A Landscape Evolution Modeling Approach for Predicting Three-Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth, L-p, and a hillslope diffusion coefficient, D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC-depth relations and the spatial pattern of remote sensing-derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.

Automated summary

Soil erosion leads to the loss of soil organic carbon and reduces agricultural productivity. This study develops a model that can predict soil loss and SOC distribution in agricultural fields, using topography as an input. The results show that the model accurately reproduces the spatial patterns of soil loss and can be used to estimate carbon burial and evaluate future soil loss.