TY - ADVS
T1 - Dataset for Optimizing Process-Based Models to Predict Current and Future Soil Organic Carbon Stocks at High-Resolution
AU - Pierson, Derek
AU - Lohse, Kathleen A.
AU - Wieder, Will
AU - Facer, Jeremy
AU - Patton, Nicholas
AU - Seyfried, Mark S.
AU - Will, Ryan
AU - Flerschinger, Gerald
AU - de Graaff, Marie-Anne
PY - 2022/1/14
Y1 - 2022/1/14
N2 - Soil carbon (C) management and mitigation policies are reliant on estimates of soil C stocks, especially at fine spatial scales. However, given enduring data limitations, statistical models used for such estimates are limited in their ability to predict the underlying composition and vulnerability of soil C to global change. Here we show that an optimized, process-based model is uniquely suited to fill this gap. We parameterize the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with spatially-explicit data across the Reynolds Creek Experimental Watershed in SW Idaho, USA, and illustrate that data-constrained model parameterization can reduce uncertainty in total soil C stocks (r=0.82 for an independent dataset). We produce the first high-resolution (10 m2) estimates of soil C stocks (including litter, microbial, particulate, and protected C pools) to a depth of 30 cm across the entire watershed (239 km2), and predict their respective vulnerabilities to a suite of environmental disturbances. Generating high-resolution estimates of soil C stocks and measurable underlying pools, are a critical step towards understanding soil C storage and vulnerability, and informing land management under a changing climate.
AB - Soil carbon (C) management and mitigation policies are reliant on estimates of soil C stocks, especially at fine spatial scales. However, given enduring data limitations, statistical models used for such estimates are limited in their ability to predict the underlying composition and vulnerability of soil C to global change. Here we show that an optimized, process-based model is uniquely suited to fill this gap. We parameterize the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with spatially-explicit data across the Reynolds Creek Experimental Watershed in SW Idaho, USA, and illustrate that data-constrained model parameterization can reduce uncertainty in total soil C stocks (r=0.82 for an independent dataset). We produce the first high-resolution (10 m2) estimates of soil C stocks (including litter, microbial, particulate, and protected C pools) to a depth of 30 cm across the entire watershed (239 km2), and predict their respective vulnerabilities to a suite of environmental disturbances. Generating high-resolution estimates of soil C stocks and measurable underlying pools, are a critical step towards understanding soil C storage and vulnerability, and informing land management under a changing climate.
KW - carbon pools
KW - clay
KW - high-resolution
KW - productivity
KW - projections
KW - soil carbon
KW - soil temperature
KW - spatial mapping
UR - https://scholarworks.boisestate.edu/reynoldscreek/26
U2 - 10.18122/reynoldscreek.26.boisestate
DO - 10.18122/reynoldscreek.26.boisestate
M3 - Digital or Visual Products
ER -