Watershed Sciences

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.

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.

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.

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.

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.

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 s