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Accurate modeling and prediction of soil organic carbon (SOC) stocks are critical for realistic estimates of climate‐carbon feedback and ecosystem carbon sequestration potential. Process‐based models are widely used for simulating the dynamics of SOC stocks but often have considerable uncertainty. Exploring how process‐based models might be better informed by observations is crucial for improving the ability to forecast SOC. In this study, we assimilated the SoilGrids SOC datasets into the Simplified Photosynthesis and Evapotranspiration model for 39 National Ecological Observatory Network (NEON) terrestrial sites across the continental US using the Predictive Ecosystem Analyzer platform. We explored several strategies to determine the best approach for assimilating both SOC data and other indirect data constraints. Our results show that direct and continuous constraint from SoilGrids SOC data is crucial to achieve more accurate and precise SOC and ecosystem respiration states. The effectiveness of data assimilation is strongly determined by the uncertainty of both models and observations across sites, therefore careful characterization and propagation of uncertainties are important. Indirect constraints on SOC from Moderate Resolution Imaging Spectroradiometer leaf area index, LandTrendr aboveground biomass, and Enhanced Soil Moisture Active Passive soil moisture data also help to reduce the uncertainty in SOC prediction. Overall, our results show that data assimilation represents a promising approach to providing more realistic SOC estimates for regional and global carbon budgets in the future.
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Convergence in simulating global soil organic carbon by structurally different models after data assimilation
Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.
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- PAR ID:
- 10518448
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 30
- Issue:
- 5
- ISSN:
- 1354-1013
- Subject(s) / Keyword(s):
- big data assimilation, deep learning, inter- model uncertainty, model parameterization, model structure, soil organic carbon
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/gcb.17297
