aquatic interface (TAI), such as coastal wetlands. Since TAIs are important zones for carbon processing, including them in E3SM is key to improving climate predictions. Based on measurements from a field experiment in a well-studied coastal salt marsh, in which temperature and CO₂ concentration were modified to represent potential future climate conditions, the team added new coastal vegetation types (high-elevation and low-elevation marsh) and new marsh hydrology processes (tides and interaction with tidal channels) into E3SM’s Land Model (ELM). The model was used to investigate the role of elevated CO₂ and temperature on plant productivity. Results were compared to observed responses from the field-scale experiments. The updated model captures many aspects of the field experiments, showing that plant community responses to environmental change are nonlinear, and that differences between community responses can be explained by differences in plant physiology and hydrologic setting. The study was more successful at simulating aboveground than belowground processes. Additionally, simulations of a low-elevation marsh dominated by a C₃ species were more closely aligned with field data than those for a high elevation dominated by C₄ species. Next steps will include updates to key belowground parameters such as root:shoot carbon allocation and the addition of feedbacks between plants and nutrient processing.

In contrast to terrestrial ecosystems in which climate change is thought to enhance carbon emissions, these findings suggest that coastal carbon storage may increase with climate change. This implies stabilizing feedback where elevated CO₂, warmer temperatures, and faster rates of sea level rise could potentially enhance carbon sequestration in marshes and help mitigate the impacts of CO₂ emissions.

SMARTX is a whole-ecosystem warming experiment that was established in a Chesapeake Bay tidal marsh in 2016 to understand how interacting facets of climate change influence carbon accumulation. Researchers modeled plant inputs and organic matter decomposition using data from the SMARTX project and found that sea level rise and elevated carbon dioxide (CO₂) interact to strongly enhance soil volume and carbon accumulation. This effect was driven primarily by the tendency for sea level rise to cause a vegetation shift toward a more flood tolerant plant community that in turn is more responsive to elevated CO₂.

Coastal marshes play a disproportionate role in regulating Earth’s climate because they sequester carbon at rates that are an order of magnitude higher than terrestrial environments. However, the response of these carbon pools to interacting facets of climate change is not well understood, and there is concern that carbon stored in marshes will be vulnerable to future sea level rise. This study uses data from the SMARTX experiment to develop a new computer model that simulates how marshes and their carbon pools will change in response to accelerating rates of sea level rise and enhanced CO₂ in the atmosphere. Researchers find that sea level rise leads to a change in vegetation type that is more responsive to elevated CO₂. This vegetation shift led to both increased productivity and decomposition of soil organic matter in the model. However, the net impact was that elevated CO₂ allowed marshes to survive faster rates of sea level rise and accumulate more carbon in their soils.

Pacific Northwest National Laboratory scientists maintain a freely available database of soil respiration observations—data on how much CO₂ is being produced through time around the world by soils. This database enables powerful, large-scale studies exploring the magnitude of soil respiration, how it varies in space and time, and how it may be affected by climate change, perhaps by liberating long-stored soil carbon to the atmosphere. Data from SRDB has served as a benchmark for Earth system model performance and in sophisticated analyses aimed at better estimating and understanding parts of the global carbon cycle.

Carbon dioxide (CO₂) flows from the Earth’s soils to the atmosphere in a process known as soil respiration. Quantifying and understanding this respiration, the second-largest carbon flux in the Earth system, are critical in an era of climate change. Researchers maintain a compendium of published data about soil respiration, referred to as the Soil Respiration Database (SRDB). A new version of the SRDB features expanded data, more powerful quality-checking scripts, and a simplified, easy-to-use architecture.

Researchers compiled field-measured soil respiration data, including soil-to-atmosphere CO₂ flux observations, into SDRB, a global soil respiration database widely used by the biogeochemistry community. Emerging questions in carbon cycle sciences require updated and augmented global information with better dataset compatibility. This need led to the release of a new version of SRDB, called SRDB-V5. This updated version includes revisions of all previous fields for consistency and simplicity, along with several new fields with additional information. SRDB-V5 contains over 800 independent studies published through 2017. It features more data from the Russian and Chinese scientific literature, has greater global spatio-temporal coverage, and has improved global climate-space representation. SRDB-V5 aims to act as a data framework for the scientific community to share seasonal to annual field soil respiration measurements. It provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability of this important source of carbon flux.

While society depends on watersheds for clean water, energy, agricultural productivity, and other benefits, state-of-the-art scientific tools are not yet regularly used to underpin resource management. Recent advances in emerging technologies—together with instrumented watershed observatories, open-science principles, and new modes of collaboration—offer significant potential to transform the ability to address complex scientific questions, develop generalizable insights, and propel accurate yet tractable approaches to predict watershed hydrobiogeochemical behavior. As resource managers struggle to make increasingly difficult decisions in the coming decades, it is hoped that the concepts described in this commentary will mobilize the scientific enterprise toward the systematic developments needed to provide actionable information over space and time scales useful for such decisions.

Emerging technologies such as machine learning, exascale computing and 5g communications are advancing key elements important for predicting watershed hydro-biogeochemical behavior, including watershed characterization, data, informatics, and modeling. This invited commentary describes and recommends a systematic community development of codesign strategies, whereby the emerging technologies could seamlessly weave together characterization, data, and modeling capabilities across scales—enabling two-way, near-real time feedback between observation and modeling systems.

Several emerging technologies are now starting to reveal their promise for greatly enhancing the predictive understanding of watershed hydro-biogeochemical behavior, including machine learning, artificial intelligence, exascale computing, 5G wireless communications, and cloud data storage and compute capacity. The paper describes a codesign strategy to unify diverse characterization, data, and simulation capabilities, allowing near real-time, autonomous communication and feedback between modelling and field observation systems. Paired with watershed observatory networks, open science principles, and radical collaboration strategies, the codesign strategies are expected to enable rapid progress on challenging scientific questions, such as: how do different types of watersheds respond to different stressors, such as climate change, droughts, floods, wildfire, and land-use? How will multiple stressors impact sustainability of municipal, industry, food, and energy systems that rely on water? Can generalizable metrics of resilience be identified and tracked? What is the minimum but sufficient amount of information needed to predict watershed behavior at temporal and spatial scales critical for underpinning resource management decisions? While systematic incorporation of emerging technologies and adoption of new modes of collaboration will require substantial coordination, resources, and commitment to overcome technical, social, and organizational barriers, the many recent efforts focused on advancing collaborations and tools across watershed communities, observatories, and government agencies are encouraging.

The progression of climate change is resulting in earlier snowmelt onset and reduced snowpack in alpine regions, causing a longer baseflow (or low flow) period for alpine streams, which  subsequently impacts streambed flow and biogeochemical processes. This study characterized streambed biogeochemistry at high resolution during baseflow to constrain how nutrient cycling in alpine watersheds may shift with climate change.

Groundwater and surface water mixing in streambeds (hyporheic exchange) is important for nutrient and carbon cycling and influences the overall quality of surface water. To better understand and map relationships between hyporheic exchange, pore water chemistry, and microbial communities, a research team characterized the streambed of a prominent meander bend of the East River in Colorado during low flow conditions. They found that meander morphology of this alpine streambed caused lateral spatial variability in channel hyporheic flow and drove streambed biogeochemical conditions. Regions of the streambed with greater surface water influence had larger concentrations of dissolved oxygen and microbially available carbon compounds. The composition of streambed microbial communities also shifted with changes in pore water chemistry, though communities were all similarly diverse.

Researchers conducted a high-resolution characterization of streambed hydrology and biogeochemistry around a prominent meander bend of the East River near Crested Butte, Colo. The team observed sinuosity-induced heterogeneity in hyporheic flow, pore water chemistry, and microbial community composition. The presence of intrameander flow paths resulted in spatial heterogeneity in the upwelling and downwelling of water, and subsequent surface water influence in the streambed. Surface water downwelled in a large recharge zone on the up-valley side of the meander and discharged on the down-valley side of the meander. Variability in hyporheic flow resulted in similar patterns in pore water chemistry and concentrations of substrate for microbial metabolism. Regions of the streambed with large surface water influence had higher dissolved oxygen concentrations, relatively low metal concentrations, and more labile, fresh dissolved organic matter. In contrast, groundwater-dominated regions had low dissolved oxygen and high metal concentrations, along with more recalcitrant dissolved organic matter. Results indicate that lateral heterogeneity in pore water chemistry drives microbial community composition, although streambed microbial communities are similarly diverse. The team’s findings enhance understanding of hyporheic biogeochemical conditions during baseflow, which is expected to lengthen with climate change.

This work revealed multiple close linkages and feedbacks between physical, chemical, and microbiological processes in headwater streambed ecosystems and highlights the need for increased characterization of upland biogeochemical cycles under future climate change scenarios.

Within the East River, near Crested Butte, Colo., a team of researchers examined seasonal patterns of surface and groundwater mixing, observing shifts in microbial composition and activity in both stream water and the streambed associated with the water mixing patterns. Their observations highlight the tight linkage between seasonal changes in hydrology and microbial community assembly and function. Specifically, rates of aerobic respiration increased during spring snowmelt, linked to the influx of abundant dissolved organic carbon. Moreover, strong river water downwelling into the riverbed had the additional effect of homogenizing microbial community composition across depth profiles through the bed.

Seasonal changes in river discharge in upland watersheds affect patterns of surface and groundwater mixing in the hyporheic zone, the region in the riverbed where these two water sources interact. These changes impact how carbon compounds and dissolved metals are processed and exported from such catchments. This study focused on seasonal patterns of hyporheic mixing in the East River, Colo., watershed where seasonal snowmelt drives large fluctuations in the annual hydrograph. Using in situ depth-resolved temperature loggers and discrete sampling of pore fluids and riverbed sediments, researchers demonstrated that snowmelt-derived runoff drives increased downwelling of river water into the riverbed. Conversely, the riverbed experienced a greater influence from upwelling groundwater under low- and base-flow conditions. The movement of dissolved solutes was strongly correlated with seasonal changes in flow. Under high river discharge, increased dissolved oxygen concentrations in riverbed pore fluids stimulated aerobic heterotrophic metabolism. Conversely, this activity was depressed under baseflow conditions. Linked to changes in microbiome function, the research team demonstrated that this dynamic hydrology also influenced microbial community assembly; strong downwelling river water conditions had the effect of homogenizing microbial community composition across depth profiles through the riverbed.

The Ecology Underground group discussed the next steps in assessing and modeling the responses of belowground processes to changing environments, paving the way for next-generation research on belowground ecology.

Through two days of online synchronous presentations and discussion, Ecology Underground highlighted three research frontiers in belowground ecology: (1) the power of trait-based approaches for linking plants, microbes, and ecosystem processes; (2) the identification of relevant spatial and temporal scales for studies in belowground ecology; and (3) the development of models that connect microscale dynamics to predict belowground processes globally.

The 2020 pandemic allowed the Ecological Society of America (ESA) to enter a new digital era by holding a fully virtual conference. Early-career plant and microbial ecologists from six ESA Organized Sessions took this opportunity to organize Ecology Underground, a two-day program of live virtual talks and open discussions on integrative belowground ecology. The discussions at Ecology Underground shaped the future directions in belowground ecology. First, trait-based approaches showed promise for linking plants, microbes, and ecosystem processes. For example, functional traits that underpin ecological strategies may help to assess and model response of free-living microbes to changing environments. Second, a better understanding of the spatial and temporal consistency and turnover in microbial communities and fine-root dynamics was critical to the integration of belowground processes into Earth System Models and ecological forecasts. Third, the use of models that represent multiple scales was key to bridge the gap between soil ecological observations at locations across the globe and biosphere model predictions. Lastly, the group that participated in Ecology Underground stressed that heading in these directions will require strong global networks, cross-disciplinary collaboration, and a diversity of perspectives only achievable through a diverse community of ecologists. Creating such a community will require organizations to recruit, support, and promote Black, Indigenous, People of Color, and other historically excluded people in science.

The trees and shrubs that show greater water stress with warming may be damaged and die back during extreme drought or heat in the future. This could change the productivity of those species and their competitive ability. The result could be a change in species composition and subsequently whole-ecosystem productivity. Since boreal wetlands store a lot of carbon in the soil and plants, a loss in productivity by some species would contribute negatively to climate change.

Researchers increased the soil and air temperature in a boreal wetland forest and measured water stress of the two main shrub species and two main tree species. The higher temperatures increased water use by tamarack trees but reduced water use by spruce trees. As a result, the tamarack trees displayed more water stress than spruce trees. Water stress was also greater for the leatherleaf shrubs as compared with the Labrador tea shrubs. The addition of higher carbon dioxide (CO₂) in the air reduced water stress in spruce and Labrador tea but not for tamarack or leatherleaf plants.

Boreal peatland forests have relatively low species diversity and thus impacts of climate change on one or more dominant species could change how the ecosystem functions. Despite abundant soil water availability, shallowly rooted plants within peatlands may not be able to meet canopy demand for water under drought or heat events. As rates of leaf transpiration increase, there must be greater root uptake and transport of water to the leaves. Under such conditions, some plants will limit transpiration by closing the stomatal pores in the leaves, while others maintain water use, which can lead to water stress and even plant mortality. Elevated atmospheric CO₂ can lead to partial stomatal closure since the higher concentrations exceed that needed for photosynthesis within the leaf, which can buffer water stress. In this study, the tamarack and leatherleaf kept their stomata open under warming treatments, which may maintain rates of photosynthesis, but they had increased water stress. Alternately, the black spruce and Labrador tea closed stomata and maintained greater hydraulic safety. These latter species also responded to elevated CO₂, which further reduced water stress. The species-specific responses of peatland plant communities to drier or hotter conditions will shape boreal peatland composition and function in the future.

Tropical forests absorb large amounts of atmospheric CO₂, but substantial decreases in tropical forest gross primary productivity have been repeatedly demonstrated in the Amazon basin during periodic widespread drought associated with high temperature. Therefore, the physiological mechanisms through which tropical forests respond to high temperature are critically important to understand. While extreme warming will decrease stomatal conductance and net photosynthesis in tropical species, research observations support a thermal tolerance mechanism where the maintenance of high photosynthetic capacity under extreme warming is assisted by the simultaneous stimulation of ETR and metabolic pathways that consume the direct products of ETR including photorespiration and the biosynthesis of thermoprotective isoprenoids. Results demonstrate that models which link isoprene emissions to the rate of ETR are ideal for tropical species and provide necessary “ground-truthing” for simulations of the large predicted increases in tropical isoprene emissions with climate warming.

The increase in global temperature directly affects the net primary productivity of the forest. High temperatures can influence the rates of chemical reactions in cells, such as photosynthesis, electron transport, and isoprene emissions. In this study, researchers asked the following questions.

1) Are reductions in photosynthesis at high leaf temperatures in tropical forests linked to a reduction in g_s rather than direct negative temperature effects on photosynthesis?

2) Do current isoprene emission models that link photosynthetic electron transport rates to isoprene emissions rates as a function of temperature hold true in tropical species?

3) What is the role of isoprene on thermal tolerance of photosynthesis at high temperatures?

The research team discovered that in a thermophile early successional species in the Amazon, photosynthetic electron transport rates increased linearly with temperature in concert with isoprene emissions, even as stomatal conductance and net photosynthetic carbon fixation declined. The team observed the highest temperatures of continually increasing isoprene emissions yet reported and that blocking isoprene production induced a temperature-dependent loss of photosynthetic capacity.

Tropical forests absorb large amounts of atmospheric CO₂ through photosynthesis, but high surface temperatures suppress this absorption while promoting isoprene emissions. While mechanistic isoprene emission models predict a tight coupling to photosynthetic electron transport (ETR) as a function of temperature, direct field observations of these phenomenon are lacking in the tropics and are necessary to assess the impact of a warming climate on global isoprene emissions. Here, the researchers demonstrate that in the early successional species Vismia guianensis in the central Amazon, ETR rates increased with temperature in concert with isoprene emissions, even as stomatal conductance (g_s) and net photosynthetic carbon fixation (P_n) declined. The researchers observed the highest temperatures of continually increasing isoprene emissions yet reported (50°C). While P_n showed an optimum value of 32.6 ± 0.4°C, isoprene emissions, ETR, and the oxidation state of PSII reaction centers (q_L) increased with leaf temperature with strong linear correlations for ETR (ƿ = 0.98) and q_L (ƿ = 0.99) with leaf isoprene emissions. In contrast, other photoprotective mechanisms, such as non-photochemical quenching (NPQ), were not activated at elevated temperatures. Inhibition of isoprenoid biosynthesis repressed P_n at high temperatures through a mechanism that was independent of stomatal closure. While extreme warming will decrease g_s and P_n in tropical species, these observations support a thermal tolerance mechanism where the maintenance of high photosynthetic capacity under extreme warming is assisted by the simultaneous stimulation of ETR and metabolic pathways that consume the direct products of ETR including photorespiration and the biosynthesis of thermoprotective isoprenoids. Results confirm that models which link isoprene emissions to the rate of ETR hold true in tropical species and provide necessary “ground-truthing” for simulations of large predicted increases in tropical isoprene emissions with climate warming.

The amount of water that tropical trees lose from their stems during drought conditions, when trees lack access to soil water, was correlated with their bark water vapor conductance, which is the leakiness of bark to water vapor. This suggests that water loss through bark may be an important and overlooked mechanism that influences stem dehydration and drought performance.

The amount of water that tropical trees lose from their stems during drought conditions, when trees lack access to soil water, is correlated with their bark water vapor conductance, which is the leakiness of bark to water vapor. This suggests that water loss through bark may be an important and overlooked mechanism that influences stem dehydration and drought performance in tropical trees.

Saplings of several tree species in Panama were measured for stem water content during well-watered conditions and drought conditions in forest understories and in a shadehouse experiment to assess stem water deficit during drought. Saplings of the same species were collected and measured for bark water vapor conductance. In both datasets, bark water vapor conductance was correlated with stem water deficit among species that lacked assess to soil water.

Organic matter transformations in aquatic ecosystems are a critical source of uncertainty in global biogeochemical cycles. Environmental metabolomics, or the analysis of organic molecules in environmental samples, help characterize the interactions of organisms within their environment. Environmental metabolomics enabled by ultrahigh-resolution mass spectrometry reveals connections between organic matter character, reactivity, and inferred biochemical transformations within and across localized river corridor ecosystems. These processes, however, are not well understood at the continental-to-global scale. This work provides a foundation for understanding global patterns in river corridor biogeochemical cycles. It also demonstrates that research done using crowdsourced science can enable discoveries that are unfeasible with traditional research models.

The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a global consortium of researchers based out of the U.S. Department of Energy’s Pacific Northwest National Laboratory. The consortium uses a standardized approach to understand coupled hydrologic, biogeochemical, and microbial functions in river corridors. Now the group reports the first ultrahigh-resolution analysis of global river corridor metabolomes of both surface water and sediment across rivers spanning a wide range of sizes and ecosystem types. The scientists describe the distribution of key chemical attributes of metabolomes, including East-West gradients, in many metabolomic features across the contiguous United States. They also show that surface water metabolomes are more diverse than those in river sediments, possibly suggesting a greater diversity of biological processes occurring in surface waters.

In 2019, WHONDRS leaders worked with the global science community to develop and implement a study based on ICON-FAIR principles. In this approach, research is designed intentionally to be integrated (I) across disciplines, coordinated (C) with consistent protocols, open (O) by generating data that is Findable, Accessible, Interoperable, and Reusable (FAIR), and networked (N) so that the distributed science community is engaged in the execution of the work. Using these principles, the WHONDRS consortium collected surface water and sediment samples from 97 river corridors in 8 countries within a 6-week period across a wide range of environmental factors such as stream order, climate, vegetation, and geomorphological features. Scientists at the Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy scientific user facility located at Pacific Northwest National Laboratory, then characterized metabolomes in these samples using Fourier-transform ion cyclotron resonance mass spectrometry.

The scientists described central aspects of the metabolomes, including assigned elemental groups, chemical classes, descriptor indices, and inferred biochemical transformations. Using those features, the scientists then described key metabolome characteristics of surface water and sediment. Finally, they explored spatial patterns across East-West gradients of many of these characteristics within the United States and how they varied among surface waters and sediments. Their work provides a benchmark for understanding global patterns in river corridor organic matter chemistry and highlights the benefits of engaging in ICON-FAIR science to increase transferability of knowledge. This publication was invited to be part of a Special Issue, ‘Metabolome and Fluxomics’, in the journal Metabolites.

Geologists have long mapped hydromorphic features using subjective observations and expert knowledge. This work demonstrates a novel application of machine learning to combine hydrologic model outputs with remotely sensed data to perform this work in an objective, consistent, and automated manner. This result is an important step toward improving the ability to predict HEFs and their effects on nutrient cycling and water quality in large, complex river systems.

Interactions between flowing water and the geometry of river channels create forces that cause river water to enter the sediments surrounding the channel and eventually return to the channel. Such exchanges of surface and subsurface waters (called hydrologic exchange flows or HEFs) play a critical role in the fate of nutrients and contaminants in the river and thus significantly impact water quality. To simplify and enable computer simulation of these effects in large river reaches, this research developed a novel machine learning approach to map regions of the riverbed that have similar hydraulic and geometric characteristics (called hydromorphic features) and can be expected to exhibit similar HEFs.

A team of scientists developed a machine learning method for quantitatively defining and mapping hydromorphic classes and then demonstrated this method on the 70-km Hanford Reach of the Columbia River (southeastern Washington state, USA). The novel approach uses outputs from river flow simulation models (depths and velocities) and remotely sensed bathymetric/topographic data to objectively define and map hydromorphic features. The identified feature classes are shown to correspond to spatially contiguous regions, and these coherent features are physically interpretable and consistent over the entire reach. Classification accuracy was verified using field observations of feature geometry and riverbed textural properties. Preliminary analysis of relationships between the mapped hydromorphic features and simulated values of exchange flows and transit time distributions based on high-resolution mechanistic modeling confirm that these important characteristics of river-subsurface exchange are distinct for each feature type. These confirmations provide a rational basis for using the results of high-resolution mechanistic models (feasible only within limited spatial domains) to predict system behaviors over much larger spatial domains.

A modeling study shows fluctuating soil microbial populations promote carbon emission from soil. It suggests that the soil microbes, similar to our human body, would consume more energy in a more seasonally fluctuated climate. The study opens up a new frontier of the impacts of microbial activity at seasonal or finer timescale on soil carbon and nutrient processes.

Seasonality is a key feature of the biosphere, and the seasonal dynamics of soil carbon emissions represent a fundamental mechanism regulating the terrestrial–climate interaction. Scientists applied a microbial explicit model—CLM-Microbe—to evaluate the impacts of microbial seasonality on soil carbon cycling in terrestrial ecosystems. The scientists first validated the CLM-Microbe model in simulating belowground respiratory fluxes (i.e., microbial respiration, root respiration, and soil respiration from nine biomes). The research team then investigated the microbial seasonality effects on soil carbon cycle by comparing the model simulations of soil respiratory fluxes and soil organic carbon content in top 1 m between the CLM-Microbe model with (CLM-Microbe) and without (CLM-Microbe_wos) seasonal dynamics of soil microbial biomass in natural biomes.

The CLM-Microbe model produced good performance in capturing the seasonality in soil respiratory fluxes. Removing soil microbial biomass seasonality yielded minor impacts on root respiration, but it significantly increased the simulation bias and reduced the goodness-of-fit in microbial respiration and soil respiration. The model simulation without soil microbial seasonality led to lower soil respiratory fluxes across sites, leading to higher soil organic carbon pool size except for boreal-Arctic sites. These lines of evidence confirmed that microbial seasonality promotes soil carbon emission. The different roles of bacteria and fungi in regulating carbon flux suggest the important regulation of soil microbial community on belowground carbon biogeochemistry. Findings of the study highlight the importance of explicit representation of microbial mechanisms at the seasonal scale on simulated carbon cycling in Earth system models, insights which will both improve the simulation performance of soil respiratory fluxes and reduce the uncertainties associated with model projection in global carbon cycle under a changing climate.

Field studies have shown that microbial respiration occurs under very cold conditions and may offset the growing-season net carbon uptake. However, cold-season carbon emissions from permafrost regions are not accurately represented in earth system land models, severely impeding predictability of permafrost carbon losses under warming. Results of this study (1) led to improved capability of ELMv1 to accurately simulate soil temperature and cold-season CH₄ and CO₂ emissions at tundra sites and (2) highlighted the importance of zero-curtain periods in facilitating cold-season carbon emissions from tundra ecosystems.

Scientists have improved the Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM) land model (ELMv1) simulations of soil temperature, zero-curtain period durations (i.e., the fall period when soil temperatures linger around 0°C), and cold-season methane (CH₄) and carbon dioxide (CO₂) emissions at several Alaskan Arctic tundra sites. Results demonstrated that simulated CH₄ emissions during zero-curtain periods (Sep. and Oct.) accounted for more than 50% of total emissions throughout the entire cold season (Sep. to May). Results also showed that cold-season CO₂ emissions largely offset current warm-season net uptake and had increasing trends from 1950 to 2017.

Field measurements have shown that cold-season methane (CH₄) and carbon dioxide (CO₂) emissions contribute a substantial portion to annual net carbon emissions in permafrost regions. Accurately representing cold-season carbon emissions is crucial for models to reasonably predict the permafrost carbon-climate feedback. However, prevailing Earth system land models cannot accurately simulate cold-season carbon emissions and their contributions to the annual totals. This study used DOE’s E3SM land model (ELMv1) to tackle this challenge. Through developing an optimization framework for multi-calibration, scientists improved the ELMv1 soil water phase-change scheme, environmental controls on microbial activity, and the methane module. Results demonstrate that the optimized ELMv1 greatly improved simulations of soil temperature and duration of zero-curtain periods. Furthermore, the improved model reduced the mean absolute errors of simulated cold-season carbon emissions by more than 70%. Overall, simulated CH₄ emissions during September and October, which often includes most of the zero-curtain period in the Arctic tundra, account for more than 50% of the cold-season total emissions, agreeing very well with observations. From 1950 to 2017, both CO₂ emissions during the zero-curtain period and during the entire cold season showed increasing trends. This study highlights the importance of zero-curtain periods in facilitating cold-season carbon emissions from tundra ecosystems.

To understand future climate change, scientists need to predict the amount of methane released from wetlands. This work has advanced understanding of how plants affect the microbial communities generating and oxidizing methane within wetlands. This understanding will help scientist model and predict wetland methane emissions.

Microbial activity in wetland soil is responsible for the emission of more methane to the atmosphere than all other natural sources combined. This flux is influenced by many factors, but in all cases, the generation of methane (methanogenesis) and any oxidation of CH4 (methanotrophy), which may attenuate emissions, are microbially mediated. Methane is a greenhouse gas with a greater ability to warm the earth than carbon dioxide. Wetlands are the largest natural source of methane to the atmosphere. Microbes in wetland soils are responsible for the generation of methane and the conversion of methane into carbon dioxide (a process called oxidation). Methane oxidation can be carried out by microbes that have different life requirements. This research investigated how a common wetland sedge (Carex aquatilis) affects microbes in wetland soil. It found that plants created a soil environment that favored methane-oxidizing microbes with specific life requirements met by resources released from plant roots. Without plants, microbes had more flexible life requirements.

Microbial activity in wetland soil is responsible for the emission of more methane to the atmosphere than all other natural sources combined. This microbial activity is heavily impacted by plant roots, which influence the microbial community by exuding organic compounds and by leaking oxygen into an otherwise anoxic environment. This study compared the microbial communities of planted and unplanted wetland soil from an Alaskan bog to elucidate how plant growth influences populations and metabolisms of methanogens and methanotrophs. A common boreal wetland sedge, Carex aquatilis, was grown in the laboratory and DNA samples were sequenced from the rhizosphere, unplanted bulk soil, and a simulated rhizosphere with oxygen input but no organic carbon. The abundance of both methanogens and methanotrophs were positively correlated with methane emissions. Among the methanotrophs, both aerobic and anaerobic methane oxidizing microbes were more common in the rhizosphere of mature plants than in unplanted soil, while facultative methanotrophs capable of utilizing either methane or other molecules became relatively less common. These trends indicate that the roots in this experiment created an environment which favored highly specialized microbial metabolisms over generalist approaches. One aspect of this specialized microbiome is the presence of both aerobic and anaerobic metabolisms, which indicates that oxygen is present but is a limiting resource controlling competition.

Results clarify the factors controlling microbial priming and associated methane production within wetland soils. Understanding the causes and mechanisms of plant-stimulated microbial priming will help scientists better predict the fate of wetland soil carbon and methane production. This information will be particularly important as the world becomes more and more impacted by climate change. Warmer temperatures and elevated concentrations of atmospheric CO₂ are expected to increase plant productivity and cause plants to release more carbon from their roots into surrounding soil.

Wetlands host microbes that convert organic carbon into methane, a powerful greenhouse gas. Wetland plants can influence which carbon compounds are available to microbes by releasing organic carbon from their roots into surrounding soil. This carbon can trigger microbial priming—the process of new carbon stimulating the microbial community into processing more soil carbon than they would have otherwise. This research identified what types of molecules were created or lost during plant-stimulated microbial priming that fueled methane generation. Scientists found the molecular size and nutrient content of the molecules controlled which compounds were processed by the microbial community.

This study utilized high resolution Fourier transform ion cyclotron mass spectrometry (FT-ICR-MS) analysis to probe the composition of soil organic compounds from the rhizosphere of Carex aquatilis, a common wetland sedge, which stimulated microbial priming and methane generation within peat soil collected from a bog. The goal was to identify what types of molecules were created or lost during microbial priming in the wetland rhizosphere and thus, advance mechanistic understanding of the process. FT-ICR-MS analysis demonstrated that more microbial transformations of carbon occurred among water-soluble compounds than among hydrophobic compounds, but that some hydrophobic compounds were processed. Crucial for understanding microbial priming, plant-released carbon triggered increased processing of high molecular weight molecules regardless of nutrient content, but processing of low molecular weight compounds only occurred if they contained nitrogen or sulfur. Nitrogen and sulfur are essential nutrients for plant growth. The importance of sulfur in determining molecular utilization is noteworthy because priming literature typically focuses on nitrogen. The fact that some molecules were processed and others were not is evidence for a selective priming effect in which some carbon compounds with specific properties are used at an increased rate, while others are left unaltered.

To understand future climate change, scientists need to predict the amount of methane released from wetlands. This work has advanced understanding of plant‐soil interactions that contribute to wetland methane emissions within thawing permafrost landscapes. This understanding will help scientist model and predict wetland methane emissions.

Methane is a greenhouse gas with a greater ability to warm the earth than carbon dioxide. Wetlands are the largest natural source of methane to the atmosphere. Working in a wetland that formed due to permafrost thaw, scientists used multiple methods to identify how wetland plants influence the amount of methane: generated by soil microbes (called production), converted by soil microbes into carbon dioxide (called oxidation), and transported from soil to the atmosphere (called emission). Plants appeared to increase methane production and to surprisingly decrease methane oxidation. Scientists created a theory for why plants increased methane emissions.

In a permafrost‐thaw bog in Interior Alaska, scientists sought to disentangle mechanisms by which vascular vegetation affect methane emissions. Vegetation operated on top of baseline methane emissions, which varied with proximity to the thawing permafrost margin. Emissions from vegetated plots increased over the season, resulting in cumulative seasonal methane emissions that were ~4.5 g m−2 season−1 greater than unvegetated plots. Mass balance calculations signify these greater emissions were due to increased methane production and decreased methane oxidation. Minimal oxidation occurred along the plant‐transport pathway, and oxidation was suppressed outside the plant pathway. Suppression of methane oxidation was stimulated by root exudates fueling competition among microbes for electron acceptors. This contention is supported by the fact that methane oxidation and relative abundance of methanotrophs decreased over the season in the presence of vegetation, but methane oxidation remained steady in unvegetated treatments. Oxygen was not detected around plant roots but was detected around silicone tubes mimicking aerenchyma, and oxygen injection experiments suggested that oxygen consumption was faster in the presence of vascular vegetation. Root exudates are known to fuel methane production, and this work provides evidence they also decrease methane oxidation.

Comparable to recent western snowpack declines, future snow losses are projected to decrease 20-30% by the 2050s and 40-60% by the 2100s. But, using a portfolio of adaptation strategies could potentially build resilience to future low-to-no snow conditions. Models used to project future water cycle changes need to be improved to provide water resource managers with estimates that are better suited to decision making. The development of new atmosphere-through-bedrock modeling capabilities are needed and could greatly benefit from non-traditional scientific-stakeholder partnerships.

Mountain snowpack acts as a large natural reservoir, providing water resources to communities, ecosystems, energy, and industry upon spring snowmelt. Because up to 75% of the western region’s water resources originate in mountainous watersheds, decreasing snowpack threatens resiliency of systems that depend on snowmelt water. This research synthesized historical observations of western U.S. snow loss over the 20th century and develops a range of projected snowpack conditions in the 21st century. This study highlights that western U.S. snowpack will likely decrease substantially over the next ~35-60 years, especially if high greenhouse gas emissions continue.

This study synthesized observational evidence of snow loss in the western U.S. over the 20th century and developed a range of projected snowpack conditions in the 21st century, elevating the understanding and importance of snow loss on water resources. Results show less consensus on the time horizon of future snow disappearance, but model projections suggest that if carbon emissions continue unabated, low-to-no snow conditions will become persistent in ~35–60 years, depending on the mountain range. Researchers propose a new low-to-no snow definition that uses a percentile approach, akin to the U.S. Drought Monitor, and considers sequencing of 1, 5, or 10 low-to-no snow years via a framework describing those losses as “extreme, episodic, or persistent.” Potential trickle-down impacts on mountain landscapes, hydrologic cycles, and subsequent water supply were also discussed. For example, diminished and more ephemeral snowpacks that melt earlier will alter groundwater and streamflow dynamics, but the directions of these changes are difficult to constrain given competing factors, such as higher evapotranspiration, altered vegetation composition, and changes in wildfire behavior in a warmer world.

A re-evaluation of long-standing hydroclimatic stationarity assumptions in western U.S. water management is urgently needed, given the impending impacts of snowpack loss. These hydroclimatic changes undermine conventional western U.S. water management practices. However, proactive implementation of soft and hard adaptation strategies could potentially build resilience to extreme, episodic, and, eventually, persistent low-to-no snow conditions. Finally, suggestions are provided for the scientific breakthroughs, management strategies, and institutional partnerships that will be needed to overcome a future with less or no snow. Co-production of knowledge between scientists and water managers can help to ensure that scientific advances provide actionable insight and support adaptive decision-making processes that unfold in the context of significant uncertainties about future conditions.

A systematic review resulted in several key recommendations for researchers looking to develop data reporting formats for their diverse datasets. First, scientists suggest that GitHub, a website typically used for collaboration on computer code, can also be used for open and transparent collaboration on reporting format documentation.  Beyond using GitHub as a collaborative platform, scientists provide a review of tools within GitHub that benefit those looking to bring more researchers into the data standardization process (e.g., submitting feedback using GitHub issues or creating project websites using GitBook or GitHub Pages).

Earth and environmental data standards are an important way to make data FAIR (Findable, Accessible, Interoperable, and Reusable). However, there is no agreed upon way for groups to share and collaborate on the standards. Some groups host standards on static websites, others circulate templates in proprietary formats. Therefore, scientists working together across the Department of Energy’s (DOE) National Labs have outlined a set of best practices to guide research communities in disseminating and collaborating on standards. Their main recommendation is that researchers use the version control platform GitHub to openly share data standards, organize feedback from their user community, and clearly track changes to the standards over time.

Over the past three years, the Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository has worked with six teams of community partners across the National Lab network to develop data reporting formats for some of the complex ESS data that are submitted to ESS-DIVE. The teams needed a web platform to host their data reporting format documentation and templates that fulfilled several requirements. The web platform needed to (1) track changes to multiple documents over time, (2) facilitate collaboration between researchers, and (3) display content openly and transparently.

To determine a path forward, the teams conducted a systematic review of over 100 data standards in earth and environmental science and explored how data standards documentation was hosted on the internet. Across the 108 data standards that were reviewed, there was no universal way that researchers chose to publish their data standards. The review revealed that 32 researchers used GitHub as the platform to manage their associated documents and templates. Though GitHub is typically used for collaboration on computer code, it meets the three criteria outlined above for collaboration on reporting formats. Thus, the teams selected it as the platform for hosting ESS-DIVE’s data reporting formats.

Based on the results of this systematic review, several best practices for leveraging GitHub features for collaboration on reporting formats were identified. First, GitHub repositories should contain descriptive README files that help orient first-time users to the reporting formats and include information like usage licenses and recommended citations. Second, semantic versioning should be used to indicate when data reporting format documents have been updated in major or minor ways (e.g., v2.0.0 or v.1.1.0, respectively). Lastly, GitHub Issues are built-in to every repository, and allow anyone with a GitHub account to provide feedback on the reporting formats. Taken together, GitHub provides an open and transparent way to host, version, and collaborate on community-led earth and environmental science data and metadata reporting formats.

Methane fluxes are a metric of broader shifts in wetland biogeochemical cycling and carbon preservation. While previous studies have predicted that CH₄ emissions will increase in a warming climate, there has been minimal work to determine the underlying mechanisms, as in this study. Without knowing these mechanisms, it is difficult to develop prognostic models to forecast wetland CH₄ emissions and to scale from site-based studies to larger areas.

SMARTX is a whole-ecosystem warming experiment that was established in a Chesapeake Bay tidal marsh in 2016 to understand the ecosystem-scale effects of warming on carbon gain, via plant inputs into the soil, and carbon loss, mainly via methane (CH₄) emissions. The research team measured monthly CH₄ emissions for four years and found that 5°C of warming more than doubled CH₄ emissions compared to ambient conditions. This effect was driven by direct and indirect warming effects, but it also was dependent on plant traits and growth patterns.

Climate warming perturbs ecosystem carbon cycling, causing both positive and negative feedbacks on greenhouse gas emissions. Terrestrial ecosystem responses to warming are typically mediated by complex plant-soil interactions. While CH₄ emissions from coastal wetlands offset a portion of the carbon sequestered into these ecosystems annually, there is still minimal knowledge about how warming will alter coastal wetland CH₄ emissions, even though these feedbacks have the potential to shift coastal wetlands from being a net sink of carbon to a net source.

In SMARTX, heating treatments run year-round along a gradient from ambient to +5.1°C above ambient and warming spans from above the plant canopy to 1.5 m in soil depth. The researchers measured CH₄ emissions monthly for four years and coupled these flux measurements with analysis of porewater biogeochemistry and vegetation biomass and composition. Using these data, the team propose four mechanisms that increase CH₄ emissions under warming conditions: (1) rates of CH₄ production increase more than rates of CH₄ oxidation, (2) overall substrate availability increases, (3) sulfate (SO₄) reducers become SO₄ limited and no longer outcompete methanogens, and (4) plant traits alter substrate and oxygen supply.

The team found that TPU, a key process at the heart of many TBMs, was poorly represented in TBMs and that continued inclusion of TPU in TBM is not supported by current understanding and data. They found that inclusion of TPU limitation in TBMs resulted in unrealistic limitation of photosynthesis that in some models could lead to a marked reduction of CO₂ uptake and poor representation of the response of photosynthesis to future global change.

Terrestrial biosphere models (TBMs), used to project the response of ecosystems to global change, need to accurately represent photosynthesis, the assimilation of carbon dioxide (CO₂) by plants. As the largest carbon flux on the planet, errors in model representation of this key process can have marked impacts on projected ecosystem CO₂ exchange with the atmosphere. In TBMs the rate of photosynthesis is determined by three potentially limiting rates: fixation of CO₂ by the enzyme RuBisCO; supply of energy from electron transport; and, in some models, use of the photosynthesis products, triose phosphates. The research team investigated model representation of this third potentially limiting process—triose phosphate utilization (TPU). They found that TBM representation of TPU was based on uncertain assumptions, failed to capture important response to temperature, and was associated with an artifact that caused a marked reduction of CO₂ uptake and was rarely observed in nature. The researchers advocate for the removal of TPU limitation from TBMs.

This work brings together several recent lines of evidence and an examination of model representation of TPU that together strongly suggest that TPU should be removed from TBMs. Current formulations of TPU in TBMs are based on assumptions about the relationship between the capacity for carboxylation and the basal rate of TPU that are not based on measured TPU rates and do not account for the independent temperature response of TPU (Kumarathunge et al. 2019). TBM sensitivity analysis demonstrated a limitation of gross primary productivity by TPU at current CO₂ concentration but most markedly at high CO₂ concentration and at high latitudes (Lombardozzi et al. 2018). However, a synthesis of measurements clearly demonstrated that TPU did not limit CO₂ assimilation at current CO₂, even at high latitudes (Kumarathunge et al. 2019). In addition, it was recently demonstrated that most TBMs that include TPU also include a quadratic smoothing function of the three potentially limiting processes, introducing an artifactual forth limitation on photosynthesis and resulting in a marked reduction in modeled CO₂ assimilation (Walker et al. 2021).

Regular monitoring of the state, functioning, and biodiversity of Earth’s terrestrial, freshwater, and coastal aquatic ecosystems is essential for understanding the impacts of severe weather, disturbance, and climate change on natural resources, potential feedbacks to climate and the management of resources, and defining policy. Remote sensing technologies are essential for large-scale monitoring, but current satellite platforms are insufficient to fill this need. The SBG Designated Observable, a novel combination of high–spatial resolution spectral and thermal infrared imagery, is uniquely designed to address these challenges and provide key observations for studying hydrological, ecological, weather, climate, and solid earth dynamics.

Remote sensing has become a critically important tool for researchers who study Earth’s ecosystems and minerals. Imaging spectroscopy—or the measurement of a many, continuous spectral channels across visible and nonvisible wavelengths—and thermal infrared imagery are essential for inferring plant health, ecosystem function, biodiversity, and solid Earth research. Reviewing the requirements of the National Aeronautics and Space Administration (NASA) Surface Biology and Geology (SBG) Designated Observable, which is a proposed global imaging spectroscopy and thermal infrared Earth Observing satellite, over 130 scientists studied the current state of imaging spectroscopy algorithms and state-of-the-art methods for remote sensing of surface, terrestrial, and aquatic ecosystems.

Monitoring Earth’s diverse natural resources and managed ecosystems is a significant challenge but essential for balancing the maintenance of health, diversity, and resource utilization. Vegetation plays a key role in regulating climate and weather, while the state and health of freshwater and coastal ecosystems impact global circulation patterns, as well as fisheries and recreation. Scientists and policymakers require tools to provide the information needed to understand how the Earth is changing and to define management strategies for the maintenance of biodiversity. The 2017–2027 National Academy of Sciences Decadal Survey, Thriving on Our Changing Planet, identified the critical need for a global imaging spectrometer (IS) combined with a multispectral thermal infrared (TIR) imager with a high–spatial resolution (~30 m for the IS and ~60 m for the TIR) and submonthly temporal resolution. The SBG Designated Observable is designed to meet the needs for regular mapping of the state and changes in Earth’s resources. A team of more than 130 scientists synthesized applications and methods for using SBG to provide the observations needed to inform science and management strategies. The team also identified the necessary next steps needed to prepare for an operational SBG-like satellite to

By highlighting the link between above- and belowground properties and processes in the Arctic, these results will be useful for improving predictions of Arctic feedback to climate change. They also show that Arctic systems are changing rapidly. The data highlight that permafrost at the research team’s study site could disappear within the next decade. This process could be accelerated by changes in snowpack distribution and rainfall patterns.

When permafrost thaws, water can flow quicker through the ground, creating a complex subsurface flow system. Researchers from Lawrence Berkeley National Laboratory gained insight into these processes by measuring the electrical resistivity of the ground daily. Results show that vegetation and the snowpack that accumulates on the vegetation in winter control the temperatures of the ground and the flow of water in the ground. Where snow accumulates, temperatures stay warmer and water and energy from snowmelt and rain can flow through the ground quickly. Where the snowpack is thin, ground temperatures are colder, preventing flow.

Climate change is causing rapid changes of Arctic ecosystems. Yet the data needed to unravel complex subsurface processes are very rare. Using geophysical and in situ sensing, researchers closed an observational gap associated with thermohydrological dynamics in discontinuous permafrost systems. Monitoring for more than 2 years, their data highlight the impact of vegetation, topography, and snow thickness distribution on subsurface thermohydrological properties and processes. Large snow accumulation near tall shrubs insulates the ground and allows for rapid and downward heat flow. Thinner snowpack above the graminoid results in surficial freezing and prevents water from infiltrating into the subsurface. Analyzing short-term disturbances such as snowmelt or heavy rainfall, the team found that lateral flow could be a driving factor in talik formation. Interannual measurements show that deep permafrost temperatures increased by about 0.2°C over 2 years. The results, which suggest that snow-vegetation-subsurface processes are tightly coupled, will be useful for improving predictions of Arctic feedback to climate change, including how subsurface thermohydrology influences carbon dioxide and methane fluxes.

The study will provide a better understanding of where changes in moisture availability for plants are most severe in the tropics during El Niño to enable better predictions of impacts on the food supply and feedbacks of water from land back to the atmosphere through evapotranspiration. These findings can be used to guide decisions on where changes need to be made to water management systems during El Niño to offset expected decreases in moisture availability for crops and to improve global Earth system model predictions.

El Niño is a complex part of the climate system with extreme events occurring every 15 to 20 years that have major impacts on global water supplies. This study combined data derived from on-the-ground measurements and a suite of global datasets to determine where impacts on soil moisture from such events were most severe in the tropics and to explore possible links of these changes to other large-scale weather patterns.

El Niño is an important part of the climate system that has widespread impacts on global water resource availability. This study employed a combination of modeled soil moisture datasets and on-the-ground measurements to determine what changes to expect for soil moisture during severe El Niño events. Supplemental datasets of evapotranspiration and precipitation were used to explore the possible link of these changes to non-El Niño related weather events. The analysis was focused on the humid tropics, which is important not only because of the higher severity of impacts due to its closer proximity to the El Niño source region, but also because historical observations in this region are generally sparse, which limits the ability to predict what will happen during an El Niño. Results indicate that the northern Amazon basin, as well as maritime regions of southeastern Asia, Indonesia and New Guinea will experience the largest reductions in soil moisture during the next severe El Niño. Information gleaned from the study can be used to develop better predictions of potential impacts on plants or the food supply so mitigation measures can be implemented, or to improve the understanding of tropical moisture feedbacks and how this might impact regional water supplies or the climate system.

These results advance current understanding of how the strategies trees use to gain nutrients can influence which bacteria and fungi are in the soil and what they are doing. Further, these results show that diverse bacteria and fungi produce more diverse products when they consume leaf litter. These diverse products are sticky and can stay in the soil longer, which may store carbon in the soil rather than be released to the atmosphere.

Temperate forest trees have different strategies to acquire the nutrients they need. Researchers found that these different strategies can influence bacterial and fungal diversity in soil. They incubated soils with leaf litter traced into bacteria and fungi that were breaking it down. Results showed that more diverse bacterial and fungal communities used the leaf litter and produced more varied products than products from the less diverse communities.

Microbial decomposition transforms plant litter into microbial products that can remain in the soil and keep carbon (C) from returning to the atmosphere. Recent theories have suggested that decomposition depends on what type of carbon compounds enter the soil and how they impact the diversity of microbes and their function. This research explicitly tests these theories by using (1) quantitative stable isotope probing, which makes it possible to trace isotopically labeled litter into different microbial species as they are consumed, and (2) metabolomics, which enables researchers to see how the microbes transform this litter into new metabolic products that could stick in the soil. In both cases, the litter that is heavier in ¹³C than the more common ¹²C essentially acts as a dye that can be traced through the soil at fine scales. Results showed that differences in how trees gain nutrients through two types of helper fungi called mycorrhizae led to arbuscular mycorrhizal (AM) soils harboring greater diversity of fungi and bacteria than ectomycorrhizal (ECM) soils. When incubated with two types of ¹³C enriched litter that varied in how easily they are broken down, researchers found that the more diverse microbes in AM soils shifted their decomposition pathways depending on how easy the litter was to eat. Essentially, who was eating the litter and what products were changed by litter type. By contrast, the decomposition pathways were more static in the less diverse ECM soil. Importantly, the majority of these shifts were driven by species only present in the AM soil, suggesting a strong link between microbial identity and their ability to decompose and assimilate substrates. Collectively, these results highlight an important interaction between ecosystem-level processes and microbial diversity, whereby the identity and function of active decomposers impact the composition of decomposition products that can form stable soil C.

Understanding how forests will respond to rising CO₂ is critical for predicting changes in the Earth’s climate. The results of this study highlight the importance of understanding large tree mortality.

Researchers used simulations to explore how size- and age-dependent mortality of trees will affect changes in forest carbon storage in response to rising CO₂. They found that faster growth as a result of increased CO₂ caused increases in forest biomass that were twice as large when mortality was age-dependent compared with size-dependent.

Little is known about how the probability of death changes as trees get older and larger. However, as rising CO₂ is expected to cause trees to grow faster, it is important that we understand whether this will lead to trees growing larger or whether they will continue to die at the same size, but in less time. This has important implications for the amount of carbon stored in forest ecosystems and how long it is stored. Researchers used simulations to explore how different mechanisms of tree mortality could affect forest carbon storage. They found that increased growth from simulated increases in CO₂ caused increases in biomass that were twice as large when mortality was age-dependent compared with size-dependent. Further, they found a much larger decrease in carbon storage time when mortality was size-dependent, as trees move through their life cycles more rapidly.

This article represents a first benchmarking and parameter sensitivity of the full-complexity FATES model using multiple dimensions of plant trait variation alongside other ecosystem parameters. The team finds that the representation of competition fundamentally alters tropical forest function, and that parameters controlling the dynamics of competition, such as disturbance rate and intensity, control ecosystem structure and function.

Tropical forests are a critical ecosystem in governing terrestrial feedbacks to global change. Representing the complex ecological dynamics that determine these processes is a crucial gap in Earth system models. Researchers have developed the FATES model to explore and represent complex ecological dynamics and are testing the model at tropical forest field sites to explore how the representation of plant traits and ecosystem parameters govern forest structure and function.

Tropical forests are a critical and dynamic ecosystem, but the ecological complexity of these regions is not represented in existing Earth system models. A research team developed the FATES model for use in E3SM to address this modeling gap. The team tested FATES within the E3SM Land Model (ELM) to explore how plant trait variation and competition between different plant functional types at a tropical forest site governs model predictions of the function and structure of the forest. Using a set of 12 plant traits whose variability has been observed at the field site, the team used ensembles of model runs to explore both the trait variation and how structural differences in the representation of competition determine model outcomes. These were compared to observations at the site. The team found that adding larger numbers of competing plant types increases the productivity of the forest and thus points to a need to better represent tradeoffs that prevent any one type from dominating an ecosystem. They also found that the balance between early successional and late successional functional types is highly sensitive to the representation of disturbance intensity, disturbance extent, and the degree of determinism in light competition by the trees, thus pointing to the need to focus on these processes in testing and benchmarking the model.

Storing more carbon in the soil removes carbon dioxide (CO₂) from the atmosphere. But the extent of this effect depends on how much carbon soils could hold. This study expands our understanding of the causes of soil carbon saturation and informs how we might manage soils to store more carbon. Soil carbon storage could be limited by controls on microbes, which are easier to manipulate than soil traits. Under the right conditions, soils that seem to be saturated might be able to store more carbon. This study also highlights important microbial dynamics that are missing from current models.

Organic additions to soils increase ecosystem carbon storage, but soils have a limited capacity to store carbon. Researchers call this phenomenon “soil carbon saturation”. Normally, researchers assume that soil traits cause carbon saturation, yet microbial processes are also critical in controlling soil carbon dynamics. In this study, scientists advance a new hypothesis: soil carbon saturation can be caused by the factors that limit microbial populations. To evaluate this hypothesis, they compiled data from experiments and embedded alternative hypotheses in soil carbon models.

Increasing soil carbon storage is a key strategy to reduce atmospheric CO₂. Adding organic inputs to soils increases carbon storage, but soils can only store so much carbon. This phenomenon of “soil carbon saturation” could result from properties of soil itself. For example, there is a widely assumed upper limit to soil carbon that increases with soil clay content. In this study, researchers suggest that soil carbon saturation could also be driven by constraints on soil microbes. The authors compiled data from field and laboratory experiments and found evidence of microbial population limits as organic inputs increase. Then, they simulated these limits in a soil carbon model and found that saturation could occur even without assuming an innate upper limit. The results imply that more realistic representations of microbes in soil carbon models could help us predict how soils will respond to environmental change and could help us manage soils to store more carbon.

