Modeling soil erodibility and critical shear stress parameters for soil loss estimation

• Published in: Soil & tillage research 2022-04, Vol.218, p.105292, Article 105292
• Main Authors: Lee, Sanghyun, Chu, Maria L., Guzman, Jorge A., Flanagan, Dennis C.
• Format: Article
• Language: English
• Subjects: climate; Critical shear stress; data collection; erodibility; Illinois; Interrill erodibility; land use; regression analysis; Rill erodibility; shear stress; Soil erosion; Soil loss; tillage; Water Erosion Prediction Project; watersheds; WEPP
• ISSN: 0167-1987; 1879-3444
• DOI: 10.1016/j.still.2021.105292

The rate of soil erosion from agricultural fields is driven by climate erosivity and by soil erodibility or resistance, commonly represented through the interrill (Ki) and rill erodibility (Kr), and critical shear stress (τc) parameters. These parameters are affected by factors showing high variability in time and space, such as soil properties, land use, and management practices. Estimating the site-specific time-varying values for Ki, Kr, and τc can require laborious field or laboratory experiments and detailed datasets. The objective of this research was to develop a non-linear regression model for estimating temporal adjustments for Ki, Kr, and τc considering different types of crops and management practices. One thousand sampling sites were randomly selected across the Kaskaskia watershed in Illinois, and the needed parameters to develop the regression models were extracted from the two-year simulation results of the Water Erosion Prediction Project (WEPP) model on a daily basis. The predicted values of the regression models showed good agreements with the sample data, with the coefficient of determination (r2) ranging from 0.62 to 0.98, capturing the spatio-temporal variations of the three variables under different crops and management practices. In addition, daily soil loss estimations using the predicted adjustments showed good agreements with the WEPP simulations, with r2 ranged from 0.98 to 0.99, confirming their possible use in soil erosion models. This is especially useful as the regression models require only a few explanatory variables, which are available at high resolution across the United States. Therefore, the models will facilitate estimating spatio-temporal variations of interrill and rill erodibility, and critical shear stress and thus a new platform of large-scale soil erosion models can be developed using readily available environmental datasets. •Derived non-linear regression models for erodibility and critical shear stress.•The equations can capture the spatio-temporal variations in topsoil’s resistance.•Explanatory variables are readily available at fine resolution.•The regression models can be applied to different crops and tillage practices.•The presented work can facilitate the estimations of soil erosion at larger scales.