Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

Area of Science:

• Environmental Science
• Soil Science
• Ecology

Background:

• Soil organic carbon (SOC) is crucial for climate change mitigation and soil health.
• Accurate SOC stock estimations are vital for effective land management policies.
• Process-based models are increasingly used to simulate SOC dynamics.

Purpose of the Study:

• To calibrate the Microbial-MIneral Carbon Stabilization (MIMICS) model using field data and remote sensing.
• To generate high-resolution SOC stock estimates and uncertainty maps.
• To assess the spatial complexity of SOC vulnerability to environmental disturbances.

Main Methods:

• Applied a process-based modeling approach using the MIMICS model.
• Parameterized the model with SOC measurements and environmental data from Reynolds Creek Experimental Watershed.
• Utilized calibrated parameters to estimate SOC pools (litter, microbial biomass, particulate, protected) at 10 m² resolution.

Main Results:

• Model calibration reduced errors in SOC stock simulation by 25%.
• Achieved spatially continuous, high-resolution SOC stock estimates and uncertainty.
• Identified significant spatial variability in protected SOC stocks (mean factor of 4.4×) and potential SOC response to disturbances (-14.9% to +20.4%).

Conclusions:

• Calibrated MIMICS model provides improved SOC stock estimates and projections.
• Significant parametric uncertainty exists, particularly for protected SOC pools.
• Further measurements of soil carbon fractions and turnover times are needed to enhance model confidence.