The model provides a physically-based and rapid approach to estimate the seasonal discharge of water from tundra soils to bodies of surface water, while accounting for the complex geomorphology of ice-wedge polygons. Simulating seasonal discharge mechanisms is becoming increasingly important to understanding the tundra hydrologic and nutrient cycles as climate change causes small-scale surface water bodies to expand across the Arctic. Insights from this work will be used to improve representations of Arctic surface hydrology in global-scale Earth System Models.

A new mathematical model explores how the unusual geomorphology of tundra features, known as ice-wedge polygons, influences the export of shallow groundwater into surface drainage networks. The model reveals that the fraction of the subsurface which “flushes” into surface water, potentially carrying dissolved organic carbon and other nutrients with it, is strongly impacted by geometric features that can be measured from space. This research opens the door to using ongoing remote sensing work to improve knowledge of tundra hydrologic and nutrient cycles.

Ice-wedge polygons segment the soil of tundra landscapes into distinctive units resembling the cells of a honeycomb that measure up to thirty meters across. Individual polygons are often bounded by rims of soil up to a half meter high and function as miniature basins, storing surface water in the central depression. This unique geomorphology strongly impacts the partitioning of precipitation into infiltration, evaporation, and runoff. It also results in complex drainage processes, whereby the water in the central depression slowly flushes outward through the soil of the rims over the course of the summer, discharging into a network of troughs resembling a gutter system at the polygon boundaries. This flushing has the potential to mobilize large amounts of soil organic carbon and dissolved nutrients as climate change causes polygonal troughs to deepen and the discharge of water to intensify.

A new model developed by researchers at Los Alamos National Laboratory simulates this complex hydrology in three dimensions, allowing scientists to predict how quickly the ponds in the centers of polygons drain and what fraction of the subsurface is flushed by this drainage. The model reveals that drainage is strongly influenced by geometric attributes, which can be easily measured through remote sensing. Ice-wedge polygons with small diameters, for example, drain more quickly than others, and a greater fraction of the interior soil is flushed by shallow groundwater. The model also reveals that as climate change increases the depth of seasonally thawed soil in ice-wedge polygons, the fraction of the thawed portion of the subsurface which is flushed grows, increasing the potential for export of soil organic carbon and other nutrients into surface water.

Because the model uses an analytical approach to simulate groundwater flow, it runs very fast and, thus, is an ideal tool to efficiently identify the mechanisms and processes acting to move water across these unique landscapes, which are present throughout the circumpolar Arctic. Insights from the model will eventually be used to improve representation of the complex near-surface hydrology of polygonal tundra landscapes in coarse-resolution Earth System Models, such as DOE’s Energy Exascale Earth System Model (E3SM).

The Root Ecology Handbook will improve trait comparisons across studies and integration of information across databases by providing standardized methods, controlled vocabularies, and ecological context to improve our quantification of important belowground traits around the world.

A large team of experts in belowground ecology developed a Root Ecology Handbook, the first “Community Resource” published by the international plant journal New Phytologist. The handbook provides standardized methodology, controlled vocabulary, and important ecological context for developing field and laboratory studies to quantify aspects of belowground ecology, ranging from root physiology to root distribution across spatial gradients. A companion paper critically evaluated the current strengths and gaps in belowground plant trait knowledge and identified future research challenges in the field of root ecology.

Due to a recent influx of research on plant root functions and their impact on the environment, root ecologists are being challenged to continue generating cutting-edge, meaningful, and integrated knowledge. However, methodology to examine belowground plant ecology is disparate and sometimes inappropriate. A Root Ecology Handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardized methods, controlled vocabularies, and ecological context for the observation of important belowground processes, ranging from root physiology to the distribution of roots across spatial gradients. In a companion paper, the team draws on literature in plant physiology, ecophysiology, ecology, agronomy, and soil science to review several aspects of plant and ecosystem functioning and their relationships with a number of root system traits, including aspects of architecture, physiology, morphology, anatomy, chemistry, biomechanics, and biotic interactions. Most importantly, the team found that belowground traits with the broadest importance in plant and ecosystem functioning are not those most commonly measured. Taken together, the aim of these companion papers is to help break down barriers between the many subdisciplines of root ecology and ecophysiology; broaden researchers’ views on the multiple aspects of root study; and encourage new, comprehensive experiments on the role of roots in plant and ecosystem functioning.

A fundamental ecological goal is to use easily-measured plant characteristics, traits, to predict plant function. Aboveground, ecologists use the ‘leaf economics spectrum’ of acquisitive to conservative leaf traits to predict photosynthesis and leaf lifespan across the world and in response to changing environmental conditions. The belowground ecologist working group investigated whether there is a parallel ‘root economics spectrum’ belowground that predicts plant resource acquisition based on root traits. Instead of a one-dimensional root economic spectrum that parallels leaves, the group found that a two-dimensional economic space was needed to encompass root resource acquisition strategies, because unlike leaves, roots have the ability to outsource resource acquisition to mycorrhizal fungi partners (Bergmann et al. 2020).

A working group of belowground ecologists from around the world met three times over a period of two years at the German Centre for Integrative Biodiversity Research (iDiv) in Leipzig, Germany with an overarching goal of improving our understanding of how root traits vary among species and around the world. New understanding was based on the foundation of data compiled in the DOE-funded global Fine-Root Ecology Database (FRED), and the species-specific data subset of these data compiled in the Global Root Trait (GRooT) Database (Guerrero‐Ramírez et al. 2020).

Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from “live fast and die young” to “slow and steady,” but the economy defined by root strategies belowground remains unclear. Here, the belowground ecologist working group takes a holistic view of the belowground economy and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1810 species across the globe confirm a classical fast-slow “conservation” gradient but show that most variation is explained by an orthogonal “collaboration” gradient, ranging from “do-it-yourself” resource uptake to “outsourcing” of resource uptake to mycorrhizal fungi. This broadened “root economics space” provides a solid foundation for predictive understanding and model representation of belowground responses to changing environmental conditions.

Despite the complex and highly dynamic nature of flood processes, this study demonstrated the ability of a physically based inundation model in E3SM for realistic simulation of floodplain inundation. Global simulations of flood inundation provided insights on the mechanisms for floods and their trends in major basins around the world.

Floods account for a significant and increasing number of reported natural hazards globally. As extreme precipitation is projected to increase in a warmer climate, there is an urgent need to improve understanding and modeling of floods to improve flood prediction and inform infrastructure planning. Analyses of flood characteristics have focused on using streamflow data, but flood inundation area has more direct societal and ecological implications.

A team led by scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory calibrated and evaluated a newly developed floodplain inundation model in the Energy Exascale Earth System Model (E3SM). Global simulations of flood inundation area for 1953-2004 revealed significant changes in flood generation mechanisms in some basins around the world. In the Amazon basin, for example, increasing concentration of extreme rainfall events within the wet season has increased its contribution to floods in the recent decades by synchronizing the occurrence of extreme rainfall more often with saturated soil in the wet season.

In this study, scientists applied a newly developed, physically based inundation model coupled with a river routing model (Model for Scale Adaptive River Transport, MOSART) within the Energy Exascale Earth System Model (E3SM) framework to investigate flood inundation dynamics. After calibration using observed streamflow and satellite-derived flood extent, the model was used to simulate global flood inundation from 1953 to 2004. The mean date and seasonality of annual maximum flood, defined based on flood extent, exhibit significant regional differences across 16 major basins.

Generally, soil moisture and monthly maximum daily rainfall are the dominant drivers of floods in tropical basins while monthly maximum daily snowmelt is the dominant driver in high latitude basins. From 1953-1982 to 1975-2004, significant changes in flood generation mechanisms are found in some basins such as Amazon, Lena, Yenisey, and Kolyma. Analysis of the rainfall seasonality and water balance at grid scale reveals during the later period, the occurrence of extreme rainfall has concentrated more in the wet season in the Amazon, which increases the co-occurrence of extreme rainfall and wet soil to produce flooding.  Fewer extreme rainfall events and increasing soil moisture reduced the contribution of monthly maximum rainfall and increased the role of monthly maximum snowmelt in floods in the Lena and Yenisey basins, respectively. Lastly, increased soil moisture and frequency of large monthly maximum snowmelt reduced the contribution of the latter to floods in the Kolyma basin. This study demonstrates the usefulness of the floodplain inundation model in E3SM for understanding floods and predicting their future changes.

The team provided and tested methods for improving carbon cycle predictions through advancing model predictions of leaf area. Tree‐level carbon allocation to leaves should be derived from first principles using mechanistic plant hydraulic processes in vegetation models.

Trees adjust their leaf area based on their traits and environmental conditions, which has enormous impacts on global carbon fluxes. A research team from the University of Utah used first principles to predict leaf area adjustment in response to global change.

Forest leaf area has enormous impacts on the carbon cycle because it mediates both forest productivity and resilience to climate extremes. Trees are capable of adjusting to changes in environment, yet many vegetation models use fixed carbon allocation schemes independent of environment, which introduces uncertainty in predictions. A team of researchers developed an optimization‐based model in which tree carbon allocation to leaves is an emergent property of environment and plant traits. A combination of meta‐analysis, observational datasets, and model predictions show strong evidence that optimal hydraulic–carbon coupling explains observed patterns in leaf allocation.

Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture but require calibration. In this study, a team of researchers from Lawrence Berkeley National Laboratory developed the first field‐based calibration of TDR sensors in an old‐growth upland forest in the central Amazon, evaluated the performance of the calibration, and then applied the calibration to determine the dynamics of soil moisture content within a 14.2‐m‐deep soil profile. They found that the widely used Topp model underestimated volumetric water content by 22 to 42%, suggesting that site‐specific calibration of TDR sensors for tropical soils is necessary. This new calibration will enable more accurate measurements of soil moisture in tropical soils, improving model representation of system hydrology and providing researchers with a better understanding of drought effects, forest vulnerability to water stress and mortality, vegetation succession under changing environmental conditions, and water cycling across the soil-plant-atmosphere system.

Soil moisture plays a key role in the hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. However, accurate soil moisture measurements in the Amazon are difficult because of logistical constraints. To improve the understanding of ecohydrological processes within tropical forests, realistic soil moisture data in the Amazon are required. Such data also would improve models of these systems in the face of changing environmental conditions.

Depth-specific TDRs were calibrated using local soils in a controlled laboratory experiment, producing a novel calibration. The sensors were later installed to their specific calibration depth in a 14.2 m pit. The widely used Topp model underestimated the site-specific volumetric water content (θ_v) by 22-42%, indicating significant error in the model when applied to well-structured, clay-rich tropical forest soils. The calibrated wet- and dry-season θ_v data showed a variety of depth and temporal variations, highlighting the importance of soil textural differentiation, root uptake depths, and event- to seasonal-precipitation effects.

Studies including root data in Puerto Rico are representative for the tropics. However, fine-root functional trait data for tropical ecosystems have not been fully explored. The research team’s synthesis will be used to enrich root database representation for the tropics, and ultimately better inform broader Earth System Models.

Researchers synthesized and analyzed studies and raw data on root systems in Puerto Rican tropical forests, including data from Spanish-language publications not previously published in English. They compared these studies and data with other tropical studies and identified key knowledge gaps to be addressed for future studies.

Fine roots play an important role in plant nutrition, as well as in carbon, water and nutrient cycling. Fine roots account for a third of terrestrial net primary production (NPP), and inclusion of their structure and function in global carbon models should improve predictions of ecosystem responses to climate change. Unfortunately, studies focusing on underground plant components are less frequent than those on aboveground structure. This disparity is more marked in the tropics, where one third of the planet’s terrestrial NPP is produced. Available tropical forest fine root data in Puerto Rico is overrepresented considering its land cover. This Caribbean island’s biodiversity, frequency of natural disturbances, ease of access to forests, and long-term plots have created an ideal place for the study of tropical ecological processes. This literature review emphasizes 50 years of root research and patterns revealed around Puerto Rico. The data in this review were compiled from scientific publications, conference reports, symposiums, and include new raw data shared by some researchers. Emergent patterns for fine roots include the shallower distribution of fine roots compared to other tropical forests, the greater root:shoot ratio compared to other tropical meta-analysis, the little variation in root phosphorus concentrations among forest types, and the slow recovery of root biomass after hurricane disturbance. Because more than half of the data on roots come from the wet tropical Luquillo Experimental Forest, other habitat types are under-represented. Gaps in knowledge about fine roots in the Puerto Rico’s ecosystems, are noted as examples to promote and guide future studies.

This study presents new direct estimates of fine‐root productivity and turnover in a Central Amazonian plateau tropical forest, as well as the factors controlling their dynamics, which are crucial to the understanding of above‐ versus below-ground trade‐offs and linkages determining forest function. The findings demonstrate a relationship between fine‐root dynamics and precipitation regimes and emphasize the importance of deeper roots for accurate estimates of primary productivity and the interaction between roots and carbon, water, and nutrients.

A common assumption in tropical ecology is that root systems respond rapidly to climatic cues but that most of that response is limited to the uppermost layer of the soil with relatively limited changes in deeper layers. However, this assumption has not been tested directly, preventing models from accurately predicting the response of tropical forests to environmental change.

The objective of this study was to quantify the patterns and controls of fine-root productivity, standing stock, and mortality and fine-root population turnover across the vertical soil profile in an Amazonian plateau tropical forest. The team measured seasonal dynamics of fine roots with high spatial and temporal resolution using minirhizotrons to see below the surface in a mature forest in Central Amazonia. Minirhizotron measurements were calibrated with fine roots extracted from soil cores. Direct observations of fine‐root dynamics to a depth of 90 cm enabled researchers to reach three important advances in understanding fine‐root dynamics in this site. First, although the largest fraction of fine‐root biomass and productivity is in the top 10 cm of the soil profile, a substantial fraction is deeper than 30 cm (46.1% and 40.6%, respectively). Second, as is often assumed but rarely observed, fine‐root turnover declined with depth. Third, seasonal variation in precipitation drives root dynamics, but the direction and strength of the influence of precipitation varies with depth. Fine‐root productivity and mortality in surface layers were positively related to precipitation. Fine‐root stock was greater in dry periods in the deepest layer where water is likely more available at that time. Results from this study extend the quantification of root dynamics to deeper in the soil profile than previous studies in tropical forests, contributing to our understanding of ecosystem NPP, carbon cycling, and environmental controls on fine‐root dynamics.

While previous studies synthesized trait data and tree mortality through meta-analysis, this study is one of the first to investigate tree mortality in response to drought for a large number of tropical tree species. Even though it requires more effort to measure hydraulic traits, they best predict mortality rates among species. Results are being used to improve how tree mortality is modeled, and the data set from this study has already been downloaded 30 times from a digital archive.

Drought-related tree mortality may increase with future changes in rainfall. However, researchers lack a complete understanding of which trees and species are most vulnerable to drought. This project used long-term records of tree death and databases of functional traits and distribution patterns to understand the responses of 53 species to extreme drought in a seasonally dry tropical forest in Costa Rica. Mortality rates during the drought ranged from 0 to 34 percent among tree species. Hydraulic traits were best correlated with mortality rates. These results suggest which traits to measure to predict future changes in tropical forest composition.

Drought-related tree mortality is now a widespread phenomenon predicted to increase in magnitude with climate change. However, the patterns of which species and trees are most vulnerable to drought and the underlying mechanisms have remained elusive—in part due to the difficulty of predicting the location of catastrophic drought years in advance. This research used a 10-year record of tree mortality rates and extensive databases of functional traits and distribution patterns to understand the responses of 53 species to an extreme drought in a seasonally dry tropical forest in Costa Rica, which occurred during the 2015 El Niño Southern Oscillation event. In this biodiverse forest, species-specific mortality rates during the drought ranged from 0 to 34 percent and varied little as a function of tree size. By contrast, hydraulic safety margins were well correlated with probability of mortality among species, while soft traits such as wood density or specific leaf area were not. This firmly suggests hydraulic traits as targets for additional research and provides an approach to predict which species and forests will be vulnerable to future droughts.

Future projections of net primary productivity (NPP) under climate change scenarios reveals species-specific differences in seasonal leaf development and function should be included in modeling. Inclusion of species-specific seasonal photosynthetic parameters should improve estimates of boreal ecosystem-level NPP, especially if impacts of seasonal physiological development can be separated from seasonal acclimation to prevailing temperature.

Earth system models are used to understand carbon, water and energy fluxes between forests and the atmosphere. Models often use a single parameter value to represent a process, such as the rate photosynthesis, but not allowing for seasonal changes in that process reduces the predictive capacity of the model.

Researchers measured seasonal photosynthetic capacities for seven dominant vascular species in a wet boreal forest peatland, then applied data to a land surface model parametrized to the study site (ELM-SPRUCE) to test if seasonality in photosynthetic parameters results in differences in simulated plant responses to elevated CO₂ and temperature. The team found significant interspecific seasonal differences in specific leaf area, nitrogen content and photosynthetic parameters (i.e., maximum rates of Rubisco carboxylation (Vcmax), electron transport (Jmax) and dark respiration (Rd)). Application of these observations to the ELM-SPRUCE land model by species (or plant functional type) indicated that the model was particularly sensitive to parameter seasonality under simulations with higher temperature and elevated CO₂, suggesting a key hypothesis to address in future studies.

Earth system models typically group different species into plant functional types, such as boreal evergreen shrubs. However, this research shows that even species from the same family (Ericaceae) can have very different phenological and physiological responses to changes in environmental conditions. As such, it is important to consider the differential acclimation of individual species in order to improve the performance of model projections.

Plant species growing at the southern edge of their ranges must acclimate to warming temperatures if they are to remain competitive. Understanding how different species respond seasonally to new conditions will provide insight into potential shifts in community composition and ecosystem function in the future.

Researchers measured seasonal patterns of photosynthesis, respiration, and non-structural carbohydrates for two dominant woody evergreen shrubs in a wet boreal forest peatland exposed to whole ecosystem warming and elevated carbon dioxide concentrations. Warming created a longer active season for both species, and there was evidence of thermal acclimation of both photosynthesis and respiration, although this varied seasonally and between species. Chamaedaphne (leatherleaf) photosynthesis increased with temperature under moderate warming but declined above +4 °C, while there was no evidence of this thermal acclimation for Rhododendron (Labrador tea). Overwintered Rhododendron leaf respiration rates decreased with temperature up to +9 °C, while there was no evidence of this thermal acclimation for Chamaedaphne. Under elevated CO₂ conditions, both species had large increases in leaf sugars and starch, and this coincided with a reduction in both N content (Chamaedaphne only) and photosynthetic capacity. Species-specific performance and vigor depends on the balance between thermal and CO₂ acclimation and how those processes play out over longer-time scales.

Low-lying coastal areas in the mid-Atlantic region are subject to TC-induced compound flooding due to the co-occurrence of river floods and coastal storm surges. Increasing coastal resilience to compound flooding necessitates understanding the effect of different factors on compound flooding and the spatial distribution of TWLs in the estuary. This research identified three different zones in the DBE where floods are dominated by river flow (upstream), storm surge (downstream), and a combination of the two (transition). Information obtained from this study can be used to support the development of adaptation and mitigation strategies for coastal communities.

Tropical cyclones (TCs) can generate extreme coastal storm surges and river flooding, causing devastating damage to coastal communities. However, little is known about how interactions between storm surges and river floods exacerbate coastal flooding, especially at the top of high tides. Through a detailed modeling analysis of the Delaware Bay Estuary (DBE), researchers separated the total water level (TWL) induced by storm surge, river flow, and tides into components to investigate contributions of the different factors and their interactions to coastal flooding. Based on the simulation results, researchers identified three distinct zones along the DBE, including a transition zone where the interaction of a river flood and storm surge can result in compound flooding.

This study investigated the effect of nonlinear interactions on coastal compound flooding induced by the co-occurrence of river floods and coastal storm surges in the DBE using a 3-D, high-resolution storm surge model. Specifically, the coastal flooding, or TWL, induced by historical extreme weather events—Hurricanes Irene (2011), Sandy (2012), and Isabel (2003) and Tropical Storm Lee (2011)—were simulated and analyzed. Simulated water levels were decomposed to astronomical tides, low-frequency surge, and nonlinear interactions. The effects of the nonlinear interactions on the TWL were further analyzed. Model results show that the DBE can be divided into three zones: (1) the river dominated, (2) the storm surge dominated, and (3) the in-between transition zones. Analysis results indicate the effect of tide–surge–river interactions on TWL in the transition zone was more noticeable during compound flooding events. Sensitivity analyses also indicate that the transition zone of compound flooding shifts downstream as river flow increases.

Disturbance from cyclones impacts the structure and function of forests. Therefore, it is important to understand how forests in different regions were affected by past cyclones and gain improved insights for future cyclones. This study reveals the links between remote sensing of forest disturbance intensity and the factors of wind and rainfall, forest structure, terrain features, and soil properties at the landscape scale, and discusses the possibility of using machine learning to help predict the impact of hurricanes on forests.

Scientists used satellite images of the impacts of multiple tropical cyclones to study what factors contribute to different impacts on forests brought by hurricanes. Scientists found that a 40 m/s wind speed threshold affects a cyclone’s impact, but discovered little consistency in the influence of other variables. Each cyclone interacted with the landscape in a unique way. In addition, the researchers discussed the difficulties for building a model that can predict the location or damage of future cyclones.

This study addressed the importance of climate variables, terrain features, and forest properties in predicting tree damage caused by cyclones. Wind, elevation, and pre-disturbance vegetation condition are strong predictors. Cyclones interacted with the landscape in unique ways, and there are no consistent rules can be applied to all the cyclones. Machine learning technologies were used to build cyclone impact models, and this study showed the limitations of machine learning models in cyclone effects prediction. The models worked well on hold out test data, but they had weak predictability on unseen cyclones. The authors believe that finer scale data can be helpful to build local models that work with similar ecosystems and landscapes; however, the complexities of cyclone effects coupled with landscapes, soils, states of affected systems, and climate change lead to questions regarding the existence of an omnipotent cyclone impact model that works for the globe.

Plant scientists require detailed and extensive information on the concentration and distribution of physiological and structural leaf properties to study vegetation responses to environmental change, monitor plant health, and facilitate the rapid screening of different plant phenotypes. Traditional approaches to measure these traits directly are expensive and logistically challenging. Therefore, scientists developed an alternative spectroscopic approach for the rapid, accurate and non-destructive estimation of traits using remote sensing data, along with tools for broadening its use and standardizing its application.

The estimation of leaf traits, such as leaf nitrogen, from hyperspectral reflectance data enables rapid, high-throughput, non-destructive characterization of leaf function and plant phenotyping with applications in ecosystem characterization and monitoring. However, lack of a standard approach for developing and reporting this information has limited the wider application of the technique. To address these challenges, scientists developed a detailed description of the use of partial least squares regression (PLSR) to predict leaf traits with spectra and offer recommendations for best practices across all steps of the process: from experimental design and data collection, to PLSR model building, model application, and reporting of results. Hands-on tutorials are also provided to assist users to in understanding these best practices for PLSR modeling and application with their own data.

Plant physiologists and ecologists regularly measure leaf functional traits, including leaf nitrogen or photosynthetic rate, across a range of leaves, plants, species, or environments. These direct measurements, while very accurate for characterizing leaf structure and function, are typically slow, expensive, and can be logistically challenging. In addition, many ecological or phenotyping studies require many samples, which can be impractical with traditional methods. On the other hand, remote sensing methods have been shown to be effective for the rapid estimation of many of key leaf traits; however, inconsistent usage of the methods have led to challenges in the wider application across the plant sciences. To address this challenge and to help standardize the approach across studies to facilitate wider adoption, scientists provide a detailed summary of the spectral method of leaf trait estimation. Clear examples and tutorials as well as a range of suggested best practices are also provided to illustrate how to use the approach. Importantly, scientists also highlight how the same approach can be scaled up to estimate vegetation traits across landscapes using non-contact remote sensing data.

The ability to accurately predict the response of tropical forests to future climate scenarios depends on appropriate understanding and representation of key ecosystem processes. The inconsistencies between model projections and real-world measurements of tropical forest carbon, water, and energy fluxes identified in this study help point to a need to improve representation of the processes by which tropical trees use, store, and move water and reflect light in current models to enable accurate predictions of the Earth system.

Forests help regulate the exchange of water and carbon dioxide between the land surface and the atmosphere. However, their influence depends on how efficiently forests access and use light and water during the year. Scientists used measurements from four flux towers in tropical forests of the Amazon to evaluate a predictions of carbon, water, and energy exchange from four  forest simulation models, and found that models predict Amazon forests dry-up too often and too quickly and  reflect more of the incoming solar radiation, when compared to observations.

Using data collected in four eddy flux towers across the Amazon, scientists quantified the seasonal cycle of sensible heat flux, evapotranspiration, emission of thermal infrared radiation, and optical properties of forest canopies. The seasonal cycle of these parameters was also simulated  using four terrestrial biosphere models that are often used to predict the future of the Amazon (namely IBIS, ED2, JULES, and CLM3.5). Comparing model predictions with tower measurements revealed that most models predict a strong seasonality of the Bowen ratio (i.e., ratio between sensible and latent heat flux), and overall low water use efficiency. Consequently, models predicted that Amazon forests experience more frequent water stress than had been observed. Likewise, models predicted that forest canopies would reflect more light than observed.  Three possible explanations for such differences are suggested. First, models do not represent when leaves shed or replace leaves, which may bias the canopy reflectance. Likewise, models seem to exaggerate the canopy interception of rainfall, which reduces the predicted soil available water. Finally, inaccurate estimates of water stress lead to discrepancies between predicted and observed outgoing longwave radiation. These findings can be used as references for future model development.

Carbon sequestered by tropical forests during normal and wet years can be released during drought years due to tree mortality and reduced ecosystem productivity. Recent drought-related plant mortality has been attributed to drier air from increasing vapor pressure deficit (VPD) associated with climate change. This research disentangled the relative impact of VPD and soil water stress on canopy water conductance that controls plant transpiration at a tropical forest site in Panama, highlighting the need for new data collection and improved model representation of drought response mechanisms to improve predictive understanding of tropical forest responses to drought.

Water stress from dry soil or dry air  can trigger plant drought responses to limit water loss through transpiration. However, separating the cause of the response between these two interactive, co-occurring stresses is challenging. To disentangle these stresses, this study used statistical models based on field observations and results from a land surface model with an added capability to simulate water movement in the soil and water transport within the plant at a tropical forest site in Panama. Researchers found that dry soil is more influential than dry air in limiting plant water loss at the site during an El Niño drought.

In this research, field data and numerical modeling were used to isolate the impact of dry soil and VPD on evapotranspiration (ET) and gross primary productivity (GPP) at a tropical forest site in Barro Colorado Island (BCI), Panama, focusing on their response to the drought induced by the El Niño event of 2015-2016. Numerical simulations were performed using a plant hydrodynamic scheme (HYDRO) and a heuristic approach that ignores stomatal sensitivity to leaf water potential in DOE’s Energy Exascale Earth System Model (E3SM) Land Model (ELM). The sensitivity of canopy conductance to VPD obtained from eddy-covariance fluxes and measured sap flux shows that, at both ecosystem and plant scale, soil water stress is more important in limiting canopy conductance than VPD at BCI during the El Niño event. The model simulations confirmed the importance of water stress limitation on canopy conductance, but overestimated the VPD impact compared to that estimated from the observations. During the dry season at BCI, seasonal ET, especially soil evaporation at VPD > 0.42 kPa simulated using HYDRO and ELM, was too strong and will require alternative parameterizations.

Rooting depths are a critical unknown for modeling forest response to droughts, which are projected to intensify. Due to challenges in measuring rooting or water-sourcing depths, researchers have relied on above-ground traits to assess the likelihood of drought-induced tree mortality. The models developed through this research will allow wider integration of rooting depths and drought exposure in drought resilience studies.

In a rainforest of Barro Colorado Island, Panama, scientists from Los Alamos National Laboratory and other institutions as part of the Next Generation Ecosystem Experiment (NGEE)-Tropics developed and tested the first inverse model of trees’ rooting depths that is integrated with plant physiology. Deep-rooted tree species had water transport systems that were likely to fail when water stressed. However, across a variety of drought conditions, deep-rooted species were less dehydrated and survived better than shallow-rooted tree species, especially among evergreen trees. This emphasizes the need to incorporate drought exposure risk in evaluating tree drought resilience.

Deep-water access is arguably the most effective, but under-studied, mechanism that trees employ to survive during drought. Functional traits such as the degree of vulnerability of trees’ water-conduits to blockage due to air-entry (embolism) can predict mortality risk at given levels of dehydration, but deep-water access may delay tree dehydration. Here, scientists tested the role of deep-water access in enabling survival within a diverse tropical forest community in Panama using a novel data-model approach.

Scientists inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990–2015) to vapor pressure deficit, water potentials in the whole-soil column, and leaf hydraulic vulnerability curves. They validated ERD estimates against existing isotopic data of potential water-access depths.

Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35 years (1981–2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress-related mortality risk through deep-water access.

The role of deep-water access in mitigating mortality of hydraulically-vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

Landfalling TCs are major drivers of catastrophic flood hazards in the Mid-Atlantic region, where reducing flood risk and damage involves crucial coastal management decisions. However, existing research exploring TC-related hydrological extremes focuses mostly on regional scales, which are too coarse for realistic decision-making, which often occurs at local or catchment scales. Through analysis of extreme events based on long-term, spatially distributed observational datasets, this research reveals substantial spatial variability in TC impacts on the severity of floods and droughts. The results of this research highlight the need to prioritize sites for coastal hazard risk management and adaptation. They also demonstrate the importance of high-resolution modeling for characterizing spatial heterogeneity in processes and system responses.

Researchers lack knowledge about the climatological characteristics of landfalling tropical cyclones (TCs) and their local-scale hydrological impacts over the Mid-Atlantic region. Through analyzing long-term observational datasets, this research found strong spatial variability in how TCs affect floods and droughts within the region. For instance, while TCs appear to have negligible impacts on flooding in the northern part of the region, they increase the magnitude of 100-year floods by over 50% for the southwestern portion. However, to varying degrees, TCs can alleviate hydrological droughts (in frequency and duration) for most of the region.

Researchers performed a climatological analysis of long-term (from 1950‒2019), spatially distributed observational datasets of hurricane tracks, precipitation, and streamflow. The analysis provided local scale understanding of the climatological characteristics and hydrological impacts of TCs over the Mid-Atlantic region, defined as the Delaware River Basin (DRB) and Susquehanna River Basin (SRB). Although TCs make limited contributions to regional precipitation (<9%), they are the major trigger of most extreme floods in the southern part of DRB (tributaries of the Christina River and lower Delaware River) and the southwestern portions of SRB (tributaries of the Lower Susquehanna and Junita River). TCs also alleviate droughts in these areas to a comparatively higher degree. Importantly, researchers observed a strong spatial variability of TC’s impact on floods and droughts within and across the basins. For instance, while the TC effect on flood is negligible for the high-elevation, northern part of the region, TCs increase the magnitude of the 100-year flood by up to 19.6% in DRB and 53.0% in SRB. TCs also reduce the duration of short-term extreme hydrological drought by up to 25.0% in SRB and 24.7% in DRB, respectively.

Tree root fungal association has a significant effect on local soil ecosystems and carbon and nitrogen cycling. This paper provides a better understanding of the effects of human intervention and climate change on root fungus type dominance and identifies shifting patterns associated with the effects. These findings are critical for improving ecosystem models to predict forest ecosystem processes and functions in global climate change.

Researchers used forest inventory data from the U.S. Department of Agriculture to create the first comprehensive distribution map of root fungus association of more than three million trees in the continental United States. Additionally, researchers used soil carbon and nitrogen stock data to determine soil organic material relationships to fungal type and broader human-induced changes to root-fungus association across the eastern United States and global impacts.

Research found that most regions in the eastern United States are shifting to one primary plant-fungal association (arbuscular mycorrhizal; AM), and away from the other (ectomycorrhizal; EM). These shifts include a higher dominance of AM saplings than adult trees in 7 of 11 ecoregions, meaning that this trend is going to continue and potentially escalate in the future. Further analysis shows that of all the human-induced global changes, nitrogen deposition, fire frequency, and climate change are driving factors for the shift in mycorrhizal association.

Tree root fungus association is often used as a useful predictor of fungal biomass and microbial interaction of soils. Knowledge of these associations informs ecosystem models that lack the data necessary to integrate fungal contributions to decomposition processes, nutrient cycling, and soil carbon storage. Shifts in fungal association of forests can have predictable and scalable impacts on fungal biomass and biogeochemical processes in soil, and including this information leads to better predictive ecosystem models.

Prior to this study, only one type of tree root fungus at one time was studied for their effect on the microbial and fungal biome of local soils. Researchers studied the complexity within plots with varying percentages of two types of fungal association to determine intermingled linkages.

This study shows that the dominant fungal (i.e., mycorrhizal) association of trees alters carbon and nutrient cycling by selecting for microbial groups with distinct enzyme function for nutrient acquisition. Furthermore, if soil carbon-nitrogen ratio and fungal association percentages of tree species are known in a given area then microbial and fungal biome characteristics can be estimated using the MANE (mycorrhizal associated nutrient economy) framework. Thus, by using a mycorrhizal-driven, trait-based approach, ecosystem models can start to predict the effects of species shifts on soil biogeochemical processes, especially at large spatial scales.

Nitrogen is one of the primary limiting factors for plant photosynthesis, which is, in turn, a limiting factor of how much CO₂ a plant can uptake from photosynthesis. In a previous synthesis, researchers (Terrer et al. 2016) showed that systems dominated by arbuscular or ectomycorrhizal fungi differed in sensitivity to elevated CO₂. This review provides context to those findings, highlighting how other changes in plants under elevated CO₂ can have cascading effects. The incorporation of a plant-microbe system, which models dynamic nitrogen acquisition strategies, is more accurate than previous methods that forced fixed nitrogen limitations for land surface models.

A major determining factor in how a plant acquires nutrients is its associated soil microbial community. In the case of symbiotic mycorrhizal fungi, plants provide the fungi with sugars in exchange for nutrients acquired from soil by the fungi. This exchange is especially important under atmospheric elevated CO₂, as plants provision some of the “extra” sugar to promote more nutrient-acquisition by the fungi. In this synthesis, researchers explored how plants from two of the most dominant mycorrhizal groups — arbuscular and ectomycorrhizal fungi — dictate the carbon cost of nitrogen acquisition in an elevated CO₂ environment, which may determine ecosystem sensitivity to elevated CO₂.

This paper outlines a plant economics framework in which a plant’s efficiency in acquiring nitrogen is measured by the “return on investment” received for the carbon it puts into building roots and fueling soil microbes. Plants are broken into three groups of soil microbe association: arbuscular mycorrhizae (AM, one type of root fungus), ectomycorrhizae (ECM, the other root fungus), and N-fixing bacteria (which retrieve nitrogen from air to give to trees). Researcher found that ECM-associated trees had the best return on investment, meaning that ECM plants received large amounts of nitrogen while putting comparatively less carbon into acquisition. In contrast, AM trees were the least effective at this return on investment. These findings are important information for determining the future of terrestrial ecosystems in an elevated CO₂ environment and showcase the importance of soil-microbe association of plants in modeling techniques.

The results of this study show that ecosystem responses to global change may hinge on the balance between a plant’s ability to decompose soil organic matter and a plant’s efficiency at exploring surrounding soils for physically protected nutrients. This research ultimately highlights the importance of dynamically linking plants and microbes in terrestrial biosphere models.

Plants and the fungus that grow on their roots have symbiotically evolved together and adapted unique strategies for acquiring nutrients from soil that is dependent on the species of fungus. This association can determine a wide variety of things from leaf litter decay rates to plant carbon allocation to wood, roots, and sugars provided to the root fungus in trade for nutrients. This paper takes a look at the complexities of these interactions and provides a new ecosystem model that uses these interactions to improve accuracy of carbon and nitrogen cycle estimations.

Nutrients in soils can be protected in many ways. For instance, chemical composition of soil can make it energetically demanding to decompose organic matter, or nutrients can be easily decomposed but protected behind some kind of physical location or barrier. Plants and their root fungus often determine these factors, and have adapted specialized tactics for gathering nutrients in these environments. This paper describes a new model created to account for plant-microbe symbioses for better estimations of their effects in global land models.

In the search to manage, measure, and predict climate change interactions with terrestrial ecosystems, researchers provide a staunch reminder of thoughtfulness in choosing variables for terrestrial models. With the flood of data from new imaging and genetic techniques it is important not to lose sight of less complex and cheaper proxies that could provide just as much value. A closer examination of the current knowledge gaps in soil carbon cycling and of the proxies researchers already use may allow us to develop new hypotheses and specify criteria for new and needed proxies.

Finding the ideal measurable characteristics to accurately represent complex ecological interactions is the holy grail of modeling research. Sometimes, measurements of a system are not easily obtainable or even impossible to acquire, and thus a substitute variable known as a proxy is used in place of the more complex variable. Ideal proxies are easy to measure and have high predictive value for the characteristic or system they are attempting to represent, but in practice have a range of ease and value. This paper emphasizes the thoughtful use of proxies to maximize predictive value and illustrates this concept with practical examples, outlining future measurements and techniques for modeling soil carbon dynamics.

The soil carbon cycle is highly complex and driven by a vast suite of environmental, physical, and biological factors. Various proxies for soil characteristics are evaluated as correlative representations (meaning they can be used in place of more complex variables) or integrative frameworks (combinations of measurements used to describe the underlying mechanism) for soil carbon dynamics. The authors provide a glimpse into the future with new and emerging proxies focusing predominantly on genome-sequence data and how they will help evolve our understanding of the terrestrial ecosystem.

The findings of this study allow for a better understanding of the production and storage of SOM by the different tree fungus types. The results also provide insight into the long-term storage and stability of SOM based on fungus type. These insights will lead to a better understanding of SOM dynamics and support the use of fungus type as an important measurable biotic factor that can be used to improve land surface models.

Researchers measured carbon and nitrogen soil organic matter (SOM) levels along with other soil composition metrics for trees with varying symbiotic root fungi dominance and at different sample depths down to one meter. The goal was to determine how fungus type affects the soil composition of forests and what implications this may have for forest longevity and stability.

The two primary root fungus types associated with trees are arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM). AM dominated trees were found to have higher levels of SOM at lower depths than ECM dominated trees while ECM soils had higher levels of SOM in upper surface soils. These findings, combined with the fact that carbon has a slower turnover time at lower depths in the soil, implies greater long-term storage and greater SOM stability in AM-dominated soils. Auxiliary measurements of soil composition reveal these patterns were driven by an accumulation of microbial residues in AM-dominated soils, which supports emerging theory on SOM formation and plant/mycorrhizal effects on soil.

Despite a high demand for data on plant-fungal relationships, this study is the first to synthesize plant-fungal associations into a global distribution map based on field data. It is becoming more and more evident that plant-fungal associations are essential for understanding how nutrients are cycled and stored in the ecosystems. Inclusion of plant-fungal distributions into vegetation models could provide a benchmark for testing hypotheses about how fungi affect ecosystems.

The relationship that plants form with root fungus is most often mutually beneficial, but sometimes can be harmful and growth-stunting to the plant. This study demonstrates that a particular type of root fungus, ectomycorrhiza (EcM), generally “walks the line” between mutualism and parasitism versus other species of fungus. Because EcM usually require more sugars (carbon) in return for the nutrients they scavenge for the plant, the fungus and root system tend to grow larger, while aboveground mass of the plant remains smaller. This paper shows that root fungus association is related to carbon stocks above- and belowground on a global scale, while also demonstrating the negative impacts agriculture may have on belowground carbon stocks.

The plant-fungal distribution maps were derived from land vegetation cover maps. With information about which species of plants typically inhabit a land cover type on a specific continent and which fungus typically colonizes that species the authors were able to derive the global distribution. Changes and losses in fungal colonization illustrated by this map could have a strong negative effect on carbon stocks, ultimately leading to less healthy soils and ecosystems.

Earth system models (ESMs) require detailed information on the structural and functional properties of leaves across global biomes in order to simulate vegetation responses to global change and inform policy decisions. Traditional approaches used to characterize plant properties which are key inputs for ESMs are slow and limited to small geographic areas. On the other hand, remote sensing approaches that this research enables can be used to remotely measure these traits over large areas and through time.

The traditional approaches used to measure many leaf functional traits, including the amount of leaf mass per unit area (leaf mass per area, LMA) are destructive, laborious, time consuming, and expensive. Researchers developed a novel spectroscopy approach, which utilizes measurements of light reflected by leaves, which can be used as an alternative to rapidly and non-destructively infer foliar traits across plants growing from the high Arctic to the tropics.

Leaf mass per area (LMA) is a key plant trait used in ecological research and climate modeling. LMA reflects fundamental tradeoffs between in resource investments to leaf photosynthesis, longevity or robustness, and structure. Characterizing the within and across biome spatial and standing goal of ecological research and is an essential component for advancing Earth system models (ESMs). In this study, researchers explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m^-2. Researchers used leaves collected from a wide range of locations encompassing broad- and needleleaf species, and upper- and lower-canopy (i.e., sun and shade) growth environments. They demonstrated the ability to rapidly estimate LMA using only leaf reflectance data with high accuracy and low error. These findings highlight the fact that the leaf economics spectrum is mirrored by corresponding variation in leaf optical properties, which paves the way for this technology to predict the diversity of LMA, and potentially a range of other leaf traits, in ecosystems across global biomes.

Many research and management applications use or rely on greenhouse gas and water fluxes measured at tower stations. Such applications include Earth or ecosystem models and remote-sensing products that are designated at fixed and explicit spatial extents. In contrast, flux measurements have relatively dynamic source areas. Such spatial mismatch leads to unknown uncertainties and biases. New research evaluates the potential biases resulting from the spatial mismatch at hundreds of tower stations. The study also provides general guidance for using flux data across many stations and paves the way for better integration and synergy among flux measurements, models, and remote-sensing.

Ecosystem-scale greenhouse gas and water fluxes are measured with the eddy-covariance technique at hundreds of tower stations across the Americas. These direct measurements are used in many research and management applications. A major challenge in using the flux data is their unknown and dynamic source areas, which vary with measurement heights, wind direction, and atmospheric conditions. Now scientists have developed a robust method to trace the source areas at hundreds of tower stations for the first time. They also proposed a simple index that can be used to identify sites suitable for specific applications.

Large datasets of greenhouse gas and water fluxes measured with the eddy-covariance technique (e.g., AmeriFlux and FLUXNET) are widely used to benchmark models and remote-sensing products. This research addresses one major challenge facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? The study evaluates flux footprints—the temporally dynamic source areas contributing to fluxes—and the representativeness of these footprints for target areas often used in synthesis and modeling studies. Scientists examine the land-cover composition and vegetation characteristics across AmeriFlux sites and evaluate potential biases due to the footprint-to-target-area mismatch. Monthly footprints vary across sites and through time ranging four orders of magnitude from 1,000 to 10,000,000 m². Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that adopt a fixed-extent target area across sites introduce biases on the order of 4%–20% for vegetation characteristics and 6%–20% for the dominant land cover percentage. The findings highlight the need for flux datasets to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information.

Climate change impacts global vegetation and threatens the health of temperate and tropical forests. Traditional methods for studying tree photosynthesis are slow and expensive, limiting the amount of usable information for investigating forest responses to climate change. To address this major challenge, scientists have developed new remote sensing methods that allow for more rapid estimation of photosynthesis. These methods provide significantly more data for the same amount of time used with traditional approaches and will help to better inform models.

Computer models used to simulate vegetation under different environmental conditions require detailed information on the properties of leaves that regulate photosynthesis. Traditional collection methods for these properties are slow and expensive. On the other hand, measuring the light reflected from leaves allows scientists to non-destructively infer these photosynthetic properties. Also, these more rapid and robust remote sensing approaches allow researchers to collect substantially more data than traditional methods. By leveraging these tools, scientists can provide improved information for models.

Remote sensing approaches, from leaf to whole-landscape scales, can fill critical observation gaps in scientific understanding of global vegetation. Using spectrometer instruments, scientists can quickly measure light reflected from leaves and infer the underlying photosynthetic properties needed to investigate vegetation responses to climate change. This study leveraged spectrometers to illustrate how scientists can develop simple, general approaches for examining photosynthesis across a wide variety of trees. It also revealed how spectrometers were more effective than other alternative approaches currently used by researchers. By continuing to improve and use reflectance measurements, scientists will be able to obtain the information needed to make models better at predicting how plants will change in the future.

This study presents a novel methodological approach to quantify how hillslope subsurface flow and chemical transport contribute to stream flow and water quality.

Quantifying connections between snowmelt infiltration and seasonal variations in solute export to surface waters is frequently confounded by a lack of critical measurements.  This study introduces a novel approach whereby distributions of fluid flow paths are highly resolved through the use of critical subsurface measurements to reveal their strong temporal sensitivity to snowpack accumulation and melt timing.

Although most of the water entering watersheds permeates through soil and underlying bedrock before entering rivers, subsurface flow paths and their influence on river water chemistry are poorly understood. This study presents a new framework for quantifying depth- and time-dependent subsurface flow and solute transport along an intensively studied hillslope that utilizes in-situ hydrologic and geochemical measurements to constrain predictions. Results quantify the importance of abrupt groundwater excursions accompanying snowmelt for mobilizing dissolved chemicals in soil and weathered bedrock, with the latter responsible for the greatest contribution to solute export. The new concept of subsurface concentration-discharge relations was developed through this work that provides information needed to mechanistically explain solute concentrations and flow measured in rivers. With information on topography, meteorology, and subsurface hydraulic properties, this framework is broadly transferrable to other hillslope and watershed settings.

Lead is highly toxic, and its consumption in any amount is considered unsafe. As a result of mining activities and leaded gasoline, soil lead contamination is widespread. It is critical to understand the fate of Pb in soils to assess the risks it presents to freshwater quality. Dissolved Pb is particularly dangerous, as it is easily transported and consumed. The research findings reveal that although common solid Pb phases are dissolved during changes in water levels in floodplain soils, released Pb is immediately retained on particulate organic matter, and dissolved Pb remains low. Thus, although the soils studied contain appreciable Pb, it likely does not pose a threat water quality in dissolved form.

Lead (Pb) contamination in soils is a major threat to water quality. Although Pb tends to occur in sparingly soluble minerals, changes in dissolved oxygen concentrations can promote dissolution of these minerals, potentially causing spikes in dissolved Pb concentrations and transport of dissolved Pb to drinking water sources. Researchers examined the fate of Pb during changes in oxygen concentrations in contaminated floodplain soils and found that Pb released from mineral phases is retained by particulate organic matter (POM). Thus, POM limits spikes in dissolved Pb concentrations and prevents transport of dissolved Pb.

Objectives were to resolve Pb speciation and partitioning across hydrologically controlled redox transitions and to determine the extent of Pb release during these transitions. To examine the effects of soil redox transitions on Pb partitioning, researchers tracked solid-phase Pb speciation and dissolved Pb concentrations in mining-affected floodplain soils near Crested Butte, CO. Groundwater levels at the study site varied seasonally, driving changes in soil redox conditions. The team collected depth-resolved soil and porewater samples at 2 – 4 week intervals between June 2 and October 26, 2018, while monitoring groundwater levels hourly. Findings determined solid phase Pb speciation using Pb L3-edge extended X-ray absorption fine structure (EXAFS) measurements. When water levels were high in June and early July, iron- and sulfate-reducing conditions developed in the soils—dissolving Fe(III)-(hydr)oxides, releasing associated Pb, and promoting PbS formation. As water levels declined into August, oxygen was reintroduced to the soil profile, and Fe(III)-(hydr)oxides precipitated while PbS was dissolved. A beaver dam was built near the site in late August, which caused water levels to rise again, resulting in Fe reducing conditions. As reducing conditions transitioned to oxidizing conditions and vice versa, researchers observed an increase in Pb adsorbed on particulate organic matter. They also did not observe increases in dissolved Pb concentrations. Taken together, this indicates that particulate organic matter retains Pb released during dissolution of Fe(III)-(hydr)oxides and PbS, thereby limiting its dissolved concentrations in porewater.

This study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

One fundamental uncertainty in terrestrial biosphere models relates to model parameters, configuration variables internal to the model whose value can be estimated from data. To address this uncertainty, a team of researchers incorporated a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. The team examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). Different parameterizations of TEM were set up across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula.

TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and leaf stomatal responses to ambient light conditions. Results also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had nearly equal uncertainty, while respiration from soil microbes had higher uncertainty than the pool of soil C.

Previously, oxidation of U(IV) has been posited as the dominant mechanism by which U is released into groundwater. However, depending on the redox-buffering capacity of the sediment, U(IV) can persist during influxes of oxidants. An additional mechanism of U mobilization from anoxic sediments is therefore needed to explain persistent elevated groundwater concentrations. This work suggests that adsorbed U(IV), whether complexed by organic matter or clay mineral surfaces, could be mobilized by desorption (e.g., by changing pH or alkalinity). Second, this work provides a mechanistic context for colloidal mobilization of U(IV). Researchers speculate that U associated with POC could be mobilized as that POC is transformed into smaller, more oxidized and more soluble units through hydrolytic degradation reactions. Also, disaggregation of organo-mineral aggregates under changing geochemical conditions (pH, ionic strength, redox) causes the release of organic matter into the dissolved and colloidal phase, along with associated metals. Thus, the team concludes that the dominance of organic matter (and clay mineral)-associated U provides a new framework to understand U mobility in the subsurface.

In contaminated aquifers, hydrologic and geochemical conditions can cause tetravalent uranium (U(IV)) – a form of uranium once considered largely immobile – stored in the sediments to be mobilized in the groundwater, and elevate the groundwater uranium concentration above the regulatory limit. However, the mechanisms by which U is released from sediment to solution remain unknown. This work combined nano-scale imaging (nano secondary ion mass spectrometry and scanning transmission X-ray microscopy) with a density-based fractionation approach to physically and microscopically isolate organic and mineral matter from anoxic alluvial sediments contaminated with U (collected from the Riverton, WY site). Previous research applied a combined spectroscopy-microscopy approach to examine U behavior in model systems, which allowed the research team to unambiguously identify U(IV) adsorption, as opposed to precipitation, as the major mechanism of U(IV) retention in aquifer sediments. Through examination of unaltered sediment from the Riverton site, researchers have extended and deepened their analysis, leading to the identification of two distinct populations of complexed U control its behavior in anoxic sediments: (1) U adsorbed to organic matter (including particles rich in both carboxylate and phenolic functional groups derived from both plant and microbial material) and (2) U adsorbed to organic-clay aggregates. This is the first study to demonstrate unambiguously a major role for organic matter as a U(IV) sorbent in unaltered sediments from an alluvial aquifer.

Uranium contamination threatens the availability of safe and clean drinking water globally. This toxic element occurs both naturally and as a result of mining and ore-processing in alluvial sediments, where it accumulates as tetravalent U [U(IV)], a form once considered largely immobile. Changing hydrologic and geochemical conditions cause U to be released into groundwater. Knowledge of the chemical form(s) of U(IV) is essential to understand the release mechanism, yet the relevant U(IV) species are poorly characterized. There is growing belief that natural organic matter (OM) binds U(IV) and mediates its fate in the subsurface. In this work, researchers sought to examine the speciation of U in sediment from a contaminated alluvial aquifer to definitively determine whether OM was the dominant U(IV) sorbent. They applied nanoscale chemical imaging and X-ray absorption spectroscopy to density fractionated sediments in which organic matter was separated from minerals, thereby allowing the team to assess the U speciation in each pool. Researchers identified two populations of U (dominantly +IV) in anoxic sediments. Uranium was retained on OM and adsorbed to particulate organic carbon, comprising both microbial and plant material. Surprisingly, U was also adsorbed to clay minerals and OM-coated clay minerals. The dominance of OM-associated U provides a framework to understand U mobility in the shallow subsurface, and, in particular, emphasizes roles for desorption and colloid formation in its mobilization.

In low-salinity, low-sulfate groundwater systems, common in many floodplains, sulfidation of ferrihydrite will generate FeS nanoclusters that will remain suspended and can be transported by groundwater. These materials can sorb metal micronutrients (e.g., Mn) and contaminants (e.g., Zn), allowing them to be mobilized to surface waters or to reactive zones in the aquifer where they may be utilized by microorganisms or accumulate as contaminant loads. These observations highlight the potential for sulfidic conditions to mobilize trace metals and promote their biogeochemical cycling. These conclusions place a large asterisk on the conventional view that sulfidic conditions generally stabilize metals through precipitation reactions.

