Modeling WEPP erodibility parameters in calcareous soils in northwest Iran

Modeling soil detachment rates at the regional scale is important for better understanding of the processes of erosion and the development of erosion models. Soil erodibility is an important factor for predicting soil loss, but its direct measurement at the watershed scale is difficult, time-consuming and costly. This study used stepwise multiple-linear regression (MLR) and artificial neural networks (ANNs) to model Water Erosion Prediction Project (WEPP) soil erodibility parameters, including the baseline inter-rill erodibility (K_ib), baseline rill erodibility (K_rb) and critical shear stress (τ_cb) of cropland conditions in calcareous soils of northwest Iran. Simulated inter-rill and rill erosion experiments were conducted at 100 locations with three replications. K_ib, K_rb and τ_cb and basic soil properties were measured at each location. Auxiliary variables related to soil erodibility were derived from a Landsat 7 satellite image and a 30 m × 30 m digital elevation model (DEM). MLR and ANN models were employed to predict K_ib, K_rb and τ_cb using two groups of input variables: i) more easily measurable basic soil properties (pedo-transfer functions (PTFs)) and ii) more easily measurable basic soil properties and auxiliary data (soil spatial prediction functions (SSPFs)). The results indicated that the WEPP models performed poorly in comparison to the derived models. PTFs and SSPFs generated from ANN models provided more reliable predictions than the MLR models. ANN-based SSPF models yielded the best results (with the highest R² and lowest RMSE values) for predicting Kib and Krb. ANN-based PTF model performed reasonably well for predicting τ_cb. These results show that information from terrain attributes and remote sensing data are potential auxiliary variables for improving prediction of soil erodibility parameters.