Comparison of predictive modeling approaches to estimate soil erosion under spatially heterogeneous field conditions

Abstract

The accuracy of soil erosion models in agroecosystems with heterogeneous field conditions is challenging due to uncertainties from soil water fluxes and crop growth. In this study, we coupled two modeling methods (Freebairn and Rose) to represent soil erosion with a process-based crop and runoff models within the SIMPLACE framework. Their accuracy was compared to a statistical model developed using 16 erosion plots (each of 625 cm²) within the same field. Uncertainty analysis showed that runoff and slope angle were the most critical components for predicting sediment yield in both models, followed by soil erodibility in the Freebairn model and entrainment efficiency in the Rose model. However, due to plot size constraints, slope-length effects were not examined. The Freebairn model had a slightly higher accuracy (RMSE = 0.69 t ha⁻¹ d⁻¹) of sediment yield predictions than the Rose model (RMSE = 0.83 t ha⁻¹ d⁻¹). Both models are effective for predicting soil loss with appropriate parameter values.