Nanometer to micrometer sized mineral particles (often associated with organic carbon and often referred to as colloids) that remain suspended in water can play major roles in mediating the mobility of nutrients, metals and radionuclides in groundwater. Yet, the factors controlling their occurrence and stability are poorly understood. The reaction of common soil Fe(III) oxyhydroxides with dissolved HS- has been proposed as a pathway by which sulfidic nanoparticles can be naturally generated in groundwater. This study confirms that this process can form stable iron monosulfide nanoclusters. (Clusters are defined here as precursors of nanoparticles) The rate of sulfidation, ionic strength of the groundwater, and abundance of organic compounds, were found to control the stability of FeS nanocluster suspensions generated from ferrihydrite sulfidation. This research provides a conceptual model for predicting the conditions under which sulfidation of ferrihydrite will generate FeS nanoclusters.

Synchrotron-based EXAFS spectroscopy, transmission electron microscopy, Fourier-transform ion-cyclotron-resonance mass spectrometry, and aqueous measurements were used to determine the stability and molecular structure of nanoclusters generated by sulfidation of ferrihydrite and to identity the composition of natural organic carbon compounds associated with them. This research shows that sulfidation of ferrihydrite generates nm-scale aqueous FeS clusters. Their tendency to condense into nanoparticles, aggregate, and settle, was directly related to the sulfide/Fe ratio. At sulfide/Fe ratios ≤0.5, FeS nanoclusters and larger nanoparticles remained in suspension for up to several months. At sulfide/Fe ratios >0.5, sulfidation reaction rates were rapid and FeS nanocluster aggregation was accelerated. The presence of organic compounds increased the time of suspension of FeS nanoclusters, whereas increased ionic strength inhibited the generation of FeS nanoclusters.

FeS nanoclusters are responsible for electron transfer in many biogeochemical pathways. Thus, suspended FeS nanoclusters could function as electron shuttles, influencing geochemical processes and heterotrophic microbial activity in aquifers. Moreover, FeS nanoclusters can directly bind nutrients and contaminants via sorption reactions and contribute to their transport in (sub)surface waters. This statement is corroborated by numerous previous studies proposing that contaminant mobility in groundwater can be directly associated with FeS mobility in the aqueous fraction.

The required climate-change mitigation efforts depend directly on the evolution of future terrestrial carbon storage. Researchers integrated observational evidence from forests, tree-rings, volcanic CO₂ springs, atmospheric and ice-core measurements, satellites, and flux-towers with experiments to provide a robust foundation for future research into plant and soil carbon storage, a crucial ecosystem service. Developing this robust and integrated foundation of literature will enable the research community to better quantify historical carbon uptake, and more accurately predict future carbon uptake. Improved understanding will inform better natural resource and ecosystem service management.

The global responses of plants and soils to increasing atmospheric carbon dioxide are slowing the rate of climate change, but these responses are complex and process understanding remains unresolved. A large amount of data have been collected, but have never before been integrated. Evidence supports the idea that plants and soils store more carbon in response to increasing atmospheric CO₂. However, the size of this response is uncertain, and other agents of global change (e.g., land cover change) are also important contributors. Despite uncertain size, this change in carbon storage is likely to decrease going into the future.

Atmospheric CO₂ is increasing, leading to climate change. Increasing CO₂ also increases leaf-scale photosynthesis and water-use efficiency. These direct responses have the potential to increase plant biomass and soil organic matter, removing carbon from the atmosphere into terrestrial ecosystems (a carbon sink) and slowing the pace of climate change. However, ecosystem CO₂-responses are complex or confounded, and evidence for a CO₂-driven terrestrial carbon sink can appear contradictory. An international team of over 60 scientists, led by researchers at Oak Ridge National Laboratory, synthesized theory and broad, multidisciplinary evidence for the effects of increasing CO₂ on the global terrestrial carbon sink.

Evidence for increasing terrestrial ecosystem carbon storage caused by increasing atmospheric CO₂ indicates a highly valuable ecosystem service that effectively subsidizes fossil fuel emissions by slowing the rate of CO₂ accumulation in the atmosphere. However, due to concurrent changes caused by other global change factors, the size of this subsidy remains unclear. Based on diminishing direct physiological responses, likely increasing nutrient limitations, increasing mortality, and other negative temperature-related effects, it is highly likely that increases in terrestrial carbon storage due to increasing atmospheric CO₂ will decline into the future. A decline in this subsidy will result in accelerated climate change per unit of anthropogenic CO₂ emissions.

Researchers provided the first observational evidence of a reduction in the response of urban phenology to temperature in major U. S. cities. The research team discovered these urban-rural phenology differences are mainly associated with the changes of background climate and urban heat island (UHI) effect intensity.

Researchers investigated changes in the start of season (SOS) and the covariation between SOS and temperature () investigated using remote-sensing SOS observations and process-based phenology models for 85 large cities and adjacent rural areas across the conterminous United States between 2001–2014.

Urbanization causes environmental changes, such as urban heat islands, which affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Researchers investigated the changes in the satellite-derived and the  in 85 large cities across the conterminous United States between 2001–2014. They found that (1) the SOS came significantly earlier (6.1 ± 6.3 days) in 74 cities, and  was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); (2) the decreased magnitude in  mainly occurred in cities in relatively cold regions with an annual mean temperature of <17.3°C (e.g., Minnesota, Michigan, and Pennsylvania); and (3) the magnitude of urban-rural difference in both SOS and  was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in  magnitude in urban areas. These findings provide the of reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in non-urban environments may decline in the future.

The modeling results suggest that landscapes with older, larger ice wedges are among the most vulnerable to climate change. This finding may help improve global-scale assessments of the permafrost-climate feedback by improving the representation of tundra landscapes in earth system models.

Much of the northern permafrost zone contains ice wedges, or large bodies of buried ice, within a couple meters of the ground surface. New modeling results indicate climate change may cause older, larger ice wedges to melt before younger ones, altering surface topography and ecosystem functioning.

Across the Arctic, an area ten times the size of Britain is underlain by large bodies of nearly pure ice, known as ice wedges. In recent years, climate change has caused many ice wedges to start melting from the top down, causing depressions known as thermokarst troughs to develop at the surface. These thermokarst troughs often fill with water, while the surrounding soil becomes better drained, thereby altering rates of carbon dioxide and methane emissions from the landscape. We constructed a numerical model of thawing processes beneath developing thermokarst troughs to assess factors controlling permafrost vulnerability. The results indicate that thaw intensity is strongly impacted by trough width. The permafrost beneath wide, flooded troughs may degrade much more rapidly than the permafrost beneath narrow troughs, due to a contrast between the efficiency with which ponded water absorbs solar radiation and sensible heat in summer, and the inefficiency with which that energy is released back to the atmosphere in winter, often via conduction through the adjacent, non-inundated sediments. Additionally, the permafrost beneath wide, flooded troughs may be more sensitive than other permafrost to changes in winter air temperatures and snow depths. These findings are important because they imply that areas of old permafrost (i.e., areas that haven’t been affected by recent erosion or sedimentation), which tend to have the largest and widest ice wedges, may be the most vulnerable to rapid changes in ecosystem functioning caused by ice wedge degradation as air temperatures rise. The sensitivity of wide ice wedges to winter conditions is important, because in many areas of the Arctic, changes to winter air temperatures and snow depths have been even more pronounced than changes to summer air temperatures.

Understanding the plant nutrient cycle is a key component for predicting adaptation of the terrestrial ecosystem to climate change. This and many other studies found that mycorrhizal association has a large impact on nutrient cycling, and is also detectable from satellite images and should thus be included in modern land surface models.

The ability of plants to resorb nutrients from leaves before they die off, either stress induced or as part of developmental aging, is a large component of nutrient cycling in the terrestrial ecosystem. Researchers found that the amount of nutrients resorbed from leaf death during developmental aging differs between plants based on the type of fungus (i.e., mycorrhizae) that grows on their roots.

Trees typically associate with one of two main root fungi at a time: ectomycorrhizal (ECM) or arbuscular mycorrhizal (AM) fungi. The results of this study suggest that trees with different mycorrhizal associations show different nutrient resorption patterns across global, biome, and local scales. For example, trees have a higher resorption rate in nutrient starved boreal regions but lower resorption in tropical areas probably due to rapid litter decay being a more efficient source of nutrients. These results illustrate the complex and multifaceted nature of the nutrient cycle, and demonstrates that mycorrhizal association plays a large role in determining plant nutrient uptake and resorption strategies.

More accurate estimates of the amount of CO₂ taken up terrestrial ecosystems can be reported by including plant-microbe symbioses in climate models. The findings from this paper suggest that ecosystems that depend on different nitrogen acquisition methods are key to understanding rates of future climate change. To reach an increase in global net production and higher CO₂ use, there would need to be a shift to species with better nitrogen acquisition methods.

Ecosystems remove CO₂ from the atmosphere and in doing slow climate change. However, given that low availability of soil nitrogen can limit how much CO₂ plants can take up, factors that control nitrogen cycling are key modulators of an ecosystem’s carbon uptake potential. Two groups of microbes that live in symbiosis with plants control nitrogen cycling in most ecosystems: fungi, which extract nitrogen from detritus and exchange it with plants, and bacteria, which take up atmospheric nitrogen from the atmosphere and exchange it with plants. Both mechanisms provide plants with sources of nitrogen that allow them to process more CO₂, but these mechanisms have not been incorporated into existing climate models.

Simulations were run using an existing land model that simulates carbon cycling in vegetation and soil, as well as water and energy fluxes. This was combined with a new coupled carbon-nitrogen cycle framework and an explicit model of plant-microbial symbioses to create a more robust global land model that accounts for nitrogen constraints of vegetation responses to elevated CO₂. Results of the new model illustrate the major drivers in carbon-nitrogen cycling, global patterns of nitrogen acquisition, and the global response to an increase in CO₂.

This research incorporated an extensive experimental design, which provides a framework for testing future hypotheses about litter-soil organic matter interactions and identifies novel mechanisms that necessitate further exploration in situ. This study lays the groundwork for further research to determine the generality of tree root fungus influence on litter decay.

Root litter represents a significant carbon input to soil organic matter; however, few studies have considered how soil environment affects root litter decay rates, or how decaying roots influence the decay of leaf litter and soil organic matter. Given that forest soil environments are in large part determined by the type of root fungus that associates with trees, researchers investigated how these tree-associated fungal groups affect litter decay.

Researchers used a factorial combination of fungal (i.e., mycorrhizal) soil type (including mixtures of mycorrhizal soils), litter treatments (roots, leaves, and combinations), litter mycorrhizal types, and replications, resulting in 144 different microcosms to measure the interactions of these factors on litter decay. Of the two primary root fungus types, arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM), researchers found that AM root litters decompose faster. The study also demonstrated that decaying roots increased leaf litter mass loss, but only in microcosms containing soils of the same origin. Overall, these results suggest that features of root, leaf and soil organic matter decay are intertwined, and that measurements of these processes in isolation may lead to incorrect estimates of the magnitude and source of carbon losses from soils.

A new hybrid model combines a process-based model with empirical relationships to reveal the fundamental mechanisms of soil thickness and understand spatial variability. This hybrid model generalizes the mechanisms and is therefore applicable to various sites. The soil thickness map can be an essential input for Earth System models, particularly for land surface models.

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness—here defined as the mobile regolith layer—at high spatial resolution remains challenging. An accurate soil thickness map can improve the estimation of water, carbon, nitrogen, and other element dynamics for hydrologic and biogeochemical modeling, but, because of the complexity of factors that affect soil thickness, it remains a key uncertainty.

Researchers developed a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). This model was applied to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River Watershed in Colorado. Two independent measurement methods—auger and cone penetrometer—were used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model.

Sensitivity analysis using the hybrid model revealed that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, results from both sampling and modeling showed that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further revealed that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties.

With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction, but also integrates the strengths of both statistical approaches and process-based modeling approaches.

The increasing number of belowground plant trait observations from around the world has greatly improved scientific knowledge of intricate connections in the hidden world beneath our feet. These observations are brought together in the Fine-Root Ecology Database (FRED), a freely available and searchable database (https://roots.ornl.gov) that focuses on narrow-diameter plant roots. FRED is at the forefront of a burgeoning ‘Belowground Data Revolution’ that spans topics ranging from fungal genomics to improving wheat yield.

Researchers brought together the newest science that updates and adds to current understanding of the role of root and rhizosphere traits in broader ecosystem processes in a Virtual Special Issue that encompassed more than 40 papers published in New Phytologist over the last two years. Advances in scientific understanding of belowground plant traits ranged from new understanding of understudied processes and new observations from underrepresented biomes, like the tundra and tropics, to new and developing linkages among above- and belowground plant traits.

The belowground world is one of the final frontiers in terrestrial ecology. The tangling of plant roots with the surrounding soil below is a lifeline for the humble forbs and towering trees above, and roots play a key role in shaping ecosystem carbon, water, and nutrient cycling. Ecologists have long sought to better understand the ecosystem-scale consequences of differing plant strategies, above- and belowground, by relating plant characteristics, or traits, to plant function. While developing trait–function linkages is arguably more difficult for plant traits that are hidden belowground, root and rhizosphere ecologists continue to fan out across grasslands and forests with their shovels, isotopes, and specialized cameras, seeking a better understanding of the secret lives of roots. Over the years, the international plant journal, New Phytologist, has served as a virtual town square for scientists to discuss their hard-won observations on the interplay among belowground plant traits, microbial activity, and edaphic and environmental conditions from biomes around the world. In a Virtual Special Issue that brought together more than 40 papers published in New Phytologist over the last 2 years, researchers highlighted the newest science that updates and adds to current understanding of the role of root and rhizosphere traits in broader ecosystem processes.

Researchers discovered that the new calculations will modify the estimation of a key physiologic variable, the concentration of carbon dioxide inside the leaf. This modification will improve estimates of other key photosynthesis variables at the leaf level. Ultimately, the new calculations could change projections of forest carbon dioxide uptake and water vapor release, particularly during drought.

Forests capture atmospheric carbon dioxide and transpire a large amount of water vapor. Models used to project the effect of climate change on forests rely heavily upon data and understanding gathered by leaf-level measurement of photosynthesis. Recently, a new theory has improved the calculations underlying instrument operations for photosynthesis measurements. A team of researchers from Brookhaven National Laboratory set out to assess the effect of those new calculations on the measurements themselves, and on the models that are end-users of that data.

Researchers analyzed the effect of the new theory presented by Marquez et al. (2021) on measurements of gas exchange variables by photosynthesis measurement systems. The new theory now includes representation of the cuticular conductance pathway, which was not considered in the previous theory by von Caemmerer and Farquhar (1981). Marquez et al.’s theory also improves representation of conditions at the leaf surface and better represents the collision between gas molecules. The main impact of applying the new theory is a reduction in the estimation of intercellular CO₂ concentration (C_i) during photosynthesis. Parameter estimates that are dependent upon measurement of C_i will be impacted. Notably, this includes V_cmax, the maximum carboxylation capacity of Rubisco, which is a key parameter of Earth system models. These improvements will not only enhance gas transport representation in models but also explicitly account for fluxes through the leaf cuticle, which are currently estimated using stomatal models.

Increased freshwater export has implications for salinity and other components of the lagoon aquatic environment. Increased runoff in late summer or autumn could support increasing biological production in the lagoons during a time when nutrient levels are lower, compared to late spring. These results highlight the need for dedicated measurement programs of climate change impacts on coastal zone processes in Arctic regions.

Mounting evidence shows that climate change is impacting flows of water and carbon in Arctic rivers. In northern Alaska, field sampling data is too limited to differentiate new from normal baseline conditions. This research applied numerical modeling to investigate climate changes impacting a coastal lagoon over recent decades. The simulation reveals significant increases in freshwater and dissolved organic carbon exports. Large increases in subsurface freshwater and carbon flows during autumn are congruent with expected impacts from sea ice losses across the nearby Beaufort and Chukchi Seas.

This study applied numerical modeling to investigate trends in freshwater and dissolved organic carbon (DOC) exports from land to Elson Lagoon in Northwest Alaska over the period 1981–2020. The model simulation reveals significant increases in surface, subsurface (suprapermafrost), and total freshwater exports. Findings included significant increases in surface and suprapermafrost DOC production and export. The largest changes in subsurface components are noted in September, which has experienced a 50% increase in DOC export from suprapermafrost flow. Direct coastal suprapermafrost freshwater and DOC exports in late summer more than doubled between the first and last five years of the simulation period, with a large anomaly in September 2019 representing a more than fourfold increase over September direct coastal export during the early 1980s. The changes are linked to increasing precipitation, particularly during summer-autumn, and the effects of warming and thawing soils. The largest freshwater and DOC increases occur in autumn, consistent with significant losses in sea ice across the nearby Beaufort and Chukchi Seas, in turn connected to Earth’s warming climate.

This research improves understanding of the Arctic’s carbon cycle; the way that carbon is transferred between the land, ocean, and atmosphere. The modeling framework provides a basis for understanding carbon export to coastal waters and for assessing impacts of water cycle intensification and permafrost thaw with ongoing warming in the Arctic. It can help to refine baseline magnitudes and better understand how global warming is altering the Earth’s carbon cycle.

Arctic rivers export large amounts of freshwater to coastal waters. The water is rich with organic matter. Mobilization and land-to-ocean transfer of dissolved organic carbon (DOC) in Arctic watersheds is linked with the region’s climate and water cycle, which is at risk of changing as a result of a warming climate. In this research, scientists applied a modeling framework to simulate dynamics of permafrost hydrology, DOC leaching, and river loading. Results revealed a marked east-west gradient in simulated spring and summer DOC concentrations of 24 river basins on the North Slope of Alaska. These findings are consistent with independent river sampling data. Nearly equivalent loading occurs to rivers which drain north to the Beaufort Sea and west to the Bering and Chukchi Seas.

Arctic rivers transfer a relatively large amount of freshwater to the Arctic Ocean compared to other oceans. These rivers contain organic carbon dissolved in the water, with the bulk arriving during the high flow in spring that follows snowmelt. Because evidence shows that climate warming is thawing permafrost and resulting in more carbon traveling through rivers to the Arctic Ocean, it is important to understand how much enters river networks from soils. To estimate how much dissolved organic carbon is loaded to rivers in the western Arctic over the period 1981 to 2010, scientists used a computer model designed to capture the seasonal thawing and freezing of Arctic soils and seasonal snowpack accumulation. For northern Alaska rivers, the simulation shows a gradient in dissolved organic carbon concentration from the east side to the west, similar to the pattern in independent data derived from river measurements. These new estimates suggest that roughly equivalent amounts of dissolved organic carbon are loaded to rivers which empty north to the Beaufort Sea, and west to the Bering and Chukchi Seas. Ultimately, the modeling provides an enhanced understanding of how climate change impacts flows of water and carbon into the Arctic Ocean.

Researchers from NGEE-Tropics found that changes in wind and light availability drive giant tree distribution as much as precipitation and temperature, together shaping the forest structure of the Brazilian Amazon. The location of giant trees should be carefully considered by policy-makers when identifying important hot spots for conserving biodiversity.

Tall trees are key drivers of ecosystem processes in tropical forests, but the mechanisms controlling the distribution of the very tallest trees remain poorly understood. The recent discovery of giant trees taller than 80 meters in the Amazon forest requires re-evaluating current thinking.

In this study, a research team used the largest airborne lidar data collection in the Amazon to contribute to the understanding of (1) how resources and disturbances shape maximum tree height distribution across the Brazilian Amazon and (2) what drives the occurrence of giant trees (taller than 70 m). They conducted an extensive analysis relating environmental variables to the maximum height recorded in the lidar transects (see figure). Common drivers of height development are fundamentally different from those influencing the occurrence of giant trees. Results indicate that changes in wind and light availability drive giant tree distribution as much as precipitation and temperature, together shaping the forest structure of the Brazilian Amazon. Ultimately, the association between environmental conditions and mechanisms of natural selection are key to understanding the complexity of this process in a changing climate.

Previously, scientists need to step into the field and measure the forest damage after hurricanes. It is a difficult job and also takes long time to get only a small plot of data. This research mapped the forest disturbance on the landscape scale, so we can quickly get access to the damage level on the whole island of Puerto Rico. To better understand the disturbance level, we also explored a number of factors that affect the spatial variation of the disturbance intensity.

Satellite images before and after hurricane María show a major shift in color from green to reddish, indicating widespread impact on forests. Most intense forest disturbance were found on steeper slopes, high elevations, wind-facing direction, close to hurricane track. Different types of trees respond differently to hurricanes.

Widely recognized as one of the worst natural disaster in Puerto Rico’s history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María’s effect on Puerto Rico’s forests as well as the effect of landform and forest characteristics on observed disturbance patterns. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

A better understanding of the biogenic sources of NO_y will help in reducing sources of emissions originating from human activity, help determine hotspots of NO_y emissions, and lead to better land surface models for predicting soil NO_y emissions into the atmosphere.

Reactive nitrogen oxides (NO_y; NO_y = NO + NO2 + HONO) decrease air quality and trap heat and radiative energy in Earth’s atmosphere, yet the factors responsible for their emission from soils remain poorly understood. The difficulties in determining this at a large scale is due to the variability of nitrogen cycling processes within and between large forest systems. This study looks to the two types of symbiotic root fungus that often determine soil biotic composition for insight into reactive nitrogenous gas fluxes in forests.

A common way to determine ecosystem scale effects of forests is to look toward the dominating symbiotic root fungus of the forest because of the large influence the fungus has on soil characteristics and biotic composition of the surrounding soils. There are two primary types of root fungus associated with trees; arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM). It was determined that the soil characteristics and biotic makeup of AM dominated trees resulted in higher production of NO_y. These finding allowed for the prediction of NO_y flux throughout the eastern United States based on percentage ECM tree abundance.

Although this study focuses on the impact of invasive grasses, the ability of these invasive species to deposit carbon was used as a tool to observe how non-specific sources of foreign carbon inputs into soil causes plants and microbes to alter their nutrient acquisition strategies. The results of this study emphasize the importance of resolving the long-term and global effects of enhanced carbon inputs in natural systems.

Most trees associate with one of the two main types of root fungus or the other. The relationship between tree and fungus affects how a tree gains nutrients and alters and processes the soil around it. Invasive plants that invade wide-ranging habitats, accumulate biomass rapidly, and contribute copious amounts of carbon to soil can have significant effect on soil composition. This study shows that these grasses change soil organic matter of trees differently based on their fungal association.

An invasive grass that had a particular carbon isotope signature was observed in plots of plants with a different carbon signature such that the carbon contributions to the soil from the invasive species could be measured and compared to pre-invasion measurements. It was found that one of the root-fungal association, arbuscular mycorrhizal (AM), was generally unresponsive to invasion while the other type, ectomycorrhizal (ECM), altered the composition of its soil presumably to access more nitrogen. This alteration of soil organic matter may cause long term or global effects to carbon cycling that needs to be studied further.

Since David Read’s seminal 1991 paper “Mycorrhizas in ecosystems”, few papers have concisely synthesized our understanding of how and why ectomycorrhizal fungi differ in their effects on soil organic matter dynamics, and why this matters for understanding ecosystem responses to global change. This paper should lead to an improved understanding of these dynamics, a novel framework for contextualizing future results, and a blueprint for improving representations of plant-soil dynamics in models.

This two-day workshop at the University of Michigan consisted of presentations and discussions about the role of ectomycorrhizal fungi in ecosystems. The workshop focused on resolving the processes that underlie the seemingly disparate empirical evidence that currently exists regarding the ability of ectomycorrhizae to provide plants with nitrogen from soil and in doing so, to modify soil organic matter. In this paper, the results of the workshop discussions are summarized, and future steps to resolve the lack of information is outlined. Key take away points involve drawing distinctions between fungal-mediated saprotrophy and fungal-mediated soil organic matter modification (enzymatic and non-enzymatic), and considering the role of fungal lineage and interspecific interactions in assessments of how ectomycorrhizae affect ecosystem processes.

While much has been learned about the role of ectomycorrhizae in ecosystems from site-level studies of a few taxa, it’s important to understand how and why ectomycorrhizal fungi differ in their effects on ecosystems. This review article synthesizes what is known and proposes new avenues of research that hold promise for resolving contrasting empirical observations in the field.

Prior to this study it had only been shown that saplings had distinct distribution patterns within a forest based on the fungus that grows on their roots. This study shows that there are distinct distributions of old-growth trees as well. Root fungal association along with other important community structure mechanisms like seed dispersal and seed germination can be used to predict future spatial structures of forests.

Plants alter the composition of the soils that they grow in to either keep plants with negative interactions away or bring plants with positive interactions closer. Most plants and trees also have a symbiotic relationship with fungus that grow on their roots that work together to cycle nutrients in the soil and help each other grow. This paper shows that the root fungus association of plants governs the spatial distribution of both saplings and old-growth trees in mature forests.

It was shown through spatial analysis of saplings and old-growth trees that root fungus association plays a large part in the distribution of plants in a forest. One hypothesis for why this happens is that some fungal associations are prone to pathogens so these plants tend to grow farther away from each other so they’re less likely to spread the pathogens. The other hypothesis is that one type of fungal association has enzymes used to extract nutrients from the soils around them so it is advantageous for these plants to huddle together to better break down soils in the same plot. These hypotheses do not contradict each other and together could explain the community structures.

This work shows the importance of nutrient cycling to climate in Earth system models from a plant-microbe interaction standpoint. This important process had been missing in Earth system models until now—these models are now improved because of this work.

The results of this study suggest that carbon expenditures to support nitrogen-acquiring microbial symbionts have critical impacts on Earth’s climate, and carbon–climate models that omit these processes will over-estimate the land carbon sink and under-predict climate change.

The carbon spent on supporting symbiotic nitrogen uptake reduced net primary production by 8.1 Pg C yr^-1, with the largest absolute effects occurring at low-latitudes and the largest relative changes occurring at high-latitudes. There are strong regional climate impacts if the carbon spent on supporting symbiotic nitrogen uptake is considered in the Community Atmosphere Model (CAM), with the largest impact occurring in high-latitude ecosystems, where such costs were estimated to increase temperature by 1.0 °C and precipitation by 9 mm yr^-1. Thus, our results suggest that carbon expenditures to support nitrogen-acquiring microbial symbionts have critical consequences for Earth’s climate.

This describes recent trends in published experimental work and offers suggestions for potential future directions of experimental work associated with global change biology.

The 25-year history of experiments reported in Global Change Biology was summarized to reveal past trends, and the authors offer subjective educated views on potential future directions.

This commentary summarizes the publication history of Global Change Biology for works on experimental manipulations over the past 25 years and highlights a number of key publications. The retrospective summary is then followed by some thoughts on the future of experimental work as it relates to mechanistic understanding and methodological needs. Experiments for elevated CO₂ atmospheres and anticipated warming scenarios which take us beyond historical analogs are suggested as future priorities. Disturbance is also highlighted as a key agent of global change. Because experiments are demanding of both personnel effort and limited fiscal resources, the allocation of experimental investments across Earth’s biomes should be done in ecosystems of key importance. Uncertainty analysis and broad community consultation should be used to identify research questions and target biomes that will yield substantial gains in predictive confidence and societal relevance. . A full range of methodological approaches covering small to large spatial scales will continue to be justified as a source of mechanistic understanding.  Nevertheless, experiments operating at larger spatial scales encompassing organismal, edaphic, and environmental diversity of target ecosystems are favored, as they allow for the assessment of long term biogeochemical feedbacks enabling a full range of questions to be addressed. Such studies must also include adequate investment in measurements of key interacting variables (e.g., water and nutrient availability and budgets) to enable mechanistic understanding of responses and to interpret context dependency.  Integration of ecosystem-scale manipulations with focused process-based manipulations, networks, and large-scale observations will aid more complete understanding of ecosystem responses, context dependence, and the extrapolation of results.  From the outset, these studies must be informed by and integrated with ecosystem models that provide quantitative predictions from their embedded mechanistic hypotheses. A true two-way interaction between experiments and models will simultaneously increase the rate and robustness of Global Change research.

Both soil carbon and belowground microbial biomass peak at high latitudes, while biodiversity peaks at low latitudes. The emerging understanding highlighted in this paper shows strong and predictable effects of functional diversity on soil microbial respiration and soil carbon stocks, opening the door for improved modeling of soil elemental cycling.

This review paper identified global patterns of biodiversity, organic carbon, and heterotrophic respiration in soils.

Soils harbor a rich diversity of invertebrate and microbial life, which drives biogeochemical processes from local to global scales. Relating the biodiversity patterns of soil ecological communities to soil biogeochemistry remains an important challenge for ecologists and Earth system modelers. We review the state of science relating soil organisms to biogeochemical processes, focusing particularly on the importance of microbial community variation on decomposition and turnover of organic matter. Although there is variation in soil communities across the globe, ecologists are beginning to identify general patterns, e.g., different kinds of mycorrhizal fungi, that may contribute to predicting biogeochemical dynamics under future climate change.

Distinctions between the effects that varying types of tree root fungus have on leaf litter may improve predictions of species effects on ecosystem processes, particularly in temperate forests where the two primary fungus species commonly co-occur. This would lead to a better predictive framework for linking litter quality, organic matter dynamics, and nutrient acquisition in forests.

Leaf litter decay data from previous studies combined with previously unavailable data from the TRY global plant traits database were used to determine the effect of tree root fungus on litter decay at varying latitudes. The goal of the study was to determine the difference in litter decay rates for temperate and sub/tropical forests.

There are two primary types of root fungus associated with trees; arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM). The researchers of this paper hypothesized that AM litters would decompose quicker than ECM litters throughout all latitudes of Earth but instead found that while AM litters decomposed more quickly than ECM litters in temperate forests, this pattern weakened at lower latitudes (i.e. sub/tropical forests). This shows that root fungal type is not necessarily a direct influencer of litter decay but more likely an indirect contributor to some of the many factors controlling litter decay with varying degrees of influence throughout latitudes.

Iron minerals in transient and dynamic (bio)geochemical settings, such as sediment deposits in lakes or ponds, are subject to dissolution and phase transformation reactions, and the fate of sorbed species of contaminants during these processes is currently unknown. For example, iron oxide transformation reactions are expected to affect the molecular-level structure of plutonium associated with these important minerals. These transformation reactions may determine the long-term fate of plutonium in contaminated environments (e.g., contaminated soils and sediments) and engineered environments (e.g., underground nuclear waste repositories). Moreover, these same reactions are likely to play an important role in the mobility, cycling, and availability of other heavy metals and radionuclides in the environment.

Plutonium contamination in the environment threatens water quality and human health. Once released into the environment, plutonium interacts with groundwater, minerals, microbes, and soil. Plutonium and many other heavy metals have a particularly strong affinity for iron oxide minerals. These iron oxide minerals are commonly subject to dissolution and transformation reactions in the environment, and the fate of plutonium during these processes is currently unknown. A multi-institutional team of scientists has recently demonstrated that these transformation reactions will impact plutonium’s molecular-level structure and its subsequent mobility.

The production and testing of nuclear weapons, nuclear accidents, and authorized discharges of radioactive effluents have contributed significantly to plutonium released into the environment. Once released, plutonium has a particularly high affinity for iron oxide minerals, which are common in soils and sediments. These iron oxide minerals are subject to dissolution and phase transformation reactions, but the fate of plutonium during these transformations is not well understood. In laboratory experiments, a multi-institutional team of scientists synthesized an amorphous iron mineral (ferrihydrite) with varying quantities of plutonium, following either a sorption or coprecipitation process. The ferrihydrite was then aged hydrothermally to yield a crystalline product (goethite). This is a common reaction process in nature. In samples prepared following the sorption method, plutonium was identified both as PuO₂ precipitate and as a surface complex. For the samples prepared via coprecipitation, no PuO₂ formation in the ferrihydrite precursor or in the low plutonium concentration goethite was observed. In these coprecipitation products, plutonium was found to be strongly bound to the minerals through either formation of an inner sphere complex, or an incorporation process. The results indicate that iron oxide transformation reactions will affect the molecular-level structure of plutonium associated with these important minerals. These same reactions are likely to play an important role in the mobility, cycling, and availability of other heavy metals and radionuclides in the environment.

Model simulations based on long-term experiments predicted small gains in soil organic carbon, similar to observations from many long-term field warming experiments.

As the climate warms, soil carbon decomposition by microbes may be accelerated to release more carbon dioxide, but most predictions are based on short-term laboratory incubations that might not reflect rates in situ. Here the authors optimize model projections with the Microbial-ENzyme Decomposition (MEND) model using parameters derived from short- and long-term incubations, and find that only the projections from long-term incubations match long-term field-scale observational changes in soil organic carbon.

Predictions of long-term changes in soil organic carbon are needed to understand future climate, but most projections are derived from model simulations based on lab incubations of short durations, e.g., hours to days. Here, model projections were compared from incubation datasets ranging from days to years, from four paired forest and grassland sites, and using substrates glucose and cellulose. Model projections derived from short-term experiments predicted greater losses of soil carbon than projections derived from long-term experiments. The projections from the long-term incubations (> 1.5 y) were more similar to the results of a meta-analysis of warming experiments in the field, which predicted small gains in soil carbon over 1- to 10-year time frames. Mechanistically, the findings represent feedbacks in the microbial community, where warming initially releases more organic carbon substrate for decomposition, but later limits reproduction and growth of the microbial community causing small positive increases in soil organic carbon. These findings suggest that long-term incubation experiments are required to accurately model long-term behavior of soil organic carbon.

The model has been validated against a 22-day polygon drainage event at the Barrow Environmental Observatory. The model simulates the drainage event in under 5 seconds on a 3.1 GHz processor and requires no external libraries, making the model amenable to inclusion in Earth system models.

The timing and flow patterns of ice-wedge polygon drainage have important hydrological, ecological, biogeochemical, and thermal implications for polygon tundra landscapes. Understanding the basic hydrological unit of polygonal tundra landscapes (the single polygon) is key to understanding the overall drainage of these landscapes. To this end, the researchers have developed an efficient model of inundated ice-wedge polygon drainage based on fundamental hydrologic first-principles.

As ice wedge degradation and the inundation of polygonal troughs become increasingly common processes across the Arctic, lateral export of water from polygonal soils may represent an important mechanism for the mobilization of dissolved organic carbon and other solutes. However, drainage from ice wedge polygons is poorly understood. The researchers constructed a model which uses cross-sectional flow nets to define flow paths of meltwater through the active layer of an inundated low-centered polygon towards the trough. The model includes the effects of evaporation and simulates the depletion of ponded water in the polygon center during the thaw season. In most simulations, the team discovered a strong hydrodynamic edge effect: only a small fraction of the polygon volume near the rim area is flushed by the drainage at relatively high velocities, suggesting that nearly all advective transport of solutes, heat, and soil particles is confined to this zone. Estimates of characteristic drainage times from the polygon center are consistent with published field observations.

As a way to separate plant and soil respiration from the carbon dioxide (CO₂) measured at the ecosystem scale, the researchers included environmental data, such as gross primary productivity, soil temperature, and soil moisture, that gave their model more information and helped them to better understand which environmental conditions contribute to higher soil decomposition. Accounting for plant and soil respiration at the ecosystem scale is important because higher soil decomposition in permafrost can increase the release of greenhouse gases to the atmosphere and worsen the impacts of climate change.

Waterlogged permafrost soil can decrease old soil carbon decomposition deep in the soil layer, but when terrain dries, old soil carbon loss can increase up to 30 times. Old soil carbon has been stored for hundreds to thousands of years, and its release to the atmosphere has implications for climate change.

Almost half global terrestrial soil carbon (C) is stored in the northern circumpolar permafrost region, where air temperatures are increasing two times faster than the global average. As climate warms, permafrost thaws and soil organic matter becomes vulnerable to greater microbial decomposition. Long-term soil warming of ice-rich permafrost can result in thermokarst formation that creates variability in environmental conditions. Consequently, plant and microbial proportional contributions to ecosystem respiration may change in response to long-term soil warming. Natural abundance d¹³C and D¹⁴C of above- and belowground plant material and of young and old soil respiration were used to inform a mixing model to partition the contribution of each source to ecosystem respiration fluxes. The researchers employed a hierarchical Bayesian approach that incorporated gross primary productivity and environmental drivers to constrain source contributions. They found that long-term experimental permafrost warming introduced a soil hydrology component that interacted with temperature to affect old soil carbon respiration. Old soil carbon loss was suppressed in plots with warmer deep soil temperatures because they tended to be wetter. When soil volumetric water content significantly decreased in 2018 relative to 2016 and 2017, the dominant respiration sources shifted from plant aboveground and young soil to old soil respiration. The proportion of ecosystem respiration from old soil carbon accounted for up to 39% of ecosystem respiration and represented a 30-fold increase compared to the wet-year average. The study’s findings show that thermokarst formation may act to moderate microbial decomposition of old soil carbon when soil is highly saturated. However, when soil moisture decreases, a higher proportion of old soil carbon is vulnerable to decomposition and can become a large flux to the atmosphere. As permafrost systems continue to change with climate, the thresholds that may propel these systems from a carbon sink to a source must be understood.

Simulating microbial community improves the mechanistic understanding of carbon cycle and reduces uncertainties in global carbon projection.

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal and bacterial biomass C from nine biomes, the researchers parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling.

The CLM-Microbe model is able to reasonably capture the seasonal dynamics of fungal and bacterial biomass across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland. The researchers found good consistencies between simulated and observed fungal and bacterial biomass on average across biomes, although the model is not able to fully capture the large variation in observed biomass. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic carbon density. The further application of the CLM-Microbe model would deepen the understanding of microbial contributions to the global carbon cycle.

Most observations indicate that tundra shrubs are expanding mainly on hillslopes, although the controlling processes remain unclear. This study found that ignoring topographically driven drainage led to substantially underestimated shrub growth, compared to observations. This finding is important because more than a third of Arctic landscapes are classified as hills and mountains, and current land models (i.e., ELM) do not represent these spatially explicit processes.

Researchers examined how topography affects shrub expansion by analyzing site observations, multi-decadal remote sensing, and a three-dimensional ecosystem model (ecosys) at the Kougarok site on the Seward Peninsula, Alaska. The team found that topographic controls on lateral fluxes of water, nutrients, and energy strongly affect shrub productivity and explain observed changes in tundra shrub cover.

Observations indicate shrubs are expanding across the Arctic tundra, mainly on hillslopes and primarily in response to climate warming. However, the impact topography exerts on hydrology, nutrient dynamics, and plant growth can make untangling the mechanisms behind shrub expansion difficult. Modeled biomass of the dominant plant functional types agreed very well with field measurements (R²=0.89) and accurately represented shrub expansion over the past 30 years inferred from satellite observations. In the well-drained crest position, canopy water potential and plant nitrogen (N) uptake was modeled to be low from plant and microbial water stress. Intermediate soil water content in the mid-slope position enhanced mineralization and plant N uptake, increasing shrub biomass. The deciduous shrub growth in the mid-slope position was further enhanced by symbiotic N₂ fixation primed by increased root carbon allocation. The gentle slope in the poorly-drained lower-slope position resulted in saturated soil conditions that reduced soil O₂ concentrations, leading to lower root O₂ uptake and thereby lower nutrient uptake and plant biomass. A simulation that removed topographical inter-connectivity between gridcells resulted in (1) a 28% underestimate of mean shrub biomass and (2) over- or under-estimated shrub productivity at the various hillslope positions. Results indicate that land models need to account for hillslope-scale coupled surface and subsurface hydrology to accurately predict current plant distributions and future trajectories in Arctic ecosystems.

Anticipated warming in permafrost regions is likely to increase the rates of greenhouse gas emissions produced by decomposition of the large organic carbon stocks that have accumulated in regional soils. This updated assessment of regional carbon distributions suggests more carbon is stored closer to the surface (where it is more vulnerable to top-down warming) than previously thought. The new spatially explicit organic carbon estimates will provide a crucial benchmark for improving the representation of high-latitude carbon stocks in land surface models used to predict changes to the global carbon cycle and resulting feedbacks to future climate.

The first high-resolution maps of soil organic carbon distributions for the northern hemisphere permafrost region were produced by combining over 2,700 field measurements with spatially explicit information on environmental factors that influence soil formation. Geospatial analysis identified dominant environmental predictors of soil carbon quantities and their uncertainties in different geographic areas and for sequential depth intervals to 3 meters below the surface. Total regional carbon amounts were similar to earlier assessments, but access to new observational data, coupled with geospatial prediction methods, provided new insight into the spatial patterns and depth distributions of carbon storage across the region.

Large organic carbon stocks have accumulated in soils of the northern hemisphere permafrost region, but their current magnitude and future fate remain uncertain. Scientists coupled a new database of soil profile observations with a high-resolution dataset of environmental factors in a geospatial framework to generate spatially explicit estimates of permafrost-region soil carbon stocks, quantify prediction uncertainties, and identify key environmental predictors. The team estimated 1,014 Pg C is stored in the top 3 meters of northern hemisphere permafrost-region soils. Although the total amount is slightly lower than earlier estimates, this new assessment suggests more carbon is stored within a meter of the surface and thus is more vulnerable to top-down warming. The greatest prediction uncertainties occurred in toe-slope positions of the northern circumpolar region and in flat areas of the Tibetan region. Soil wetness and elevation were the dominant topographic controllers of soil carbon stocks. Significant climatic controllers were surface air temperature in the circumpolar region and precipitation in the Tibetan region. The study produced the first high-resolution geospatial assessment of permafrost-region soil organic carbon stocks and their relationships with environmental factors. Such information is crucial for modeling efforts to predict the responses of permafrost-affected soils to changing climatic conditions.

Tropical rainforests are key ecosystems which not only host an incredible biodiversity but also help regulate the global weather. Here, we have shown that future climate might alter one of the key controls of plant diversity (negative density dependence). This suggests that future climate could lead to a decrease in plant diversity in these forests.

Rainforests are going to experience warmer and drier climate than current conditions but little is known about how plants will respond. Using a warming field experiment in Puerto Rico, we showed that plant growth and survival are altered by warming and drought. These changes might threaten the future of these diverse forests.

Climate change is predicted to result in warmer and drier Neotropical forests relative to current conditions. Plant enemies inflict negative density-dependent feedbacks, where by plants growing at high density experience more negative effects from enemies than plants growing at low density. These negative feedbacks are key to maintaining the high diversity of tree species found in the tropics, yet we have little understanding of how projected changes in climate are likely to affect these critical controls. Over three years, we evaluated the effects of a natural drought and in situ experimental warming on density-dependent feedbacks on seedling demography in a wet tropical forest in Puerto Rico. In the +4^oC warming treatment, we found that seedling survival increased with increasing density of the same species. If positive density-dependent feedbacks are not transient, the diversity of tropical wet forests, which may rely on negative density dependence to drive diversity, could decline in a future warmer, drier world.

The combination of high-resolution drone imagery and ground-based field work has great potential to improve the understanding of the structure and dynamics of old-growth tropical forests with dense understories. These results help scientists understand the proportion of trees in canopy and understory in relation to tree size, the contributions of canopy and understory trees to carbon stocks and wood productivity, and differences in stem growth and size distributions between canopy and understory trees.

Whether or not trees are in the canopy has long been recognized as a critical determinant of tree performance. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. In a new study, the integration of remote sensing and ground-based data enabled this determination and measurements of how canopy and understory trees differ in structure and dynamics in the Central Amazon. Researchers found that canopy trees constituted 40% of the inventoried trees with diameter at breast height (DBH) >10 cm and accounted for ~70% of aboveground carbon stocks. Diameter growth was on average twice as large in canopy trees as in understory trees, and the size distribution was also differed.

Canopy trees constituted 40% of the inventoried trees with DBH >10 cm and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees >25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models.

Uncertainty in land model representations of the processes associated with tundra shrub expansion are uncertain and result in large uncertainties in the magnitude and direction of carbon-climate feedbacks. Prediction of tundra carbon dynamics requires land models that consider the wide array of relevant ecological processes and their interactions. This study explored and synthesized the literature to explain the key climatic and environmental drivers and controlling mechanisms for shrub expansion across the Arctic.

Age tracer observations in streamflow provide a novel and relatively cost-effective method to indirectly characterize bedrock properties in a steep, snow-dominated watershed that can lead to new insights into watershed functioning. The added information from the tracer data suggests more deeper groundwater flow occurs than previously thought. Collecting stream water gas data also helped identify groundwater flow path sensitivity to climate and land-use change. Under wetter conditions, groundwater flow paths and ages are insensitive to climate change or forest removal. A sensitivity analysis indicates that the basin is close to a precipitation threshold. With only small shifts toward a drier state, groundwater flow paths will become increasingly deeper and groundwater age in the stream increasingly older.

Older groundwater that flows through deep bedrock in mountain watersheds could be important to stream water, but limited data on bedrock properties often limits the ability to examine and understand its role. To address this, the authors combined a novel stream water gas tracer experiment in a steep mountain stream in a Colorado River headwater basin (24 km²) with a previously published hydrologic model to examine relationships between streamflow age variability, shallow and deeper groundwater flows, and climate conditions. Results indicate streamflow age in the late summer varies interannually (3–12 years) as a function of shallow, subsurface flow (<1 year) that is controlled by snow dynamics. In contrast, deeper groundwater ages remain stable (12 years) across historical conditions.

There is growing awareness that deep bedrock in steep, mountain watersheds could be an important part of a watershed’s hydrologic system, but the true importance of deeper groundwater flow remains largely unknown. Here the scientists present a proof of concept for a new and efficient approach to characterize deeper groundwater flow in a mountain watershed using stream water concentrations of N₂, Ar, CFC-113, and SF₆. Using gas tracer observations, the scientists provide solid evidence of nontrivial groundwater flow to streams that occurs at considerable depth in a mountain watershed underlain by fractured crystalline rock.

The implication for this revised conceptual model of groundwater flow in this mountain watershed is substantial. Using age tracers to inform an integrated hydrologic model, the scientists move Copper Creek from a topographically controlled basin with hyper-localized groundwater flow paths (young ages) that are insensitive to changes in precipitation to a borderline recharge-controlled groundwater basin in which groundwater flow paths are extremely sensitive to increased aridity and forest structural change. This study clarifies the importance of characterizing the bedrock groundwater system in steep mountain watersheds to predict how groundwater and surface water interactions may respond to future changes in climate, land cover, or land use.

Monitoring greenhouse gas exchange between the soil and the atmosphere is important in tracking worldwide CO₂emissions. Despite this, many regions are either inaccessible or do not have the resources to undertake rigorous research to monitor soil respiration. In this study, researchers found that soil respiration measured at annual mean temperature can be used to predict annual soil respiration. The findings could be used to reduce soil respiration measurement frequency and greatly decrease cost– enabling easier measurements in low income and inaccessible regions worldwide.

Soil respiration—the flow of CO₂ from the soil surface to the atmosphere—is one of the largest carbon fluxes in the terrestrial biosphere. In recent DOE-funded study, researchers created a model that predicted annual soil respiration in different parts of the world based on average air temperature for each region.

Led by  Pacific Northwest National Laboratory, this internationally diverse research collaboration used data from more than 800 site-year observations worldwide. The team developed a predictive model to test the relationship between annual soil respiration and instant soil respiration rate at mean annual temperature among diverse ecosystems and climates throughout the world. Air temperature data is more common than soil temperature data, making it a more achievable measurement to gauge carbon emissions in lower income countries. Their results were recently published in Agricultural and Forest Meteorology.

The experimental simulations show substantial intra-seasonal variability in the temperature sensitivity of CH₄ production and emission. These findings demonstrate the uncertainty of inferring CH₄ production or emission rates from temperature alone and highlight the need to properly represent microbial and abiotic interactions in terrestrial biogeochemical models.

Wetland methane (CH₄) emissions are likely increasing and important in global climate change assessments; however, the temperature sensitivity of CH₄ production and emission remains very uncertain. Here researchers from NGEE-Arctic use a well-tested mechanistic ecosystem model to examine the observed apparent CH₄ emission hysteresis to air and soil temperatures. Their simulations indicate that these hysteretic relationships are driven by substrate-mediated microbial and abiotic interactions: seasonal cycles in substrate availability favors CH₄ production later in the season, leading to higher CH₄ production and emission rates at the same temperature.

Methane (CH₄) emissions from wetlands are likely increasing and important in global climate change assessments. However, contemporary terrestrial biogeochemical model predictions of CH₄ emissions are very uncertain, at least in part due to prescribed temperature sensitivity of CH₄ production and emission. While statistically consistent apparent CH₄ emission temperature dependencies have been inferred from meta-analyses across microbial to ecosystem scales, year-round ecosystem-scale observations have contradicted that finding. Here, researchers from NGEE-Arctic show that apparent CH₄ emission temperature dependencies inferred from year-round chamber measurements exhibit substantial intra-seasonal variability, suggesting that using static temperature relations to predict CH₄ emissions is mechanistically flawed. The model results indicate that this intra-seasonal variability is driven by substrate-mediated microbial and abiotic interactions: seasonal cycles in substrate availability favors CH₄ production later in the season, leading to hysteretic temperature sensitivity of CH₄ production and emission. These findings demonstrate the uncertainty of inferring CH₄emission or production rates from temperature alone and highlight the need to represent microbial and abiotic interactions in wetland biogeochemical models.

The experimental simulations show that short-term warming resulted in a much higher rate of soil carbon loss relative to multi-decadal responses. This can partly be attributed to long-term perturbation occurring at a lower rate of change. However, the short-term warming experiments favor heterotrophic activity, and hence soil carbon loss, and generally are not designed to capture longer-term, non-linear dynamics of vegetation, that occur in response to thermal, hydrological, and nutrient transformations belowground.

Climate warming is occurring fastest at high latitudes; however, a question remains as to how representative short-term warming manipulations are of tundra responses to a changing climate. Here researchers from NGEE-Arctic use a well-tested mechanistic land model to examine differences in ecosystem carbon cycle responses between observed and modeled short-term (<10 year) warming experiments and modeled long-term (100 year) changes under 21^st century expected temperature, precipitation, and CO₂ concentrations. Their simulations show that short-term experiments disturb the tundra carbon cycle in ways that are inconsistent, and stronger, than ecosystem responses to multi-decadal climate change (Bouskill et al., 2020).

Climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. Scientists from NGEE-Arctic show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (< 10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. They first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. Next they found  that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. These results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.

The expansion of deciduous plants in a warmer climate may result in several ecological and climatic feedbacks that affect the carbon cycle of northern ecosystems. For example, increases in surface litter input and lower litter lignin content results in positive feedbacks to more rapid microbial decomposition and nutrient cycling, changes seasonal phenology, and increases transpiration and thus summer longwave radiative forcing. Declines in herbaceous plant productivity may also affect the amount and distribution of summer forage, and thus change habitat for moose and other animals.

Researchers from Lawrence Berkeley Natioanl Lab applied a well-tested mechanistic model, ecosys, to examine how different plant types (evergreens, graminoids, deciduous, moss, and lichen) across the boreal forests and Arctic tundra of Alaska will respond to projected 21^st century changes in climate and fire. They modeled, consistent with changes during the Holocene, that changes in 21^st century climate and fire will favor Alaskan deciduous plants, making them dominant in northern ecosystems. These changes occurred because of complex interactions between enhanced soil microbial activity early in succession and competition for light later in succession.

High-latitude regions have experienced the most rapid warming in recent decades and this trend is projected to continue over the 21^st century. Fire is also projected to increase with warming. Researchers from LBNL show here, consistent with changes during the Holocene, that changes in 21^st century climate and fire are likely to alter vegetation composition of Alaskan boreal forests and tundra. They hypothesize that tradeoffs in competition for nutrients after fire in early succession and for light later in succession in a warmer climate will cause shifts in plant functional types. Consistent with observations, evergreen conifers were modeled to be the current dominant trees in Alaska. However, under future climate and fire, our study suggests the relative dominance of deciduous broadleaf trees nearly doubles, accounting for 58% of Alaska ecosystem net primary productivity (NPP) by 2100, with commensurate declines in contributions from evergreen conifer trees and herbaceous plants. The relative dominance of both deciduous and evergreen shrubs were shown to increase in much of the Arctic tundra, particularly in the Northern Slopes and Brooks Range, consistent with field experiments and observations indicating that climate warming will increase shrub cover in Arctic tundra. Post-fire deciduous plant growth under future climate was sustained from enhanced microbial nitrogen mineralization caused by warmer soils and deeper active layers. Expansion of deciduous trees and shrubs will affect the carbon cycle, surface energy fluxes, and ecosystem function, thereby affecting multiple feedbacks with the climate system.

