Embracing Fine‐Root System Complexity in Terrestrial Ecosystem Modeling

An explicit but tractable approach to model fine-root system structure and functioning.

Schematic of the three-pool transport and absorptive fine roots with mycorrhizal fungi (TAM) structure governed by interrelated processes of partitioning, phenology, and distribution under environmental constraints.

[Reprinted with permission from Wang, B., et al. "Embracing Fine‐Root System Complexity in Terrestrial Ecosystem Modeling." Global Change Biology 29 (11), 2871–85 (2023). DOI:10.1111/gcb.16659. © 2023 John Wiley & Sons Ltd.]

The Science

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).

The Impact

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.


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.

Principal Investigator

Daniel Ricciuto
Oak Ridge National Laboratory

Co-Principal Investigator

Colleen Iversen
Oak Ridge National Laboratory

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science


This work was supported by the Biological and Environmental Research (BER) Program within the U.S. Department of Energy’s (DOE) Office of Science under contract DE-AC05-00OR22725. FRED is a part of the Terrestrial Ecosystem Science (TES) Science Focus Area at Oak Ridge National Laboratory.

Related Links


Wang, B., et al. "Embracing Fine‐Root System Complexity in Terrestrial Ecosystem Modeling." Global Change Biology 29 (11), 2871–85  (2023). https://doi.org/10.1111/gcb.16659.

Nair, R., et al. "Rooting Vegetation Models in Realism." Global Change Biology 29 (11), 2868–70  (2023). https://doi.org/10.1111/gcb.16699.