Abstract
Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon–climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental United States, and two machine learning approaches (random forest [RF] and generalized additive modeling [GAM]) to (a) select important environmental predictors of SOC stocks, (b) derive empirical relationships between environmental factors and SOC stocks, and (c) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy to the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon–climate feedbacks.
| Original language | English |
|---|---|
| Pages (from-to) | 1611-1624 |
| Number of pages | 14 |
| Journal | Soil Science Society of America Journal |
| Volume | 86 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2022 |
Funding
This study was supported jointly by the Laboratory Directed Research and Development program of Sandia National Laboratories and the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE‐NA‐0003525. Lawrence Berkeley National Laboratory (LBNL) is managed by the Regents of the University of California for the U.S. Department of Energy under Contract no. DE‐AC02‐05CH11231. Oak Ridge National Laboratory (ORNL) is managed by UT‐Battelle, LLC, for the U.S. Department of Energy under Contract no. DE‐AC05‐00OR22725. Thanks to S. Wills for providing access to the SOC profile data. USDA is an equal opportunity provider and employer. US Department of Energy Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE‐NA‐0003525. Lawrence Berkeley National Laboratory (LBNL) is managed by the Regents of the University of California for the U.S. Department of Energy under Contract no. DE‐AC02‐05CH11231. Oak Ridge National Laboratory (ORNL) is managed by UT‐Battelle, LLC, for the U.S. Department of Energy under Contract no. DE‐AC05‐00OR22725. Funding information informationUS Department of Energy Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525. Lawrence Berkeley National Laboratory (LBNL) is managed by the Regents of the University of California for the U.S. Department of Energy under Contract no. DE-AC02-05CH11231. Oak Ridge National Laboratory (ORNL) is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract no. DE-AC05-00OR22725.This study was supported jointly by the Laboratory Directed Research and Development program of Sandia National Laboratories and the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525. Lawrence Berkeley National Laboratory (LBNL) is managed by the Regents of the University of California for the U.S. Department of Energy under Contract no. DE-AC02-05CH11231. Oak Ridge National Laboratory (ORNL) is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract no. DE-AC05-00OR22725. Thanks to S. Wills for providing access to the SOC profile data. USDA is an equal opportunity provider and employer.