The new map visualizes the spatial distributions of low-centered polygons (LCPs)—which are associated with pristine conditions—and high-centered polygons (HCPs)—which are associated with thawing permafrost, and emit elevated amounts of carbon dioxide to the atmosphere—in ultra-high resolution. The map can be used to estimate landscape-scale carbon fluxes and to monitor contemporary rates of permafrost degradation, by measuring increases in the spatial coverage of HCPs in the future.

A machine-learning based approach was used to map the occurrence of tundra landforms known as ice wedge polygons across a ~1,200 km² landscape in northern Alaska. Microtopographic relief was measured in over one million polygons, revealing complex spatial patterns in permafrost degradation with unprecedented detail.

Ice wedge polygons are tundra landforms that cover an estimated 2.5 million square kilometers in the circumpolar Arctic. Most polygons fit between two geomorphic endmembers: low centered polygons (LCPs), which are characterized by rims of soil at the edges; and high-centered polygons, which resemble mounds surrounded by a network of troughs, and usually reflect thaw in the underlying permafrost. Understanding the spatial distributions of LCPs and HCPs is important, because the two morphologies are associated with pronounced differences in runoff generation, soil moisture, and greenhouse gas emissions. However, high-resolution mapping of ice wedge polygons is difficult, as several thousand polygons may occupy as single square kilometer of terrain, and the microtopographic features distinguishing LCPs and HCPs commonly represent only a few tens of centimeters of relief. Researchers from NGEE-Arctic employed a novel machine learning-based approach, built around a cutting-edge algorithm known as a convolutional neural network, to map the boundaries of more than one million ice wedge polygons across a ~1,200 km² landscape near Prudhoe Bay, Alaska, using a high-resolution digital elevation model generated through an airborne lidar survey. We then measured the relief at the center of each ice wedge polygon, to place it on a spectrum between LCP and HCP. Their map reveals complex trends in ice wedge polygon form, on spatial scales varying from meters to tens of kilometers, with unprecedented detail. This high-resolution quantification of ice wedge polygon form provides rich spatial context for extrapolating ground-based measurements of carbon emissions from tundra soils, and parameterizing microtopography within earth system models. It also represents an extensive baseline dataset for quantifying how contemporary rates of permafrost degradation vary across a landscape, by observing where new high-centered polygons form as air temperatures in the Arctic continue to rise.

Even though higher CO₂ concentrations in future decades can increase GPP, low soil water availability and disturbances associated with droughts could reduce the benefits of such CO₂ fertilization. This study conducted the first global analysis to quantify potential impacts of drought on future GPP, which could guide future modeling and field experiments.

This paper showed an increasingly stronger impact on terrestrial gross primary production (GPP) by extreme droughts than by mild and moderate droughts over the twenty-first century. Specifically, the percentage contribution by extreme droughts to the total GPP reduction associated with all droughts was projected to increase from ~28% during 1850–1999 to ~50% during 2075–2099.

Terrestrial gross primary production (GPP) is the basis of vegetation growth and food production globally and plays a critical role in regulating atmospheric CO₂ through its impact on ecosystem carbon balance. Here researchers from LANL and NGEE-Tropics analyzed outputs of 13 Earth system models to show an increasingly stronger impact on GPP by extreme droughts than by mild and moderate droughts over the twenty-first century. The droughts were defined on the basis of root-weighted plant accessible water. Due to a dramatic increase in the frequency of extreme droughts, the magnitude of globally averaged reductions in GPP associated with extreme droughts was projected to be nearly tripled by the last quarter of this century (2075–2099) relative to that of the historical period (1850–1999) under both high and intermediate greenhouse gas (GHG) emission scenarios. By contrast, the magnitude of GPP reductions associated with mild and moderate droughts was not projected to increase substantially. These drought impacts were widely distributed with particularly high risks for the Amazon, Southern Africa, Mediterranean Basin, Australia and Southwestern United States. This analysis indicates a high risk of extreme droughts to the global carbon cycle with atmospheric warming; however, this risk can be potentially mitigated by positive anomalies of GPP associated with favorable environmental conditions.

Now used in hundreds of publications, FLUXNET2015 serves many applications, from ecophysiology and remote sensing studies, to development of ecosystem and Earth system models. The new features motivated by this paper will further fuel scientific investigation while allowing credit attribution to data contributors.

FLUXNET2015 is the largest and most complete dataset of land-atmosphere fluxes ever produced. This paper is the definitive documentation for its production and the open-source software pipeline (ONEFlux), metadata, and led to a more open data policy.

FLUXNET2015 provides ecosystem-scale data on CO₂, water, and energy exchange between the biosphere and atmosphere from 212 sites around the globe who voluntarily contributed their data. Data were quality controlled and processed using uniform methods to improve consistency and intercomparability across sites. It includes gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, and uncertainties. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Tropical forests contribute substantial amounts of water vapor to the atmosphere that falls as rain downwind. Today more tropical forests are degraded than intact and this may be changing the amount of atmospheric water vapor. Future exploration of these results may help to explain why tropical forest regions are suffering longer and more severe droughts.

Normally moist tropical forests suffer from the effects of droughts. In a model study researchers found that degraded forests (forests that had serious impacts from logging and or burning) absorbed less carbon dioxide from the atmosphere, evaporated less water and got hotter relative to intact forests under mild to moderate drought conditions. Under severe drought conditions, all forests had similar responses in terms of water loss and warming.

Researchers from NGEE-Tropics integrated small-footprint airborne lidar data with forest inventory plots across precipitation and degradation gradients in the Amazon. They provided the forest structural information to the Ecosystem Demography Model (ED-2.2) to investigate how degradation-driven forest structure affects sensible heat, evapotranspiration and gross primary productivity. Tropical forest degradation effects were the strongest in seasonal forests. Increased water stress in degraded forests resulted in up to 35% reduction in evapotranspiration and gross primary productivity, and up to 43% increase in sensible heat flux. Relative to intact forests, degradation effects diminished during extreme droughts, when water stress dominated the response in all forests. These results indicate a much broader influence of land cover and land use change in energy, water, and carbon cycles that is not limited to deforested areas and highlight the relevance models that consider of forest structure such as FATES in predicting biophysical and biogeochemical cycles.

This research significantly advanced our understanding of how tropical forest soils respond to warming, to hurricane disturbance, and to the interactive effects of both warming and hurricanes. The work helps us understand and successfully manage these forests into the future, as well as improves our ability to forecast future carbon cycling and climate at the global-scale.

BER supported researchers used a one-of-a-kind tropical forest warming experiment in Puerto Rico to determine how warming affects critical soil processes, such as soil carbon storage, in tropical forests. After a year of warming, Hurricanes Irma and Maria greatly altered the site, allowing us to assess how hurricanes and warming temperatures interact to affect microbes and chemistry for tropical forests soils.

Tropical forests represent <15% of Earth’s land surface yet support >50% of the planet’s species and play a disproportionately large role in determining climate due to the vast amounts of carbon they store and exchange with the atmosphere. Currently, disturbance patterns in tropical ecosystems are changing due to factors such as increased land use pressure and altered patterns in hurricanes. At the same time, these regions are expected to experience unprecedented warming before 2100. Despite the importance of these ecosystems for forecasting the global consequences of multiple stressors, our understanding of how changes in climate and disturbance will affect tropical forests remains extremely poor. Until now, no studies have evaluated forest recovery following hurricane disturbance within the context of concurrent climatic change. Here, researchers from USGS, USFS, and Michigan Tech present soil results from a tropical forest field warming experiment in Puerto Rico where, a year after experimental warming began, Hurricanes Irma and María greatly altered the forest, allowing a unique opportunity to explore the interacting effects of hurricanes and warming. They tracked post-hurricane forest recovery for a year without warming to assess legacy effects of prior warming on the disturbance response, and then reinitiated warming treatments to further evaluate interactions between forest recovery and warmer temperatures. The data showed that warming affected multiple aspects of soil cycling even in the first year of treatment, with particularly large positive effects on soil microbial biomass pools (e.g., increases of 54%, 33%, and 38% relative to the control plots were observed for microbial biomass carbon, nitrogen, and phosphorus, respectively after 6 months of warming). They also observed significant effects of the hurricanes on soil biogeochemical cycling, as well as interactive controls of warming and disturbance. Taken together, these results showed dynamic soil responses that suggest the future of soil function in this tropical wet forest will be strongly shaped by the directional effects of warming and the episodic effects of hurricanes.

These results show that warming may not be the most consequential short-term effect of climate change for tropical forest understories. Rather, the increase in climate extremes, such as hurricanes, are more likely to cause abrupt changes in tropical forest understories.

Herbaceous plants that were warmed 4°C in the understory of a tropical forest in Puerto Rico for a year to mimic future climate change showed little change in leaf cover or species composition. However, this herbaceous understory increased dramatically in leaf area after the forest overstory was disturbed by two hurricanes.

BER supported researchers studied the effects of experimental warming on the abundance and composition of a tropical forest floor herbaceous plant community in the Luquillo Experimental Forest, Puerto Rico. This study was conducted within Tropical Responses to Altered Climate Experiment (TRACE) plots, and used infrared heaters under free-air, open-field conditions, to warm understory vegetation and soils +4 °C above nearby control plots.  Results showed that one year of experimental warming did not affect the cover of individual herbaceous species, fern population dynamics, species richness, or species diversity.  After one year of the warming experiment, Hurricanes Irma and María damaged the heating infrastructure and opened up the forest overstory.  One year after this hurricane disturbance, when plots were not experimentally warmed, herbaceous plant cover increased from 20% to 70%, bare ground decreased from 70% to 6%, and species composition changed. The negligible effects of warming may have been due to the short duration of the warming treatment or an understory that is somewhat resistant to higher temperatures.

The study found that where rain falls within a Colorado River headwater basin strongly effects whether that rain makes it to the stream. Rain falling in the upper elevations, where water is plentiful soils are thin, and vegetation is sparse, added to streamflow. In the lower elevations, dense conifer and aspen forests consumed much of the additional water provided by the monsoon rain to limit its impact on streamflow. Summer rains produced more streamflow in cooler years and those years with a lot of snow. These complex dynamics mean that even strong summer rains cannot fully replenish water from lost snow. In a warmer future, summer rains are likely to produce less streamflow, adding to water challenges caused by decreasing snowpack.

In snow-dominated western watersheds, summer monsoon rains can provide significant rainfall, but these inputs do not always translate into significant streamflow. Scientists used a hydrological model to examine how efficient monsoon rains were at producing streamflow over several decades. Results showed monsoon rains produced half the amount of streamflow compared to spring snow of the same water input. Streamflow increases from rain were limited to high elevations and strongly influenced by temperature and the previous season’s snowpack. Understanding the dynamics between snow, rain, and streamflow in these western watersheds is important, particularly given a warmer future with less snow.

A data-modeling framework indicates summer rains occur when atmospheric demand for water is high, soil moisture is waning, and the bulk of rain serves to moisten very dry soils and does not generate streamflow. Instead, water is quickly consumed by vegetation, with the largest increases in plant consumption of water by aspen and conifer forests. As a result, streamflow contributions from rain are half those generated by equal amounts of spring snowfall that occur when atmospheric water demand is low and soils moisture is high. Most of the rain-generated streamflow occurs at higher elevations in the watershed where soil moisture storage, forest cover, and energy demands are low. Mean elevation is the single most important predictive metric of the ability of summer rain to generate streamflow in the East River, and extrapolation estimates across the Upper Colorado River Basin indicate that streamflow generation from monsoon rains, while limited to only 5% of the region by area, can produce substantive streamflow. Interannual variability in monsoon efficiency to generate streamflow declines when snowpack is low and aridity is high. This underscores the likelihood that the ability of monsoon rain to generate streamflow will decline in a warmer future with increased snow drought.

Although tropical forests play a critical role in the global carbon cycle, there is a high level of uncertainty on how they will respond to ongoing global environmental changes. This uncertainty is partially attributed to the poor representation of tree mortality in vegetation demographic models. To improve the mechanistic inclusion of mortality in vegetation models, researchers designed a standardized field protocol to evaluate tree vigor, biomass loss, and factors likely to be associated with future tree death. Improving tree mortality representation in models is a research priority that will enable more accurate estimates of terrestrial carbon budgets and predictions of future carbon cycle–climate feedbacks.

Information about how multi-frequency variation in river flow affects the transport of dissolved chemicals is relevant to risk analysis and remediation of contaminated sites. These results suggest that high-frequency river fluctuations may have little impact on how contaminants that are distant from the shoreline migrate. This may partially explain why contaminants persist in river corridors that have highly dynamic fluctuations in the river water levels.

The results also potentially suggest that significant spreading of contamination plumes is caused by the interaction between flow variation and heterogeneity within the river corridor in highly permeability aquifers, such as at the Hanford site. Therefore, solute and thermal mixing might be highly underestimated when using large scale models with homogeneous assumptions. Understanding the impact of high-frequency fluctuations relative to low-frequency fluctuations can provide insights on what sampling frequency or numerical time step may be employed in order to better allocate both site characterization efforts and modeling studies.

Natural- and human-induced factors, such as snow melting cycles and upriver dam operations, induce multi-frequency river flow variations on scales ranging from hours to seasons. In addition, the interactions between these river flow temporal variations and aquifer spatial heterogeneity enhance both the spread and mixing of contaminant plumes in river corridors. A team of scientists at the University of Southern California and the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) studied how the interactions between short- and long-term flow fluctuations and spatial heterogeneity in aquifer physical properties affect the transport of dissolved solutes from a groundwater aquifer to the adjacent river. Theoretical modeling of solute transport dynamics and measurements from the Hanford Reach reveal that a plume’s center of mass is largely controlled by fluctuations that happen over longer time periods, while its spreading could be significantly enhanced in the presence of physical heterogeneity within the river corridor.

Many factors influence how groundwater and surface water interact in river corridors. Precipitation affects aquifer recharge and river discharges, and snow melting cycles may affect river stage. Irrigation, dam operations, and groundwater pumping also have effects, too. These combined changes cause multi-frequency river flow variations on scales ranging from hours to seasons.

In this work, a team of scientists investigated how these variations interact with aquifer heterogeneity and affect a plume of dissolved contaminants moving into a river. To do this, they analyzed a simplified flow field composed of horizontal and longitudinal components with two different sets of characteristic frequencies of variations. Then the scientists compared the changes in the mass of the plume center and how it spreads over time by sequentially removing variations of sub-weekly, weekly, and monthly frequencies. Their results indicated that the plume’s center of mass is mainly controlled by low-frequency fluctuations of the flow field that are driven by seasonal variation in river stages, while the impacts of higher-frequency flow variations on plume spreading are more complex.

Increasing shrub cover and height in the Arctic is a key control on large-scale changes in plant biodiversity, energy balance, and biogeochemical cycling. The resolution of traditional satellite remote sensing is too coarse to capture the fine-scale surface heterogeneity in arctic landscapes, creating significant challenges in understanding the impacts of “shrubification” on tundra ecosystems. To address this challenge, scientists employed a novel multi-sensor UAS to investigate the influence of arctic shrubs on plant community composition and surface energy balance at a very high spatial resolution. The use of this UAS platform allowed for a deeper understanding of fine-scale controls on vegetation structure, composition, and energy cycling. This information will be used to improve scaling efforts that employ other airborne or satellite platforms.

The Arctic is warming faster than anywhere else on Earth, driving important changes in vegetation composition, structure, and function. Traditionally, satellite remote sensing has been used to monitor changes in the Arctic; however, the heterogeneity of tundra landscapes and the coarse resolution of satellite data have left critical gaps in our understanding of Arctic vegetation. Unoccupied Aerial Systems (UASs) can provide the rich, spatially detailed information on vegetation dynamics necessary to improve the remote sensing of tundra vegetation. Using a novel UAS, scientists found that deciduous tall shrubs had strong localized effects on surface energy balance and vegetation composition, where increased tall shrub cover led to a significant reduction in surface temperature and the abundance of other plant species.

Changes in vegetation composition, structure, and function have strong impacts on terrestrial ecosystems and feedbacks to global climate. In the Arctic, average temperatures are warming twice as fast as the global average, with important implications for tundra vegetation dynamics. Remotely monitoring these changes, however, is challenging given the high spatial heterogeneity of tundra vegetation and surface properties in these ecosystems. To address the challenges of characterizing the fine-scale patterns of arctic vegetation and primary controls on composition, structure, and surface energy cycling, researchers used a novel multi-senor UAS platform to map the spatial patterns of vegetation composition, structure, and function across multiple arctic watersheds. Results show that the fine-scale details provided by UAS platforms can significantly improve understanding of the drivers of arctic vegetation distribution, structure, and thermoregulation. In particular, the researchers found a significant localized ‘cooling’ effect in areas of higher tall shrub abundance that has important implications for surface energy balance. The establishment of tall shrub individuals also reduced the abundance of other vegetation types in arctic plant communities, due to increased competition for light and resources, as well as creating a more closed canopy. Importantly, these fine-scale patterns of tundra vegetation also drive the emergent, landscape-scale cycling of carbon, water, and energy in Arctic ecosystems.

The developed roadmap shows how collaboration can help to overcome the limitations of working in isolation and lead to better understanding of how grassland function and plant communities respond to global change. Ecosystems are complex systems and responses to environmental change have multiple layers that interact (see image). Community ecologists need to start understanding the causes of community changes, while modelers need to develop better ways to represent key processes in grasslands. Working together will help to accelerate these goals.

A plant community is the different types of plants and how abundant they are in an ecosystem. Changes in a grassland community can lead to changes in that grassland’s function. Community changes have been observed in reduced rainfall experiments, with changes in rainfall affecting grassland function both directly through plant responses to reduced rainfall and indirectly through shifts in the plant community. Yet, computer models of grassland function have limited capabilities in predicting these community changes. This study develops a roadmap for scientific collaboration between experimenting and modeling scientists.

Grassland plant communities have been observed to shift in response to experimentally altered environmental conditions. The shifts in community structure led to major shifts in the functioning of these grassland ecosystems. Yet models of grassland ecosystem function are unable to accurately predict these shifts in community structure. On the other hand, the changes in community structure observed in the experiments cannot be explained mechanistically. In other words, scientists cannot pinpoint why these changes happened. This study describes the discussions and conclusions from a series of meetings supported by the National Center for Ecological Analysis and Synthesis (NCEAS). These meetings brought together community ecologists and modelers to better understand grassland responses in global change experiments. To understand and predict shifts in community structure in response to global change and its effect on ecosystem functioning, scientists must identify: 1) the key mechanisms driving shifts in community structure; 2) how functional traits of plants change interact with these mechanisms to drive changes in community structure, and 3) how community dynamics alter the distribution of traits across the entire community and alter ecosystem function. These goals will be best achieved when experimenters and modelers (theorists) work together to overcome some of the limitations of isolated empirical studies and incomplete models.

Unique background ¹⁴C tracer studies demonstrate root production and turnover to be the primary source of deep mineral soil C. Surface leaf litter inputs accumulate in the organic layers of these forest soils.

A litter manipulations study based on enriched background levels of carbon-14 (¹⁴C) and their manipulation in an upland deciduous forest on the Oak Ridge Reservation enabled scientists to separate the fate of surface applied leaf litter from that of belowground root production and turnover. Detailed analysis of the fate of the enriched carbon (C) allowed scientists to trace C accumulation within these mineral soils.

To a large degree, the sources and stability of soil organic carbon remain poorly constrained. A clear understanding of links among the components of the soil C cycle is hampered by the complexity of the system as well as challenges associated with partitioning bulk soil C into meaningful fractions. A large accidental ¹⁴CO₂ release at the Oak Ridge Reservation in Tennessee, USA provided a strong label pulse into adjacent, well-studied oak forests, resulting in highly elevated Δ¹⁴C values in leaf litter (~1000‰) and roots (~260–450‰). A four-year manipulative study was conducted to determine the relative contribution of litter versus roots to the bulk mineral soil C pool, as well as to free light, occluded light and heavy fractions. The heavy fraction was further split into fractions with densities of 1.7–2.4 g cm^-3 and >2.4 g cm^-3 to test the homogeneity of the mineral-associated fraction of C. Substantial concentrations of label were detected in all soil fractions within a year of the ¹⁴CO₂ release, indicating rapid incorporation of newly fixed photosynthates in all fractions of soil organic C. This rapid incorporation of new carbon occurred only in treatments where roots were enriched, indicating that roots are the major source of inputs to mineral soil C stocks at these sites. Separation of the heavy fraction into subfractions of intermediate (1.7–2.4 g cm^-3) and high (>2.4 g cm^-3) density indicated that both subfractions incorporated label at similar rates, despite significant differences in degree of microbial processing. In general, the rate of label incorporation suggested a much faster turnover for all fractions than indicated by natural radiocarbon abundance values. This suggests that within each soil fraction there are portions of slow-cycling and fast-cycling materials, and the determination of an average turnover time or mean age is dependent on experimental approach. The rapid incorporation of label into all fractions within a year implies a high degree of heterogeneity in all fractions regardless of how finely the soils are partitioned. Further refinement of the nature and drivers of this heterogeneity could yield important insights into the soil C cycle.

Earth’s climate continues to warm, which has direct effects on plant physiology and resultant growth rates of forests and biomass plantations. Understanding the physiological limits of thermal acclimation of important forest species and their interaction with other environmental conditions is necessary to understand terrestrial carbon uptake and potential climate feedbacks due to changes in productivity. Environmental warming occurs concurrently with periodic changes in other stressors, like pathogens or drought. Thus, although these results point to the ability of poplar trees to acclimate to and benefit from future temperature conditions, there may be other mitigating controls on ecosystem productivity.

Researchers examined the effects of warming conditions on young Populus trichocarpa trees. They used climate-controlled growth chambers and measured carbon uptake (as photosynthesis) and carbon loss (as respiration) from leaves, roots, and soil. Findings show that photosynthetic rates increased with temperature at monthly time scales, but that warming also increased leaf, root, and soil respiration. Total plant growth was greatest at intermediate levels of warming (+4 °C) as compared with un-warmed control (+0 °C) or severely warmed (+8 °C) trees. Root respiration rates were dependent on root nitrogen content in the warmest treatment, indicating the importance of plant-soil interactions and their role in plant adjustment to climate change.

Plant metabolic acclimation to thermal stress remains underrepresented in current global climate models. Gaps exist in our understanding of how key metabolic processes (i.e., photosynthesis, respiration) adjust and acclimate over time and how aboveground versus belowground acclimation differs. Researchers compared aboveground vs. belowground physiological responses to warming over time for ninety genetically identical ramets of Populus trichocarpa.  After establishment at 25°C for six weeks, sixty clones were warmed to either +4°C or +8°C and monitored for ten weeks, measuring photosynthesis and respiration from the leaves, roots, and soil. Results showed thermal acclimation (beneficial adjustment to the new temperature) in both photosynthesis and respiration, with rates initially increasing, then declining as the optimal temperature for photosynthesis and the temperature-sensitivity (i.e., Q₁₀) of respiration adjusted to warmer conditions.

In addition to a higher optimal temperature for photosynthesis, the maximum rates of photosynthesis were also higher. Belowground, soil respiration decreased with warming, while root-free soil respiration declined abruptly, then remained constant with additional warming. Plant biomass was greatest at +4°C, with 30% of structural carbon allocated belowground. Rates of root respiration were similar among treatments, however, root nitrogen increased at +8°C leading to lower rates of root respiration per unit of nitrogen. The exponential (Q₁₀) temperature relationship of R_r was affected by warming, leading to differing values among treatments. By measuring carbon uptake and carbon losses from each treatment, these results suggest that moderate climate warming (+4°C) may lead to optimized plant adjustments to temperature and increased plant growth, but those increases could be limited with severe warming (+8°C).

These findings identify key traits that influence tree responses to drought and contribute to an enhanced understanding of how tropical forests respond to drier climates. In addition, these data can be used to parameterize and validate models to predict the future of tropical forests under climate change, which has implications for understanding biodiversity, community dynamics, and biogeochemical cycles.

Drought impacts tropical forests across the globe, but scientists do not fully understand what controls tree responses to drought. Researchers measured hydraulic traits for 27 tree species across a rainfall gradient in Panama and leveraged historical forest censuses to examine how these traits varied across sites. From this data, the researchers determined which traits explained moisture requirements and mortality, finding that hydraulic traits sorted into two main groups. The first group included traits associated with plant water status, and the second group included mainly leaf associated traits. The researchers found that safety from leaf wilting plays an important role in tree mortality, while the ratio of leaf area to sapwood area informs tree moisture needs.

Intensified droughts are affecting tropical forests across the globe. However, the underlying mechanisms of tree drought response and mortality are poorly understood. Hydraulic traits and hydraulic safety margins—the extent to which plants buffer themselves from water stress thresholds—provide insights into species-specific drought vulnerability. This study investigated the degree that tree hydraulic traits varied across the Isthmus of Panama rainfall gradient and the relationships between hydraulic traits and species-specific optimal moisture and mortality rates. Researchers found strong coordination among traits, with a network analysis revealing two major groups of correlated traits. One group included plant water status, leaf wilting point, stem water storage, stem density, hydraulic safety margins, and mortality rate. The second group has leaf mass per area, leaf dry matter content, hydraulic architecture (leaf area to sapwood area ratio), and species-specific optimal moisture. These results demonstrated that while species with greater safety from turgor loss had lower mortality rates, only hydraulic architecture explained species’ moisture dependency. Species with a greater leaf area to sapwood area ratio were associated with drier sites and reduced their dry season transpirational demand via deciduousness.

This new method enables the estimation of daytime ecosystem carbon exchanges at submeter resolution on any given day. In addition, scientists analyzed NEE-day integrated over the growing season, which suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Land-atmosphere carbon exchange is known to be extremely varied in arctic ice-wedge polygonal tundra regions, which cover much of the high-Arctic. Accurate mapping of net ecosystem exchange (NEE) at the resolution that resolves microtopography is needed to quantify the overall NEE as well as to understand the potential effects of geomorphological changes on NEE associated with permafrost thaw. Although there are many new remote sensing and sensor technologies, a major challenge remains integrating all relevant measurements.

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, scientists developed a Kalman filter-based method to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEE-day) at 0.5-m resolution over a 550 m by 700 m study site. Scientists integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. Scientists took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. Scientists then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes, such as the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers, and rims). This study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Soil moisture fluctuations are increasing globally, but researchers possess limited understanding of how different soils respond to these environmental changes. The researchers found no uniform response to drought or flood across the three soils, and soil response varied widely by site. They were able to infer that soil texture—which influences pore size distribution and the spatial distribution of water—may drive soil chemical responses to new hydrologic conditions, whereas environmental conditions and disturbance history may drive the soil microbial response to new hydrologic conditions. However, the variabilities in the results highlights the need to consider the origin and structure of the soils when studying responses to environmental changes. It demonstrates a long-recognized problem in soil and environmental science: it is difficult to unambiguously identify universal soil responses because of the complexity and heterogeneity of soils. This study emphasizes the importance of incorporating environmental history and soil physicochemical properties when studying soil carbon-moisture dynamics at the pore-to-core scale.

Climate change is increasing the frequency of droughts and floods. Because water is an important driver of soil carbon dynamics, understanding how moisture disturbances will affect carbon availability and fluxes in soils is crucial for predictions of the terrestrial carbon cycle. A new experiment compared soils from Alaska, Florida, and Washington state to determine how different soils would respond to the same moisture treatments. Overall, drought had a stronger effect on soil respiration, pore-water carbon, and microbial community composition than flooding. The soil response was not consistent across sites and was influenced by site-specific composition and environmental factors. The high clay content in the Washington soils helped them retain water, which may buffer the microbial community against drought stress.

Climate change is intensifying the global water cycle, with droughts and floods becoming increasingly frequent. Water is an important driver of soil carbon dynamics, and it is crucial to understand how moisture disturbances will affect carbon availability and fluxes in soils.

Researchers investigated the role of water in substrate-microbe connectivity and soil carbon cycling under extreme moisture conditions. They collected soils from three U.S. states, Alaska, Florida, and Washington, and incubated them under drought and flood conditions. The researchers found that soil texture strongly influenced the chemical response, whereas environmental history strongly influenced the microbial response. Drought had a stronger effect than flood on soil respiration and soil carbon chemistry, especially in the Alaska soils. The Florida soils, which are sandy and adapted to moisture extremes, showed minimal response to the treatments. The Washington samples were from a floodplain and likely adapted to fluctuating saturated conditions. Soil texture and porosity can influence microbial access to substrates through the pore network. The microbial communities have adapted to the historic stress conditions at their sites and demonstrate site-specific responses to drought and flood. A portion of this research was performed at the Environmental Molecular Sciences Laboratory (EMSL), a Department of Energy (DOE) Office of Science user facility

Land model representations of processes associated with tundra shrub expansion are uncertain, yet have large impacts on high-latitude carbon cycling. This study shows that plants acquire 5-50% of their annual nutrient demands during the non-growing season, and these interactions strongly impact shrub expansion predictions. Models must account for these dynamics to accurately predict 21st century carbon cycling.

Nutrient constraints on high-latitude carbon cycling remains uncertain in land models, yet critical for 21st century prediction. This study shows that improving land models requires better representation of winter soil biogeochemical and plant processes. The commonly applied approach to represent competition for nutrients (called Relative Demand) is unable to represent these non-growing season dynamics.

Permafrost soils contain as much carbon as currently exists in the atmosphere, and these soils are vulnerable to releasing that carbon as the Earth warms. However, the net effect of climate change on the carbon balance of these ecosystems also depends on plant growth, which will likely be enhanced by warming. Current land models used for carbon cycle predictions remain uncertain, and a large part of this uncertainty stems from the role of plant nutrient constraints. Although it is widely recognized that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process.

In this paper researchers applied a well-tested (including at several NGEE-Arctic sites) mechanistic model to explore the role of non-growing season processes on vegetation dynamics and 21^st century carbon cycling. The team found that non-growing season nutrient uptake ranges between 5 and 50% of annual uptake, with large spatial variability and plant type dependence. This plant nutrient acquisition strongly enhances 21^st century shrub expansion, and thereby ecosystem carbon storage. This work highlights the importance of including non-growing season plant processes in large-scale land models, such as DOE’s ELM

Stream temperature is an important water quality variable for aquatic ecosystems. Stream warming can affect fish populations and drive changes in species composition. This sensitivity leads to significant concern about stream warming in response to climate change. Analysis of headwater stream observations found that in Arctic regions, thaw-induced changes in water flow paths may partially counter the effects of increasing air temperatures and result in cooler streams than would be expected from increasing air temperatures alone.

Permafrost influences the flow of water in Arctic landscapes and, as a result, has the potential to influence streamflow and stream temperature. Analysis of observations from 11 headwater streams in Alaska show that July water temperatures were higher in catchments with more near‐surface permafrost. Hillslope-scale simulations using a fully coupled cryohydrology model show that observed trends are consistent with thaw-induced flowpath changes. Specifically, degrading permafrost leads to deeper flow paths, which buffers seasonal extremes in air temperature and leads to cooler streams in the summer.

Daily stream temperatures in July in headwater streams from the Noatak River Basin were found to be positively correlated with percent permafrost coverage. Researchers used the integrated surface/subsurface code Amanzi-ATS model configured in cryohydrology mode to investigate whether the impact of permafrost on flow path depth could cause a similar pattern in temperatures of groundwater discharging from hillslopes to streams. The model simulates surface energy and water balances, snow, and subsurface water and energy dynamics. The numerical experiments used two‐dimensional hillslopes with varying permafrost extents. Researchers found that hillslopes with continuous permafrost have shallower flow paths compared to hillslopes with no permafrost. The deeper flow paths in permafrost‐free simulations buffer seasonal temperature extremes so that summer groundwater discharge temperatures are highest with continuous permafrost. Results suggest that permafrost thawing alters groundwater flow paths and can lead to decreases in summer stream temperatures and reductions in evapotranspiration in headwater catchments.

Tree species that increase photosynthesis and reduce respiration with warming will display greater net carbon uptake and thus benefit by temperature increases. Since only tamarack trees increased photosynthesis, and neither tree species reduced respiration, the tamarack trees may have more carbon available for growth, maintenance, and defense than black spruce trees. This, in turn, could affect relative rates of success and, over time, impact the balance of species in the ecosystem with implications for carbon sequestration.

Researchers measured leaf photosynthesis, respiration, and nitrogen content of mature boreal trees in the Spruce and Peatland Responses Under Changing Environments (SPRUCE) whole ecosystem warming enclosures, one or two years after treatments began. Over time, tamarack trees displayed increased photosynthesis with warming, due to higher nitrogen content and by keeping their stomatal pores open to allow for CO₂ uptake by the leaves. In contrast, the black spruce trees reduced their stomatal pore opening, and photosynthesis did not change with temperature. Respiration rates were not down regulated with warming, indicating a lack of acclimation, thus higher temperature led to greater rates of carbon use by respiration.

The decade-long SPRUCE experiment consists of five warming treatments (+0 to +9°C) with or without the addition of elevated CO ₂ (+500 ppm) in a southern boreal bog ecosystem. Enclosures warm the air and deep belowground, providing scientists a glimpse of potential futures. Results from this work demonstrate that increased growth temperatures, in the range expected for the next century at high latitudes, have contrasting effects on stomatal behavior, photosynthetic performance, and respiration of two common boreal tree species in North America. These factors could affect the future growth and competitive ability of the trees. Though both species had higher leaf nitrogen in the warmer plots, tamarack was better able to maintain photosynthetic performance under warmer, drier conditions than was black spruce, due to different stomatal behaviors. This implies that tamarack may be better able to maintain favorable carbon balance and growth under warmer climates, provided that soil water is available. However, by maintaining greater stomatal pore opening in tamarack, the trees will use more water, which could lead to water stress and increased sensitivity to drought in the future. These data suggest that species-specific responses to future climate change may dictate how forest carbon and water fluxes change over the next few decades in boreal forests.

While soil carbon has accumulated over millennia in peatlands, these results demonstrate that vast, deep carbon stores are vulnerable to microbial decomposition in response to warming, and since elevated rates of methanogenesis are fueled by plant metabolites, increased rates are likely to persist and result in amplified climate-peatland feedbacks.

In the U.S. Department of Energy’s (DOE) Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment, air and soil are experimentally warmed from +0 up to +9°C above ambient temperatures to greater than 2 m deep in a northern Minnesota bog. These warming treatments simulate the effects of climate change on the carbon cycle at the whole ecosystem scale over the long term. The production of the potent greenhouse gas methane (CH₄) was shown to increase at a faster rate in comparison to carbon dioxide (CO₂) in response to warming, and evidence indicates that soil respiration and methanogenesis are stimulated by the release of plant-derived metabolites. Results suggest that as peatland vegetation trends towards increasing vascular plant cover with warming, a concomitant shift towards increasingly methanogenic conditions and amplified climate-peatland feedbacks can be expected.

Northern peatlands store approximately one-third of Earth’s terrestrial soil organic carbon due to their cold, water-saturated, acidic conditions that slow decomposition. These investigations leverage the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment, where air and peat warming were combined in a whole ecosystem warming treatment. Scientists hypothesized that warming would enhance the production of plant-derived metabolites, resulting in increased labile organic matter inputs to the surface peat, thereby enhancing microbial activity and greenhouse gas production. In support of this hypothesis, significant correlations were observed between metabolites and temperature consistent with increased availability of labile substrates, which may stimulate more rapid turnover of microbial proteins. An increase in the abundance of methanogenic genes in response to the increase in the abundance of labile substrates was accompanied by a shift towards acetoclastic- and methylotrophic methanogenesis. Results suggest that as peatland vegetation trends towards increasing vascular plant cover with warming, a concomitant shift towards increasingly methanogenic conditions and amplified climate-peatland feedbacks can be expected.

As climate change intensifies the global water cycle, researchers predict that soil moisture fluctuations will become more frequent and intense. Understanding how these fluctuations will impact soil carbon processes can improve the predictive capacity of soil carbon models. This research explains changes occurring in soils during wetting and drying, which influence carbon stabilization and destabilization in soils. Soil carbon models thus must factor in the moisture history of soils to produce more accurate predictions.

Researchers know that soil moisture influences soil carbon dynamics but understand little about the impact of previous moisture conditions. A new experiment explored the influence of recent soil moisture history on soil carbon cycling. A simulated drought history increased carbon availability in soils compared to a simulated flood history, even after rewetting/drying and incubating the soils at the same moisture conditions. Drought caused a release of protected carbon previously bound to mineral surfaces, as well as a release of organic molecules from ruptured microbial cells. The increased availability of carbon resulted in greater microbial respiration when these drought-affected soils were rewet, whereas short-term flooding did not strongly alter soil carbon availability or carbon forms.

Soil moisture influences soil carbon dynamics, including microbial growth and respiration. Researchers generally assume that soil response to moisture changes is linear and reversible. Current models do not account for previous, or antecedent, soil moisture conditions when determining soil respiration. Researchers conducted laboratory incubation experiments to determine how the antecedent conditions of drought and flood influenced soil organic matter chemistry, bioavailability, and respiration. Rewet soils (with antecedent drought) had greater organic carbon and respiration compared to the drying soils (with antecedent flood). In addition, simulated drought soils had the highest organic carbon concentrations, with a strong contribution from proteins found in soil microbes. Various analyses identified increased contribution of complex aromatic groups/molecules in the rewet soils, compared to the drying soils. Drying introduced organic matter into the available pool via desorption of aromatic molecules from mineral surfaces and/or microbial cell lysis, thereby stimulating microbial respiration. This work indicates that even short-term shifts in antecedent moisture conditions can strongly influence soil carbon dynamics. This improved understanding of soil water-carbon dynamics may help researchers develop more accurate predictive models.

This study provided some of the first evidence that warmer temperatures lead to a significant drop in the stored carbon stock in deep forest soils. This result suggests that there can be net transfer of carbon from the soil, where it was sequestered as organic carbon, to the atmosphere, where it is released as a greenhouse gas. The experiment, in California’s Sierra Nevada Forest, found that the carbon content in subsoils dropped 33% over five years. In addition, warming the soil led to a 30-35% increase in CO₂ emissions each year. The world’s forests are currently a net sink of atmospheric  CO₂ and have the potential to sequester large amounts of atmospheric  CO₂ in coming decades. However, a better understanding of how deep soils will respond to warming is needed to accurately predict and plan for long-term changes. This cycle, if confirmed over longer time scales, could constitute positive feedback to climate change.

Soils contain twice as much carbon as the atmosphere, and deeper soils (more than 20 centimeters deep) are believed to account for roughly half of soil carbon. While it is known that warmer temperatures can stimulate microbial decomposition of this organic soil carbon, scientists are uncertain whether warming leads to a significant loss of soil carbon stocks and consequently an increase in atmospheric carbon dioxide (CO2) levels. This study found that five years of experimental warming led to a significant reduction in carbon stock stored in deep forest soils, providing empirical confirmation for possible positive feedback to warming.

Subsoils below 20 cm are an important reservoir in the global carbon cycle, but little is known about their vulnerability under climate change. Scientists conducting this field experiment artificially heated plots of soil down to 1 meter deep by 4°C, which is the amount of heating projected by century’s end in business-as-usual climate scenarios. Heating mimicked natural daily and seasonal cycles. The scientists measured a statistically significant loss of subsoil carbon (−33 ± 11%) in warmed plots of a conifer forest after 4.5 years of whole-soil warming (4°C). The loss of subsoil carbon was primarily from unprotected particulate organic matter. Warming also stimulated a sustained 30 ± 4% increase in soil CO₂ efflux due to increased CO₂ production through the whole-soil profile. These field observations of a decline in subsoil carbon stocks with warming are strong evidence for a positive soil carbon-climate feedback, which could not be concluded based on increases in CO₂ effluxes alone. The high sensitivity of subsoil carbon and the different responses of soil organic matter pools suggest that models must represent these heterogeneous soil dynamics to accurately predict future feedbacks to warming.

Soils are a globally important store of carbon. Geochemical properties are emerging as important predictors of carbon in soils. Calcium can play a role in the accumulation of soil organic carbon, but few studies have investigated the exact processes behind this accumulation. This study is one of few that investigates how soil organic carbon is stored in soils with a variation in calcium content.

This study investigates soils from a grassland in Switzerland. Researchers found that carbon content was twice as high in soils with more calcium. The team separated these soils into individual components or pools to determine how this carbon was stored differently in the soil samples. Carbon content was higher in the soils with more calcium because it was trapped on the surface of minerals or rocks. These results highlight the role of calcium in causing carbon to be complexed onto mineral surfaces, and what this mechanism could mean for the global carbon cycle in soils.

Geochemical indicators are emerging as important predictors of soil organic carbon (SOC) dynamics, but evidence concerning the role of calcium (Ca) is scarce. This study investigates the role of Ca prevalence in SOC accumulation by comparing otherwise similar sites with calcium carbonate (CaCO₃)-bearing) or without carbonates (CaCO₃-free). Researchers measured the SOC content and indicators of organic matter quality (C stable isotope composition [δ¹³C values] and thermal stability) in bulk soil samples. Researchers then used sequential sonication and density fractionation to separate two occluded pools from free and mineral-associated SOC. The SOC content, mass, and δ¹³C values were determined in all the fractions. X-ray photoelectron spectroscopy was used to investigate the surface chemistry of selected fractions. The team hypothesized that occlusion would be more prevalent at the CaCO₃ -bearing site due to the influence of Ca on aggregation, inhibiting oxidative transformation, and preserving lower δ¹³C values. Results indicated that bulk SOC content was twice as high in the CaCO₃ -bearing profiles, which also had lower bulk δ¹³C values, and more occluded SOC. Yet, contrary to the researchers’ hypothesis, occlusion only accounted for a small proportion of total SOC (< 10%). Instead, it was the heavy fraction (HF), containing mineral-associated organic C, which accounted for the majority of total SOC and for the lower bulk δ¹³C values. Overall, an increased Ca prevalence was associated with a near-doubling of mineral-associated SOC content. Future investigations should aim to isolate Ca-mediated complexation processes that increase organo-mineral association and preserve organic matter with lower δ¹³C values.

The differential C-Q analysis is a valuable tool for assessing differences across stream reaches, comparing accumulation and mobilization of harmful chemicals within and across reaches, and monitoring solute behavior in the face of hydrologic and climatic perturbations. This approach can therefore aid watershed and land managers in identifying the stream segments that are essential to monitor and in designing pollution prevention or intervention strategies.

Concentration–discharge (C-Q) relationships reflect sources, storage, reactions, and transport of solutes in watersheds. Compared to traditional C-Q approaches, a new “differential C-Q” approach performed well and enabled researchers to identify critical stream segments that assimilate harmful chemicals such as nitrate and phosphate. The new, easy-to-use approach can account for gains, losses, and/or fractional solute turnover over each stream segment. It also yielded a better accounting than traditional approaches of the specific sources, hillslope contributions, and critical stream segments that can adversely impact river water quality.

Concentration-discharge (C-Q) relationships are often used to describe how water moves through streams and the chemicals that are transported with it. These relationships are typically examined at individual sampling stations, which do not provide sufficient information about accumulation or mobilization of harmful chemicals, pesticides, or other solutes. In this study, the researchers present a new differential C-Q approach that can capture the increase, decrease, and/or the fractional solute turnover over each stream segment. To evaluate and compare this differential approach with traditionally used approaches, the team used water quality data collected at the East River, Colorado, watershed. The traditional C-Q patterns showed a consistent “L” shape for nitrate across three stations of the East River watershed. In comparison, differential C-Q approach showed gains in nitrate in the upstream reach and losses in the downstream reach during high gains in discharge. In contrast to nitrate, gains in phosphate, organic carbon, molybdenum, and several other solutes were observed in the downstream reach due to its low-relief, meandering terrain. In this manner, the new C-Q approach clearly indicated when and where small increases in nutrients like phosphorus and nitrate can be particularly concerning, given the potential for algal growth and eutrophication. Overall, the differential C-Q approach holds potential for aiding water quality managers in the identification of critical stream reaches that assimilate harmful chemicals.

Understanding how soil organic matter stores carbon and nutrients and acts as a sink for atmospheric carbon dioxide is important to agriculture and the global climate. Until now, most research has focused on organic matter’s adsorption to mineral surfaces in acidic conditions. This study adds new insight into organic matter accumulation in alkaline soils and suggests a different, unexpected stabilization mechanism.

Using sophisticated instrumentation and techniques available at the Environmental Molecular Sciences Laboratory (EMSL) as part of its Biogeochemical Transformations and Isotope & Chemical Analysis Integrated Research platforms, scientists conducted experiments using methods including X-ray diffraction, 57Fe-Mössbauer spectroscopy, X-ray photoemission spectroscopy, nanoscale secondary ion mass spectroscopy, and transmission electron microscopy to examine the organic matter stabilization processes in alkaline soils. They found that most of the organic matter was bound within calcium aggregates, rather than adsorbing to iron or aluminum minerals, as would be expected in acidic environments.

A research team, which included scientists from Pacific Northwest National Laboratory, Oregon State University, Sandia National Laboratories, and Iowa State University studied alkaline soil samples taken from eastern Washington State, conducting soil sorption experiments with two types of iron-binding compounds (pyoverdine and enterobactin) known as siderophores. They examined the minerology and distribution of carbon- and nitrogen-containing siderophores on individual fine particles within the soil. Results indicated that siderophores aggregated with calcium-rich organic matter coatings rather than with bare mineral surfaces. This discovery suggests an adsorption mechanism by which organics aggregate within alkaline soils via cation bridging. The mechanism results in greater sorption of the more water soluble siderophore onto soil particles. This finding may help scientists understand the composition of organic carbon and nutrients that accumulate in alkaline soils.

In hydrogeology, substrate size is a proxy for permeability. Spatial mapping of grain-size distribution within the hyporheic zone and along a given length of a river enables a better understanding of the hydrological variation and complexity in the system. It also provides information useful for modeling transport properties, such as hydrologic exchange flows and residence times.

The hyporheic zone beneath and alongside a riverbed, where groundwater and surface water mix, is a key area of river corridors. This mixing provides and controls transport of dissolved nutrients, contaminants, and microbes. Modeling flow and transport in this region is challenging due to natural variation and dynamic influences from water, sediment, and microbial processes. Researchers at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL) used a type of machine learning to predict sediment grain size along the Hanford Reach of the Columbia River. Their resulting substrate-size maps filled in gaps not provided through field data and improved spatial coverage and resolution by learning existing, but unevenly, sampled measurements.

Measurements of sediment grain size cover about 70% of the entire Hanford Reach of the Columbia River, but the spatial resolution is too coarse to infer continuity or variation. Bathymetry measurements and hydrodynamic simulations of this area provide higher spatial resolution data. In this study, researchers used machine learning to link this higher-resolution information to predicted substrate size, so they could develop a spatial map capturing the natural sediment variation along the 70-km reach.

Researchers trained the machine-learning model with data from more than 13,000 samples of dominant substrate sizes previously collected along the Reach. They also included measurements of bathymetry, slope, and aspect, as well as simulated hydrodynamic properties, such as water depth, velocity, and river bottom shear stress. The researchers used a bagging-based machine-learning technique, called Random Forest, to develop unbiased predictions with minimal over- fitting. The resulting model accurately predicted the measured substrate size and could be applied to a grid of 5- to 10-m resolution across the Hanford Reach. These algorithms enable gap filling and refining for mapping spatial grain-size distribution by learning predictive relationships between substrate size and multiple types of complementary information.

This new RS reporting format  was developed with considerable community input, and provides a realistic and flexible framework for data providers, instrument manufacturers, and database designers. More generally, such reporting formats  provide consistency and interpretability, making data more findable (by providing a pathway to data archiving), accessible (through free and open data repositories), and usable.

Field observations of the soil-to-atmosphere carbon dioxide (CO₂) flux—soil respiration (RS)—are a prime example of ‘long tail’ data, or data that existed in many dispersed publications and incompatible formats, without either centralized databases or a standard reporting format.  These storage and formatting gaps have hindered scientific transparency, analytical reproducibility, and syntheses with respect to this globally important component of the carbon cycle. To begin addressing these gaps, scientists developed a reporting format focused on RS fluxes, with the goal of optimizing data discoverability and usability while not placing an undue burden on data contributors.

Soil respiration—the soil-to-atmosphere CO₂ flux— observations have historically lacked centralized databases and standard reporting formats, thereby hindering scientific transparency, analytical reproducibility, and syntheses with respect to this globally important component of the carbon cycle. To develop a relevant and useful reporting format, scientists investigated previous RS data collection efforts, examined lessons learned from related databases and data-oriented networks (e.g., FLUXNET) in earth and ecological sciences, and engaged in the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO₂ concentration) and associated ancillary measurements. Drawing from research community input , the research team also developed research data and metadata templates to support data collection that adheres to the reporting format. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable (FAIR) data, while providing ‘future-proofing’ capabilities to support re-analyses using as yet unknown algorithms or approaches. This proposed reporting format is openly available online and is intended to be a dynamic document, subject to further community feedback and/or change.

The deep learning method yielded realistic permeability estimations for a real watershed system, with an improved match between the predicted and observed stream discharges. This work demonstrates that deep learning can be a powerful tool for estimating watershed parameters from indirect but relevant observations. By successfully using deep learning to map the nonlinear relationship between permeability and stream discharge, this work presents new opportunities for improving the subsurface characterization of large-scale watersheds. It paves the way to help develop more generalized watershed model calibration strategies for complex systems that involve multiple parameters and multiple types of observation data.

Subsurface permeability, a measure of how well liquids flow through belowground rocks and soils, is a key parameter that determines subsurface flow and transport processes in watersheds. However, permeability is difficult and expensive to measure directly at the scale and resolution required by watershed models. On the other hand, stream flow monitoring data is widely available. The links between permeability and stream flow provide a new route to estimating subsurface permeability. Scientists used deep learning that more accurately estimates the subsurface permeability of a watershed from stream discharge data than is possible with traditional methods. This improvement will help calibrate watershed models and reduce the uncertainty in stream discharge predictability.

Subsurface permeability is a key parameter that controls the contribution of the subsurface flow to stream flows in watershed models. Since directly measuring permeability at the spatial extent and resolution required by watershed models is difficult and expensive, researchers commonly estimate it through inverse modeling. The wide availability of stream surface flow data compared to groundwater monitoring data provides a new data source for integrated surface and subsurface hydrologic models to infer soil and geologic properties.

Scientists trained deep neural networks (DNNs) to estimate subsurface permeability from stream discharge hydrographs. First, the DNNs are trained to map the relationships between the soil and geologic layer permeabilities, and the simulated stream discharge obtained from an integrated surface-subsurface hydrologic model of the studied watershed. The DNNs yielded more accurate permeability estimates than the traditional inverse modeling method. The DNNs then estimated the permeability of a real watershed (Rock Creek Catchment in the headwaters of the Colorado River) using observed stream discharge from the study site. The watershed model with permeability estimated by DNNs accurately predicted the stream flows. This research sheds new light on the value of emerging deep learning methods to assist integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

Model predictions of the Arctic carbon cycle are important for understanding future climate because the Arctic region contains vast amounts of carbon. Improved models that include more types of Arctic vegetation can simulate the different responses of various plant types and vegetation communities to warming and other environmental changes in high latitude ecosystems. These improvements will allow more accurate predictions of how carbon cycling in the Arctic will change in the future.

Arctic ecosystems are home to specialized plant communities that have adapted to cold winters and short growing seasons. Researchers used measurements of aboveground and belowground plant biomass across different plant communities in the Seward Peninsula of Alaska, USA, to add additional Arctic plant types to an ecosystem model. The new plant types allowed the model to simulate how ecosystems dominated by tall shrubs could gain biomass at much faster rates than ecosystems with thin soils and small plants.

Accurate simulations of high latitude ecosystems are critical for confident Earth system model projections of carbon cycle feedbacks to global climate change. Many ecosystem models represent high-latitude vegetation, using only two plant functional types (PFTs) representing shrubs and grasses, thereby missing the diversity of Arctic vegetation growth patterns. This study used field observations of above- and belowground vegetation biomass and traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to incorporate nine Arctic-specific PFTs into an ecosystem model. The newly developed PFTs included: (1) mosses and lichens, (2) deciduous and evergreen shrubs of various height classes, including an alder shrub PFT, (3) graminoids, and (4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through the year 2100 showed alder-dominated plant communities gaining more biomass and lichen-dominated communities gaining less biomass compared to original model PFTs. These results highlight how representing the diversity of Arctic vegetation and confronting models with measurements from varied plant communities improves the representation of Arctic vegetation in terrestrial ecosystem models.

Enabling proper citation of large data collections will provide tracking of citations to individual datasets, and allow machine learning and AI to use large-scale integrated datasets and cite them accurately. We aim to support discoverable and reusable data through accurate citation counts so that authors receive appropriate credit for their work.  

Easy-to-use data citation methods are needed to address current challenges around integrating data from dozens or hundreds of datasets that large-scale DOE projects generate.  

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of those who contribute and use the data, the most pressing need is to create user-friendly solutions that provide credit and enable accurate citation of integrated data.

Providing easy-to-use data citations is needed to address social and technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We propose a new mechanism to enable citation of multiple datasets and credit to data producers, and convene a community of practice to address current social and technical challenges.

Persistent identifiers for samples, along with common metadata to describe a variety of biological and environmental sample types, provides essential information to improve the efficiency of sample tracking, and makes sample data more findable, accessible, interoperable, and reusable (FAIR).

Many Environmental System Science projects have complicated sample analysis workflows and need an efficient system for tracking samples as they are sent to different collaborators, labs, user facilities, and published online. Work to improve project sample tracking for multidisciplinary projects was driven by the user community of the US Department of Energy’s (DOE’s) data repository for Earth and environmental sciences—Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE).  We provide recommendations for assigning sample identifiers and associated metadata to describe a variety of sample types for multidisciplinary ecosystem science projects.  

Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.

This biochemical pathway is a previously unknown way that bacteria can influence gaseous conditions in the soil and atmosphere through natural metabolism under oxygen-free conditions. In soil, ethylene can restrict plant growth, and methane in the atmosphere affects climate. This new pathway could also explain the long-standing mystery of the origins of ethylene production in oxygen-depleted soils. Early attempts to isolate ethylene-producing microbes from these soils only revealed bacteria that required oxygen to grow.

Soil microbes produce methane and ethylene, gases that affect soil and atmospheric conditions. Scientists have now identified a biochemical pathway in terrestrial and freshwater bacteria that generates these gases. This pathway, present in a number of species, uses a series of enzymes that function similar to an ancient class of proteins that process nitrogen gas. Instead of reducing nitrogen, this system reduces carbon-sulfur bonds in common volatile organic sulfur compounds, producing methane, ethylene, and the amino acid methionine, which is a protein building block.

To identify this new metabolic pathway, a team of scientists from multiple universities and two national laboratories grew freshwater bacteria in soil under oxygen-free conditions where they were known to produce ethylene. Then the scientists compared protein levels in these bacteria to the same microbes grown under conditions that suppressed ethylene production. Proteins with some of the highest abundance increases had unknown function, yet they were encoded by several gene clusters that resembled genetic information for nitrogen-fixing enzymes.

To identify the function of the abundant proteins, scientists used genetic engineering to delete all the genes in each cluster and sequentially re-introduce them to the bacteria. They fed bacteria known molecular precursors to methane and ethylene production. With each genetic addition, the scientists monitored bacterial growth and gas production to see which proteins were key to metabolism.

At the Environmental Molecular Sciences Laboratory (EMSL), scientists used Cascade, a high-performance computer, to calculate the energy required to convert various sulfur-containing molecules to metabolites with methane and ethylene as additional products. They found all the reactions were thermodynamically favored and consistent with experimental results, helping to verify the molecular mechanisms. This work was part of the Biogeochemical Transformations Integrated Research Platform at EMSL, an Office of Science user facility.

For the studied levels of warming, carbon losses far exceed historical carbon accumulation rates. Land surface ecosystem models are capable of capturing carbon cycle responses to temperature under ambient CO₂ conditions but overpredict sensitivity to elevated CO₂ compared to the effect of warming as observed at the SPRUCE experiment.

The Spruce and Peatland Responses Under Changing Environments (SPRUCE) team at Oak Ridge National Laboratory used in in situ whole-ecosystem manipulations to evaluate peatland carbon cycle changes to a range of warming conditions and elevated carbon dioxide (CO2). Warming caused variable responses for vegetation and losses of both CO₂ and methane (CH₄), resulting in an ecosystem carbon loss response rate of 31.3 grams of carbon per m² per year (g C·m⁻²·year⁻¹)).

Northern bogs and fens have accumulated carbon in deep deposits of peat—dead and decaying plant material high in carbon content—for millennia under wet, cold, and acidic conditions. The SPRUCE team experimentally warmed and added CO₂ to a series of bog plots in northern Minnesota to investigate whether the altered environment would lead to the increased decomposition and loss of carbon from bogs to the atmosphere, where it would contribute further to warming. The team found that warming changed the nature of these bogs from carbon accumulators to carbon emitters—where carbon was increasingly lost to the atmosphere in the form of greenhouse gases CO₂ and CH₄ as the level of warming increased. This carbon loss was faster than historical rates of carbon accumulation, demonstrating the significant impact of global warming on naturally stored carbon. Improved peatland ecosystem models are capable of capturing the temperature responses but overpredict responses to elevated CO₂.

Leaf photosynthesis models simulate the rhythms of carbon dioxide (CO₂) transfer from the atmosphere to plants. This study highlights a key shortfall in photosynthesis modeling and in the general approach to developing and using predictive models of the terrestrial biosphere. Models can reach the same endpoint in multiple ways, leading to models “getting it right for the wrong reasons.” The multi-hypothesis approach will help to identify key processes causing model differences and evaluate alternative hypotheses to describe those processes, ultimately leading to more robust predictions of terrestrial ecosystems.

Scientific hypotheses describe how processes might work in the natural world. Computer models are built from mathematical descriptions of these hypotheses. Alternative hypotheses are common, especially in environmental sciences, yet most computer models cannot easily switch among these alternative hypotheses. With funding from the U.S. Department of Energy, scientists have developed a new model that can switch between hypotheses and prioritize which process to study further, a “multi-hypothesis model.” Using the model to study common leaf photosynthesis models, scientists found the surprising importance of a process that has previously received little attention. New data were then collected to discriminate among the alternative hypotheses, finding support for the more traditional approach.

Leaf photosynthesis models are the beating heart of global carbon cycle models. These photosynthesis models simulate the rhythms of CO₂ transfer from the atmosphere into plants and terrestrial ecosystems. The reigning king of photosynthesis models is the Farquhar model published in 1980. However, despite its almost ubiquitous use, there are a number of variations in how the mechanics of various component processes are mathematically described (i.e., there are various mathematical hypotheses that describe some of the sub-processes within the overarching Farquhar model). The consequences of these alternative choices have never been formally investigated, in part because methods to formally investigate model sensitivity to variation in how processes are represented have only recently been developed. Novel multi-hypothesis modeling methods were applied to investigate the influence of 14 parameters and four processes with alternative representations in photosynthesis models, finding the surprising dominance of a process that has not been extensively evaluated with data. Running the alternatives of this dominant process in global models resulted in a difference in photosynthesis equivalent to annual human CO₂ emissions. This multi-hypothesis model evaluation identified as important two alternative hypotheses for photosynthetic limiting rate selection. Novel, high-resolution photosynthesis measurements were designed and undertaken to discriminate among these hypotheses. General support for the original Farquhar implementation was found and is recommended for use to reduce uncertainty in global photosynthesis simulations.

The release of volatile mercury from soils and sediments is a critical process in the global movement of mercury; however, the transformation of mercury(II) (a form of mercury that tends to remain in the soil/sediment) to mercury(0) (a form that can escape the soil/sediment as a gas) is not well understood. It has been known that bacteria and other microorganisms can transform mercury(II) to mercury(0); however, this study shows that clay minerals commonly found in soils can also cause this transformation. This insight will help improve models of the global transport of mercury, thereby advancing efforts to protect human health and the environment.

Mercury is a common pollutant in soils and sediments due to natural and man-made sources. Because of its toxicity to humans and wildlife, it is a major environmental concern. This research found that iron in sedimentary minerals can transform mercury(II) to volatile mercury(0).

Mercury is found in the environment due to release from volcanoes, mining activity, the burning of fossil fuels, and industrial and consumer use. As such, mercury is a common contaminant in many terrestrial and aquatic environments, and its bioaccumulation in organisms, including humans, is a major environmental concern. Mercury in the environment is present as either mercury(II), which tends to remain in soils/sediments, or mercury(0), which as a gas can escape into the atmosphere and be mobile on a global scale. Thus, the reduction of mercury(II) to mercury(0) in soils and sediments is a key control on its distribution between the atmospheric and aquatic/terrestrial reservoirs and on the overall biogeochemical cycling of mercury. The transformations between the two forms of mercury can be caused by microorganisms or by chemical reactions. Researchers used X-ray spectroscopic capabilities at the Advanced Photon Source at Argonne National Laboratory to show that iron(II) in clay minerals commonly found in soils/sediments can reduce mercury(II) to mercury(0), a previously unknown process in the biogeochemistry of mercury. This finding that clay minerals may play a role in the emission of mercury(0) from soils and sediments can lead to improved models of global mercury cycling and better protection of human health and the environment.

This is the first data-rich study to quantify the long-term effectiveness of a wetland to immobilize inorganic elements. The wetland concentrated the uranium three thousand times greater than typical background sediment uranium concentrations. Uranium distribution in this wetland system appeared to be largely controlled by local stream velocity and the movement of uranium in particulate form. Such information can now be used to model present and future wetland water quality resulting from contaminant and nutrient releases into the environment.

The Science
Wetlands play important roles in the hydrologic cycle, including maintaining water quality by controlling nutrient availability and removing surface and groundwater contaminants. The objective of this study was to determine the mass of inorganic contamination remaining in a watershed 50 years after 43.5 metric tonnes of uranium were released into it upstream from a former DOE fuel fabrication facility on the Savannah River Site (SRS). High-resolution maps were generated of uranium concentrations in the watershed that were determined with portable gamma and X-ray detection equipment, revealing that 83% of all the released uranium remains immobilized in a relatively small 28-hectare wetland area within the SRS.

Wetlands possess a diverse set of biogeochemical properties that originate from their unique hydrological regime. These wet environments promote conditions that accumulate organic carbon and create steep biogeochemical redox, organic carbon, and microbial gradients that together can enhance binding of nutrients and groundwater contaminants within their sediments. The long-term attenuation of nutrients and contaminants by wetlands is susceptible to well- documented human-induced activities, as well as natural perturbations that can be chronic or episodic, such as droughts, floods, and fires. Significant environmental changes, such as those associated with hydrology, forest fires, or real-estate development, may alter the complex hydro-biogeochemical interactions within wetlands, causing the degradation of its water quality. Results from this work will advance models that consider the role wetlands play in controlling water quality and wetland responses to natural and human-induced disturbances.

Due to their tremendous biogeochemical importance, ammonia-oxidizing microbial communities have been previously characterized in a wide variety of natural and engineered environments, including oceans, lakes, rivers/streams, estuaries, soils, sediments, caves, aquaria, and wastewater treatment plants, among others. However, until now, no study has ever systematically examined these key N-cycling communities within the riparian subsurface. By examining multiple sediment cores/depths and across five geographically-distinct DOE legacy sites within the intermountain west, we were able to gain unprecedented insights into the environmental factors potentially driving the distribution and diversity of subsurface AOA and AOB communities, including depth, salinity, and total N. Our results also suggest that archaeal ammonia oxidation may predominate within the terrestrial subsurface beyond a meter belowground.

Subsurface microbial communities mediate key biogeochemical transformations that drive both local and ecosystem-level cycling of essential elements, including nitrogen (N). By linking the most reduced and oxidized pools of N in the biosphere, nitrification plays a critical role in the global N cycle. Ammonia oxidation is the first and rate-limiting step of nitrification and also represents a major biological source of the potent greenhouse gas, N2O. While ammonia-oxidizing bacteria (AOB) have been well-studied for over a century, the capacity for ammonia oxidation was only recently discovered within the domain Archaea (<15 years ago). Ammonia-oxidizing archaea (AOA) are now recognized as one of the most abundant microbial groups on the planet—often comprising over 20% of single-celled life in the deep ocean—and have also been studied extensively in topsoils (<30 cm) worldwide. However, their presence in the continental riparian subsurface beyond a meter belowground has been largely unexplored. To fill this knowledge gap, in this study, we examined the microbial ecology of ammonia oxidation within the terrestrial subsurface of five semi-arid riparian sites spanning a 900 km N-S transect within and surrounding the upper Colorado River Basin. Overall, our study identified ammonia oxidizer diversity and community composition trends through depth and at a regional-scale. The data suggest that the AOA and AOB “ecotypes” within these terrestrial subsurface soils are primarily associated with conditions influenced by water table position, the location of reducing zones, or both. Furthermore, our study revealed that AOA outnumber their bacterial counterparts by several orders of magnitude at sites we sampled across this region.

Nitrification is a critical branch of the subsurface N cycle within semi-arid floodplain environments, but the underlying microbial communities have not been thoroughly characterized to date. Both AOA and AOB use a multi-subunit ammonia monooxygenase (AMO) enzyme for ammonia oxidation. Although AOA and AOB can be detected using 16S rRNA-based approaches, the amoA gene (encoding the a-subunit of AMO) is the most commonly used genetic marker for specifically analyzing AOA and AOB populations in the environment. In this study, the diversity and abundance of ammonia-oxidizing microbial communities were evaluated in the context of subsurface geochemistry and hydrology by applying a combination of amoA gene sequencing, quantitative PCR, and geochemical techniques. Analysis of ~900 amoA sequences from AOA and AOB revealed extensive ecosystem-scale diversity, including archaeal amoA sequences from four of the five major AOA lineages currently found worldwide as well as distinct AOA ecotypes associated with key depths and hydrogeochemical zones (unsaturated, capillary fringe, and saturated). Interestingly, the most abundant and cosmopolitan archaeal amoA sequence type in our dataset (representing ~25% of all amoA sequences, from all five sites) was closely related to AOA from marine/estuarine ecosystems. The widespread presence of “aquatic” AOA potentially adapted to high salinities within the terrestrial subsurface is intriguing and suggests that hydrological and geochemical variability proximal to the water table likely exerts a strong influence over the community membership found in the surrounding sediment. The key finding that AOA outnumber AOB by 2- to 5,000-fold within these sediments highlights the future need to employ a combination of meta-omic and biogeochemical approaches to examine both the detailed ecophysiology and activity of subsurface AOA communities in this region.

The NGEE Arctic team, underpinned by a strong safety culture at the National Laboratories and partner institutions, has made the safety of individuals and of the team its number one concern before, during, and after field and laboratory campaigns. The team adopted a safety mindset that underlies all their work, a heightened understanding of the need for respect and common purpose, and a broad set of values endorsed by everyone: safe and harassment-free work environments, respect for local culture and knowledge of the environment in areas and communities where they are guests, and collaboration and open science.

Intentionally create a project-wide culture of safety, inclusion, and trust that facilitates strong cross-disciplinary collaboration and exciting scientific discoveries.

Increasingly, scientists from around the world and across a wide spectrum of disciplines are working together to advance our understanding of the vulnerable and globally important Arctic biome. As scientists become part of larger teams and join broader and more diverse scientific endeavors, they must all become leaders in creating cultures of safety, inclusion, and trust to facilitate the physical and emotional well-being of individuals in scientific teams and in the local communities where scientists work. Then NGEE Arctic team shares lessons learned from an “experiment within an experiment” begun as part of a large-scale, decade-long research project in Alaska. The experiment was focused on answering the question: How can we intentionally create a project-wide culture of safety, inclusion, and trust that facilitates strong cross-disciplinary collaboration and exciting scientific discoveries?

The researchers observed an increase in aboveground vascular plant biomass accumulation, due primarily to an increase in shrub abundance. Overall species diversity decreased substantially, likely due in part to shading by increases in shrub density. The main driver of change in the vascular plant community was temperature, with minimal effects of CO₂ evident. Dominant growth increases of woody shrubs hid similarly dramatic reductions in common and overtopped forb vegetation.

Peatlands store a significant amount of terrestrial organic carbon in plant biomass and soils. The Spruce and Peatland Responses Under Changing Environments (SPRUCE) project is a warming and elevated carbon dioxide (eCO₂) experiment designed to test how the carbon sequestration and storage capacity of peatland ecosystems will respond to climate change. Here, the researchers report changes in the vascular plant community that have occurred during the first five years of SPRUCE. The team tracked species composition, diversity, and aboveground net primary production in chambers warmed at a wide range of temperatures (+0, +2.25, +4.5, +6.75, +9^°C), and two CO₂ levels (~400 [ambient] and 900 parts per million) related to near- and long-term climate possibilities.

Summary
Project results indicate an overall increase in shrub-layer vegetation suggesting that warmer future climates could enhance productivity of woody peatland vegetation, but at a cost to the diversity comprising that community. Such data are an essential component of carbon cycle models of wetland ecosystems key to the global assessment of terrestrial carbon cycles.

Recent modeling studies suggest that Amazonian peatlands play an important role in the global methane budget and are highly sensitive to climate change. Here, the team provide some of the first ecosystem scale observations to help provide a better biophysical understanding of the role that these Amazonian palm swamp peatlands play in carbon cycling. Study observations and analyses will be important for constraining models that aim to scale up the carbon budget for this region and will be critical for understanding how sensitive carbon dioxide (CO₂) and methane CH₄) emissions are to interannual variability in climate and hydrology.

Tropical peatlands are a major, but understudied, biophysical feedback factor on the atmospheric greenhouse effect. To address this knowledge gap, the research team established an eddy covariance flux tower in a natural palm swamp peatland near Iquitos, Peru to improve the understanding of how these forested peatlands exchange carbon dioxide and methane with the atmosphere over a two-year period. The land-atmosphere carbon fluxes indicated that the peatland was a significant carbon dioxide sink but a methane source under current conditions, but evidence indicates that the sink strength is vulnerable to future climate change.

This study reports ecosystem-scale CO₂ and CH₄ flux observations for an Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on CO₂ and CH₄ fluxes. High air temperature and vapor pressure deficit exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration was either inhibited or enhanced depending on water table position. The cumulative net ecosystem carbon dioxide exchange indicated that the peatland was a significant carbon dioxide sink ranging from –465 (–279 to –651) g C m^–2 y^–1 in 2018 to  –462 (−277 to −647) g C m^–2 y^–1 in 2019. The forest was a methane source of 22 (20 to 24) g C m^–2 y^–1, similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Thus, this Amazonian palm swamp peatland appears to be a major carbon sink and to drive net negative radiative forcing under current hydrometeorological conditions, but evidence indicates that the sink strength is vulnerable to future climate change.

While soil carbon has accumulated over millennia in peatlands, these results demonstrate that the vast deep carbon stores become destabilized under prolonged warmer conditions (>1 year), providing important insights into climate-peatland interactions.

To investigate the effects of long-term climate change on peatland carbon stability, the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment warmed the air and soil (to >2 m deep) of a Minnesota bog up to +9°C above ambient temperatures with a subset additionally subjected to doubled atmospheric carbon dioxide (CO₂) concentrations. Following five years of warming, both surface and deeply buried peat served as carbon sources contributing to large increases in methane (CH₄) and CO₂ emissions, suggesting that the massive carbon banks stored as peat are vulnerable to climate change.

Northern peatlands contain one half of Earth’s soil organic carbon due to their cold, water-saturated, acidic conditions that slow decomposition. The investigators at the University of Oregon working with the Oak Ridge National Laboratory whole-ecosystem manipulation SPRUCE project hypothesized that warmer temperatures—due to changing climate—would increase emissions of the greenhouse gases carbon dioxide (CO₂) and methane (CH₄). To test this hypothesis, air and peat (to >2 m deep) at SPRUCE were warmed to five temperatures (0°C, +2.25°C, +4.5°C, +6.25°C, and +9°C above ambient), with and without elevated air CO₂ concentrations. Previous results after more than 1 year of treatment showed that the exponential increase in CH₄ emissions with warming was solely due to surface processes. After 5 years of treatment, large increases in both CH₄ and CO₂ emissions were observed in response to warming. Further, radiocarbon dating and laboratory incubations revealed that the increased emissions resulted from both surface peat and destabilization of deep ancient peat. Decomposition has become more methanogenic with warming as indicated by a decrease in the CO₂:CH₄ ratio in anaerobic respiration, which is particularly concerning given the large global warming potential of CH₄. Elevated air CO₂ concentrations to date have only had small effects on CH₄ processes. These results highlight the vulnerability of massive peatland carbon banks to a warmer climate and suggest a positive feedback that is expected to exacerbate climate warming.

Soil respiration is one of the largest fluxes in the global carbon cycle, providing critical insights into biological activity in the underlying soil. This new global map of soil respiration and its uncertainties provides modelers and experimentalists with a “gold standard” benchmark dataset identifying areas with the highest uncertainties to target in the future.

Scientists at the Pacific Northwest National Laboratory have developed and continue to maintain a global database of measurements made of soil-to-atmosphere CO₂ flows, termed soil respiration. A research team at the University of Delaware has leveraged these observations in a machine-learning approach to create a new high-resolution global map of soil respiration and its uncertainties.

Soils emit large amounts of carbon dioxide to the atmosphere every year via the process of soil respiration. Rates of soil respiration are highly variable in space, however, limiting scientists’ ability to balance global carbon budgets and forecast climate change. This study used a novel machine learning approach to predict soil respiration rates at high resolution (1 km²) globally, based on how observations of soil respiration were related to climate (annual temperature, annual and seasonal precipitation) and vegetation. It also examined the spatial patterns of the associated uncertainty of these predictions. Predicted annual soil respiration and prediction uncertainty varied across ecosystem types and regions, with evergreen tropical forests dominating global annual soil respiration emissions. Dryland, wetland, and cold ecosystems had the highest associated prediction uncertainties, suggesting that future soil respiration measurements would be especially useful in these areas. The high spatial resolution of these predictions will help researchers studying the carbon cycle at local to global scales and provide a high-quality benchmark dataset for Earth System Models.

Projected permafrost degradation may result in several ecological and climatic feedbacks that affect the carbon cycle. Permafrost regions store huge amount of carbon, which may be available for microbial decomposition. Uncertainties in projected 21^st century precipitation trends strongly affect simulated permafrost degradation. Earth system models, including DOE’s ELM, which do not account for changes in soil thermal regime driven by precipitation heat transfer, likely underestimate predicted increases in thaw depth and therefore their effects on high-latitude carbon interactions with the atmosphere.

A study at the North Slope of Alaska shows that increased precipitation accelerates permafrost degradation beyond the degradation caused by recent and 21^st century climate surface air warming. The study examines (1) how changes in precipitation affect active layer depths under recent and future climate and (2) the relative importance of changes in surface air temperature and precipitation on permafrost degradation in the continuous permafrost zone (>90% of the area underlain by permafrost).

Permafrost is critical to future carbon-climate feedback predictions, yet permafrost degradation and surface energy budgets of high-latitude permafrost systems are poorly represented in Earth System Models (ESMs). A potentially important factor in permafrost degradation neglected so far by ESMs is heat transfer from precipitation, although increases in soil temperature and thaw depth have been observed following increases in precipitation. Using a mechanistic ecosystem model, ecosys, modeled active layer depth (ALD) in simulations that allow precipitation heat transfer agreed very well with observations from 28 Alaska Circumpolar Active Layer Monitoring (CALM) sites (R²=0.63; RMSE = 10 cm). Simulations that ignored precipitation heat transfer resulted in lower spatially-averaged soil temperatures and a 39 cm shallower ALD by year 2100 across the north slope of Alaska. Results from our sensitivity analysis show that projected increases in 21^st century precipitation deepen the active layer by enhancing precipitation heat transfer and ground thermal conductivity, suggesting that precipitation is as important an environmental control on permafrost degradation as surface air temperature. We conclude that ESMs that do not account for precipitation heat transfer likely underestimate ALD rates of change, and thus likely predict biased ecosystem responses.

Increases in shrub abundance, or ‘shrubification’, of northern peatlands under warmer conditions has been seen aboveground in previous studies. However, this work highlights belowground mechanisms that enable shrubs to rapidly adapt to warmer and drier conditions. Fine-root data and responses from this study will also enable improved representation of peatlands into Earth system models.

Peatlands store a significant amount of global soil carbon and are vulnerable to global change. The warming response of peatland plants is expected to influence future carbon uptake and storage but is poorly understood, especially belowground. Researchers at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) whole-ecosystem warming experiment found warming (and soil drying) significantly increases fine-root growth. The magnitude of this belowground response is 20× higher than previously observed in similar experiments from upland ecosystems.

Belowground responses to climate change remain a key unknown in the Earth system. Plant fine-root response is especially important to understand because fine roots respond quickly to environmental change, are responsible for nutrient and water uptake, and influence ecosystem carbon cycling. However, fine-root responses to climate change are poorly constrained, especially in northern peatlands, which contain up to two-thirds of the world’s soil carbon. Using a whole-ecosystem warming manipulation, researchers at SPRUCE found that warming strongly increased ecosystem fine-root growth. Shrub fine-root production increased linearly by 1.2 km m^-2 year^-1 for every degree increase in soil temperature. Soil moisture was negatively correlated with fine-root growth, highlighting that drying of these typically water-saturated ecosystems can fuel a surprising burst in shrub belowground productivity; one possible mechanism explaining the ‘shrubification’ of northern peatlands in response to global change. This previously unrecognized belowground mechanism sheds light on how peatland fine-root response to warming and drying could be strong and rapid, with consequences for the belowground growing season duration, microtopography, vegetation community structure and ultimately, carbon function of these globally relevant carbon sinks.

This study highlights the need to consider the interdependence between temperature and hydrology when attributing the relative contribution of these factors to interannual variability of the terrestrial carbon cycle. Specifically, when offline models are forced with observations or reanalysis, the contribution of temperature may be overestimated when its own variability is modulated by hydrology via land-atmosphere coupling.

During El Niño events, atmospheric teleconnections with sea surface temperature (SST) anomalies in the equatorial Pacific cause higher temperatures and reduced rainfall in the Amazon, leading to increased CO₂ emissions. While some of the temperature increase results directly from the SST-atmosphere teleconnection, drier soil resulting from reduced rainfall can also contribute to higher temperatures and resulting CO₂ flux anomalies. Researchers from the University of California, Irvine and the Oak Ridge National Laboratory modified the Energy Exascale Earth System Model (E3SM) to decouple the direct effects of SST anomalies from the resulting soil moisture anomalies, in order to determine the relative importance of each of these drivers.

Soil moisture variability was found to amplify and extend the effects of SST forcing on eastern Amazon temperature and carbon fluxes in E3SM. During the wet season, the direct, circulation-driven effect of ENSO SST anomalies dominated temperature and carbon cycle variability throughout the Amazon. During the following dry season, after ENSO SST anomalies had dissipated, soil moisture variability became the dominant driver in the east, explaining 67–82% of the temperature difference between El Niño and La Niña years, and 85–91% percent of the difference in carbon fluxes. The research team demonstrated that in E3SM, soil moisture anomalies resulting from SST variability extended and strengthened the temperature and CO₂ flux anomalies associated with ENSO. This indicates the need to consider the interdependent relationship between temperature and the hydrologic cycle when attributing mechanisms to ENSO-driven variability in the tropical terrestrial carbon cycle.

Team members now have a capability to study fungi beneath peatlands and other ecosystems. As rising temperatures rapidly modify the boreal biome, an understanding of peatland belowground biodiversity is crucial to predict the role of peatlands in carbon cycles and climate change.

Researchers at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) whole-ecosystem warming experiment pioneered the use of high-resolution minirhizotrons in a forested peat bog to explore temporal variation in the abundance and growth of plant fine roots and their fungal partners under variable temperature and moisture conditions. The research team demonstrated that the abundance and growth of shrub roots and light-colored, thick fungal vegetative parts will increase and the belowground active season will be extended under warmer peat temperatures. These changes may reduce peat carbon accumulation on the boreal landscape.

Mycorrhizal fungi enable plants to thrive in the cold, waterlogged, organic soils of boreal peatlands and, with saprotrophic fungi, largely contribute to the sequestration of atmospheric carbon in peat. Hence, fungi support the contribution of peatlands to global climate regulation, on which society depends. Here the team used high-resolution minirhizotrons for an unprecedented glimpse of the belowground world of a forested bog and highlighted linkages between environmental change and the abundance, dynamics, and morphology of vascular plant fine roots and fungal mycelium. These changes may have implications for peat carbon accumulation on the boreal landscape.

Evergreen trees invest in long-lived leaves that provide C uptake over multiple years. As climate changes, there may be more extended or frequent drought events. If drought occurs during spring budbreak, it can restrict branch expansion and physiological capacity, which in turn may impact net annual C uptake over multiple years. These findings could be useful for informing Earth system models that simulate spring canopy development and ecosystem carbon exchange.

Springtime budbreak and branch development induces substantial carbon (C) costs in trees. New branch growth in evergreen trees relies on both old and new C from stored sugars and spring photosynthesis, respectively. Drought stress during branch development can hinder translocation of stored sugars and reduce new C uptake.

The researchers studied effects of drought and re-hydration on early season branch development, carbon uptake, and internal carbon cycling in 10-year-old Picea mariana (black spruce) trees. During a six-week drought treatment, researchers measured dynamics of key morphological and physiological processes. Development of the photosynthetic apparatus was delayed in droughted trees by two weeks in comparison with that of well-watered trees. Drought stress reduced springtime carbon availability for growth, and the drought-stressed trees prioritized use of new C uptake for respiration over structural branch and leaf components. Drought during branch expansion ultimately resulted in longer branches, but the display of leaves was more compact, with reduced branch volume. Since evergreen species such as black spruce retain active leaves for multiple years, impacts of early season drought on plant vigor could be carried forward into subsequent years.

Soil microbes, including bacteria, regulate many processes essential to sustaining life on Earth, including carbon and nutrient cycling. Yet, scientists have limited understanding of how environmental changes affect soil microbial communities and their ability to perform vital ecosystem functions. This study revealed that permafrost microbial communities can respond to Arctic warming by rapidly shifting bacterial abundances to maximize functional strategies that take advantage of short-term resource changes caused by abrupt thawing and higher temperatures. It also suggests that soil chemistry will likely play a large role in shaping the structure and function of bacterial communities over the long-term.

Bacteria living in soil respond to environmental conditions, such as moisture, temperature, and soil chemistry (e.g., pH, nutrients, and minerals). In the Arctic, global warming is causing permafrost (continuously frozen ground) to thaw and bacterial communities to change. Researchers investigated how warming affects bacteria in different soil layers sampled from tundra sites in northern Alaska. When permafrost thawed, bacteria that grow quickly in nutrient-rich environments increased, while bacteria that grow slowly and survive on limited resources decreased. In contrast, bacterial responses to warming of surface soil layers that thaw every summer were less drastic and more influenced by soil chemistry.

Arctic soils hold a substantial portion of the global carbon pool because cold temperatures have limited microbial decomposition of soil organic matter (SOM) and carbon mineralization for millennia. Yet, warming is occurring much faster in the Arctic than in other regions. Understanding how soil microorganisms respond to warming-induced changes—in both the seasonally thawed active layer and underlying permafrost—will inform efforts to project the fate of soil carbon in the Arctic.

Upper permafrost and two contrasting active layer soils (organic and mineral) were sampled at four tundra sites with differing soil properties, incubated at five temperatures ranging from −1°C to +16°C, and analyzed for chemical composition and bacterial community structure. Bacteria known to thrive in carbon- and nutrient-rich environments responded positively when permafrost was incubated above 0°C, reflecting increased access to labile SOM upon thaw. While bacterial community structure was driven primarily by site and soil layer type, soil chemistry data improved predictive models. Although the largest pre- to post-incubation shifts in bacterial abundance occurred in permafrost, correlations between SOM chemistry and bacterial abundance were far greater in active layer soils, suggesting annual thawing produces a more stabilized community that is attuned to its specific environment.

The study demonstrated that the inclusion of stem capacitance in CLM4 can significantly improve the model’s capability to simulate the response of water and heat fluxes of tropical rainforests to drought conditions.

Scientists at Oak Ridge National Laboratory incorporated a tree stem-water component into the version 4 of Community Land Model (CLM4) to characterize the dynamic stem-water storage and its impacts on the daily transpiration rate. The updated model was evaluated at an Amazonian rainforest site to assess model capability to simulate diurnal and monthly dynamics in water and energy fluxes

A novel tree stem-water model was developed to capture the dynamics of stem- water storage and its contribution to daily transpiration. The module was incorporated into the Community Land Model (CLM), where it was used to test model sensitivity to stem-water content for an NGEE-Tropics evergreen rainforest site in Amazonia, that is, the BR-Sa3 eddy covariance site. With the inclusion of the stem-water storage, CLM produced greater dry-season latent heat flux that was closer to observations, facilitated by easier canopy access to a nearby stem-water source rather than being solely dependent on soil water. The simulated stem-water content also showed seasonal variations in magnitude corresponding with seasonal variations in sap flow rate. Stored stem-water of a single mature tree was estimated to contribute 20–80 kg/day of water to transpiration during the wet season and 90–110 kg/day during the dry season, thereby partially replacing soil water and maintaining plant transpiration during the dry season. Diurnally, stem-water content declined as water was extracted for transpiration in the morning and then was refilled from soil water beginning in the afternoon and through the night. The dynamic discharge and recharge of stem storage was also shown to be regulated by multiple environmental drivers. Our study indicates that the inclusion of stem capacitance in CLM significantly improves model simulations of dry-season water and heat fluxes, in terms of both magnitude and timing.

Given the multiphysics nature of permafrost simulations, multiple types of field observations across multiple years are needed to refine and thoroughly evaluate emerging permafrost models. This comparison against multiple types of data collected by the Next-Generation Ecosystem Experiments (NGEE) –Arctic team provides new confidence in their representations of permafrost physical processes and projections of permafrost thermal hydrology and the fate of stored organic carbon.

Models are essential tools to study the complex permafrost environment. The research team have used multiple field observations in multiyear fine-scale simulations to evaluate and build confidence in emerging permafrost models that couple processes across the surface and shallow subsurface.

Highly resolved three-dimensional (3D) simulations of integrated surface/subsurface permafrost thermal hydrology were compared, for the first time, to multiple types of observations. The simulations using the Advanced Terrestrial Simulator (ATS) successfully reproduced multiple types of observations collected by the NGEE-Arctic team over multiple years, including winter snow depth; summer water table; and soil temperature at several depths in the trough, center, and rim of an ice-wedge polygon. After applying a simple upscaling procedure, evaporative fluxes were in reasonable agreement with evapotranspiration fluxes from a nearby eddy-covariance flux tower. Simulations showed sensitivity to parameters in the bare soil evaporation model, snow model, and the lateral hydraulic conductivity. This study provides new confidence in our models for the responses of Arctic polygonal tundra to a warming climate at fine and intermediate scales, which provides, in turn, the basis for parameterizing processes in the Earth System Model (ESM) to reduce uncertainty in ESM projections of permafrost landscapes.

This article, part of an invited collection of Grand Challenges papers published in AGU journals, seeks to focus LSM developers on the largest problems facing land surface science. Highlighting key barriers in LSM science, this article seeks to galvanize the community to focus on problems related to process complexity, the many dimensions of sub-gridscale heterogeneity, and the representation of coupled physical-ecological dynamics.

The land surface is a crucial part of the Earth system, and land surface models (LSMs) are key to some of the most important problems facing society today.  But large uncertainty in LSM predictions, and a poor ability to attribute the sources of that uncertainty, mean that new strategies are needed.  Researchers at Lawrence Berkley National Laboratory identify three “grand challenges” facing LSM developers and propose strategies to help overcome these problems.

LSMs are critical pieces of Earth system models, needed for projection of the extent of global change as well as impacts on critical terrestrial systems such as agriculture, freshwater resources, ecosystems, and built infrastructure. However, LSM predictions show a stubborn uncertainty that has been difficult to attribute to specific process representations and parameter values.  At the same time, the scope of LSMs has grown complex—for valid reasons—because of the many interacting processes that make up terrestrial systems. The researchers argue that, as a first “Grand Challenge”, the LSM community must focus more clearly on the process complexity of LSMs, in order to better allow scaling from simplified models to highly interacting representation of a full LSM.  A second “Grand Challenge” relates to the differing views of heterogeneity in LSMs, which focus on various subsets and combinations of, e.g., disturbance, hillslopes, microclimate, vegetation communities, recent weather, snow depth, land management, and others.  But a general approach to identifying what are the dominant dimensions of heterogeneity at a given location, and how to most efficiently resolve that heterogeneity has not yet emerged.  A third “Grand Challenge” is on how to understand the dynamics of model parameters that are governed by complex interactions between physical and ecological dynamics; in particular the article reviews three leading approaches (empirical, optimality-based, and competition-resolving) and identifies questions about when to use each of these, how different aspects may and may not be combined, and what the implications are for each of these.

Permafrost-affected regions store a huge amount of frozen organic carbon. The fate of the stored organic carbon depends on the thermal stability of those regions. This study shows that the timing of snow accumulation, with stable air temperatures and snow magnitude, can significantly impact permafrost degradation. Thereby, snowfall timing has the potential to accelerate the release of stored carbon to the atmosphere and permafrost thermal hydrology.

Predicting how permafrost soil changes when climate changes is not an easy task. NGEE-Arctic researchers at Oak Ridge National Laboratory have used computer models to design experiments to study how snow accumulation timing can expose previously stored frozen organic carbon to microbial decomposition.

Computer models allow the design of experiments in a well-controlled environment, an  impossibility in observational and manipulated experiments in field studies. In this work, snow manipulation numerical experiments using fully coupled process-based integrated surface/subsurface thermal hydrology models show that even small shifts in the timing of snow accumulation can significantly affect permafrost degradation. In the study’s numerical scenarios, simulations were forced by the same meteorological data, except the snow precipitation, which was systematically altered to change timing of snowfall. The scenarios represent subtle shifts in snow timing, but the snow onset/melt days, the end of winter snowpack depth, and the total annual snow precipitation are the same in all scenarios. The simulations show that subtle shifts in the timing of snowfall can affect the active layer and even lead to talik formation when snowfall arrives earlier in the winter. The team found that the shifts in snow timing have a stronger impact on wetter regions as compared to drained regions. This study highlights the importance of understanding potential changes in winter precipitation patterns for reliable projections of active-layer thickness in a changing Arctic environment.

As fire regimes and our relationships with fire continue to change, prioritizing these research areas and emergent themes will facilitate understanding of the ecological causes and consequences of future fires and fire management.

The Future of Fire Consortium (FFC), composed of ecologists from around the globe with expertise ranging from paleoecology to atmospheric science, identified critical research frontiers in six areas of fire ecology and three emergent themes for future fire ecology research including: (1) the need to study fire across temporal and spatial scales, (2) the need to assess the mechanisms underlying a variety of feedbacks in the fire system, and (3) the need to improve representation of fire in a range of modeling contexts.

In this review, critical research frontiers in six areas of fire ecology were identified: (1) expanding concepts of fire regimes, (2) understanding changing fire regimes, (3, 4) examining fire effects on aboveground and belowground ecology, (5) increasing fuels characterization in determining fire behavior, and (6) improving representation of fire processes in a variety of modeling contexts. Within these areas, three emergent themes for future fire ecology research including: (1) the need to study fire across temporal and spatial scales, (2) the need to assess the mechanisms underlying a variety of feedbacks in the fire system, and (3) the need to improve representation of fire in a range of modeling contexts. This review offers guidance to further our effort to understand both the fundamental role of fire in ecological systems and the human role in shaping fire activity. As fire regimes and our relationships with fire continue to change, prioritizing these research areas and emergent themes will facilitate understanding of the ecological causes and consequences of future fires and fire management.

A new synthesis of carbon dioxide flux observations in the northern permafrost region indicates that current carbon losses from soils to the atmosphere during the winter season (October through April) exceed average contemporary estimates of growing-season carbon uptake derived from process models. Extending model predictions to 2100, winter carbon losses from warming permafrost soils might increase by up to 41% under future climate change scenarios.

A new synthesis of carbon dioxide flux observations in the northern permafrost region indicates that current carbon losses from soils to the atmosphere during the winter season (October through April) exceed average contemporary estimates of growing-season carbon uptake derived from process models. Extending model predictions to 2100, winter carbon losses from warming permafrost soils might increase by up to 41% under future climate change scenarios.

Permafrost region soils have captured and stored carbon for tens of thousands of years. However, the recent pace of warming in northern latitudes is accelerating the rate of decomposition of soil organic matter and the subsequent release of carbon dioxide (CO₂) to the atmosphere. Yet, the amount of CO₂ released during winter is highly uncertain and is not well represented by land models or by empirically based estimates. A large community of researchers synthesized regional in situ observations of CO₂ flux from arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost region. The researchers estimated present-day regional CO₂ emissions during winter (October through April) to be 1.7 Pg C yr^-1. This loss exceeds process model projections of contemporary regional carbon uptake by plants during the growing season (-1.0 Pg C yr^-1). Extending model predictions to warmer conditions in 2100 indicates that winter CO₂ emissions might increase 17% under a moderate mitigation scenario (RCP 4.5) but could rise 41% under a business-as-usual emissions scenario (RCP 8.5). Synthesis results provide a new baseline for winter CO₂ emissions from northern terrestrial regions and suggest that enhanced release of soil carbon due to winter warming could offset growing season carbon uptake under future climatic conditions.

In an era of rapid global change, the capacity to predict the responses of Earth’s natural systems lags behind the ability to monitor and measure changes in the biosphere. A primary bottleneck to improvements in process understanding is the lack of community tools for informing models with observations, which reduces our ability to fully exploit the growing volume and variety datasets. Addressing this challenge will require new infrastructure investments to provide accessible, scalable, and transparent tools that integrate the expertise of modelers and empiricists to accelerate the pace of discovery.

The researchers explore limitations to rapid model-data integration and provide a vision for a new community cyberinfrastructure to reduce the disconnects between empirical research and modeling, including the lags between data collection and model ingest. The team details five key opportunities for community tool development designed to improve the fidelity of the models on which scientists, managers, and policymakers rely; reduce barriers to entry; and increase the speed at which new information is synthesized into a predictive framework.

Process-based ecosystem models are a primary tool used by scientists, managers, and policymakers to understand and project the impacts of global change on Earth’s natural and managed ecosystems. In recent years, the volume and diversity of observational data have significantly increased, and yet the ability to incorporate this new information into predictive frameworks has lagged behind, slowing the pace of progress in model capacity to forecast natural systems. Furthermore, the insufficient communication between the non-modeling and modeling communities represents an additional bottleneck to improving the representation of underlying processes in models. In addition, the complexity and diversity of process models lead to a technical barrier to entry for new researchers. Given the breadth and depth of these challenges that transcend individual research groups, empirical and modeling communities, and funding agencies, the team argue for the development of a new community-wide cyberinfrastructure: a computational environment facilitating seamless data flows into and out of models to more rapidly simulate natural phenomena, test new hypotheses, perform standardized model evaluation, and more easily interpret results and compare predictions across a range of models. The researchers specifically provide a roadmap for this cyberinfrastructure, including five key opportunities for the development of community tools addressing this need. The team feel this new modeling paradigm is a critical step toward meeting the needs for science and society in the 21^st century.

Ongoing changes in both environmental drivers and disturbance regimes appear to be consistently increasing mortality and forcing forests towards shorter and younger stands. This reduces potential carbon storage and impacts the biodiversity and, ultimately, the surrounding climate. The pervasive shifts in forest vegetation dynamics are already occurring and are likely to accelerate under future global changes, with consequences for climate forcing.

As an interplay of chronic drivers and transient disturbances are introduced to forests, dynamic changes occur in the reproduction, growth, and mortality of trees. Combining analyses from multiple studies, researchers led by Pacific Northwest National Laboratory investigated the demographic drivers of forest dynamics, the disturbances that drive them, and the pervasiveness and impact of these effects worldwide.

In a collaborative study, more than twenty researchers investigated the influence of today’s climate conditions on the dynamics and stature of forests. Combining data and observations from more than 160 previous studies worldwide, the research team found that tree stands are losing their potential for obtaining height and enduring lifespans. Researchers identified consistent mortality trends resulting from specific chronically changing drivers—rising temperature, increasing CO₂ levels, and transient disturbances including wildfire, drought, and land-use change. These factors have thrown out of balance three important characteristics of a diverse and thriving forest: (1) recruitment, which is the addition of new seedlings to a community; (2) growth, the net increase in biomass or carbon; and (3) mortality, the loss of a plant’s ability to reproduce. As a result, forest vegetation and canopies decline and overall recovery is lessened, resulting in ecosystems dominated by novel species that have replaced the original community. Since the resulting forest loss comes from both natural and land-use change drivers, all of which are predicted to increase in magnitude in the future, it is highly likely that tree mortality rates will continue to increase while recruitment and growth will respond to changing drivers in a spatially and temporally variable manner. The net impact will be a reduction in forest coverage and biomass, with mixed effects on biodiversity. This study forms the basis for investigations regarding the patterns and processes underlying the shifts in forest dynamics, all of which can be tested using emerging terrestrial and satellite-based observation networks.

The dynamics of soil moisture must be considered to adequately model soil respiration and C-N cycling, and soil moisture is more sensitive to dynamics than temperature and litterfall. Dynamic data better represent real-world climate and environmental conditions, which could enable more realistic modeling and understanding of soil C and nutrient cycling in a changing world.

Soil moisture, temperature, and litterfall vary with time, but microbial models analyzing soil carbon and nitrogen cycling often don’t consider the effects of frequent changes in these parameters. Research from scientists at Oak Ridge National Laboratory shows that variation in soil moisture over time can critically affect soil respiration and carbon-nitrogen cycling.

Soil moisture, litterfall, and temperature change seasonally and diurnally, but many microbial modeling analyses use yearly averages or time-invariant data as inputs. The effect of constant versus dynamic parameterization of soil moisture, temperature, and litterfall on microbially mediated carbon and nitrogen cycling in soils was investigated using an updated version of the Microbial-Enzyme Decomposition (MEND) model.  The study found that explicit, dynamic representation of soil moisture is critical to reproduce microbial respiration and C-N cycling processes because microbes can undergo dormancy under dry conditions and microbial enzyme activities also vary with soil moisture. The model was also able to faithfully reproduce the carbon-to-nitrogen ratio in soil organic matter and microbial biomass, and to reproduce nitrate and ammonia concentrations in soils.

Globally, terrestrial plant nitrogen demand has exceeded soil nitrogen supply in unfertilized ecosystems over the past century, which could be due to either warmer climates or elevated atmospheric CO₂ increasing plant growth. Results from this experiment indicate that this imbalance is driven primarily by elevated CO₂, implying that nitrogen supply will likely increase to meet plant demand as warming exceeds 2°C.

Interactions between plant nutrient demand and nutrient supply and dictate key terrestrial ecosystem feedbacks to global climate change. Experimental warming of a tidal marsh ecosystem showed that plants and microbes respond to warming at different threshold temperatures. Modest warming (<2°C) caused plant demand for nitrogen to outpace soil nitrogen supply, while more extreme warming (3-5°C) caused the nitrogen supply to increase to meet plant nitrogen demand. This response changed further when the plants were exposed to both warming and elevated CO₂.

Terrestrial ecosystem responses to climate change are mediated by complex plant-soil feedbacks that are poorly understood, but often driven by the balance of nutrient supply and demand. SMARTX is an in situ whole-ecosystem active warming experiment crossed with elevated CO₂ that was established in a Chesapeake Bay tidal marsh in 2016 to understand the combined ecosystem-scale effects of warming and elevated CO₂. Heating treatments run year-round along a gradient from ambient to +5.1°C above ambient and warming spans from above the plant canopy to 1.5 m soil depth. Data from two years show that plants and soils respond to whole-ecosystem warming at different threshold temperatures, creating non-linear responses in biomass allocation to roots vs shoots. Peak belowground allocation occurred at 1.7°C, but declined back to ambient levels with further warming up to +5.1°C. Crossing elevated CO₂ with +5.1°C of warming reversed this pattern and dramatically increased the root-to-shoot ratio. The research team proposes that this non-linearity is explained by asynchronous patterns of increasing plant nitrogen demand vs soil nitrogen supply; even though plants increase growth and thus nitrogen demand at low temperatures, microbial nitrogen mineralization, which drives the soil nitrogen supply, does not respond until higher levels of warming.

The impact of warming on deep soils, not just surface soils, must be taken into consideration to accurately predict carbon-climate feedbacks over the 21^st century. Particularly in cold regions, using soil warming rather than air warming projections may improve predictions of temperature-sensitive soil processes, like decomposition.

The average of 14 Earth System Models predicts that globally, soils will warm by 4.5˚C or 2.3˚C by the end of the 21^st century under RCP’s 8.5 or 4.5, respectively, and that deep soils (100 cm) will warm by the same amount as near surface (1 cm) soils. In regions with snow and ice, this soil warming is predicted to occur slightly slower than air warming above it.

Despite the fundamental importance of soil temperature for Earth’s carbon and energy budgets, ecosystem functioning, and agricultural production, studies of climate change impacts on soil processes have mainly relied on air temperatures, assuming they are accurate proxies for soil temperatures. Researchers from Lawrence Berkeley National Laboratory evaluated changes in soil temperature, moisture, and air temperature predicted over the 21st century from 14 Earth system models. The model ensemble predicted a global mean soil warming of 2.3 ± 0.7 and 4.5 ± 1.1 °C at 100‐cm depth by the end of the 21st century for RCPs 4.5 and 8.5, respectively. Soils at 100 cm warmed at almost exactly the same rate as near‐surface (~1 cm) soils. Globally, soil warming was slightly slower than air warming above it, and this difference increased over the 21st century. Regionally, soil warming kept pace with air warming in tropical and arid regions but lagged air warming in colder regions. Thus, air warming is not necessarily a good proxy for soil warming in cold regions where snow and ice impede the direct transfer of sensible heat from the atmosphere to soil. Despite this effect, high‐latitude soils were still projected to warm faster than elsewhere, albeit at slower rates than surface air above them. When compared with observations, the models were able to capture soil thermal dynamics in most biomes, but some failed to recreate thermal properties in permafrost regions. Particularly in cold regions, using soil warming rather than air warming projections may improve predictions of temperature‐sensitive soil processes.

Arctic vegetation composition, structure, and function has been significantly altered by climate change. Most current remote sensing is insufficient for characterizing the fine-scale patterns of Arctic plants that are needed by computer models to predict vegetation dynamics under different climatic conditions. The Osprey multi-sensor UAS platform was designed to provide the high-resolution spatial details (i.e. centimeter-scale resolution) necessary for studying the patterns of vegetation across Arctic landscapes. Using Osprey has helped to identify the critical links between vegetation properties and environmental conditions that enable the improved simulation of plants under future climate change, particularly in Arctic ecosystems.

Climate change is impacting the health and distribution of global vegetation. While various satellite and airborne platforms have been used to monitor vegetation changes over space and time, the lower resolution of these platforms limits their utility to identify fine-scale patterns and properties of plants, particularly in the Arctic, which is characterized by high spatial heterogeneity. Recently, scientists at Brookhaven National Laboratory have adopted the use of unoccupied aerial systems (UASs) to provide high-resolution monitoring of vegetation dynamics through the Next Generation Ecosystem Experiment (NGEE)-Arctic. The spatial details provided by UASs improve understanding of plant responses to their environment, thereby enabling better prediction of how climate change will impact terrestrial ecosystems.

Unoccupied Aerial Systems (UASs) fill a critical gap in the monitoring of ecosystems by providing very-high-resolution observations. They can be deployed in remote areas with lower effort than other airborne systems, and can collect data on-demand under different conditions. This study leveraged a novel UAS platform designed to collect fine-detail information on Arctic plant structure and functional properties. The researchers show how the use of the multi-sensor platform was effective at the fine-scale mapping of vegetation patterns, properties, and health. The investigators also found that taller Arctic shrubs regulate the patterns of surface temperature and plant species composition and that these patterns could be mapped in fine-detail with a UAS. Leveraging these platforms will allow scientists to understand the key features of Artic plants that facilitate acclimation to their environment, necessary information for modeling plants under future climate conditions.

This is the first study to use radiocarbon dating to understand how quickly carbon is added to or lost from soil following conversion to oil palm plantations. It is important to understand this because of how quickly natural forests are being converted to oil palm plantations, and because there is a very large amount of carbon stored in the soils of tropical forests. These results indicate that soil carbon stocks in cleared forests may recover following reforestation to oil palm plantations to a similar degree as regrowth of secondary forests and that minimizing the time between forest clearing and oil palm plantation can prevent sustained losses of soil carbon.

Deforestation of tropical forests leads to the loss of carbon stored in the soil, but afforestation or conversion to plantation forests can result in the addition of carbon to the soil. The research team used radiocarbon and stable isotope measurements of soil organic carbon to disentangle the impacts of land cover conversion on soil carbon storage. They found conversion of forests to pastures or oil palm plantations resulted in the loss of soil carbon. In the pastures, the carbon lost from the soil tended to be very young. In the oil palm plantations, the age of the soil carbon did not change, suggesting young carbon may have been lost during conversion but the oil palms have been able to accumulate new young carbon in the soil. Additionally, the team found conversion of degraded pastures to oil palm plantations or secondary forest may help to gradually increase soil carbon stocks.

Tropical forests contain one-third of the Earth’s terrestrial carbon pool; however, rapid deforestation threatens the stability of this carbon. The team measured radiocarbon and stable isotopes of soil carbon from tropical forests and land that was converted to oil palm plantations in Peru, Indonesia, and Cameroon. Additionally, they looked at the impact of converting tropical forests to pastures in Peru and the subsequent conversion of those lands to oil palm plantations or secondary forest. The team found that even though the amount of carbon in soil decreases from the initial conversion to oil palm plantations, oil palms accumulate new carbon in the soil, potentially offsetting soil carbon losses. In contrast, clearing primary forest for crops or pasture caused losses of both total soil carbon and radiocarbon that persisted for decades following reforestation to secondary forest or oil palm plantations.

As climate shifts can lead to more extreme droughts, predictive capacity of ecosystem models must be tested with relevant data. This study provides novel insights to the resistance and resilience of roots, root-associated symbiotic fungi and free-living soil microbes, indicating differential rates of decline in respiration during drought and differential legacy effects following rehydration.

A key uncertainty in models is the impact of droughts on the different sources of belowground respiration, which vary by root traits and mycorrhizal associations. Soil respiration rates of roots, root-associated symbiotic fungi and free-living soil microbes were measured during a drought cycle. To separate the contributions of these three different components of soil respiration, scientists at Oak Ridge National Laboratory grew plants in a greenhouse in specialized pots with mesh partitions during a drought-rewetting experiment.

The researchers imposed a drought-rewetting event on mesocosms planted with maple (Acer saccharum Marshall; arbuscular mycorrhizal fungi host) or oak (Quercus alba L.; ectomycorrhizal fungi host) saplings that were separated into heterotrophic or autotrophic compartments. In maple mesocosms, respiration from the root exclusion (hyphae+microbes only) chamber was the most drought resistant, while in oak mesocosms respiration from the microbes only chamber was the most drought-sensitive. Respiration did not recover after rewatering, indicating a persistent drought legacy. In contrast, carbon degrading microbial enzyme activity returned to control functioning after 2 weeks of well-watered conditions. Their results suggest that belowground biota differ in their sensitivity to and recovery from drought, which affects the carbon processes differently. An improved ability to partition carbon fluxes into biotic sources can help to constrain predicted carbon fluxes under future climate scenarios.

The priority of climate change mitigation efforts depends directly on how future terrestrial carbon storage will evolve. This study integrates a wide range of evidence from forests, tree-rings, experiments, volcanic CO₂ springs, atmospheric and ice-core measurements, satellites, and flux towers to provide a common foundation for future research into this crucial ecosystem service. Improved understanding will help inform better management of natural resources and the services they provide to humanity.

The global responses of plants and soils to increasing atmospheric carbon dioxide (CO₂) are slowing the rate of climate change, but these responses are complicated and their understanding lacks consensus. The research team led by scientists at Oak Ridge National Laboratory integrated a range of diverse evidence, finding support for the idea that plants and soils store more carbon in response to increasing atmospheric CO₂. However, the size of this response is uncertain. Other agents of global change (e.g., land cover change) are also affecting carbon storage, complicating the attribution of change to any single factor. Despite the uncertainty, it is expected that the change in carbon storage will diminish going into the future.

Atmospheric CO₂ is increasing, leading to climate change. Increasing CO₂ also increases leaf-scale photosynthesis and water-use efficiency, which has the potential to increase plant biomass and soil organic matter. This enhanced removal of carbon from the atmosphere into terrestrial ecosystems (a carbon sink) could slow the pace of climate change. However, ecosystem CO₂ responses are complex and evidence for “CO₂ fertilization” of the terrestrial carbon sink can appear contradictory. An international team of over 60 scientists, led by scientists at Oak Ridge National Laboratory, synthesized theory and broad, multidisciplinary evidence for the effects of increasing CO₂ on the global terrestrial carbon sink.

Evidence for increasing terrestrial ecosystem carbon storage caused by increasing atmospheric CO₂ indicates a potential highly valuable ecosystem service that effectively subsidizes fossil fuel emissions by slowing the rate of CO₂ accumulation in the atmosphere. But due to concurrent changes caused by other global change factors, the size of this subsidy remains unclear. Diminishing direct physiological responses, increasing mortality, nutrient limitations, and other temperature-related restrictions are highly likely to limit future increases in terrestrial carbon storage due to increasing atmospheric CO₂. A decline in this subsidy will result in accelerated climate change per unit of anthropogenic CO₂ emissions.

This work demonstrates the first clear evidence of a discernible human fingerprint on NEL physiological vegetation changes and points to new investigations that could use detection and attribution methods to study broad-scale terrestrial ecosystem dynamics.

This study examines leaf area index (LAI; area of leaves per area of ground) during the growing season (April–October) over northern-extratropical latitudes (NEL; 30° to 75°N). Previous work assessing modeled and observed LAI focused on timing of seasonal growth, interannual variability, and multiyear trends. These earlier studies showed that spatiotemporal changes in LAI were related to variation in climate drivers (mainly temperature and precipitation). This new study adds to an increasing body of evidence that NEL vegetation activity has been enhanced, as reflected by increased trends in vegetation indices, aboveground vegetation biomass, and terrestrial carbon fluxes during the satellite era. However, this analysis goes beyond previous studies by using formal detection and attribution methods to establish that the trend of increased northern vegetation greening is clearly distinguishable from both internal climate variability and the response to natural forcings alone. This greening can be rigorously attributed, with high statistical confidence, to anthropogenic forcings, particularly to rising atmospheric concentrations of greenhouse gases.

Significant NEL land greening has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water, and carbon budgets, as well as ecosystem services across multiple scales. Discernable human impacts on Earth’s climate system have been revealed by using statistical frameworks of detection and attribution. These impacts, however, were not previously identified on the NEL greening signal, due to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and lack of suitable simulations from coupled Earth system models (ESMs). Researchers, led by Oak Ridge National Laboratory, overcame these challenges to attribute recent changes in NEL vegetation activity. They used two 30 year–long, remote sensing–based LAI datasets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Their findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. This evidence of historical, human-induced greening in the northern extratropics has implications for both intended and unintended consequences of human interactions with terrestrial ecosystems and the climate system.

The regionally observed increases in plant biomass and active layer thickening over the past 40 years not only have major implications for energy and water balance, but also have significantly altered land–atmosphere CH₄ emissions for this region, potentially acting as a positive feedback to climate warming.

Researchers measured methane (CH₄) fluxes of aquatic vegetation during 2010–2013 at sites characterized in the 1970s at the International Biological Program (IBP) research site near Barrow, Alaska. They then developed statistical models to determine the major environmental factors associated with CH₄ emissions such as plant biomass and active-layer depth. They used the IBP historic datasets to model changes in CH₄ fluxes between the 1970s and 2010s. Next, using high-resolution imagery, the researchers mapped aquatic vegetation and applied their model to estimate regional changes in CH₄ emissions.

Plant-mediated CH₄ flux is an important pathway for land-atmosphere CH₄ emissions, but the magnitude, timing, and environmental controls, spanning scales of space and time, remain poorly understood in arctic tundra wetlands, particularly under the long-term effects of climate change. CH₄ fluxes were measured in situ during the peak growing season for the dominant aquatic emergent plants in the Alaskan arctic coastal plain, Carex aquatilis and Arctophila fulva, to assess the magnitude and species-specific controls on CH₄ flux. Plant biomass was a strong predictor of A. fulva CH₄, flux while water depth and thaw depth were copredictors for C. aquatilis CH₄ flux. The researchers used plant and environmental data from 1971 to 1972 from the historic IBP research site near Barrow, Alaska, which they resampled in 2010-2013, to quantify changes in plant biomass and thaw depth. They used these data to estimate species-specific decadal-scale changes in CH₄ fluxes. A ~60% increase in CH₄ flux was estimated from the observed plant biomass and thaw-depth increases in tundra ponds over the past 40 years. Despite covering only ~5% of the landscape, the researchers estimate that aquatic C. aquatilis and A. fulva account for two-thirds of the total regional CH₄ flux of the Barrow Peninsula. The regionally observed increases in plant biomass and active-layer thickening over the past 40 years not only have major implications for energy and water balance, but also have significantly altered land- atmosphere CH₄ emissions for this region, potentially acting as a positive feedback to climate warming.

These findings, which have important implications for future atmospheric CO₂ levels, emphasize the need to incorporate better understanding of soil carbon cycling as well as ¹⁴C and other tracer diagnostics into ESMs to improve the quality of future climate projections. The work also illustrates the potential value of systematically exploiting available ecosystem measurements during model development to create more robust models.

Researchers used carbon-14 (¹⁴C) data from 157 globally distributed soil profiles to determine that current soil carbon is about 3,100 years old rather than the 450 years stipulated by many Earth system models (ESMs).  This analysis shows that the fifth Coupled Model Intercomparison Project (CMIP5), for example, underestimated the mean age of soil carbon by about a factor of six, resulting in an overestimate of soil carbon sequestration potential by a factor of nearly two. Consequently, a greater fraction of carbon dioxide (CO₂) emissions than previously thought could remain in the atmosphere and contribute to global warming.

Soil is the largest terrestrial carbon reservoir and may influence the sign and magnitude of carbon cycle–climate feedbacks. Many ESMs estimate a significant soil carbon sink by 2100, yet the underlying carbon dynamics determining this response have not been systematically tested against observations. Researchers from the University of California, Irvine; Max Planck Institute for Biogeochemistry; Lawrence Berkeley National Laboratory; Stanford University; and U.S. Geological Survey used ¹⁴C data from 157 globally distributed soil profiles sampled to 1-m depth to show that ESMs underestimated the mean age of soil carbon by a factor of more than six (430 ± 50 years versus 3100 ± 1800 years). Consequently, ESMs overestimated the carbon sequestration potential of soils by a factor of nearly two (40% ± 27%). This analysis shows that ESMs must better represent carbon stabilization processes and the turnover time of slow and passive soil carbon reservoirs when simulating future atmospheric CO₂ dynamics.

An intergovernmental partnership to compile available arctic vegetation data can be leveraged to quantify and model the biodiversity and distribution of vegetation across the Arctic, now and in the future.

The Arctic Vegetation Archive (AVA) was developed in response to a goal set by the intergovernmental Arctic Council of eight Arctic nations to better understand the biodiversity and distribution of vegetation across the circumpolar Arctic.

The AVA was conceived by the Flora Group of the Conservation of Arctic Flora and Fauna (CAFF), the biodiversity working group of the intergovernmental Arctic Council, with the goal of compiling available plot-level vegetation data to better understand the distribution of vegetation across the Arctic tundra. Each Arctic nation is tasked with developing a portion of the evolving pan-Arctic vegetation archive. The U.S. contribution, the AVA-AK was begun in 2013. To date, the AVA-AK contains more than 3,000 nonoverlapping vegetation plots from the Arctic portion of Alaska, with georeferenced locations and associated environmental data ranging from slope and altitude, to edaphic conditions, to plot-level microrelief (i.e., microtopography as in basically just small-scaled features). Plant species in the AVA-AK encompass both vascular and nonvascular plants, and span Arctic vegetation communities ranging from wet tundra to dwarf shrubs to alpine communities to snowbeds. The AVA-AK database is freely available through a web-based portal at the Alaska Arctic Geoecological Atlas (https://arcticatlas.geobotany.org), housed at the University of Alaska, Fairbanks. A preliminary cluster analysis of the data in the AVA-AK indicates the database can be used to predict patterns of vegetation composition across Alaskan tundra in relation to soil moisture and acidity, geography, and ecological affiliation. Furthermore,  data in the AVA-AK can provide a baseline of vegetation distribution across Arctic Alaska for use in terrestrial biosphere models. The Department of Energy’s Next-Generation Ecosystem Experiments (NGEE)–Arctic project joined this international collaboration and contributed species and functional type cover, along with habitat and edaphic conditions, from vegetation censuses conducted during Phase 1 of NGEE-Arctic at Intensive Site 1 on the Barrow Environmental Observatory in Barrow, Alaska. In Phase 2, NGEE-Arctic will contribute data from the Seward Peninsula, Alaska, to help address existing gaps in the AVA-AK database (e.g., large areas of Arctic Alaska not associated with permanent Arctic observatories).

Existing permafrost thermal hydrology simulation tools are limited in their capability to represent the thermal effects of surface and subsurface flows and other important thermal processes. This new process-rich, fine-scale model dramatically expands the range of permafrost thermal hydrology phenomena that can be represented in simulations and provides a community modeling tool to help advance process understanding and evaluate approximations used in Earth system models.

Researchers developed and demonstrated a new process-rich simulation capability for coupled surface and subsurface thermal hydrology in permafrost regions. The Arctic Terrestrial Simulator (ATS) represents nonisothermal surface flow, subsurface thermal hydrology, phase change, surface energy balance, and snow distribution in fully coupled three-dimensional (3D) simulations.

ATS is a collection of physics modules and physics-informed model couplers for use in a parallel, open-source subsurface flow and transport simulator called Amanzi-ATS. A team of researchers developed new models for nonisothermal overland flow and snow distribution in microtopography, new approaches for robustly coupling 2D surface and 3D subsurface models, and new strategies for managing complexity in process-rich simulations. They combined those new capabilities with a state-of-the-art model for thermal hydrology of freezing and thawing soil. Fine-scale, 100-year projections of the integrated permafrost thermal hydrological system in polygonal tundra near Barrow, Alaska, demonstrate the feasibility of microtopography-resolving, process-rich simulations as a tool to help understand possible future evolution of the carbon-rich Arctic tundra in a warming climate.

The study highlights how a belowground perspective of drought can be used in climate models to reduce uncertainty in predicting ecosystem consequences of droughts in forests.

Key data on root distributions and soil water potential from prior Department of Energy–funded precipitation manipulations on the Oak Ridge Reservation (Tennessee) were used to illustrate mechanistic modeling needs. Results show challenges and opportunities associated with managing forests under conditions of increasing drought frequency and intensity and provide a belowground perspective on drought that may facilitate improved forest management.

Predicted increases in the frequency and intensity of droughts across the temperate biome have highlighted the need to examine the extent to which forests may differ in their sensitivity to water stress. At present, a rich body of literature exists on how leaf- and stem-level physiology influence tree drought responses. Less is known, however, regarding the dynamic interactions that occur belowground between roots and soil physical and biological factors. Consequently, better understanding is needed of how and why processes occurring belowground influence forest sensitivity to drought. This study reviews what is known about tree species’ belowground strategies for dealing with drought, and how physical and biological characteristics of soils interact with rooting strategies to influence forest sensitivity to drought. Findings show how a belowground perspective of drought can be used in models to reduce uncertainty in predicting ecosystem consequences of droughts in forests. Additionally, the researchers describe the challenges and opportunities associated with managing forests under conditions of increasing drought frequency and intensity and explain how a belowground perspective on drought may facilitate improved forest management.

A simulation of the tracer release reproduced the motion of tracer from its source to the detectors, but also indicated that the uppermost detector (at 329 m above ground) was mainly sampling air from far beyond 25 km, with a minor contribution from areas within that range. Therefore, for nocturnal conditions, the researchers are confident that the tower is sampling air from over a regional-scale area (25 km to 150 km), and is only weakly influenced by nearby emissions.

At night, can an upper-level carbon dioxide sensor be overly influenced by gas released from nearby vegetation, reducing researchers’ confidence in its ability to provide information on continental-scale surface fluxes? The vertical dispersion of a gas released at night was studied with a field project in South Carolina comprising (1) the release of five perfluorocarbons (inert airborne “tracer” gases) from multiple surface locations and (2) downwind detection of the tracers at four elevations on a tall television transmitter tower.

On two nights characterized by moderate to strong vertical stability, tracer gases were released at the surface from locations upwind of a South Carolina tower equipped with sensors at 34 m, 68 m, and 329 m. The uppermost sensor was able to detect the tracer gas released from the ground at a distance of about 25 km—evidence for some vertical transport despite the weak vertical mixing on the nights it was released. Simulations of the experiment, validated against the field project data, were conducted to estimate the tower “footprint,” or total area from which tracer released at the surface will be detected by the 329-m sensor. These simulations indicate that most of the air reaching the highest tower level came from surface locations much more distant than the domain of the tracer release, with the sensor footprint extending well beyond 25 km. The low-level nocturnal jet (located at 100 m to 1000 m above ground, and at 8 to 20 m per sec speed) was an important reason for the dominant role of distant upwind sources.

These data provide baseline information on porewater chemistry in the Spruce and Peatland Responses Under Climatic and Environmental Change (SPRUCE) experimental bog, highlighting the importance of collecting samples across both space and time. Capturing temporal and spatial variability is needed especially for solute pool and flux calculations and for parameterizing process-based models.

Researchers examined weekly to monthly variation in peat porewater chemistry [pH, cations, nutrients, and total organic carbon (TOC)] depth profiles in an experimental bog in northern Minnesota and compared this temporal variation to spatial (among plot) variation in chemistry.

Research findings showed strong gradients in chemistry depth profiles. For example, ammonium increased and TOC decreased with depth, likely reflecting mineralization of deep peat or TOC. These depth profiles were also temporally dynamic, with ammonium, soluble reactive phosphorus, and potassium concentrations more temporally variable in near-surface porewater than deeper porewater; pH, calcium, and TOC concentrations were more temporally variable at deeper depths. When temporal variation in porewater chemistry at one location was compared to spatial variation in porewater chemistry across 17 locations (SPRUCE plots), findings showed that temporal variation in chemistry at one location was often greater than spatial variation in chemistry, especially in near-surface porewater. These results suggest that representative sampling of porewater requires measurements across both space and time.

This research suggests that even ecosystems that are currently quite cold, such as tundra, will continue to experience greater soil respiration with forecasted future warming. Also, many single-site studies have shown an acclimation of soil respiration to warming, but acclimation was not found in this much larger, spatially distributed dataset.

A synthesis of 27 experimental warming studies across nine biomes showed the soil respiration increased with temperature to about 25°C, with rates decreasing with further warming. No acclimation of soil microbes to warming was found.

The respiratory release of carbon dioxide from soil is a major, yet poorly understood flux in the global carbon cycle. Climatic warming is hypothesized to increase rates of soil respiration, potentially fueling further increases in global temperatures. However, despite considerable scientific attention in recent decades, the overall response of soil respiration to anticipated climatic warming remains unclear. In this study, researchers synthesized the largest global dataset to date of soil respiration, moisture, and temperature measurements, totaling >3,800 observations representing 27 temperature manipulation studies, spanning nine biomes and over two decades of warming. Their analysis reveals no significant differences in the temperature sensitivity of soil respiration between control and warmed plots in all biomes, with the exception of deserts and boreal forests. Thus, these data provide limited evidence of acclimation of soil respiration to experimental warming in several major biome types, contrary to the results from multiple single-site studies. Moreover, across all nondesert biomes, respiration rates with and without experimental warming follow a Gaussian response, increasing with soil temperature up to a threshold of ~25°C, above which respiration rates decrease with further increases in temperature. This consistent decrease in temperature sensitivity at higher temperatures demonstrates that rising global temperatures may result in regionally variable responses in soil respiration, with colder climates being considerably more responsive to increased ambient temperatures compared with warmer regions. This analysis adds a unique cross-biome perspective on the temperature response of soil respiration, information critical to improving mechanistic understanding of how soil carbon dynamics change with climatic warming.

A widely held assumption is that the representation of photosynthesis in TBMs is settled science and that model uncertainty is driven largely by other processes downstream of carbon acquisition. This study demonstrates that model divergence in the physiological response of photosynthesis to key environmental drivers is high and likely a major source of model divergence. This finding is critical because the response of the terrestrial biosphere to global change is driven by these same physiological responses and their accurate representation should be an essential component of improved TBMs. The study lays out the steps needed to improve model representation of photosynthesis.

A collaboration between modelers and plant physiologists compared the projected physiological responses of photosynthesis to key environmental drivers in seven terrestrial biosphere models (TBMs) that form the land components of major Earth system models. The study identified research activities needed to improve process representation of photosynthesis in TBMs.

Accurate representation of photosynthesis in TBMs is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. In this study, researchers compared the representation of photosynthesis in seven TBMs by examining leaf and canopy-level responses of photosynthetic carbon dioxide (CO₂) assimilation to key environmental variables: light, temperature, CO₂ concentration, vapor pressure deficit, and soil water content. They identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. The study provides a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and ESMs.

Soil respiration data can bring a range of benefits to model development, particularly with larger databases and improved data-sharing protocols that make R_S data more robust and broadly available to the research community. These efforts can help usher in new global syntheses and spark progress in both measurement and modeling of biogeochemical cycles.

Scientists have spent decades making measurements of soil respiration (R_S), the flow of carbon dioxide from the soil to the atmosphere, but only recently have they started to collect and synthesize this information. A recent review argues that these data offer untapped potential for better understanding the larger carbon cycle and improving the performance of ecosystem- to global-scale computer models.

Model-data synthesis activities are increasingly important to understand the carbon and climate systems, but only rarely have they used R_S data. In an invited review, U.S. Department of Energy researchers at Pacific Northwest National Laboratory and co-authors argue that overlooking R_S data is a mistake and identify three major challenges in interpreting and using R_S data more extensively and creatively. First, when R_S is compared to ecosystem respiration measured from eddy covariance towers, it is not uncommon to find the former to be larger, which is impossible. This finding is most likely because of difficulties in calculating ecosystem respiration, which provides an opportunity to utilize R_S for eddy covariance quality control. Second, R_S integrates belowground heterotrophic and autotrophic activity (i.e., from plant- and animal-derived carbon), and opportunities exist to use the total R_S flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. Finally, R_S is generally measured at a different resolution than that needed for comparison to eddy covariance or ecosystem- to global-scale models. Downscaling these fluxes to match the scale of R_S, and improving R_S upscaling techniques, will improve resolution challenges.

Most vegetation models assume that forest trees will track their environmental “niche” as climate warms, including upslope to higher elevations. But there is little understanding of climate constraints on seedlings, which are the future of the forest. The unexpected results of intensive field experiments in Colorado indicate that warming reduces the odds of seedlings establishing at and above their current upper limits, as well as in the forest, or provides no net benefit. Seeds sourced from higher-elevation trees also performed relatively poorly, suggesting that past genetic adaptation to local conditions may hinder upslope tree advances, a finding counter to current theory.

Using field experiments in the Rocky Mountains, scientists tested the sensitivity of emerging tree seedlings to artificial warming and watering at three locations along a mountainside to understand whether trees will be able to migrate upward in elevation as the climate changes.

Climate warming is expected to promote upslope shifts in forests. However, common gardens sown with seed collected from two different elevations and subjected to climate manipulations using infrared heaters and manual watering indicate that warming and local genotype may constrain tree seedling recruitment above current treeline. Negative effects of warming in forest, treeline, and alpine sites were partly offset by watering, suggesting growing season moisture may limit establishment of future subalpine forests. Greater climate sensitivity of Engelmann spruce compared with limber pine portends potential contraction in the elevational range of Engelmann spruce and changes in the composition of high-elevation Rocky Mountain forests. The greater availability of poorer-quality seed at the upper forest edge could act to further slow upslope shifts.

The research identified a large, underrepresented source of carbon in the Arctic. The findings suggest that the Arctic may be even less of a carbon sink than previously thought. The eddy covariance measurements imply that to calculate Arctic carbon budgets more accurately, early spring fluxes should be measured and taken into account. The dynamics of this offset in the context of climate change are not yet known, but it appears that the conditions that lead to the accumulation and abrupt emission of the stored gases may become more frequent with warming.

A multi-institution team of scientists measured a large pulse of greenhouse gases [carbon dioxide (CO₂) and methane (CH₄)] released from the frozen Arctic tundra during a two-week period in late May to early June 2014 when soils started to thaw. Little is known about such releases, and the researchers investigated the circumstances, mechanism, likelihood, and outcomes of these events. They show that the pulse was the result of a delayed mechanism, in which gases produced in fall were trapped in the frozen soils and released in spring. The team linked hydrology, biogeochemistry, and geophysics to uncover the pivotal roles of warmer fall weather and spring rain-on-snow events, implying these pulses may be more frequent in the future.

Measurements of a large pulse of carbon gases emitted from the tundra ecosystem were made near Barrow, Alaska, in May 2014. The pulse was large enough to offset nearly half of the following summer’s net plant CO₂ uptake and added 6% to the CH₄ summer fluxes. A similar pulse was measured 5 km away, indicating that this was a widespread phenomenon. Examination of an array of field surveys and laboratory experiments indicated that the spring carbon pulse was a result of a delayed mechanism in which gases produced in the fall are trapped in the frozen soils and released in early spring. How do gases accumulate in the soil? As temperatures drop in late fall, the mid-soil layer remains above freezing for approximately a month after the surface layer has frozen. Microbial activity in the mid-layer produced gases that are trapped beneath the surface ice. How are gases rapidly released from the soils in spring? May 2014 was unique in that several rain-on-snow events took place, with the potential to enhance soil cracking. These cracks can serve as pathways for rapid gas release as soon as the surface ice thaws. How will things change in the future? Warmer fall seasons may lead to a longer period of gas accumulation in the soils; more rain-on-snow events in spring may increase the likelihood of spring carbon pulse events.

Globally, soils store enormous quantities of organic carbon, some of which is consumed by microbes and exhaled as carbon dioxide. In this way, soils annually produce a major natural carbon dioxide flux into the atmosphere, in an amount roughly six times larger than human emissions of the same greenhouse gas. Understanding what influences this flux has enormous implications for understanding climate change, the carbon cycle, and setting emissions targets.

Researchers recently studied how moisture influences soil heterotrophic respiration, the process by which microbes convert dead organic carbon in soil to carbon dioxide. Their cost-effective modeling strategy is the first to investigate the effect of moisture on these climate-critical respiration rates at the hard-to-simulate pore scale. The study also finds that simulations must acknowledge the diversity of soil-pore spaces, moving beyond the modeling assumption that they are homogeneous.

Moisture conditions in soil affect the respiration rate of heterotrophic microbes. Soils are made of sand, silt, clays, and organic matter. Within all this material, miniature “porospheres” interlock to create microbial habitats made of water and gases. Modeling heterotrophic respiration at this “pore scale” is difficult because of two factors: (1) the computational challenges of modeling fluids at this scale and (2) the microscale differences within soil. In every soil, distribution of organic carbon is highly localized and dependent on physical protection, chemical recalcitrance, pore connectivity, nonuniform microbial colonies, and local moisture content.

This study, led by researchers at Pacific Northwest National Laboratory, is the first to conduct a pore-scale investigation of how moisture-driven respiration rates are affected by soil pore structure heterogeneity, soil organic carbon bioavailability, moisture content distribution, and substrate transport. The work provides insight into the physical processes that control how soil respiration responds to changes in moisture conditions. The study’s numerical analyses represent a cost-effective approach for investigating carbon mineralization in soils.

The simulations in this study generally confirmed that (1) the soil respiration rate is a function of moisture content, (2) such rates increase as moisture (and therefore substrate availability) increases, and (3) soil respiration decreases after some optimum because of oxygen limitation. The model’s results, also replicated by field research, show that respiration rates go up with higher soil porosity and that compacted soils (those with less porosity because they are unplowed and undisturbed) reduce the rate at which carbon dioxide escapes into the atmosphere. The study also warned of a danger to assuming uniform porosity in modeled soils; instead, the researchers found that the structural heterogeneity (diversity) of soils should be modeled as it exists in nature.

Further research is needed to determine how coupled aerobic and anaerobic processes would speed up or slow down the amount of organic carbon sequestered in soil.

These findings could provide a powerful framework for building a new generation of models simulating SOC dynamics and composition. The findings also provide insights for using natural processes to protect SOC so that it remains or decomposes in the soil rather than returning to the atmosphere.

Soil has networks of pores and channels that weave through it like interconnected straws. These networks are formed underground by the different minerals that compose soil and as a result of movements or growth by roots, insects, and other living organisms. Soil pores house gases and liquids such as soil organic carbon (SOC) and water. SOC plays a vital role in the carbon cycle. A recent study found that carbon complexity differs with the size of the pore that contains it, yet its decomposability is driven by its proximity to microorganisms, not its chemistry.

In the natural water cycle, the hydrologic connectivity of soil pores surges as soil water content increases, and when pore channels fill with water, SOC and other nutrients can mix and redistribute. Furthermore, when the soil is saturated, soil pores become increasingly connected (making them straw-like) by water, allowing movement of dissolved SOC between pores. This movement increases the likelihood that stored carbon will be transported to microbial-rich locations more favorable to decomposition. This diverse distribution of microbial decomposers throughout soil indicates that metabolism or persistence of SOC compounds is highly dependent upon short distances— think “sprints”—of transport between pores, via water, within the soil.

To demonstrate this process, researchers at Pacific Northwest National Laboratory saturated intact soil cores and extracted pore waters with increasing suction pressures to sequentially sample them from increasingly fine pore domains. The soil solutions were held behind coarse and fine pore “throats,” and revealed more complex soluble carbon in finer pores than in coarser ones. Analysis of the same samples—incubated with fungi Cellvibrio japonicus, Streptomyces cellulosae, and Trichoderma reseei—showed that the more complex carbon in fine pores is not more stable; rather, it is at least as easily decomposed as the simpler forms of carbon found in coarse pores. In fact, the decomposition of complex carbon led to greater losses of it through respiration than the simpler carbon found in coarse pore waters. This finding suggests that repeated cycles of drying and wetting in soils may be accompanied by repeated cycles of increased carbon dioxide emissions. All this raises a question: Is SOC persistence primarily a function of its isolation in different-sized pores?

All the study’s incubated samples demonstrated that the fungi could decompose the SOC in pore waters within the first 48 hours of colocation, meaning that the proximity of microbes with the substrate is the controlling factor in protecting carbon within the soil. The challenge is to use this information to improve predictions of carbon persistence in soils and perhaps determine if and how these natural processes within the soil could be exploited on a much bigger scale so that carbon releases to the atmosphere are reduced.

The magnitude, vulnerability, and spatial distribution of SOCs are major sources of uncertainty in projected carbon-climate feedbacks attributed to the permafrost region. Study results provide a spatially optimized set of locations designed to guide new field observations for constraining the uncertainties in soil carbon estimates and providing robust spatial benchmarks for Earth system model results.

Researchers used a geospatial approach that integrates existing observations with the multivariate spatial heterogeneity of soil-forming factors. The approach was developed to identify the optimal number and spatial distribution of observation sites needed to improve estimates of soil organic carbon (SOC) stocks under current and projected future climatic conditions.

Representing land surface spatial heterogeneity is a scientific challenge that is critical for designing observation schemes to reliably estimate soil properties. Researchers led by Argonne National Laboratory developed a geospatial approach to identify an optimum distribution of observation sites for improving the characterization of SOC stocks across Alaska. By using environmental data expected to influence soil formation as proxies for representing the spatial distribution of SOC stocks, the scientists determined that complementing data from existing samples with 484 new observation sites would be needed to characterize average whole-profile SOC stocks across Alaska at a confidence interval of 5 kg C per m². Estimates to depths of 0 m to 1 m and 0 m to 2 m with the same level of confidence would require 309 and 446 new observation sites, respectively. New observation needs are greater for scrub (mostly tundra) than for forest land cover types, and ecoregions in southwestern Alaska are among the most undersampled. The number and locations of required observations are not greatly altered by changes in climatic variables through 2100 as projected by Intergovernmental Panel on Climate Change emission scenarios. Study results serve as a guide for future sampling efforts to reduce existing uncertainty in SOC observations and improve benchmarks for ESM results.

Project results show that windthrows occur every year and were more frequent from September through February. Drivers of windthrows include southerly squall lines (that form in southern Amazonia and move to northeast Amazonia) that were found to be more frequent than their previously reported ~50 year interval. Project results will improve representations of tree mortality in ESMs and, in particular, the ACME Land Model (ALM).

Windthrows (gaps of uprooted or broken trees) are a recurrent disturbance in Amazonia that affects the persistence of woody biomass, which, in turn, affects patterns of productivity and biomass, floristic composition, and soil composition in the basin. Windthrows are produced by severe convective events that are expected to be more frequent with climate change. Yet, the variability of windthrows over time has not been investigated. Studying the frequency of windthrow occurrence is key to understanding the atmospheric conditions that produce these events.

Windthrows are a recurrent disturbance in Amazonia and are an important driver of forest dynamics and carbon storage. In this study, researchers present, for the first time, the seasonal and interannual variability of windthrows, focusing on central Amazonia, and discuss the potential meteorological factors associated with this variability. Landsat images from 1998 through 2010 were used to detect the occurrence of windthrows, which were identified based on their spectral characteristics and shape. They were found to occur every year, but were more frequent between September and February. Organized convective activity associated with multicell storms embedded in mesoscale convective systems—such as northerly squall lines (that move from northeast to southwest), and southerly squall lines (that move from southwest to northeast)—can cause windthrows. The researchers also found that southerly squall lines occurred more frequently than their previously reported ~50-year interval. At the interannual scale, the study did not find an association between El Niño–Southern Oscillation and windthrows.

Since plants interact with their environments through their traits, bacteria may be an important middleman in determining how plants will respond to changing environmental conditions.

Plant traits, such as root and leaf area, influence how plants interact with their environment and how bacteria living within plant tissues can determine morphology (plant form and structure) and physiology (how they function). To understand how different microbes shaped plant morphology and physiology, researchers inoculated cottonwood seedlings with three different strains of root-dwelling bacteria. They found that the bacteria did not change photosynthesis rates or total biomass, but bacteria regulated where carbon was allocated and how plants used it. Additionally, the researchers found closely related bacteria can have vastly different effects on plant growth.

Bacteria living within plant tissues (endophytes) can change how plants express traits such as root and leaf growth rates and the ratio of root to leaves. Small changes in these traits could build up to alter how plants survive, adapt, and compete within their environment. In a recent study, researchers either inoculated cottonwood seedlings with one of three endophytic bacterial stains or left the plant un-inoculated as a control. They then looked at several responses including root and leaf growth rate, plant biomass, photosynthetic rate, and the ratio of roots to leaves. They found that inoculation was linked to an increase in root and leaf growth rate, but that this increase in growth rate did not lead to an increase in plant biomass or photosynthetic efficiency. These findings indicate bacterial endophytes can change where and how carbon is used in a plant but may not increase the overall amount of carbon fixed by photosynthesis and stored in the plant’s biomass.

Because MF alter resource availability, it may seem obvious that they will alter plant coexistence. Until now, however, mathematical models did not include MF. Including MF in models will lead to better predictions, which can enable better understanding of patterns in nature and how they might be altered by climate change.

The coexistence of plants in an ecosystem is regulated by resource availability and competition for those resources. Mycorrhizal fungi (MF), a root symbiont that helps plants obtain nutrients, can alter how plants compete for resources, which can alter patterns of plant coexistence. MF are found almost everywhere that plants grow, so leaving them out of climate models can cause inaccurate predictions of ecosystem patterns such as plant coexistence. Researchers recently developed a new mathematical model that includes MF for the first time.

Mycorrhizal fungi can alter plant coexistence patterns by changing the host plant’s ability to compete for resources in the soil. How MF change plant coexistence patterns depends on how dependent the host plant and MF are on one another for survival, the rate at which plants and MF exchange nutrients, and how plant growth patterns respond to the cost-benefit ratio of their symbiotic relationship with MF. A new model, which explicitly includes MF, shows that there are tradeoffs to the symbiosis. At times, the carbon cost of MF is balanced by the increase in nutrient availability; however, it is also possible for the carbon cost to outweigh the nutrient benefits and for MF to become detrimental to the host plant’s growth. The balance of the symbiotic relationship can affect plant competition for resources, which can lead to changes in plant coexistence. This model will enable future empirical studies to form hypotheses in light of a better understanding of MF’s role in plant coexistence patterns.

This new hypothesis has significant impact on the seasonal cycle of atmospheric carbon dioxide (CO₂), an important performance metric for global carbon cycle models. The fate of excess carbon can have significant impact on other ecosystem processes.

An innovative and low-cost field experiment provided new results regarding the fundamental process of photosynthetic carbon uptake in the face of varying levels of nutrient limitation. Experimental results refute current modeling approach for instantaneous downregulation of carbon uptake and support new hypothesis for long-term storage and release of excess carbon.

Models predicting ecosystem CO₂ exchange under future climate change rely on relatively few real-world tests of their assumptions and outputs. This work demonstrated a rapid and cost-effective method to estimate CO₂ exchange from intact vegetation patches under varying atmospheric CO₂ concentrations. Findings showed that net ecosystem CO₂ uptake (NEE) in a boreal forest rose linearly by 4.7% ± 0.2% of the current ambient rate for every 10 ppm CO₂ increase, with no detectable influence of foliar biomass, season, or nitrogen fertilization. The lack of any clear short-term NEE response to fertilization in such a nitrogen-limited system is inconsistent with the instantaneous downregulation of photosynthesis formalized in many global models. Incorporating an alternative mechanism with considerable empirical support—diversion of excess carbon to storage compounds—into an existing Earth system model brings the model output into closer agreement with the team’s field measurements. A global simulation incorporating this modified model reduced a long-standing mismatch between the modeled and observed seasonal amplitude of atmospheric CO₂. Wider application of this chamber approach would provide critical data needed to further improve modeled projections of biosphere-atmosphere CO₂ exchange in a changing climate.

The extended model captures pH dynamics reasonably well in Arctic soil incubations. Temperature and pH sensitivity for microbial reactions is highlighted as an important area for further research.

Explicit aqueous phase redox, pH, and mineral interaction dynamics were coupled to the Converging Trophic Cascade (CTC) decomposition model, enabling prediction of carbon dioxide (CO₂) and methane (CH₄) production from Arctic polygonal tundra soils under laboratory incubations over a range of temperatures.

Soil organic carbon turnover and CO₂ and CH₄ production are sensitive to redox potential and pH. However, land surface models typically do not explicitly simulate the redox or pH, particularly in the aqueous phase, introducing uncertainty in greenhouse gas predictions. To account for the impact of availability of electron acceptors other than oxygen (O₂) on soil organic matter (SOM) decomposition and methanogenesis, the research team extended an existing decomposition cascade model (Converging Trophic Cascade model or CTC) to link complex polymers with simple substrates and add iron [Fe(III)] reduction and methanogenesis reactions. Because pH was observed to change substantially in the laboratory incubation tests and in the field and is a sensitive environmental variable for biogeochemical processes, the researchers used the Windermere Humic Aqueous Model (WHAM) to simulate pH buffering by SOM. To account for the speciation of CO₂ among gas, aqueous, and solid (adsorbed) phases under varying pH, temperature, and pressure values, as well as the impact on typically measured headspace concentration, they used a geochemical model and an established reaction database to describe observations in anaerobic microcosms incubated at a range of temperatures (–2, +4, and +8°C). The study’s results demonstrate the efficacy of using geochemical models to mechanistically represent the soil biogeochemical processes for Earth system models. The modeling approach demonstrated in this work will be evaluated against additional field and laboratory data and incorporated in new Earth system modeling development to improve prediction of greenhouse gas fluxes in Arctic tundra environments.

Knowledge of the causes of variation among models is now guiding data collection in the experiment, with the expectation that the guided experimental data collection will optimally inform future model improvements.

Quantitative model projections were made for the recently established Eucalyptus Free-Air CO₂ Enrichment (EucFACE) experiment in Australia. Model simulations were designed to evaluate the experiment data as they are collected and to identify key measurements that should be made to discriminate among competing model assumptions.

A major uncertainty in Earth System models (ESMs) is the response of terrestrial ecosystems to rising atmospheric carbon dioxide (CO₂) concentration, particularly in nutrient-limited environments. The EucFACE experiment, established in a nutrient- and water-limited woodland, presents a unique opportunity to address uncertainty in ESMs, but it can best do so if key model uncertainties have been identified in advance. The research team applied seven representative vegetation models to simulate a priori possible outcomes from EucFACE. Simulated responses to elevated CO₂ of annual net primary productivity (NPP) ranged from 0.5% to 25% across models. The simulated reduction of NPP during a low-rainfall year varied even more widely than the CO₂ response—from 24% to 70%. Key processes where assumptions caused disagreement among models included nutrient limitations to growth, feedbacks to nutrient uptake, autotrophic respiration, and the impact of low soil moisture availability on plant processes.

Stable carbon isotopes provide a useful and independent constraint upon stomatal conductance, an important ecosystem parameter that controls carbon and energy balance at the land surface. Isotopes also can help guide improvements in how nitrogen limitation is represented within the land model component of a climate model.

Researchers used continuous observations of stable carbon isotopes that are exchanged between the land and atmosphere to better understand how a forest in the Colorado Rocky Mountains responded to stressful growing conditions.

Researchers used stable carbon isotopes of carbon dioxide (CO₂) to improve the performance of a land surface model, a component within Earth system climate models. They found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and rate of carbon assimilation (photosynthetic rate). This information provided by isotope observations can go beyond what has traditionally been provided by land surface exchange of carbon, heat, and water measured from towers. Unexpectedly, the study also found that isotope observations provided guidance on how nitrogen limitation should be represented within models. Therefore, the study concludes that isotopes have a unique potential to improve model performance and provide insight into land surface model development.

The study determined that soil respiration of carbon drives variability in ¹⁴CO₂ during the summer months and that simulations from the Carnegie-Ames-Stanford Approach (CASA) model underestimate the biospheric ¹⁴CO₂ source compared to observations at the Wisconsin Tall Tower. This approach has the potential to better constrain the long-term carbon balance of terrestrial ecosystems and the short-term impact of disturbance-based loss of carbon to the atmosphere, highlighting areas for continued land-surface/biogeochemistry model development.

A recent study demonstrates a novel methodology for constraining the net exchange of carbon dioxide (CO₂) between the landscape and atmosphere using ¹⁴CO₂ observed from a tall tower in the midwestern United States. Exchanges include net ecosystem respiration (including belowground carbon), fires, and anthropogenic sources.

A recent study found that during the summer months the biospheric component dominates the atmospheric ¹⁴CO₂ budget at the Park Falls AmeriFlux/WLEF Tall Tower in northern Wisconsin. Respiration of carbon from soils is an important component of the global carbon cycle, returning carbon previously taken up via photosynthesis over decadal time scales back to the atmosphere. For 2010, observations from 400 m above ground indicate that the terrestrial biosphere was responsible for a 2 to 3 times higher contribution to total ¹⁴CO₂ than predicted by the CASA terrestrial ecosystem model. This finding indicates that the model is underpredicting ecosystem respiration and net primary production. Based on back-trajectory analyses, this bias likely includes a substantial contribution from the North American boreal ecoregion, but transported biospheric emissions from outside the model domain cannot be ruled out. The ¹⁴CO₂ enhancement also appears somewhat decreased in observations made over subsequent years, suggesting that 2010 may be anomalous. Going forward, this isotopic signal could be exploited as an important indicator to better constrain both the long-term carbon balance of terrestrial ecosystems and the short-term impact of disturbance-based loss of carbon to the atmosphere.

Researchers in every field of science are being pushed—by funders, journals, governments, and their peers—to increase transparency and reproducibility of their work. A key part of this effort is a move towards open data as a way to fight post-publication data loss, improve data and code quality, enable powerful meta- and cross-disciplinary analyses, and increase public trust in and the efficiency of,publicly funded research. The approach described used in this study is a way to help researchers achieve these goals and may serve as a model for interested researchers.

Scientists conducted an “open experiment” in which every aspect of a laboratory experiment was documented online and in real time. This model pushed the researchers to write higher-quality analysis code, shortened the time required to do so, enabled them to quickly identify problems, and resulted in a stronger publication.

In early 2015, Department of Energy scientists at Pacific Northwest National Laboratory planned a laboratory incubation experiment to characterize the chemical and biological properties of sub-Arctic, active-layer soils subjected to changes in temperature and moisture. This experiment required (1) a multidisciplinary team that was not located in one time zone; (2) integration of various data; (3) rapid performance of quality control and diagnostics, so that if instrument problems arose the team would lose only the minimum amount of time and data; and (4) tight integration of data, statistical analyses, and manuscript results. The team designed a data processing and analytical system written in an open-source and widely used language for statistical computing and graphics, and placed it in a publicly available “repository” that stored all code and data, making them available in real time. Using an automated analytical pipeline in an open repository provided significant advantages for the project, but the costs of such an approach and investments required should also be considered.

Model changes substantially improved CH₄ emission predictions compared to observations. Cold season CH₄ emissions estimates remain biased low, motivating more observations during this period. Large CH₄ emissions uncertainties are also generated by uncertainties in wetland hydrology.

Wetlands are the largest global natural methane (CH₄) source, yet predictive capability of land models is low. In a recent study, researchers improved the methane module in the Community Land Model (CLM) and Accelerated Climate Modeling for Energy (ACME) Land Model (ALM) and compared predictions with tower and aircraft observations and atmospheric inversions. The findings highlight new observations and model requirements to improve global CH₄ predictions.

The study compared wetland CH₄ emission model predictions with site- to regional-scale observations. A comparison of the CH₄ fluxes with eddy flux data highlighted needed changes to the model’s estimate of aerenchyma area, which were implemented and tested. The model modifications substantially reduced biases in CH₄ emissions when compared with CarbonTracker CH₄ predictions. CLM4.5 CH₄ emission predictions agree well with Alaskan growing season (May–September) CarbonTracker CH₄ predictions and site-level observations. However, the model underestimated CH₄ emissions in the cold season (October–April). The monthly atmospheric CH₄ mole fraction enhancements due to wetland emissions also were assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model and compared with measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold season CH₄ emissions. The greatest uncertainties in predicting the seasonal CH₄ cycle are from the wetland extent, cold season CH₄ production, and CH₄ transport processes. Predicted CH₄ emissions remain uncertain, but the study’s findings show that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

Community-level methods were developed and shown capable of quantifying the net flux of the important greenhouse gases CO₂ and CH₄ in a raised bog setting to capture heterogeneous conditions. The method allows for intact assessments of net ecosystem exchange of carbon from the bog community in a manner that does not disturb the experimentally manipulated plots.

Researchers evaluated seasonal patterns of net carbon dioxide (CO₂) and methane (CH₄) flux from an experimental bog in northern Minnesota to establish a baseline for whole-ecosystem warming studies.

Evaluation of the net carbon flux from peatlands under a warming global climate is key to the projection of future greenhouse gas emissions to the atmosphere. The method developed in this study, as part of the Spruce and Peatland Responses Under Climatic and Environmental Change (SPRUCE) experiment, enabled these measurements as well as an estimation of seasonal carbon flux of CO₂ and CH₄ for a temperate bog ecosystem.

Researchers mapped the complex interaction of isolated environmental conditions that govern permafrost conditions. As a result, Arctic tundra response to changing conditions either by naturally occurring environmental gradients or by climate-induced perturbations can be inferred.

Researchers used a physics-based numerical model validated at the Barrow (Alaska) Environmental Observatory to simulate the subsurface thermal hydrological response in permafrost tundra due to changing environmental conditions in organic soil layer thickness, snow depth, soil saturation, and ponded depth.

The collective work provides details on active layer thickness (ALT), or annual thaw depth above permafrost, related to three important environmental conditions characteristic of Arctic permafrost tundra: (1) organic soil layer thickness, (2) snow depth, and (3) unsaturated to inundated conditions. The work teases out how ALT will change as gradients along these environmental conditions are traversed in either space or time. One finding indicates that wetting or drying of polygonal tundra appears to have a minor effect on ALT compared to organic layer thickness and snow. At the same time, however, the inundation state is very interactive and can act to amplify other conditions that influence ALT; so much so, that subsurface thermal tipping points can be crossed. For example, the combined effect of inundation depth and snow can cause taliks, zone of year-round unfrozen soil, to form.

Wetlands are critical for storage, filtration, and supply of freshwater. However, the dual impacts of human land use and climate drying due to warmer temperatures place these wetlands at risk, particularly in low-latitude regions where dense human populations are expanding. The feedback between external drying driving shrub encroachment and autogenic drying by those shrubs can degrade wetland habitat quality, biodiversity, and ecosystem function, compromising regional hydrology and carbon storage.

Water is a defining characteristic of wetlands and a key influence on biodiversity and biogeochemistry. Unfortunately, climate change and water management are making water a waning commodity in freshwater wetlands, facilitating the spread of woody shrubs into wetland sedge communities. Working in subtropical Florida peatlands, researchers found that the leaves of these shrub invaders use water less efficiently, resulting in increased loss of water to the atmosphere despite small increases in carbon uptake.

Studying sawgrass peatlands of south Florida, researchers from Florida Atlantic University quantified differences in plant photosynthetic efficiency and canopy structure between the historic dominant sedge and encroaching native willow to determine the degree to which vegetation carbon and water cycling is altered by shifts in community dominance. Leaf gas exchange of both carbon dioxide (plant photosynthetic uptake) and water (plant transpiration release) was greater for willow, which also used water less efficiently during photosynthesis (greater water loss per carbon gain). Additionally, the willow’s spreading, multitiered branch growth pattern produced more than double the leaf area index (leaf area per ground area). When scaled to the landscape, the elevated water loss rate and leaf density result in substantial increases in wetland water loss through transpiration with even small spatial extent of shrubs. Autogenic drying of wetlands may also accelerate litter and soil decomposition by increasing aerobic conditions, further compromising the health of these peatlands.

This experimental system allows researchers to study a broad range of organisms (microbes, moss, shrubs, trees, and insects) and ecosystem processes (carbon cycle and water use) under realistic field environments for a broad range of alternative environments that may occur in the future.

Scientists at Oak Ridge National Laboratory (ORNL) have documented an experimental system that combines aboveground and deep-soil heating approaches to provide researchers with a plausible method with which to glimpse future environmental conditions for intact peatland ecosystems.

This study describes methods to achieve and measure both deep-soil heating (0 m to 3 m) and whole-ecosystem warming (WEW) appropriate to the scale of tall-stature, boreal forest peatlands. The methods were developed to provide scientists with a plausible set of ecosystem-warming scenarios within which immediate and longer-term (1-decade) responses of organisms (microbes to trees) and ecosystem functions (carbon, water, and nutrient cycles) could be measured. Elevated carbon dioxide (CO₂) was also incorporated to test for interactions with temperature. The WEW approach was successful in sustaining a wide range of aboveground and belowground temperature treatments (as much as +9°C) in large 115-m², open-topped enclosures. The system is functional year round, including warm summer and cold winter periods. The study contrasts its WEW method with prior closely related field-warming approaches and includes a full discussion of factors that must be considered in interpreting experimental results. The WEW method enables observations of future temperature conditions not available in the current observational record, thereby providing a plausible glimpse of future environmental conditions.

The study’s findings showed that biotic and abiotic extremes and legacies can introduce variations to annual ecosystem carbon balance. These variations are different from those that might be explained by the fast responses to factors like light and temperature.

Long-term carbon flux measurements over Mediterranean-type ecosystems enabled observations of ecosystem metabolism responses to a wide range of physical, biological, and ecological conditions.

Many ecophysiological and biogeochemical processes respond rapidly to changes in biotic and abiotic conditions, while ecosystem-level responses develop much more slowly (e.g., over months, seasons, years, or decades). To better understand the role of the slow responses in regulating interannual variability in net ecosystem exchange (NEE), the study partitioned NEE into two major ecological terms: gross primary productivity (GPP) and ecosystem respiration (Reco). The researchers tested a set of hypotheses on seasonal scales using flux and environment data collected from 2000 to 2015 in an oak-grass savanna area in California, where ecosystems annually experience a wet winter and spring and five-month-long summer drought. Results showed that the spring season (April through June) contributed more than 50% of annual GPP and Reco. An analysis of outliers found that each season could introduce significant anomalies in annual carbon budgets. The magnitude of the contribution depends on biotic and abiotic seasonal circumstances across the year and the particular sequences. The study found that (1) extremely wet springs reduced GPP in the years of 2006, 2011, and 2012; (2) soil moisture left from those extremely wet springs enhanced summer GPP; (3) groundwater recharged during the spring of 2011 was associated with the snowpack depth accumulated during the winter between 2010 and 2011; (4) dry autumns (October–December) and winters (January–March) decreased Reco significantly; and (5) grass litter produced in previous seasons might increase Reco, and the effect of litter legacy on Reco was more observable in the second year of two consecutive wet springs. These findings confirm that biotic and abiotic extremes and legacies can introduce variations to annual ecosystem carbon balance, other than those that might be explained by the fast responses.

Fine roots play an important role in ecosystem carbon, water, and nutrient cycling. However, fine-root traits are underrepresented in global trait databases, hindering efforts to link belowground plant function with changing environmental conditions and contributing to the coarse representation of fine roots in terrestrial biosphere models. FRED represents a critical step toward improving understanding of belowground plant ecology and its effects on ecosystem functioning.

Researchers have organized tens of thousands of data points describing the functional characteristics of small-diameter “fine” plant roots across environmental gradients into a single common framework, the Fine-Root Ecology Database (FRED). These data, which are freely available to the public (see http://roots.ornl.gov), will improve understanding and model representation of belowground processes.

Variation and tradeoffs within and among plant traits are increasingly being harnessed by empiricists and modelers to understand and predict ecosystem processes under changing environmental conditions. While fine roots play an important role in ecosystem functioning, fine-root traits are underrepresented in global trait databases. This deficiency has hindered efforts to analyze fine-root trait variation and link it with plant function and environmental conditions at a global scale. The new database called FRED, which so far includes more than 70,000 observations encompassing a broad range of root traits and also includes associated environmental data, represents a critical step toward improving understanding of belowground plant ecology. For example, FRED facilitates the quantification of variation in fine-root traits across root orders, species, biomes, and environmental gradients, while also providing a platform for assessments of covariation among root, leaf, and wood traits; the role of fine roots in ecosystem functioning; and the representation of fine roots in terrestrial biosphere models. Continued input of observations into FRED to fill gaps in trait coverage will improve understanding of changes in fine-root traits across space and time.

The impact of warming on soil carbon dioxide (CO₂) flux is a major uncertainty in climate feedbacks. This whole-soil warming experiment found a larger respiration response than (1) many other controlled experiments, which may have missed the response of deeper soils; and (2) most models. Thus, the strength of the soil carbon-climate feedback may be underestimated.

Scientists created a field experiment in a conifer forest in California to explore, for the first time, what happens to organic carbon trapped in soil when all soils are warmed. In this case, the soil layers extended to a depth of 100 cm. Warming the whole profile by 4°C increased annual soil respiration by 34% to 37%. More than 40% of this increase in respiration came from below a 15-cm depth (i.e., below the depth considered by most studies).

Soil organic carbon harbors three times as much carbon as Earth’s atmosphere, more than half of that below 20-cm depth. The response of whole-soil profiles to warming has not been tested in situ. In this deep warming experiment in mineral soil, CO₂ production from all soil depths increased significantly with 4°C warming; annual soil respiration increased by 34% to 37%. All depths responded to warming with similar temperature sensitivities, driven by decomposition of decadal-aged carbon. Whole-soil warming reveals a larger soil respiration response than many in situ experiments, most of which only warm the surface soil, and models.

In this year-round experiment, plots were warmed by a ring of 22 vertical heating cables installed to 2.4-m depth. Three plots (3-m diameter each) were warmed, and three served as controls. Soil respiration was measured by chambers at the surface and gas tubes at five depths. Radiocarbon content of CO₂ and soil fractions suggests that respiration—and its warming response—was dominated by decadal cycling carbon.

Virtually all life on this planet depends on photosynthesis. The study found that the observation-based carbonyl sulfide (COS) record is most consistent with simulations of climate and the carbon cycle that assume large gross primary productivity (GPP) growth during the 20th century (31% increase).

The team of researchers discovered the record of global photosynthesis by analyzing Antarctic snow data captured by the National Oceanic and Atmospheric Administration (NOAA). Gases trapped in different layers of Antarctic snow allow scientists to study global atmospheres of the past. This study focused on a gas stored in the ice that provides a record of the Earth’s plant growth.

The scientists analyzed the COS gas. It is a cousin of carbon dioxide (CO₂). Plants remove COS from the air through a process that is related to the plant uptake of CO₂. While photosynthesis is closely related to the atmospheric COS level, other processes in oceans, ecosystems, and industry can change the COS level also.  To account for all of these processes, the interdisciplinary team of scientists developed an Earth system model of COS sources and sinks. Although this COS analysis does not directly constrain models of future GPP growth, it does provide a global-scale benchmark for historical carbon cycle simulations.

Snow plays a critical role in Arctic ecosystem functioning, as it influences permafrost thaw, water delivery, and carbon exchange. Snow depth is, however, extremely heterogeneous and traditionally difficult to map in sufficient resolution using conventional point measurements. Although there have been significant technical advances in measuring snow depth (e.g., geophysics and remote sensing), it is still challenging to integrate all these state-of-art data in a harmonized manner due to their different scales and accuracy. The developed Bayesian approach will be an integrating framework for these advanced datasets, allowing the measurement of snow depth at high resolution over a large area.

This project developed a Bayesian approach to integrate a variety of state-of-art snow sensing techniques—in situ measurements, ground-penetrating radar, phodar on unmanned aerial system (UAS), and airborne lidar—for mapping highly heterogeneous snow depth over ice-wedge polygonal tundra. Project analysis also showed that the end-of-winter snow depth was highly variable in several-meter distances, influenced by microtopography.

This paper aims to develop an effective strategy to characterize heterogeneous snow depth over the Arctic tundra, using state-of-art techniques (ground-penetrating radar and UAS phodar) and also to quantify the relationship between snow depth and topography. All the techniques provided fairly accurate estimates of snow depth, while they have different characteristics in term of acquisition time and accuracy. The team of researchers then investigated the spatial variability of snow depth and its correlation to micro- and macrotopography using the wavelet approach. The researchers found that the end-of-winter snow depth was highly variable over several-meter distances, affected primarily by microtopography. In addition, the team developed and implemented a Bayesian approach to integrate multiscale measurements for estimating snow depth over the landscape.

Studies of methane fluxes in upland forests have focused on exchanges between the atmosphere and soils, but the scientists conclude that methane fluxes across tree surfaces are also potentially important for upland forest methane budgets.

Upland forest soils remove methane from the atmosphere and are represented in global budgets as net methane sinks. However, this study demonstrates that upland trees can also emit methane.

Upland forests remove methane from the atmosphere and are represented in global budgets as net methane sinks. However, this view is based almost entirely on measurements of methane exchange across forest soil surfaces, with little attention to the exchange of methane across plant surfaces. Here the team report that methane is emitted from the stems of dominant tree species in a temperate upland forest. The source of the methane emitted from these trees is uncertain but may include transport in the transpiration stream from anoxic groundwater, or methane produced inside the tree itself. High-frequency measurements revealed diurnal patterns in the rate of tree-stem methane emissions that support a groundwater source. A simple scaling exercise suggested that tree emissions offset 1% to 6% of the growing season soil methane sink, and the forest may have briefly changed to a net source of methane to the atmosphere due to tree methane emissions.

The scientists highlight barriers limiting knowledge of how fine roots work in ecosystems and, importantly, suggest tractable ways in which to possibly overcome those barriers. Refocusing their efforts to measure multiple aspects of roots traits and function in ways that can be rigorously compared across species will rapidly improve understanding of terrestrial ecosystems.

Fine roots play important roles acquiring soil nutrients and water for plant growth. However, it has been difficult to determine how traits of fine roots change across environments and how these changes impact plant and ecosystem processes.

Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. This study discusses three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These areas include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine root–trait frameworks, and the need for improved scaling of traits measured on individual roots to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how belowground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.

Models and observations currently disagree over how Arctic warming will affect the CO₂ balance of tundra ecosystems, and few studies combine warmer air temperatures and permafrost thaw to evaluate ecosystem CO₂ balance. This work demonstrates that tundra CO₂ uptake and loss responded much more strongly to permafrost thaw than to warmer air temperatures alone. Rapid permafrost thaw did initially stimulate CO₂ uptake during the summer, but the effect leveled off with very deep thaw. In all years of the experiment, summer CO₂ uptake was insufficient to offset year-round CO₂ losses.

Frozen in permafrost soil, northern latitudes store almost twice as much carbon as is currently in the atmosphere. Rapid Arctic warming is expected to expose previously frozen soil carbon to microbial decomposition and increase carbon dioxide (CO₂) release to the atmosphere. The impact of permafrost thaw on the CO₂ balance is, however, unclear because warmer temperatures and nutrients released from thawing permafrost also increase plant growth and could offset CO₂ losses. The scientists used an experimental warming manipulation to distinguish the effect of warmer air temperature from the effect of warmer soil and permafrost thaw on tundra ecosystem CO₂ uptake and loss.

Seven years of experimental air and soil warming in tundra show that soil warming and permafrost thaw had a much stronger effect on carbon balance than air warming. Permafrost thaw initially stimulated greater summer CO₂ uptake than CO₂ loss; however, the initial increases were not sustained. As thaw continued to progress, summer CO₂ uptake and CO₂ loss leveled off. Leveling off CO₂ uptake and release could be explained by slowing of plant growth and greater soil saturation as thaw caused the ground surface to collapse. The complex interactions between permafrost thaw, plant growth, and soil moisture could be captured mathematically by a quadratic relationship showing that the effect of thaw on CO₂ uptake and loss changed over time. Models and measurements used to estimate CO₂ losses during the winter found that the tundra was losing CO₂ on an annual basis, even during those summers when thaw stimulated high plant growth and CO₂ uptake.

The study’s The observationally constrained nitrogen allocation estimates provide insights on mechanisms that operate at a cellular scale within leaves, and can be integrated with ecosystem models to derive emergent properties of ecosystem productivity at local, regional, and global scales.

Nitrogen is one of the most important nutrients for plant growth and a major constituent of proteins that regulate photosynthetic and respiratory processes. This study integrated observations from global databases with photosynthesis and respiration models to determine plant-functional-type-specific allocation patterns of leaf nitrogen for photosynthesis and respiration.

The scientists developed here a comprehensive global analysis of nitrogen allocation in leaves for major processes with respect to different plant functional types. Based on analysis, crops partition the largest fraction of nitrogen to photosynthesis and respiration. Tropical broadleaf evergreen trees partition the least to photosynthesis and respiration. In trees (especially needle-leaved evergreen and tropical broadleaf evergreen trees) a large fraction of nitrogen was not explained by photosynthetic or respiratory functions. Compared to crops and herbaceous plants, this large residual pool is hypothesized to emerge from larger investments in cell wall proteins, lipids, amino acids, nucleic acid, carbon dioxide (CO₂) fixation proteins (other than Rubisco), secondary compounds, and other proteins. The resulting pattern of nitrogen allocation provides insights on mechanisms that operate at a cellular scale within leaves and that can be integrated with ecosystem models to derive emergent properties of ecosystem productivity at local, regional, and global scales.

In addition to characterizing waterbody distributions where detailed information exists, the scientists link results with observations of permafrost extent, ground ice volume, geology, and lithology to extrapolate waterbody statistics to regional landscape units. They also provide historical imagers from 1948 to 1965 with a resolution of 6 m or better. These large-scale waterbody distribution estimates, and their temporal trajectories, will help land modelers improve their representation of surface energy and carbon representations, an exercise the team is pursuing for the ACME Land Model.

CE1 ponds and lakes are abundant in Arctic permafrost lowlands and play important roles in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, waterbodies with surface areas smaller than 10⁴ m² (ponds) have not been inventoried or characterized in a manner amenable to improving land models. The Permafrost Region Pond and Lake (PeRL) database addresses this problem with a circum-Arctic characterization of ponds and lakes from modern (2002–2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better. Project researchers found that ponds are the dominant waterbody type by number in all landscapes.

Waterbodies in Arctic permafrost lowlands strongly affect wetland ecosystem processes of carbon, water, and energy fluxes important in regional- to global-scale models. However, there is no robust theory for the distribution or temporal dynamics of these surface features, nor do land models have accurate characterizations. The open source PeRL database is a critical first step in developing such theories and model representations. Project findings that small waterbodies dominate the number density of all waterbodies, and that their distributions are temporally dynamic, are motivating ongoing work in conceptualizing process representations that can be integrated in land models to improve prediction of high-latitude terrestrial processes.

The observed differences in plant hydraulic traits enhances understanding of important controls over tropical forest dynamics, an advancement which is critical for informing the parameterization of hydrodynamic formulations used in Earth system models.

This study demonstrated that tropical tree species that were tolerant of an experimental drought had hydraulic traits that differed from those that were intolerant. The hydraulic traits of the measured species were not aligned with their early- versus late-successional life histories, thus revealing an important drought-tolerance control over tropical forest dynamics.

This study found a characteristic pattern in the measured leaf and xylem traits of several tropical tree species that was consistent with their demographic responses to an experimentally imposed drought. This study provides valuable insight into the traits controlling drought tolerance of tropical rainforest trees and provides much needed information for parameterizing more realistic water-stress functions in Earth system models. Finally, understanding the variability in plant hydraulic traits that exists among tropical tree species is critical for determining the fate of the Amazon rainforest if precipitation patterns change substantially.

The data and derived products in the FLUXNET2015 dataset are consistently quality controlled and gap filled, made simple to use, and can be used to validate satellite measurements, inform Earth system models, and provide insight into ecology and hydrology questions. They can also be used to fuel novel applications, many harnessing big data tools, from the scales of microbes to continents. As an indication of the expected impact of this data release, only one year after its first announcement, the FLUXNET2015 dataset had been downloaded by more users than the previous release in the entirety of its nearly 10-year lifetime. In its first 15 months, FLUXNET2015 had over 87,000 site-data downloads, more than twice the total number for the previous release (LaThuile 2007: 41,000). Many factors contribute to the high scientific demand, including the enhanced derived products and long time series in the dataset as well as growing emphasis on confronting data with models, more advanced data tools, and a more open data policy.

FLUXNET2015 is the largest and most complete dataset of land-atmosphere fluxes ever produced, including data from 212 sites in 30 countries. The FLUXNET and U.S. Department of Energy AmeriFlux Management Project teams created the dataset, in a large-scale collaborative endeavor with regional networks and site teams from around the world.

In the mid 1990s, regional networks like AmeriFlux and the European Fluxes Database were established to enable sharing of data and methods from measuring carbon, energy, and water exchanges between land and the atmosphere. FLUXNET brought these networks together and allowed the creation of global synthesis datasets: Marconi dataset in 2000, LaThuile Dataset in 2007, and now FLUXNET2015 dataset. These datasets were key to answering science questions on themes ranging from soil microbiology to the global carbon cycle. Among the new features for FLUXNET2015 are intensive data quality checks; energy corrections applied to achieve energy balance closure, potentially making the data more useful to climate and ecosystem models requiring closed energy budget; estimation of uncertainties for processing steps, leading to uncertainty quantification suitable for use in data-model integration; and improved accuracy of gap-filled data and aggregated products (e.g., daily or yearly sums) through use of downscaled ERA-Interim reanalysis data.

Carbon cycle models used for regional or global simulations are known to perform poorly when used to simulate a specific site. Researchers identified a number of areas for carbon cycle model improvement. Model development to improve the accuracy of grassland simulations should focus on improving the realism of the controls of water availability on growth and soil nitrogen in these nonforested  ecosystems.

Researchers challenged ten carbon cycle models, often used to simulate ecosystem responses to environmental change, to simulate a grassland in Wyoming subjected to experimental carbon dioxide (CO₂) enrichment and increased temperature.

Multifactor experiments are often advocated as important for advancing terrestrial biosphere models, but this claim is rarely tested. As part of the U.S. Department of Energy–supported Free Air CO₂ Enrichment Model Data Synthesis (FACE-MDS) project, researchers aimed to investigate how a CO₂ enrichment and warming experiment can be used to identify a road map for carbon cycle model improvement. Researchers found that the ten models tested simulated a wide spread in annual aboveground growth in current environmental conditions (i.e., not experimentally manipulated conditions). Comparison with data highlighted that the reasons for these model shortcomings were poor representation of: carbon allocation, seasonality of growth, impact of water stress on the seasonality of growth, sensitivity to water stress, and soil nitrogen availability. In response to the experimentally manipulated conditions, models generally overestimated the effect of warming on leaf onset and were lacking the mechanism to allow CO₂-induced water savings to extend the growing season. However, when both CO₂ and warming were increased, the observed effects of the experimental increase in CO₂ and temperature on plant growth were subtle and contingent on water stress, phenology, and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. The study outlines a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

The scientists use four censuses of a 25.6-ha ForestGEO forest dynamics plot to assess mortality patterns. With such a large sample size it is possible to characterize mortality rates by size, species, plant functional type, and microhabitat, allowing for detailed understanding of the drivers of mortality. The method developed forms the basis of a protocol now being applied at 10 large-scale tropical ForestGEO plots under the Next-Generation Ecosystem Experiments (NGEE)–Tropics project.

Forest mortality has overriding control on the forest carbon cycle. However, the drivers of mortality in forests are not well understood, and are consequently not well represented in Earth system models. This study develops a method for assessing how trees die and how mortality rates differ among species, size classes, and functional groups. The new method will capture rare mortality events and detect mortality events that may be linked to environmental change.

Since understanding fine-scale mortality processes is essential for modeling forest responses to changing climatic and environmental conditions, this work makes important progress in providing empirical observations that will inform future modeling activities in the NGEE-Tropics project. Furthermore, widespread application of annual tree mortality surveys on large forest dynamics plots will provide greater insights into the annual variability of forest structural and compositional changes that result from tree death associated with anthropogenic, ecological, or climatic disturbances.

This review paper provides many testable hypotheses for scientists ranging from modelers to empiricists to land managers.

This paper summarizes the current state of knowledge on belowground responses of forests to drought and how this knowledge may be employed to improve model predictions of forest drought sensitivity, as well as forest management to avoid drought-induced growth declines and mortality.

This paper summarizes current knowledge of abiotic and biotic factors below ground that influence plant responses to drought. It sets forth hypotheses that are testable, and thus should be well cited. It also identifies what is known with good confidence regarding the belowground system and provides suggestions to modelers and land managers.

This approach allows rapid, high-frequency, accurate estimates of evapotranspiration across the globe. The applications are extensive and range from forecasting to policymaking to simulation.

This paper describes new, publicly available, high-frequency (8-day), 1-km, satellite-based estimates of evapotranspiration at the global scale, that was assessed on many continents in tropical, temperate, and boreal ecosystems.

Models and land managers require estimates of global evapotranspiration for drought impact predictions. The approach developed in this paper allows rapid and precise estimates of evapotranspiration at the global scale at the nearly weekly temporal resolution. The approach validated well in a global test. This approach will be highly valued by both modelers, who need data for evaluation of their predictions, and land managers, who need data to assess water-stress impacts on ecosystems.

This paper provides the first large advance in the understanding of leaf thermoregulation, and is thus likely to be tested widely.

This research uses a variety of global datasets to support theory suggesting that plants maximize carbon gain in part via myriad traits that regulate temperature near the optimum for photosynthesis.

Leaf thermoregulation has been rarely documented, and its control is unknown. However, leaf temperature is one of the most critical parameters regulating photosynthesis in Earth system models. Improving its understanding has widespread fundamental and applied (e.g., modeling) value. The scientists tested a novel carbon- and energy-based theory using multiple global datasets of leaf temperature and photosynthesis, along with myriad leaf traits. The theory was supported by the data, and demonstrated that leaf thermoregulation does act to maximize photosynthesis. This research has broad implications for fundamental biology and for applied modeling of ecosystems.

This model will radically simplify, yet improve, Earth system models, once incorporated.

This study introduces a new approach to modeling stomatal function based on trait-based hydraulics.

Earth system models do not simulate stomatal conductance, and hence photosynthesis, correctly. Here, a team from the Next-Generation Ecosystem Experiments (NGEE)–Tropics project introduce a modeling approach that is simple yet mechanistically accurate. The model validated particularly well against multiple empirical datasets.  Furthermore, the researchers propose ways this model can be incorporated into Earth system models, thus greatly improving their realism and accuracy.

This study proposes new frontiers in research on how trees die and survive during drought, and how they recover post-drought.

This paper synthesizes research to generate hypotheses on how nutrient availability influences the likelihood of drought-induced mortality, and the recovery of ecosystems after drought.

Global forests are experiencing hotter temperatures and more frequent droughts, causing an acceleration in tree mortality. Current research on drought-induced mortality is focused on the carbon- and water-related mechanisms of death, and so far have ignored the potentially critical role of nutrients.  High nutrient availability is likely a detriment to drought survival, thus areas of nitrogen deposition should be more predisposed to death. Nutrients are released after drought ceases, and thus recovery may be a strong function of the ability of trees to acquire this transient pulse of resource availability.  This study provides a testable framework by which the role of nutrients in drought-induced mortality and recovery may be understood.

Project results revealed that more frequent storms led to a switch in simulated carbon accumulation from negative (i.e., source) to positive (i.e., sink), with coarse woody debris and leaf production being major carbon components that should be included in disturbance modeling. While there is evidence that hurricane intensity has been increasing in the Atlantic Basin over the past 30 years, team researchers predict the long-term forest structure and productivity will not be largely affected in relationship to storm intensity alone. Additionally, project results suggest that subtropical dry forests will remain resilient to hurricane disturbances.

Caribbean tropical forests are subject to hurricane disturbances of great variability. In addition to natural storm incongruity, climate change can alter storm formation, duration, frequency, and intensity. This model-based investigation assessed the impacts of multiple storms of different intensities and occurrence frequencies on the long-term dynamics of subtropical dry forests in Puerto Rico. This is the first attempt to model hurricane effects for dry forests of Puerto Rico—a unique, overlooked, and threatened biome of the world.

For this study, the project used a previously validated individual-based dynamic vegetation gap model, and developed a new hurricane damage routine parameterized with site- and species-specific hurricane effects. Increasing the frequency of hurricanes decreased aboveground biomass by between 5% and 39%, and increased net primary productivity (NPP) between 32% and 50%. In contrast, increasing hurricane intensity did not create a large shift in the long-term average forest structure, NPP, or annual carbon accumulation (ACA) from that of historical hurricane regimes, but it did produce large fluctuations in biomass. With an increase in the frequency of storms, the total ACA switched to positive due to shifts in leaf production, annual litterfall, and coarse woody debris inputs, indicating a carbon sink into the forest over the long term and major carbon components that should be included in disturbance modeling. Project results suggest that subtropical dry forests will remain resilient to hurricane disturbance. However, carbon stocks will decrease if future climates increase hurricane frequency by 50% or more. These results, and the new disturbance damage routine, are being considered for DOE’s new dynamic vegetation model, Functionally Assembled Terrestrial Ecosystem Simulator (FATES), which is being integrated into the Accelerated Climate Modeling for Energy (ACME) Land Model version 1 (ALMv1) and used by the Next-Generation Ecosystem Experiments (NGEE)–Tropics project.

Project results highlight that fungal groups were strongly influenced by relatively high quality organic carbon, but bacterial groups are positively correlated with low-quality carbon compounds. This contrasts with the observations of leaf litter decomposition and will provide a key insight toward a better wood decomposition model in the U.S. Department of Energy’s (DOE) Earth system model.

During wood decomposition, microbial community composition shifted from fungi-dominated at early stages to relatively more bacteria-dominated ones at later stages. Fungal community dominance during early decomposition stages is associated with relatively high quality carbon compounds and low wood-moisture contents.

Although decaying wood plays an important role in global carbon cycling, how changes in microbial community are related to wood carbon quality and then affect wood organic carbon loss during wood decomposition remains unclear. In this study, a chronosequence method was used to examine the relationships between wood carbon loss rates and microbial community compositions during Chinese fir (Cunninghamia lanceolata) stump decomposition. Results showed that the microbial community shifted from fungi-dominated community at early stages to relatively more bacteria-dominated ones at later stages during wood decomposition. Fungal phospholipid fatty acid content primarily explained wood carbon loss rates during decomposition. Interestingly, fungi biomass was positively correlated with proportions of relatively high quality carbon (e.g., O-alkyl-C), but bacterial biomass was positively correlated with low-quality carbon. In addition, fungi biomass dominance at the early stages (0 to 15 years) was associated with low wood moisture (<20%), while the increase in bacteria biomass at later stages (15 to 35 years) was associated with increasing wood moisture. Project findings suggest that the fungal community is the dominant decomposer of wood at early stages and may be positively influenced by relatively high quality wood-carbon and low wood-moisture contents. Bacteria were positively influenced by low-quality wood-carbon and high wood-moisture contents at later stages. Enhanced understanding of microbial responses to wood quality and environment is important to improve predictions in wood decomposition models.

A substantial amount of diversity in tropical forests can be represented by a reduced set of model parameters and dimensions. This submodel can be used in conjunction with other demographic ecosystem models to predict how forest composition evolves under a changing climate.

This project developed a trait-based plant hydraulics model for tropical forests. It successfully predicts how individual trees in a forest vary in water status based on their size, canopy position, and hydraulic traits, which improved simulations of total ecosystem transpiration.

The project developed a plant hydraulics model for tropical forests based on established plant physiological theory, in which all parameters of the constitutive equations are biologically interpretable and measureable plant hydraulic traits (e.g., the turgor loss point, hydraulic capacitance, xylem hydraulic conductivity, water potential at 50% loss of conductivity for both xylem and stomata, and the leaf:sapwood area ratio). Next the researchers synthesized how plant hydraulic traits coordinate and trade off with each other among tropical forest species. The team first show that a substantial amount of trait diversity can be represented in the model by a reduced set of trait dimensions. They then used the most informative empirical trait-trait relationships derived from this synthesis to parameterize the model for all trees in a forest stand. The model successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, and it also improved simulations of total ecosystem transpiration. Collectively, these results demonstrate the importance of plant hydraulic traits in mediating forest transpiration and overall forest ecohydrology. When used in conjunction with other demographic ecosystem models, this modeling approach can be used to predict how forest composition evolves under a changing climate.

The scientists argued for development of Decomposition Functional Types (DFTs), analogous to plant functional types (PFTs), for use in global models. They applied cluster analysis to produce example DFTs based on the global variability in 11 biotic and abiotic factors that influence decomposition processes.

This study proposed improving representation of heterotrophic respiration (HR) in Earth system models (ESMs) by grouping metabolism and flux characteristics across space and time.

Heterotrophic respiration, the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes the ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, the scientists suggest that a useful step to improve this situation is the development of DFTs. Analogous to PFTs, DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelers and experimentalists to efficiently group and vary these characteristics across space and time. The team applied cluster analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and they demonstrated that these groups are distinct from, but complementary to, PFTs. In this position paper, they suggested priorities for next steps to build a foundation for DFTs in global models to provide the ecological and climate change communities with robust, scalable estimates of HR.

This research helps bridge the gap between studies of litter decomposition and SOM by tracing how litter becomes SOM over a decade. The results back the recent paradigm shift in the understanding of soil carbon research be demonstrating that the long-term retention of litter-derived carbon and nitrogen soil is an ecosystem property dependent on the soil horizon in which the litter was placed.

In one of the few studies examining litter decay over a decade, Lawrence Berkeley National Laboratory (LBNL) scientists used stable isotope labels to trace plant litter–derived carbon and nitrogen as they decomposed and formed soil organic matter (SOM). They found that the litter type (needles or roots) and the soil environment (organic or mineral horizon) both affected decomposition, but at different timescales.

The scientists found that the legacy of the type of plant inputs (root or needle litter) affected total carbon and nitrogen retention over 10 years, but that the soil horizon affected how the litter-derived SOM is stabilized in the long term. In the organic (O) horizon, litter was retained in the coarse particulate size fraction (>2 mm) over 10 years, likely due to conditions that limited its physical breakdown. In the mineral (A) horizon, litter-derived carbon and nitrogen were retained in a finer size fraction (<2 mm), likely due to association with minerals that prevent microbes from accessing the carbon and nitrogen. Litter type had no effect on the stabilization of litter-derived carbon and nitrogen in mineral-associated pools. After 10 years, 5% of initial carbon and 15% of initial nitrogen were retained in organo-mineral associations, which form the most persistent organic matter in soils. Very little litter-derived carbon moved vertically in the soil profile over the decade, but nitrogen was significantly more mobile.

This study shows that the coupling of phosphorus cycle in land surface model results in more realistic spatial pattern of simulated ecosystem productivity in the Amazon region. Through exploratory simulations, this study points to the need for more tropical field measurements under different temperature and humidity conditions with different soil phosphorus availability.

Phosphorus has been generally considered to be the most limiting nutrient in lowland tropical forests. Several recent field studies in the Amazonia have highlighted the importance of phosphorus in tropical forest productivity and function. Despite the importance of phosphorus in tropical carbon cycling, most Earth system models do not currently include phosphorus cycling and phosphorus limitation. This study investigates how phosphorus cycling dynamics might affect tropical ecosystem responses to changes in atmospheric carbon dioxide (CO₂) and climate using a phosphorus-enabled land surface model.

It is being increasingly recognized that carbon-nutrient interactions play important roles in regulating terrestrial carbon cycle responses to increasing CO₂ in the atmosphere and climate change. Nitrogen-enabled models in CMIP5 showed that accounting for nitrogen greatly reduces the negative feedback between land ecosystems and atmospheric CO₂. None of the CMIP5 models has considered phosphorus as a limiting nutrient, although phosphorus has been considered the most limiting nutrient in lowland tropical forests. In this study, scientists from Oak Ridge National Laboratory investigated the effects of phosphorus availability on carbon cycling in the Amazon region using a phosphorus-enabled land surface model. Model simulations demonstrate that the CO₂ fertilization effect in the Amazon region may be greatly overestimated if phosphorus cycling were not considered. Exploratory simulations highlighted the importance of considering the interactions between carbon, water, and nutrient cycling (both nitrogen and phosphorus) for the prediction of future carbon uptake in tropical ecosystems.

Forest carbon loss from wind disturbance is sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

Two factors, differential mortality and the spatial structure of mortality, acted independently to affect total necromass (dead plant material) on the landscape. Simple relationships relating tree mortality to disturbance metrics in tropical forests can oversimplify the complex processes that create important variation in tree mortality related to tree and landscape characteristics.

Wind disturbance can create large forest blowdowns, which greatly reduce live biomass and add uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence on tree mortality from wind disturbance has seldom been quantified, a gap which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. The scientists examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect landscape-level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of nonrandom mortality for landscape production of necromass through a simulation model. Tree mortality was highly nonrandom within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the nonspatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. The study suggests that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

This study provides a basis for improving models of photosynthesis based on phosphorus nutrition and thereby increasing the capability of models to predict future conditions in phosphorus-limited tropical forests.

Gas exchange and nutrient content data were collected from upper canopy leaves of 144 trees at two forest sites in Panama, differing in species composition, rainfall, and soil fertility. Relationships between photosynthesis, foliar nitrogen and phosphorus, and wood density were evaluated against mechanistic and empirical models.

The objective of this study was to analyze and summarize data describing photosynthetic parameters and foliar nutrient concentrations from tropical forests in Panama to inform model representation of phosphorus limitation of tropical forest productivity. Gas exchange and nutrient content data were collected from upper canopy leaves of 144 trees from at least 65 species at two forest sites in Panama, differing in species composition, rainfall, and soil fertility. The relationships between photosynthetic parameters and nutrients were of similar strength for nitrogen and phosphorus and robust across diverse species and site conditions. The strongest relationship expressed maximum electron transport rate (J_max ) as a multivariate function of both nitrogen and phosphorus, and this relationship was improved with the inclusion of independent data on wood density. Models that estimate photosynthesis from foliar nitrogen content would be improved only modestly with the inclusion of additional data on foliar phosphorus, but doing so may increase the capability of models to predict future conditions in phosphorus-limited tropical forests, especially when combined with data on edaphic conditions and other environmental drivers.

The impacts of land use and land cover on the carbon cycle are not restricted to deforestation, and this work identified that carbon losses from logging and fire can be large and persistent: in the most extreme cases, biomass was reduced by more than 90% and remains with 40% less biomass than intact forests even after 15 since last disturbance. The pantropical biomass maps did not capture these patterns and consistently overestimated biomass in degraded forests. These maps need frequent updates to capture the rapid changes in biomass in frontier forests.

The scientists integrated forest inventory plots and high-density airborne lidar data from 18 regions across the Brazilian Amazon, to build a statistical model relating aboveground biomass to lidar metrics across a broad range of degraded forests. Relatively simple models captured the variation of biomass, including  persistent and significant carbon losses at the most degraded areas. The authors also found that pantropical maps overestimate carbon stocks in many areas with active logging and burning, and underestimate biomass at intact forests.

The role of tropical forest degradation in the the carbon cycle is highly uncertain. The scientists used 359 forest inventory plots co-located with 18,000 hectares (ha) of airborne lidar data in the Brazilian Amazon and developed statistical models to predict biomass based on airborne lidar metrics of forest structure. Degraded forest areas lost significant portions of their original biomass. The degree of carbon loss was influenced by the intensity of disturbance with a range of more than 90% carbon loss for forests subject to multiple fires to only 4% to 20% for reduced impact logging. The scientists compared lidar biomass estimates with pantropical maps; they found that these maps consistently overestimated biomass at the most degraded forests and underestimated biomass at intact forests, and failed to capture the fine-scale variability of carbon stocks. The differences in carbon stocks indicate that the use of such maps in frontier forests leads to significant biases in estimates of baseline carbon stocks, and they should be improved and updated more frequently to better characterize the effects of forest degradation in the carbon cycle.

Given that β-ocimenes are highly reactive with respect to both atmospheric and biological oxidants, the results suggest that highly reactive β-ocimenes may play important roles in the thermotolerance of photosynthesis by functioning as effective antioxidants within plants and as efficient atmospheric precursors of secondary organic aerosols. Thus, monoterpene composition may represent a new sensitive thermometer of leaf oxidative stress and atmospheric reactivity, and therefore a new tool in future studies of warming impacts on tropical biosphere-atmosphere carbon cycle feedbacks. Plant response to warming may involve a single enzyme or gene (ocimene synthase), insertion into transgenic plants will facilitate quantitative studies on the role of light-dependent monoterpenes in oxidative stress responses including thermotolerance of photosynthesis. This presents opportunities for the development of the ‘monoterpene thermometer’ gene in agricultural plants as a sensor of plant oxidative stress during environmental extremes.

Tropical forests absorb large amounts of atmospheric carbon dioxide (CO₂) through photosynthesis, but elevated temperatures suppress this absorption while promoting biochemical emissions of monoterpene. Plant monoterpenes are hypothesized to be involved in thermotolerance of photosynthesis, but observations are scarce and global models assume that tropical monoterpene emissions are dominated by α-pinene. Moreover, models assume that monoterpene emissions composition is insensitive to temperature. Using ¹³CO₂ labeling, this study shows that monoterpene emissions from tropical leaves derive from recent photosynthesis and demonstrate distinct temperature optima for five groups, potentially corresponding to different enzymatic temperature-dependent reaction mechanisms within β-ocimene synthases. As diurnal and seasonal leaf temperatures increased during the Amazonian 2015 El Niño event, leaf and landscape monoterpene emissions showed strong linear enrichments of the highly reactive β-ocimenes (Group 1) at the expense of other monoterpene isomers (Groups 4–5).This high positive sensitivity of Group 1 monoterpenes and negative temperature sensitivity of α-pinene (Group 2), typically assumed to be the dominant monoterpene with moderate reactivity, was not accurately simulated by current global emission models.

Tropical forests are increasingly threatened by increased temperatures that can lead to oxidative stress, but the physiological mechanisms plants use to cope with these conditions remain poorly understood. This study reports the discovery of a tropical forest monoterpene thermometer where the composition of monoterpene emissions changes as a function of temperature. The scientists found a high-temperature sensitivity of the composition of tropical leaf monoterpene emissions across a wide range of temporal (minutes to seasons) and spatial (leaf to ecosystem) scales. As monoterpene emissions increased with temperature, the composition shifted such that highly reactive monoterpenes accounted for a larger fraction of the total under high-temperature stress. This result suggests a biological function of these highly reactive monoterpenes in the tropics. Given their high reactivity to both atmospheric and biological oxidants, the results suggest that monoterpenes play important roles in the thermotolerance of photosynthesis by functioning as effective antioxidants within plants and as efficient atmospheric precursors of secondary organic aerosols, thereby enhancing surface cooling and water recycling. Thus, monoterpene composition may represent a new sensitive ‘thermometer’ of leaf oxidative stress and atmospheric reactivity, and therefore a new tool in future studies of warming impacts on tropical biosphere-atmosphere carbon cycle feedbacks.

The emerging field of volatile ecosystem metabolomics integrates chemical, physical, and biological processes involved in the metabolism of volatiles within the land-atmosphere interface including potential perturbations of the system by anthropogenic activities such as climate warming. An emerging approach evaluated in this study is the use of volatiles as sensitive ecosystem biomarkers of response to abiotic stress including temperature and drought. Examples include temperature-dependent isoprenoid composition and oxidation product formation; senescence and mortality through green leaf volatiles and isoprenoid emissions from storage resins; fermentation volatiles; and volatiles associated with cell wall growth, stress, and repair. The integration of volatiles into plant central metabolism is discussed in term of a predictive understanding of the integration of land processes (plant physiology and biochemistry) with atmospheric processes (atmospheric chemistry and climate). Therefore, volatile metabolomics provides noninvasive techniques to study plant metabolism from a variety of spatial and temporal scales. The application of these methods in the tropics may improve the mechanistic understanding of how environmental and biological variables associated with climate and land-use change affect the carbon and energy metabolism of natural and managed forests. Genetic engineering of plant metabolism of volatiles is highlighted as a new research tool with application in enhancing plant productivity and abiotic stress tolerance in agricultural, biofuel, and biomaterial industries.

Many cellular processes leave unique volatile fingerprints behind that can be studied through the acquisition of gas-phase metabolite profiles in the headspace atmospheres of plants across a wide range of spatial and temporal scales, from enzymes to ecosystems and from seconds to seasons. While generally studied for their strong impact on atmospheric properties, recent research results from U.S. Department of Energy (DOE)–funded GOAmazon 2014/5 and Next-Generation Ecosystem Experiments (NGEE)–Tropics projects in the central Amazon highlight the potential for emissions of volatile metabolites as quantitative tracers of biological processes including carbon and energy metabolism (photosynthesis, photorespiration, respiration, and fermentation), cell wall expansion and growth, acetyl-CoA and fatty acid metabolism and degradation, and antioxidant defense and signaling during abiotic and biotic stress.

Biogenic volatile organic compounds (BVOCs) are produced directly within plants via biochemical pathways associated with primary and secondary metabolic processes. Although nonvolatile metabolites are typically bound within specific cellular organelles in lipids or aqueous phases, BVOC volatile metabolites can readily partition between these phases and the intracellular airspace. Thus, many BVOCs may freely exchange among cellular organelles, cells, and tissues, contributing to an integration of whole-organism carbon and energy metabolism. Moreover, exchange of the intracellular airspace with the atmosphere may help coordinate the metabolisms of different plants within an ecosystem in response to environmental and biological factors. In addition, land-atmosphere exchange of VOCs integrates local and regional atmospheric chemistry with plant metabolism. In this chapter, select examples of the physiological roles BVOCs in plants is presented with a focus on key results from the DOE-funded GOAmazon 2014/5 project in central Amazonia.

This mechanistic approach allows the comparison of tree vulnerability and resistance to snapping and uprooting across tropical and temperate forests and facilitates the use of current findings in the context of ecosystem models. Higher wind-induced tree mortality observed on plateaus and top of slopes may be explained by different wind speeds and gusts direction (valleys have different aspects and the wind can blow parallel or perpendicular), rather than by differences in soil-related factors that might affect the critical turning moment (M_crit).

Through a tree-pulling experiment, the scientists found that tree resistance to failure (uproot or snapping) increased with size (diameter at the breast height, DBH, 1.3 m) and aboveground biomass, AGB) and differed among species.

High descending winds generated by convective storms are a frequent and major source of tree mortality disturbance events in the Amazon, affecting forest structure and diversity across a variety of scales, and more frequently observed in western and central portions of the basin. Soil texture in the Central Amazon also varies significantly with elevation along a topographic gradient, with decreasing clay content on plateaus, slopes, and valleys, respectively. In this study, the scientists investigated the critical turning moments (M_crit – rotational force at the moment of tree failure, an indicator of tree stability or wind resistance) of 60 trees, ranging from 19.0 to 41.1 cm in diameter at breast height (DBH) and located in different topographic positions and for different species, using a cable-winch load-cell system. The approach used torque as a measure of tree failure to the point of snapping or uprooting. This approach provides a better understanding of the mechanical forces required to topple trees in tropical forests, and it will inform models of windthrow disturbance. Across the topographic positions, size-controlled variation in M_crit was quantified for cardeiro [Scleronema mincranthum (Ducke) Ducke], mata-matá (Eschweilera spp.), and a random selection of trees from 19 other species. The analysis of M_crit revealed that tree resistance to failure increased with size (DBH and ABG) and differed among species. No effects of topography or failure mode were found for the species either separately or pooled. For the random species, total variance in M_crit explained by tree-size metrics increased from an R2 of 0.49 for DBH alone, to 0.68 when both DBH and stem fresh wood density (SWD) were included in a multiple regression model. This mechanistic approach allows the comparison of tree vulnerability induced by wind damage across ecosystems, and facilitates the use of forest structural information in ecosystem models that include variable resistance of trees to mortality inducing factors. Project results indicate that observed topographic differences in windthrow vulnerability are likely due to elevational differences in wind velocities, rather than by differences in soil-related factors that might effect M_crit.

Some tree species remained well hydrated even after three months without water. These species had reduced root surface area in the drought treatment, suggesting a role for root abscission in preventing water loss from roots to soil during severe drought.

To test the ability of tropical tree saplings to avoid dehydration during severe droughts, as well as the mechanisms and traits associated with dehydration avoidance, potted saplings were subjected to three months without water and their water relations were compared to well-watered control plants.

Tree species vary greatly in their ability to extract water from drying soil, yet it is unclear how much they vary in their ability to remain hydrated when soil water is unavailable. To explore variation in the ability to regulate plant water status, this study subjected potted saplings of tropical trees to extreme drought and compared their responses to well-watered plants. After three months, soil in the drought treatment was extremely dry, yet some species had 100% survival and maintained water status similar to well-watered plants (i.e., dehydration-avoiding species). Other species had low survival and reached low water status. The dehydration-avoiding species had traits that favor water storage (e.g., low tissue density), which could provide a reservoir that buffers water status despite water loss, yet they maintained most of their stored water during the drought. The dehydration-avoiding species also had low lateral root area, which was further reduced in the drought treatment. This may slow water loss into dry soil. Together, these results suggest that the ability to avoid dehydration during extreme drought varies greatly among species and is dependent on retaining stored water within the plant.

Observations in Amazonian forests consistently show that seasonality in GPP is driven by endogenous biological cycles of leaf flushing and associated age-related trends in leaf-level photosynthetic capacity. This intercomparison makes an important link between model deficiencies in seasonal carbon flux dynamics with the missing biological mechanisms driving photosynthesis and leaf and stem growth in seasonal Amazon forests. It therefore guides model development with these seasonal carbon flux benchmarks and by highlighting leaf age and carbon sink limitation as key mechanisms underlying these patterns.

This study compared and contrasted the observed and modeled seasonality of ecosystem photosynthesis (gross primary productivity, GPP), leaf, and wood production (NPPleaf and NPPwood) at four sites across the Amazon basin, spanning dry season lengths of 1 to 6 months. Observations came from a network of eddy covariance towers and associated ground-based measurements; models were IBIS, ED2, JULES, and CLM3.5, many of which are used in coupled climate–carbon cycle simulations.

Using dynamic global vegetation models (DGVMs) for prediction requires that they be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. In this data-model intercomparison of DGVMs and observations of carbon fluxes at four forests in the Amazon basin, the scientists found that most DGVMs poorly represented the annual cycle of GPP, of photosynthetic capacity (Pc), and of leaf and stem growth. Because these mechanisms are absent from models, modeled GPP seasonality usually follows that of soil moisture availability, which only agrees with observations at the driest, southernmost site. Furthermore, observations suggest that seasonality in growth (NPP) arises from lags or other processes limiting the allocation of GPP to leaves and stems, mechanisms also absent from models. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Previous work had supposed that spatial patterns in AGB in Amazon forests were mediated by a positive association between woody NPP and stem mortality rates inducing reductions in AGB. In contrast, the scientists found that woody NPP and stem mortality are not correlated and, instead, that spatial variability in AGB is controlled primarily by stem mortality (not woody biomass loss).

This study provides several key benchmarks for vegetation models in the Amazon basin via (1) spatial pattern maps of mortality, woody net primary productivity (NPP), and aboveground biomass (AGB) and (2) the underlying mechanisms controlling these patterns.

Understanding the processes that determine AGB in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody NPP and the rate at which carbon is lost through tree mortality. Here, the scientists test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP control variation in AGB among 167 plots in intact forest across Amazonia. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand-size structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influences AGB. In contrast to previous findings, the study finds that woody NPP was not correlated with stem mortality rates and is weakly positively correlated with AGB. The spatial pattern maps of mortality, NPP, and AGB, as well as the underlying mechanisms controlling these patterns, provide key benchmark targets for DGVMs in Amazonia.

This study has three important implications for the broader ecology, evolutionary biology, plant physiology, and modeling communities: (1) challenges modeling approaches that assume tropical forest photosynthesis is primarily controlled by the environment at both short and long timescales; (2) advances ecophysiological understanding of resource limitation (i.e., light versus water) and the temperature sensitivity of tropical evergreen forest; and (3) highlights the importance of accounting for differential regulation of tropical forest photosynthesis at different timescales and of identifying the underlying feedbacks and adaptive mechanisms.

Tropical forest photosynthesis varies with the environment and with biotic changes in photosynthetic infrastructure, but the scientific understanding of the relative effects of these factors across timescales is limited. Here, the scientists used a statistical model to partition the variability of seven years of eddy covariance–derived photosynthesis in a central Amazon evergreen forest into two main causes (i.e., environmental versus biological) and identified the differential regulation of tropical forest photosynthesis at different timescales.

Canopy-scale photosynthesis (i.e., gross ecosystem productivity, GEP) of a central Amazonian evergreen forest in Brazil was derived from the k67 eddy covariance tower (2002–2005 and 2009–2011), using the standard approach to partition ecosystem respiration from eddy covariance measurements of net ecosystem exchange . This work used statistical models to partition the variability of seven-year eddy covariance–derived GEP into two causes: variation in environmental drivers (solar radiation, diffuse light fraction, and vapor pressure deficit) and biotic variation in canopy photosynthetic light-use efficiency. The “full” model was driven by both environmental and biotic factors and the “Env” model was driven by environmental factors only. The models were trained by using the hourly data of years 2003, 2005, 2009, and 2011, and validated by the independent data of years 2002, 2004, and 2010, including the aggregation to different timescales (i.e., daily and monthly). Study results showed that both models (full versus Env) simulated photosynthetic dynamics well at hourly timescales; however, when aggregating the model results into other timescales (i.e., daily, monthly, and yearly), the Env model showed continuous decline in model performance. By contrast, the full model consistently simulated the photosynthetic dynamics across all timescales. The results thus suggest that environmental variables dominate photosynthetic dynamics at shorter timescales (i.e., hourly to daily) but not at longer timescale (i.e., monthly and yearly), and they highlight the importance of accounting for differential regulation of GEP at different timescales and of identifying the underlying feedbacks and adaptive mechanisms.

This study has three important implications for the broader plant science, remote sensing, and modeling communities: (1) It shows that it is possible to monitor and map leaf age of tropical forest canopies and landscape using an imaging spectroscopy approach. (2) In combination with previous spectroscopy work that demonstrated the possibility to retrieve plant functional traits from leaf spectral signatures, this work highlights the possibility to use a spectroscopy approach to reconstruct temporal dynamics of leaf traits (i.e., morphological, physiological, and biochemical). (3) This work enables the retrieval of age-dependent plant functional traits that can be used to parameterize new model structures in future terrestrial biosphere models.

In tropical forests, knowing leaf age is a key component of understanding seasonal dynamics in carbon assimilation. However, a robust method for efficiently estimating leaf age across multiple species and environments did not exist. Here, scientists measured leaf age and leaf reflectance spectra and showed that their statistical model was able to predict leaf age across two contrasting forests in Peru and Brazil, and through diverse vertical gradients within the canopy.

Leaf age was estimated by tagging developing leaves at budburst and following their development with repeated in situ photo documentations. Scientists assembled 759 leaves from 11 tree species covering four canopy environments in an Amazonian evergreen forest in Brazil (August 2013–August 2014), including canopy sunlit leaves (red, n=4 trees), canopy shade leaves (yellow, n=4), mid-canopy leaves (green, n=3), and understory leaves (blue, n=4). Project results showed that a previously developed spectra-age model for Peruvian sunlit leaves also performed well for independent Brazilian sunlit and shade canopy leaves (R2 = 0.75–0.78), suggesting that canopy leaves (and their  associated  spectra) follow constrained developmental  trajectories even in contrasting forests. The Peruvian model did not perform as well for Brazilian mid-canopy and understory leaves (R2 = 0.27–0.29), because leaves in different environments have distinct traits and trait developmental trajectories. When the team accounted for distinct environment-trait linkages by re-parameterizing the spectra-only model to implicitly capture distinct trait trajectories in different environments, the resulting, more general, model was able to predict leaf age across diverse forests and canopy environments.

Fine roots contribute to ecosystem biogeochemical cycles through resource acquisition and respiration, as well as their death and decay, but are understudied in peatlands. Changes in the distribution of roots throughout the peat profile, across the landscape, and over time could alter the delicate balance of peat accumulation.

As one of the few studies to adapt minirhizotron technology for use in waterlogged peatlands, this project was able to provide a rare glimpse into the hidden patterns of root distribution and dynamics in a forested, ombrotrophic bog.

In this fundamental study, scientists aimed to determine how the amount and timing of fine-root growth in a forested, ombrotrophic bog varied across gradients of vegetation density, peat microtopography, and changes in environmental conditions across the growing season and throughout the peat profile. they quantified fine-root peak standing crop and growth using nondestructive minirhizotron technology over a two-year period, focusing on the dominant woody species in the bog. They found that fine-root standing crop and growth varied spatially across the bog in relation to tree density and microtopography, and they observed tradeoffs in root growth in relation to aboveground woody growth rather than environmental variables such as peat temperature and light. A shallow water table level constrained living fine roots to the aerobic zone, which is extremely poor in plant-available nutrients, and ancient, undecomposed, fine roots in peat below the water table suggest a significant contribution of roots to historical accumulated peat. The team expect the controls over the distribution and dynamics of fine roots in this bog to be sensitive to projected warming and drying in northern peatlands.

The study’s results show a more complex and different seasonal cycle than current land surface models predict. The study suggests a long-term decline in evapotranspiration from the forest, due to ecosystem functional change at the scale of the entire basin.

The Next-Generation Ecosystem Experiments (NGEE)–Tropics research team combined satellite measurements of rainfall and gravity anomalies with Amazon river flow data to derive a seasonally resolved estimate of evapotranspiration for the entire Amazon basin. The team then analyzed the seasonal cycles and long-term variation of this measurement and compared it to process-based land surface model predictions.

Evapotranspiration, which comprises the sum of all moisture fluxes from an ecosystem directly to the atmosphere, is a crucial quantity at the center of the terrestrial energy, water, and carbon cycles. Because measurements of evapotranspiration are typically made at local scales, and are sparse over remote locations such as the Amazon, the larger-scale fluxes are not well known. This study combined observations of rainfall, river discharge, and time-varying gravity anomalies to construct a water budget for the Amazon basin, which allows NGEE-Tropics researchers to solve for evapotranspiration as the missing term in the budget. This water budget–based measurement shows a complex seasonal cycle, with a deeper minimum during the wet season than is estimated by other upscaling estimates or by process-based models, and also shows that models tend to increase their seasonal evapotranspiration fluxes later in the dry season than is observed. Furthermore, a long-term analysis of evapotranspiration suggests a decline in the rate over the period of observation, which could be evidence of a large-scale change in ecosystem function.

This paper provides a clear vision of the ways in which the experimental, modeling, and remote sensing communities can use simultaneous observations of ecosystem structure, function, composition, and biochemistry from a suite of novel sensors that will be installed on the ISS. Importantly, the collection of these remotely sensed data will improve understanding of ecosystems as well as the ability to test predictive models.

Ecosystems, particularly tropical forests, play an important role in determining the rate and extent of changes in the Earth system by absorbing and storing about one-third of the carbon dioxide (CO₂) released during the use of oil, coal, and other fossil fuels. Current understanding of how ecosystems take up and store CO₂ is limited to those areas that can be reached by the scientists that study them. However, these study sites only represent a small fraction of the total land area that needs to be studied to understand how much and for how long plants will continue to help slow the rise of atmospheric CO₂ concentration. New instrumentation and technology offer the opportunity to remotely measure many important properties of plants and ecosystems that will determine how the planet will respond to changing environments and provide critical data for scientists to test models of how ecosystems will respond to changes in the Earth system. Specifically, remote measurement of tree height, temperature, CO₂ take up, and biochemical composition offers exciting new opportunities for science. This work highlights the deployment of this new instrumentation on the international space station (ISS), informs the scientific community of the opportunity presented by these measurements, and describes ways to use these unique data. The work is the result of detailed discussions and an ongoing collaboration between ecosystem modelers, experimentalists, and remote sensing scientists.

To improve prediction of the ability of plants to slow the rate of Earth and environmental change by absorbing and storing CO₂, scientists need more data about the composition, function, and structure of terrestrial ecosystems, particularly in remote regions such as the tropics. Unfortunately, current ability to measure and understand important ecosystem processes is too sparse and too spatially biased to make significant progress. Satellite observations are the only source for the required dense, frequent, and spatially and temporally extensive records. The unique collection of measurements anticipated from the ISS will yield important new insights into ecosystem structure and function and provide important new observations to evaluate the models used to understand how important ecosystems, such as tropical forests, will respond to changing conditions.

Soil analysis using traditional laboratory methods are often time consuming and expensive, and require relatively large samples—limiting the availability of information on the spatial variability of SOM composition and other soil properties. Mid-infrared spectroscopy of small soil samples proved to be a promising technique for quickly and reliably estimating carbon content and differentiating the degradation state of organic matter stored in northern cold-region soils.

Multivariate analysis of mid-infrared spectra of soils collected from a 2800-km latitudinal transect across Alaska identified spectral bands that can be used to quickly discriminate variations in soil properties, estimate the quantity and chemical composition of soil organic matter (SOM), and assess the degradation state of organic matter stored in northern cold-region soils.

The amount and vulnerability of soil carbon stocks in northern cold-region soils are major sources of uncertainty in the representation of terrestrial biogeochemical cycles in Earth system models. Researchers led by Argonne National Laboratory investigated the suitability of diffuse reflectance Fourier transform mid-infrared (DRIFT) spectroscopy—a nondestructive, cost-effective infrared light analysis method—to discriminate variations in soil physical and chemical properties needed to improve estimates of the spatial variability of carbon stocks and the extent of organic matter decomposition in these soils. Archived soils collected from a 2800-km latitudinal transect across Alaska were analyzed to provide a representative range of climate, vegetation, surficial geology, and soil types for the region. The chemical composition of organic matter, as well as site and soil properties, exerted strong multivariate influences on the DRIFT spectra. Spectral differences indicated that soils with less decomposed organic matter contained greater abundance of relatively fresh materials, such as carbohydrates and aliphatics, whereas clays and silicates were incorporated into more degraded soils. A single spectral band was identified that might be used to quickly estimate soil organic carbon and total nitrogen concentrations. Overall, the study demonstrated that DRIFT spectroscopy can serve as a valuable tool for quickly and reliably assessing variations in the amount and composition of organic matter in northern cold-region soils.

This project (1) tests a contentious hypothesis regarding hydraulic failure and carbon starvation, for the first time, at a global scale, and (2) provides modelers a direct path to improving vegetation dynamics simulations.

This is the first paper to synthesize the results on mechanisms of mortality from all known drought manipulation studies, and the synthesis found that hydraulic failure is a universal component of death while carbon starvation is frequent but not universal.

About half of carbon dioxide emissions are absorbed by plants, but this service is threatened by increasing frequency of hot droughts. One of the largest uncertainties in land surface modeling is how vegetation will respond to greater exposure to life-threatening droughts. One of the most contentious theories in ecology today regards the mechanisms of responses (e.g., how plants regulate hydraulic failure and carbon starvation, if they even occur at all) during drought. Hydraulic failure is where plants experience partial or complete interruption of the water-transporting xylem tissue function from stress-induced embolisms that inhibit water transport, leading to desiccation. Carbon starvation is a phenomenon where an imbalance between carbohydrate demand and supply leads to an inability to meet osmotic, metabolic, and defensive carbon requirements. This study reviewed and synthesized the findings on all known drought studies that killed trees and found the occurrence of hydraulic failure was a universal characteristic preceding plant death, and co-occurring carbon starvation occurred in approximately 50% of studies. The most advanced land-surface models today simulate mortality via carbon starvation but not via hydraulic failure. Therefore, current model development should incorporate hydraulic failure as a trigger to plant mortality to improve understanding and predictions of ecosystems and vegetation.

FATES will allow a richer representation of the potential ecosystem responses to weather, land-use, and atmospheric compositional changes, and of how these ecosystem changes alter the dynamics of the Earth system. The coupled ACME Earth system model (ESM) will benefit from these changes to allow it to be applied to scientific questions about the role of ecosystem change in the context of larger global changes.

The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is a dynamics vegetation model that predicts tree size distributions, disturbance dynamics, and plant trait competition. It has been integrated into the Accelerated Climate Model for Energy (ACME) Land Model and released as an open-source tool to the public.

FATES is a demographic vegetation model that includes dynamics that are not included in the current ACME Land Model, such as individual tree growth, death, and competition for light; explicit representation of both natural and anthropogenic disturbance; and competitive dynamics between different plant functional types as a result of their differing plant traits. The FATES model has been designed for modularity to allow scientific isolation of component processes and clean scientific experimental design. Because FATES makes predictions about tree size distributions, disturbance dynamics, and physiological dynamics at the level of individual trees, it can be more robustly tested against field measurements and can therefore serve as an organizing model for U.S. Department of Energy (DOE) field activities, particularly in forested ecosystems, such as the Next-Generation Ecosystem Experiments (NGEE)–Tropics project.  Now that FATES has been fully integrated into the ACME Land Model, such activities are directly feeding into ACME science.

Detailed metadata—information that describes when, where, and how data is generated—are required for interpreting, comparing, validating, and synthesizing ecohydrological observations collected with diverse methods in different ecosystems. FRAMES bridges the gap between complex data information models that are needed to organize detailed metadata and specific ecohydrological data reporting protocols that lack enough detail for Earth system science research.

FRAMES is a set of Excel and online templates that standardize reporting of diverse ecohydrological data and the necessary metadata required for data synthesis to study Earth systems.

FRAMES templates standardize reporting of diverse ecohydrological data and metadata for data synthesis required for Earth system science research. This research team developed FRAMES iteratively with data providers and consumers who are developing a predictive understanding of carbon cycling in the tropics. Key features include: (1) Best data science practices, (2) Modular design that allows for addition of new measurement types, (3) Data entry formats that enable efficient reporting, (4) Multiscale hierarchy that links observations across spatiotemporal scales, and (5) Collection of metadata for integrating data with Earth system models.

The influence of flooding on the seasonal cycle of Sphagnum photosynthesis is an advance in the understanding of these at-risk ecosystems that will help to improve model simulations under a changing environment.

Sphagnum mosses form many of the world’s peat bogs, which store huge reservoirs of submerged carbon. These ecosystems are at risk in a changing climate. U.S. Department of Energy (DOE) researchers investigated how photosynthesis in Sphagnum mosses changes though the seasons at the Marcell Experimental Forest, Minn. Researchers were surprised to find that the peak in Sphagnum photosynthesis was delayed compared with the seasonal peak in sunlight strength and showed that the delayed peak was likely due to flooding of the Sphagnum and submergence by water suppressing photosynthesis.

Sphagnum mosses are the keystone species of peatland ecosystems. With rapid rates of climate change occurring in high latitudes, vast reservoirs of carbon accumulated over millennia in peatland ecosystems are potentially vulnerable to rising temperature and changing precipitation. DOE researchers investigated the seasonal drivers of Sphagnum photosynthesis—the entry point of carbon into wetland ecosystems. Continuous measurements of Sphagnum carbon exchange with the atmosphere show a seasonal cycle of Sphagnum photosynthesis that peaked in the late summer, well after the peak in photosynthetically active radiation. Statistical analysis of oscillations in the data showed that water table height was the key driver of weekly variation in Sphagnum photosynthesis in the early summer and that temperature was the primary driver of gross primary productivity (GPP) in the late summer and autumn. A process-based model of Sphagnum photosynthesis was used to show the likelihood of seasonally changing maximum rates of photosynthesis and a previously unreported relationship between the water table and photosynthesising tissue area when the water table was at the Sphagnum surface. The model also suggested that variability in carbon dioxide (CO₂) transport through the Sphagnum tissue to the site of photosynthetic fixation, caused by changing Sphagnum water content, had minimal effect on photosynthesis. Researchers came up with a list of four specific areas to improve the modeling of Sphagnum photosynthesis.

This research highlights the need for robust estimates of global photosynthesis and a better understanding of how maximum photosynthetic rates scale across the Earth’s surface.

A major source of uncertainty in modeling of global photosynthesis, and associated carbon cycle dynamics, is the calculation of maximum photosynthetic carboxylation rate, which is one of two plant traits that closely determines photosynthetic rate. Various methods are used in terrestrial biosphere models to calculate these traits, each representing a different theory about how these traits scale, but the resultant errors have not yet been quantified.

The impact on global patterns of photosynthesis of four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) was investigated by an international team of researchers. Led by a U.S. Department of Enerby researcher at Oak Ridge National Laboratory, the study finds that global photosynthesis estimates from the different trait-scaling hypotheses ranged between 108 and 128 petagrams of carbon per year (Pg C yr^–1), representing around 65% of the uncertainty range found in photosynthesis model intercomparison exercises. The uncertainty propagated through to a 27% variation in net biome productivity, the net amount of carbon removed from the atmosphere by land ecosystems. All hypotheses produced global photosynthesis estimates that were highly correlated with proxies of global photosynthesis. Nevertheless, nutrient limitation appeared to be marginally the best method to simulate the scaling of maximum photosynthetic rates. The comparison of model photosynthesis with “observed” photosynthesis was stymied by the fact that no robust methods exist to measure photosynthesis at the global scale. For this reason, researchers used three proxies of global photosynthesis to compare with the model estimates. Interestingly, photosynthesis in agricultural regions of Earth were much higher in the satellite-based photosynthesis proxies that measure solar-induced fluorescence of the photosynthetic machinery in a leaf. Higher photosynthesis in these regions when measured from space suggests that models and other photosynthesis proxies may be missing an important component of global photosynthesis in these managed ecosystems.

A conceptual framework was developed to inspire new research aimed at the role of microorganisms in the formation of persistent SOM. New understanding on this topic is essential for model development and for informing national and global discussions on the sustainability and vulnerability of soils, including related impacts on food and biofuel production, ecosystem services, environmental health, and climate.

The concept of a soil “microbial carbon pump” is proposed as a mechanism for integrating how the contrasting breakdown and synthesis activities of microorganisms—coupled with the “entombment” of microbial residues via organo-mineral interactions—influence soil organic matter (SOM) dynamics and persistence.

The dynamic balance between inputs of organic materials versus losses (via decomposition or transport) regulates SOM cycling. In this context, microbes are widely investigated as major mediators of decomposition, particularly through the effects of their extracellular enzymes. Less studied is the impact of microbial growth and death on the creation of SOM. Because the living biomass of microbes in soil is small, microbial contributions to SOM formation have been underappreciated. But, the rapid life cycle of microbes can produce large amounts of organic residues over time. Even though microbial residues can be intrinsically easy to decompose, recent studies suggest a significant portion can be stabilized in soils by intimate physical and chemical associations with soil minerals. In this perspective article, the contrasting metabolic roles that microbes play in SOM dynamics (i.e., catabolic breakdown and anabolic formation) are reviewed. The concept of a soil “microbial carbon pump” is borrowed from marine literature and coupled with the “entombing effect” (stabilization via organo-mineral interactions) to create a framework for stimulating and guiding new research efforts targeted at the role of microbial synthesis and turnover in the formation of persistent SOM.

Leaf longevity has been recognized as critical for understanding tropical seasonality and carbon dynamics. The proposed leaf longevity model can be used in next-generation Earth system models (ESMs) to improve projections of carbon dynamics and potential climate feedbacks in the tropics.

Leaf longevity (LL), how long a leaf lives, is closely linked to plant resource use, carbon uptake, and growth strategy. In tropical forests, there is remarkable diversity in LL across species, ranging from several weeks to six years or more. However, it remains unclear how to capture such large variation using predictive models. Here, the scientists present a meta-analysis of 49 species across temperate and tropical biomes. Their results show that the leaf aging rate is positively correlated with the mass-based carbon uptake rate of mature leaves. They further developed an LL model to capture leaf aging rate and evaluated it with LL data for 105 species, measured in two tropical forests in Panama. Their results show that the new model explains over 40% of the cross-species variation in LL, including those species sampled from both canopy and understory. Collectively, the results reveal how variation in LL is constrained by both leaf structural traits and the growth environment.

The scientists use a trait-based carbon optimality approach to model LL, in days, and assess the model performance with in situ LL data for 105 species in two tropical forests in Panama. More specifically, they examine the relative impact of leaf aging rate (i.e., the rate at which leaf photosynthetic capacity declines with age) and within-canopy variation in light environment on the modeled LL. They first assumed that all species have the same leaf aging rate (i.e., the community average value) and receive the same light condition (i.e., canopy-level light). The results are correlated with coefficient r = 0.08, which is not significant. Then they performed the analysis with species-specific leaf aging rates, while assuming that all species receive the same light condition (i.e., canopy-level light), and the results are r = 0.53 and p-value <<0.001. Lastly, they performed the analysis with species-specific leaf aging rate and light environment, and the results are r = 0.66 and p-value <<0.001. Their results thus suggest that both leaf aging rate and within-canopy variation in light environment are essential for modeling LL in the tropics, and the best model can capture over 40% of interspecific variability in LL, including those species from canopy and understory.

This work highlights the poor representation of Arctic photosynthesis in terrestrial biosphere models and provides the critical data necessary to improve the ability to project the response of the Arctic to global environmental change.

Carbon uptake and loss from the Arctic is highly sensitive to climate change, and these processes are poorly represented in terrestrial biosphere models (TBMs). Uncertainty surrounding the Arctic carbon cycle is dominated by uncertainty over carbon dioxide (CO₂) uptake by photosynthesis. However, current TBMs have almost no data on Arctic photosynthesis and currently rely on understanding developed in temperate systems. This study provided the first Arctic dataset of the key photosynthetic parameters maximum carboxylation capacity and maximum electron transport rate (known as V_cmax and J_max, respectively). The scientists found that current TBM representation of these two parameters was markedly lower than the values they measured on the coastal tundra of northern Alaska, in some cases fivefold lower. On average, the capacity for CO₂ uptake by Arctic vegetation is double current TBM estimates.

The team measured V_cmax and J_max in seven species representative of the dominant vegetation found on the coastal tundra near Barrow, Alaska. They made three key discoveries: (1) The temperature-response functions of V_cmax and J_max that are used to determine how the capacity for CO₂ uptake changes with temperature were markedly different than the temperature-response functions of temperate plants. (2) V_cmax and J_max were two- to fivefold higher than the values used to parameterize current TBMs. (3) Current parameterization of TBMs resulted in a twofold underestimation of the capacity for leaf-level CO₂ assimilation in Arctic vegetation. The insight and data set provided in this study can be used to markedly improve TBM representation of Arctic photosynthesis and improve projections of how Arctic photosynthesis responds to rising temperature and CO₂ concentration. The high-impact dataset generated during this study has already been used in four additional publications.

The authors demonstrate that (1) existing Monod and SU kinetics are scaling inconsistently, (2) the new SUPECA kinetics rectifies these problems, and (3) that SUPECA is well suited to trait-based modeling approaches. The authors also show that SUPECA kinetics enables mechanistic modeling of soil moisture effects on organic matter decomposition.

Environmental biogeochemistry emerges from microbially mediated redox reactions in a complex web of consumers and substrates. The two dominant approaches to represent these reactions, Monod and synthesizing unit (SU), are unable to scale consistently across complex reaction networks and fail to include substrate limitations, respectively. The authors here extend these approaches (termed SUPECA) to general redox reaction networks to improve terrestrial ecosystem biogeochemical modeling. The authors also applied the SUPECA approach to analyze the soil moisture constraint on soil organic matter (SOM) decomposition and compared results to a benchmark dataset to show that their approach accurately represents this constraint across a wide range of soil moisture conditions. The SUPECA approach is being applied in Next-Generation Ecosystem Experiments (NGEE)–Arctic modeling analyses and in the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM) Land Model (ELM).

SOM decomposition occurs in an extremely complex network of reactions, substrates, and consumers. To address this problem in a manner amenable to land model representation (e.g., E3SM’s ELM), the authors extended the equilibrium chemistry approximation (ECA) approach to generic biogeochemical networks that include redox reactions (termed SUPECA, or SU plus ECA, kinetics). The authors demonstrated that SUPECA consistently scales from single Monod type and redox reactions to a reaction network, while the popular dual Monod kinetics and SU kinetics fail to do so. It is also demonstrated that SUPECA kinetics is superior to dual Monod kinetics in modeling substrate competition in the presence of substrate-mineral interactions. By applying SUPECA to SOM decomposition, the authors showed that soil aggregates have significant impacts and illustrate potential flaws in current ESM land model approaches. The authors are applying the SUPECA approach in NGEE-Arctic modeling analyses and in DOE’s ELM.

Quantifying the amount of carbon frozen and stored in permafrost soils is a critical challenge in the attempt to estimate the possible feedback that thawing permafrost may have on the global carbon cycle. Given the widespread distribution and potential for deposits thicker that one meter, hillslope deposits of soil carbon could be a significant, but presently unaccounted-for, store of carbon in permafrost regions. Greater study should be focused on these depositions to better constrain estimates of permafrost SOC stores.

The gradual and ongoing transport of soil and soil organic carbon (SOC) down hillslopes results in deposits at the base of hills. Limited sampling of these deposits leaves the quantity of carbon buried on hills poorly quantified. This study’s analysis suggests that accounting for carbon in these deposits could significantly alter present estimates of carbon stored in permafrost.

This study combined topographic models with soil profile data and topographic analysis to evaluate the quantity and uncertainty of SOC mass stored in perennially frozen hill toe soil deposits. The study shows that in Alaska this SOC mass introduces an uncertainty that is >200% the current state-wide estimates of SOC stocks 77 petagrams of carbon (Pg C) and that a similarly large uncertainty may also pertain at a circumpolar scale. The SOC content of permafrost hill toe deposits can meaningfully change current estimates of permafrost SOC. SOC stored in hill toe deposits is likely sensitive to climate change–induced erosion and deposition. Soil sampling and geophysical imaging efforts that target hill toe deposits can help constrain this large uncertainty.

Electrical and seismic signals during freeze-thaw cycles of saline permafrost show characteristic changes with differential hysteresis behaviors. The uncertainty associated with unfrozen water content estimation based on electrical resistivity could be large.

This study demonstrated the mechanical and electrical responses of Arctic saline permafrost during freeze-thaw processes, and suggested large uncertainty when estimating the unfrozen water content using electrical resistivity data.

This study revealed low electrical resistivity and elastic moduli at temperatures down to approximately –10°C, indicating the presence of a significant amount of unfrozen saline water under the current field conditions. The spectral induced polarization signal showed a systematic shift during the freezing process, affected by concurrent changes of temperature, salinity, and ice formation. An anomalous induced polarization response was first observed during the transient period of supercooling and the onset of ice nucleation. Seismic measurements showed a characteristic maximal attenuation at the temperatures immediately below the freezing point, followed by a decrease with decreasing temperature. The calculated elastic moduli showed a nonhysteric response during the freeze-thaw cycle, which was different from the concurrently measured electrical resistivity response where a differential resistivity signal is observed depending on whether the soil is experiencing freezing or thawing. The differential electrical resistivity signal presents challenges for unfrozen water content estimation based on Archie’s law.

Root water uptake can be linked to characteristic root traits, such as diameter or age. Comparing actual water uptake with modeled water uptake highlights problems with current model assumptions. This work points to the need for new research to understand soil hydraulic properties with and without roots present.

Neutron imaging is used to measure soil water movement and water uptake by individual roots in situ.

Knowledge of plant root function is largely based on indirect measurements of bulk soil water or nutrient extraction, which limits modeling of root function in land surface models. Neutron radiography, complementary to X-ray imaging, was used to assess in situ water uptake from newer, finer roots and older, thicker roots of a poplar seedling growing in sand. The smaller-diameter roots had greater water uptake per unit surface area than the larger diameter roots, ranging from 0.0027 to 0.0116 g/cm² root surface area/h. Model analysis based on root-free soil hydraulic properties indicated unreasonably large water fluxes between the vertical soil layers during the first 16 hours after wetting—suggesting problems with common soil hydraulic or root surface area modeling approaches and the need to further research and understand the impacts of roots on soil hydraulic properties.

This technical advance allows researchers to study the effect of greater temperature elevations than previously possible using passive (solar radiation) warming. This is particularly relevant for remote and challenging environments, such as the Arctic, that are projected to experience warming that exceeds the 1.5°C limit of current technology by the middle of the century.

Advances in understanding and model representation of plant and ecosystem responses to rising temperature have typically required temperature manipulation of research plots. In remote or logistically challenging locations, passive warming using solar radiation is often the only viable approach for temperature manipulation. However, current passive warming approaches are only able to elevate mean daily air temperature by about 1.5°C. The scientists have developed an alternative approach to passive warming that uses modulated venting to allow additional warming. The system requires no electrical power for fully autonomous operation. When tested in the research environment, the coastal tundra of northern Alaska, the project’s chambers were able to double the warming achieved by existing approaches.

The study’s zero-power warming (ZPW) chamber requires no electrical power for fully autonomous operation. It uses a novel system of internal and external heat exchangers that allow differential actuation of pistons in coupled cylinders to control chamber venting. This enables the ZPW chamber venting to respond to the difference between the external and internal air temperatures, thereby increasing the potential for warming and eliminating the risk of overheating. During the thaw season on the coastal tundra of northern Alaska the ZPW chamber was able to elevate the mean daily air temperature 2.6°C above ambient, double the warming achieved by an adjacent passively warmed control chamber that lacked the team’s hydraulic system. The team describe the construction, evaluation, and performance of their ZPW chamber and discuss the impact of potential artifacts associated with the design and its operation on the Arctic tundra. The approach described here is highly flexible and tuneable, enabling customization for use in many different environments where significantly greater temperature manipulation than that possible with existing passive warming approaches is desired.

The team developed a new model for predicting soil response to changes in soil temperature, moisture, plant inputs, and stoichiometry. This model is simple and based on well-defined physical and biological properties and could be developed to model microbial activity at larger scales.

This study developed a new model of microbial soil decomposition that successfully captured the changing relationship between temperature and microbial respiration during the growing season. The scientists showed that the soil microbial response to plant inputs depends on the nitrogen content of the added plant material.

Microorganisms that grow in the soil, like bacteria and fungi, affect how much carbon resides in the soil and how much is released to the atmosphere as carbon dioxide (CO₂). Mathematical models used to make climate change predictions often struggle to capture the activity of soil microbes in realistic ways. This study uses well-established descriptions of water and temperature effects on soil microbes to predict rate of carbon and nitrogen cycling in the soil. The new model (called the Dual Arrhenius Michaelis-Menten–Microbial Carbon and Nitrogen Physiology, or DAMM-MCNiP), reproduces the changing relationship between temperature and microbial respiration during the growing season. The study also shows using a theoretical addition of root secretions that the microbial response depends on the nitrogen content of the added plant material. This model is simple and based on well defined physical and biological properties and could be developed to model microbial activity at larger scales.

Quantitative characterization of soil organic carbon content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of organic carbon, which both in turn are important for predicting carbon-climate feedbacks. The scientists present a novel approach to estimate this soil property and its impacts on a hydrological-thermal regime including the freeze-thaw transition in the Arctic tundra based on observations of soil moisture, soil temperature, and electrical resistivity data.

The project developed and tested a novel inversion scheme that can flexibly use single or multiple datasets including soil liquid water content, temperature, and electrical resistivity tomography (ERT) data to estimate the vertical distribution of organic carbon content and its associated uncertainty in the Arctic tundra. The results show that jointly using multiple datasets helps to better estimate the organic carbon content, especially at the active layer.

Quantitative characterization of soil organic carbon content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of organic carbon, which both in turn are important for predicting carbon-climate feedbacks. The scientists present a novel approach to estimate this soil property and its impacts on a hydrological-thermal regime including the freeze-thaw transition in the Arctic tundra based on observations of soil moisture, soil temperature, and electrical resistivity data.

The study shows via a global benchmark that existing models systematically underestimate the temperature sensitivity of soil carbon decomposition, and that the solution to this is to take into account the way in which surface soils freeze.

U.S. Department of Energy–supported researchers combined global maps of productivity, soil carbon, and environment to demonstrate a basic pattern of environmental controls on soil decomposition, which is that its temperature sensitivity is highest in cold regions. From this, they derive a theory that explains the pattern as an outcome of the scaling of soil freeze-thaw processes in time and depth, and apply the benchmark to existing Earth system models (ESMs) and newer land modeling approaches.

The results show that the sensitivity of soil carbon to temperature is highest in cold climates, even for surface rather than permafrost layers, and that this global pattern can most simply be explained as an outcome of the way in which soils experience freeze-thaw processes. The team also show that all existing (CMIP5-era) ESMs systematically underestimate this temperature sensitivity, whereas newer approaches, such as the CLM4.5 representation that forms the basis of the E3SM soil biogeochemistry, can match observations. Thus the team’s approach shows two major impacts: (1) the single most important relationship that soil models must take into account is the physical scaling of freeze and thaw and (2) existing estimates systematically underestimate the long-term temperature sensitivity of surface soil carbon.

Analysis of observations of a vast amount of tree-water dynamics shows juniper and piñon trees have different physiological responses to heat and drought stress including varying ability to acclimate. The scientists’ new framework allows separation of temperature and precipitation responses in these species and provides a path forward for better model representations of how trees will function within the evolving Earth system.

A novel tree manipulation study shows the roles of hydraulic acclimation to both precipitation and temperature in two tree species and unravels their effects.

Previous findings suggested warming superimposed on drought would exacerbate drought stress and increase mortality. However, during this study’s five-year period of warmer and much drier conditions, no mortality was observed. The tree stomata adjusted to heat and drought even when other functions were drastically impaired by drought—stomata acclimation prevented tree death from the additive effects of warming and drying. Also, previous work had revealed that juniper trees can be highly resistant to drought, keeping their stomata open, while piñon shut down all functions that kept them alive. However, in this study, juniper was unable to significantly acclimate and showed strong reductions in function. Piñon, which suffered when exposed to drought, actually acclimated when warming was the only stressor, and it retained hydrological functions including sap production to repel invaders.

Coincident monitoring of the spatiotemporal distribution of and interactions between land, soil, and permafrost properties is important for advancing the predictive understanding of ecosystem dynamics. Demonstration of this first aboveground and belowground geophysical monitoring approach within an Arctic ecosystem illustrates its significant potential to remotely “visualize” permafrost, soil, and vegetation ecosystem codynamics in high resolution over field-relevant scales.

A novel monitoring strategy was developed to quantify complex Arctic ecosystem responses to the seasonal freeze-thaw growing season conditions. The spatially and temporally dense monitoring data sets revealed several insights about tundra system behavior at a site located near Barrow, Alaska.

The novel strategy exploited autonomous measurements obtained through electrical resistivity tomography to monitor soil properties; pole-mounted optical cameras to monitor vegetation dynamics; point probes to measure soil temperature; and periodic measurements of thaw layer thickness, snow thickness, and soil dielectric permittivity. Among other results, the soil electrical conductivity (a proxy for soil water content) in the active layer indicated an increasing positive correlation with the green chromatic coordinate (a proxy for vegetation vigor) over the growing season, with the strongest correlation (R = 0.89) near the typical peak of the growing season. Soil conductivity and green chromatic coordinate also showed significant positive correlations with thaw depth, which is influenced by soil and surface properties. These correlations have been then confirmed at larger spatial scale using an unmanned aerial system (UAS) platform.

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics requires advances in quantifying soil and snow properties This work enables a better understanding and quantification of the morphology and physical properties of ice-wedges and permafrost present in Arctic tundra.

The research team developed an approach to improve the estimation of ice-wedge dimension and other permafrost characteristics by integrating various geophysical imaging techniques including ground penetrating radar (GPR) and electrical resistivity tomography (ERT).

The team document for the first time that GPR data collected during the frozen season, when conditions lead to improved GPR signal-to-noise ratio, can provide reliable estimates of active layer thickness and geometry of ice wedges. They find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, they also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Quantification of the value of the GPR and ERT data collected by the team during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

The research showed that evapotranspiration from mosses and open water was twice as high as that from lichens and bare ground, and that microtopographic variations in polygonal tundra explained most of this and other spatial variation in evapotranspiration.

A group of scientists conducted field research over two summers at an Arctic tundra site near Barrow, Alaska, to measure water vapor fluxes (evapotranspiration) from different characteristic plant types, bare soil, and open water, to understand the variations and the controls over these fluxes across the landscape.

Coastal tundra ecosystems are relatively flat, yet they display large spatial variability in ecosystem traits. The microtopographical differences in polygonal geomorphology produce heterogeneity in permafrost depth, soil temperature, soil moisture, soil geochemistry, and plant distribution. Few measurements have been made, however, of how water fluxes vary across polygonal tundra plant types, limiting the ability to understand and model these ecosystems. In this study, the team investigated how plant distribution and geomorphological location affect actual ET. These effects are especially critical in light of the rapid change polygonal tundra systems are experiencing with Arctic warming. At a field site near Barrow, Alaska, USA, scientists investigated the relationships between ET and plant cover in 2014 and 2015. ET was measured at a range of spatial and temporal scales using: (1) an eddy covariance flux tower for continuous landscape-scale monitoring; (2) an automated clear surface chamber over dry vegetation in a fixed location for continuous plot-scale monitoring; and (3) manual measurements with a clear portable chamber in approximately 60 locations across the landscape. The team found that variation in environmental conditions and plant community composition, driven by microtopographical features, has significant influence on ET. Among plant types, ET from moss-covered and inundated areas was more than twice that from other plant types. ET from troughs and low polygonal centers was significantly higher than from high polygonal centers. ET varied seasonally, with peak fluxes of 0.14 mm per h in July. Despite 24 hours of daylight in summer, diurnal fluctuations in incoming solar radiation and plant processes produced a diurnal cycle in ET. Combining the patterns observed with projections for the impact of permafrost degradation on polygonal structure, the suggestion is that microtopographic changes associated with permafrost thaw have the potential to alter tundra ecosystem ET.

SOM and its main constituent, SOC, interact with several aspects of the Earth system and its services to society, including food, fiber, water, energy, cycling of carbon and nutrients, and biodiversity. It is critical that the scientific community expand its understanding of SOM and SOC so that it can improve the state of soil and ecological sustainability, as well as contribute to climate change mitigation.

Soil organic matter (SOM) sustains terrestrial ecosystems, provides food and fiber, and retains the largest pool of actively cycling carbon. Over 75% of the soil organic carbon (SOC) in the top meter of soil is directly affected by human land-use practices. Large areas with and without intentional management are also being subjected to rapid climate changes, making many reservoirs of SOC in soil vulnerable to losses by decomposition or disturbance.

To quantify potential losses of SOC or its sequestration at field, regional, and global scales, members of the International Soil Carbon Network (ISCN) posit that improvements in scientific data, modeling, and communication are necessary. They also suggest that their network could be a platform for integrating the two scientific communities dominating SOM research: one focused on soil science and soil health and the other focused on the terrestrial carbon cycle and biogeochemistry. Together, these science communities have an opportunity to combine and transform knowledge, databases, and mathematical frameworks for the benefit of environmental health and humanity.

At the global scale, SOM is one of the largest actively cycling carbon reservoirs, and direct human activities (growing crops, grazing, and forestry practices) impact over 70% of carbon stocks in the upper meter of soil. The distribution of soils in managed lands follows the distribution of human land use. Overlaying the estimated SOC stocks with human land-use data shows that the majority of near-surface SOC stocks are directly affected by human activities today.

One global initiative to reduce atmospheric carbon dioxide (CO₂) through soil carbon sequestration has demonstrated that many soils in managed systems could offer an opportunity for climate regulation. And if these gains are applied across all land management plans, there is an opportunity to offset carbon emissions from permafrost, or from the combined projected emissions from land-use change and agricultural management.

The ISCN posits that there is a need and an opportunity for the scientific community to (1) better identify datasets to characterize ecosystem and landscape properties, processes, and the mechanisms that dictate SOC storage and stabilization and their vulnerabilities to change; (2) identify, rescue, and disseminate existing datasets; (3) develop platforms for sharing data, models, and management practices for SOC science; and (4) improve the connection between the research communities related to the global carbon cycle and to soil management.

Photosynthesis is a key climate forcing process in the terrestrial biosphere. It removes CO₂ from the atmosphere and stores carbon in plants, slowing the rate of climate change. Measurements of atmospheric COS provide the first global-scale estimates of this carbon-climate feedback.

Current climate models disagree on how much carbon dioxide (CO₂) land ecosystems take up for photosynthesis. In response, atmospheric scientists, biogeochemists, and oceanographers have proposed measuring a gas called carbonyl sulfide (COS) to help quantify the contribution that photosynthesis makes to carbon uptake.

Ten years ago, scientists discovered a massive and persistent biosphere signal in atmospheric COS measurements. In these data, COS and CO₂ levels follow a similar seasonal pattern, but the COS signal is much stronger over continental regions, suggesting that the terrestrial biosphere is a sink for COS. The remarkable discovery led scientists to wonder: Could COS be used as a tracer for carbon uptake? An explosive growth in COS studies followed as scientists attempted to answer this question, including a COS record from the present to the Last Glacial Maximum, satellite-based maps of the dynamics of COS in the global atmosphere, and measurements of ecosystem fluxes of COS.

The proposed model modification addresses a long-standing problem in mechanistic models of soil biogeochemistry and improves predictions of soil carbon storage in response to long-term changes in plant productivity.

Currently, soil carbon models that explicitly represent microbial activity have large biases in predicted carbon stocks and temporal dynamics. Scientists at Lawrence Berkeley National Laboratory have showed that accounting for density-dependent microbial mortality greatly improves predictions against long-term observations and improves microbial models for inclusion in Earth system models (ESMs).

Changes in climate, atmospheric composition, and land use all have the potential to alter plant inputs to soil in ways that impact soil microbial activity. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of SOC dynamics. Most of these models, however, exhibit unrealistic oscillatory behavior, and their SOC stocks are insensitive to long-term changes in carbon inputs. U.S. Department of Energy (DOE) national laboratory scientists diagnosed the source of these problems in four archetypal microbial models and proposed a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. They compared model predictions to 24 long-term carbon-input field manipulations and identified key benchmarks. The proposed formulation reproduces soil carbon responses to long-term carbon-input changes and implies greater SOC storage associated with CO₂ fertilization–driven increases in carbon inputs over the coming century compared to recent microbial models. This study provides a simple, yet effective, modification to improve microbial models for inclusion in ESMs.

(1) This study provides direct evidence that even the most chemically labile organic substrates can be protected from microbial decomposition via association with mineral phases [in this case iron (hydro)oxide]. (2) These results support the emerging view that molecular structure is not the sole determinant of soil organic carbon (SOC) stability. (3) The efficacy of the laboratory approach demonstrates that microbial respired CO₂ can be used as a tracer for OM desorption in soil, creating additional research opportunities.

Recent field studies suggest that interactions with soil mineral phases can stabilize otherwise biodegradable organic matter (OM) in soils against microbial decomposition. To directly assess the effect of organo-mineral associations on an easily decomposable substrate (glucose), the research team conducted a series of laboratory incubations with well-characterized minerals (goethite and ferrihydrite) and native soils from three soil depths. Indeed, while free glucose added to soil was completely respired by microbes within 80 days, almost no glucose that had been sorbed to minerals before incorporation into soil was respired (~100% versus 0.4%, respectively).

Empirical field-based studies have provided indirect evidence of the capacity of soil minerals to stabilize organic carbon in soil. However, uncertainties remain as to the effect of mineral association on the bioavailability of organic compounds. To assess the impact of mineral association on the decomposition of glucose, an easily respirable organic substrate, a series of laboratory incubations was conducted with soils from 15, 50, and 85 cm. ¹³C-labeled glucose was added either directly to native soil or sorbed to one of two synthetic iron (Fe) (hydr)oxides (goethite and ferrihydrite) that differ in crystallinity and affinity for glucose. This study demonstrates that association with Fe (hydr)oxide minerals effectively reduced decomposition of glucose by ~99.5% relative to the rate of decomposition for free glucose in soil. These results emphasize the key role of mineral-organic associations in regulating the fluxes of carbon from soils to the atmosphere by enhancing the persistence of SOC stocks.

CLM4.5 was able to reasonably simulate the observations at Wind River after significant calibration of parameters. While most of the adjustments were site specific, the adjustment of the slope of the leaf stomatal conductance equation aligned with results from other studies at different coniferous forest sites, suggesting that CLM4.5 could benefit from a revised default value. The results also demonstrate that carbon isotopes can expose structural weaknesses in CLM4.5 and provide a key constraint that may guide future model development.

Droughts in the western United States are expected to intensify with climate change. Thus, an adequate representation of ecosystem response to water stress in land models is critical for predicting carbon dynamics. The study’s goal was to evaluate the performance of Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site in the Pacific Northwest region of the United States (Wind River AmeriFlux site), characterized by a Mediterranean climate that subjects trees to water stress each summer.

U.S. Department of Energy (DOE)–supported scientists evaluated CLM4.5 against observations at an old-growth coniferous forest site that is subjected to water stress each summer. They found that, after calibration, CLM4.5 was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress (i.e., response of canopy conductance to atmospheric vapor pressure deficit and soil water content). The calibration of the slope parameter in the Ball-Berry leaf stomatal conductance model aligned with other studies, suggesting that CLM4.5 could benefit from a revised value of 6, rather than the default value of 9, for needleleaf evergreen temperate forests. This study demonstrates that carbon isotope data can be used to constrain stomatal conductance and intrinsic water use efficiency in CLM4.5, as an alternative to eddy covariance flux measurements. It also demonstrates that carbon isotopes can expose structural weaknesses in the model and provide a key constraint that may guide future model development.

Photosynthesis is the foundation of life and civilization on Earth. Yet scientists’ current ability to measure photosynthesis at large scales is extremely limited. The team shows that SIF is a direct proxy of photosynthesis and the relationship is consistent across biomes. This research opens up a new direction for photosynthesis observations at multiple scales. It also shows how ground-based observations such as those from AmeriFlux can be integrated with satellite remote sensing to advance photosynthesis research at local, regional, and global scales.

When energized by photons of sunlight, chlorophyll molecules in plant leaves emit a faint red light—solar-induced fluorescence (SIF). SIF originates directly from the core of the photosynthetic machinery and is produced concurrently with carbon fixation. Orbiting Carbon Observatory-2 (OCO-2) is capable of monitoring SIF at high spatial resolution. After validating OCO-2’s SIF measurements against ground measurements, the team related OCO-2 SIF to gross primary production (GPP) estimated from AmeriFlux sites under the OCO-2’s orbital tracks. A significant linear relationship is obtained between these two variables across different vegetation types.

Quantifying GPP remains a major challenge in global carbon cycle research. Space-borne monitoring of solar-induced chlorophyll fluorescence (SIF), an integrative photosynthetic signal of molecular origin, can assist in terrestrial GPP monitoring. However, the extent to which SIF tracks spatiotemporal variations in GPP remains unresolved. The OCO-2 SIF data acquisition and fine spatial resolution permit direct validation against ground and airborne observations. Empirical orthogonal function analysis shows consistent spatiotemporal correspondence between OCO-2 SIF and GPP globally. A linear SIF-GPP relationship is also obtained at eddy-flux sites covering diverse biomes, setting the stage for future investigations of the robustness of such a relationship across more biomes. Team findings support the central importance of high-quality satellite SIF for studying terrestrial carbon cycle dynamics.

The team’s results demonstrate that methanol activates the C₁ pathway in plants that provides an alternative carbon source for glycine methylation in photorespiration, enhance CO₂ concentrations within chloroplasts, and produce key C₂ intermediates (e.g., acetyl-CoA) for central metabolism. Their observations are consistent with previous studies that demonstrated formaldehyde integrates into photorespiration in the mitochondria by providing an alternate source of CH₂-THF used for the methylation of serine to glycine. By eliminating the need for a second glycine for the production of CH₂-THF with the subsequent loss of CO₂ and ammonia (NH₃), the integration of C₁ pathway into photorespiration may convert it from a net loss of carbon to a net gain. By also suppressing photorespiration via the production of CO₂ in chloroplasts, the study presents the hypothesis that the integration of C₁ pathway into C_2/3 metabolism may boost carbon use efficiency and therefore represent an important mechanism by trees under photorespiratory conditions (e.g., high-temperature stress). As agricultural crops are known to be high methanol producers, genetic manipulation of the C₁ pathway has the potential to improve yields and tolerance to environmental extremes, thereby providing a new tool to the agriculture, bioenergy, and biomanufacturing industries.

An oxidative C₁ pathway is known to exist in plants where intermediates with a single carbon atom beginning with methanol are oxidized to carbon dioxide (CO₂). Although the flux of carbon through the C₁ pathway is thought to be large, its intermediates are difficult to measure and relatively little is known about this potentially ubiquitous and mysterious pathway. In this study, scientists at Lawrence Berkeley National Laboratory (LBNL) evaluated the C₁ pathway and its integration with central metabolism using aqueous solutions of ¹³C-labeled C₁ and C₂ intermediates delivered to branches of the tropical species Inga edulis via the transpiration stream.

Methanol is highly abundant in the global atmosphere and is known to be tightly connected to plant growth. However, to date, it is assumed that methanol represents a byproduct of the expansion of cell walls during growth processes. Although evidence for the existence of a C₁ pathway in plants was first collected over 50 years ago, its intermediates are difficult to measure and relatively little is known about this potentially ubiquitous, yet mysterious biochemical pathway. Previous research by one of the founding fathers of photosynthesis research (Dr. Andrew Benson), for whom this paper is dedicated, found evidence for an important role of methanol in boosting plant photosynthesis, biomass, and productivity. However, this topic remains controversial as subsequent researchers were unable to observe these effects, and the biochemical mechanism(s) remain unclear.

In this paper, scientists from LBNL employ the newly developed technique in their lab termed dynamic ¹³C-pulse chase to evaluate the potential existence of the complete C₁ pathway and its integration with C_2/3 metabolism in individual branches of a tropical pioneer species using aqueous solutions of ¹³C-labeled C₁ (methanol, formaldehyde, and formic acid) and C₂ (acetic acid and glycine) intermediates delivered via the transpiration stream. They confirm that methanol initiates the complete C₁ pathway in plants (methanol, formaldehyde, formic acid, and carbon dioxide) by providing the first real-time dynamic ¹³C-labeling data showing their interdependence. The team present novel aspects about the pathway including the rapid interconversion between methanol and formaldehyde, whereas once oxidation to formate occurs, it is quickly oxidized to CO₂ within chloroplasts where it can be re-assimilated by photosynthesis. The scientists show for the first time that reassimilation of C₁, respiratory, and photorespiratory CO₂ is a common mechanism for isoprene biosynthesis; a strong linear dependence of ¹³C-labeling of isoprene on ¹³C-labeling of CO₂ was observed across all C₁ and C₂ ¹³C-labeled substrates. Thus, this analysis presents a new method for studying the reassimilation of internal CO₂ sources in plants. Finally, the LBNL research team show, for the first time, that methanol and formaldehyde delivery to the transpiration stream leads to a rapid and quantitative conversion of carbon pools used in the biosynthesis of central C₂ compounds (acetic acid and acetyl CoA) and therefore represents a new uncharacterized route to the biosynthesis of these key C₂ intermediates widely used in cells as precursors for a diverse suite of anabolic (e.g., fatty acid biosynthesis) and catabolic (e.g., mitochondrial respiration) processes.

These observations are consistent with previous studies that demonstrated formaldehyde integrates into photorespiration in the mitochondrial by providing an alternate source of CH₂-THF used for the methylation of serine to glycine. By eliminating the need for a second glycine for the production of CH₂-THF with the subsequent loss of CO₂ and NH₃, the integration of C₁ pathway into photorespiration may convert it from a net loss of carbon to a net gain. By also suppressing photorespiration via the production of CO₂ in chloroplasts, this study presents the hypothesis that the integration of C₁ pathway into C_2/3 metabolism may boost carbon use efficiency during photorespiratory conditions (e.g., high-temperature stress). As all agricultural crops have been shown to be high methanol producers, genetic manipulation of the C₁ pathway has the potential to improve yields and tolerance to environmental extremes, thereby providing a new tool to the agriculture, bioenergy, and biomanufacturing industries.

This approach to obtaining direct and quantitative treatment of the organic-mineral interface could provide fundamental information and critical new measurements that may inform the next generation of process-rich land-carbon models.

The terrestrial biosphere plays an important role in the global carbon cycle, partly through how strongly organic compounds (i.e., ligands) and soil minerals bind. These binding sites—or interfaces—play an important role in the long-term persistence of soil carbon. In a new Nature Communications paper, researchers from Pacific Northwest National Laboratory found that both carbon chemistry and environmental conditions affect carbon persistence.

The researchers used dynamic force spectroscopy (DFS) to directly measure the strength with which different types of organic carbon binds to soil minerals and the conditions under which that organic carbon is released.

Unlike previous methods, DFS allows researchers to quantify the energy needed to separate organic molecules from minerals. That allows them to compare specific functional chemical groups and mineral types and to better understand when and how carbon is retained in soils or how easily it escapes to the atmosphere.

Changes to soil moisture, such as flooding and drought, change the nanoscale chemical environment (ionic strength and pH) in ways that alter the overall quality of carbon potentially solubilized in natural soils.

Complex interactions among plants, microbes, and minerals mean soil organic matter (SOM) can reside in soils anywhere from months to millennia. In this study, researchers set out to better understand the factors that affect the SOM persistence and vulnerability at the mineral interface.

Until now, researchers had only limited, qualitative information about organic-minerals at this interface. Using DFS, however, they could make comparisons between specific functional groups and mineral types under varying environmental conditions. Their findings indicate that environmental factors, such as ionic strength and pH, produce the most drastic differences in binding energies.

Their approach to obtaining direct and quantitative treatment of the organic-mineral interface could fundamentally inform next-generation land-carbon models in which mineral-bound carbon is an important control on carbon persistence. In turn, such models would be at the cutting edge of current understanding of the terrestrial carbon cycle.

Earth system models (ESMs) poorly represent tropical forests in part due to a lack of data on both the phosphorus cycle and the belowground processes that influence them. The results can be used to improve how models represent the influence that roots and microbes have on the phosphorus cycle in tropical forests.

Phosphorus is an important nutrient for plant growth, but its availability is often limited in tropical forests. While most studies focus on either roots or bacteria, scientists from Oak Ridge National Laboratory studied an important enzyme (phosphatase) in both roots and bacteria, showing that phosphatase release varies with tree species and soil phosphorus availability.

ESMs simulate the global carbon cycle to predict how the world responds to and changes with perturbations to the carbon cycle. Tropical forests absorb a large amount of carbon in the atmosphere, making it important to understand how they grow and are influenced by environmental factors such as phosphorus. Roots and microbes interact to access nutrients and water from the soil environment. In tropical forests, roots and microbes must release phosphatase, an enzyme that breaks down phosphorus locked into organic material. Plant growth in future climates may be highly influenced by whether plants can release enough phosphatase to continue growing. Scientists from ORNL studied phosphatase activity in roots and bacteria collected from different tree species and soil phosphorus availabilities in tropical forests of Puerto Rico to better understand phosphatase activity. The influences of roots and bacteria on the phosphorus cycle are not usually included in ESMs. The study’s results can be used to help improve ESMs.

This review found that there are many potential ways for “drought” to be manifested in seasonally dry tropical forests. Importantly, most of the studies are consistent with the prediction that changing rainfall regimes will have a large effect on species composition and ecological function of these forests.

Seasonally dry tropical forests experience periodic droughts that occur each year, but it is unknown how their organisms and ecosystem processes will respond to increasing climatic variability including extreme droughts and/or changes in the timing, duration, or magnitude of rainfall regimes. This uncertainty has led to two very different predictions: some people argue that seasonally dry tropical forests will be very sensitive to changes in rainfall because they are already at hydrologic thresholds, while others claim that they will be resistant because these species are already adapted to strong seasonal drought. This research reviewed existing studies with the goals of searching for general patterns that could discriminate between these two hypotheses and also identifying gaps in the literature to guide future research.

By the end of the 21st century, climate models predict substantial changes in rainfall regimes across the seasonally dry tropical forest biome, but little is known about how dry forests will cope with the hotter, drier conditions predicted by climate models. The scientists explored two alternative hypotheses: (1) dry forests will be sensitive to drought because they are already limited by water and close to hydrologic thresholds or (2) they will be resistant or resilient to intra- and interannual changes in rainfall because they are adapted to predictable, seasonal drought. In this review of literature spanning microbial to ecosystem scales, most studies suggest that increasing frequency and intensity of droughts in dry forests will likely alter species distributions and ecosystem processes. Though these scientists conclude that dry forests will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast dry forest responses to future climate change at coarser spatial and temporal scales.

Correlative and integrative proxies in soil carbon cycle measurements have continuing importance because they yield significant insight while being simpler, easier, and cheaper to measure than the actual feature being represented. For example, it is easier to measure clay content as an indicator of soil porosity or carbon storage potential, but understanding which feature is being inferred is important to interpreting the research. The thoughtful use of proxies can lead to new hypotheses and experiments to identify causative relationships; not using proxies may result in overweighting of correlations to explain research results and the misrepresentation of mechanisms.

Near-term land management and policy decisions depend on proxies, which are used as surrogates for soil features and processes and affect long-term projections of Earth system responses to change. In a new paper, soil ecologists from Pacific Northwest National Laboratory review and classify types of complex soil measurements—called proxies for the purposes of environmental research.

In the long history of environmental, soil, and climate change sciences, researchers have always needed proxy variables to improve how complex variables and processes are measured and represented. They have used tree ring chronologies to infer past climate conditions, for instance. And both experimentalists and modelers widely use clay content as a proxy for properties such as bulk density, water-holding capacity, and soil organic matter.

Because of the complexity of processes and interactions within soil, measuring soil carbon dynamics is another case in which proxies are necessary.

In this realm, ecologists often use two types of proxies. Correlative proxies represent soil characteristics that cannot be directly measured. Integrative proxies aggregate information about multiple soil characteristics into one variable. Both of these proxies are useful for understanding the soil carbon cycle and are now being used to make predictions of the carbon fate and persistence under future climate scenarios. Still, the researchers point out, both proxies limit data interpretation.

Meanwhile, new advances in imaging and proteomics have added capabilities and variables to studying the soil carbon cycle. But so far, these methods are often more expensive and more difficult to measure directly.

The researchers advocate for the thoughtful use of appropriate proxies for predicting the soil carbon cycle. Proxies, they say, are simpler, easier, and cheaper to measure, and, if used wisely, can suggest new hypotheses and relationships for future study.

Based on evidence of organic matter hydrogenation in field samples and peat incubations, the researchers hypothesize new pathways for organic matter degradation in peatlands, whereby electrons are deposited to the organic matter itself rather than to CH₄. This mechanism has also been observed to reduce CH₄ production in the cow rumen. An examination of past research on animal hosts suggests many parallels between the chemical and microbiological hydrogenation of organic matter between peatlands and the rumen. Because CH₄ has a sustained flux warming potential about 45 times higher than that of CO₂, mechanisms that alter CH₄ production ratios during peat mineralization have important implications for environmental change.

These results highlight the utility of an “environmental metabolomics” approach that takes advantage of analytical chemistry assets at the U.S. Department of Energy’s (DOE) Environmental Molecular Sciences Laboratory (EMSL), for identifying microbial processes in organic matter decomposition that have importance in human and animal health as well as in the role of wetlands in environmental change.

In freshwater wetlands such as peatlands, soils become anoxic at the surface and the majority of organic matter is decomposed through microbial consortia that are believed to primarily terminate in methanogenesis or methane (CH₄) production. In peat from high-latitude Sphagnum-dominated peatlands that are critical to the global carbon cycle, state-of-the-art environmental metabolomics measurements revealed extensive hydrogenation of organic matter, which may serve as a predominant mechanism for producing carbon dioxide (CO₂) without CH₄, thereby explaining why less CH₄ is produced relative to CO₂ in many northern peatlands.