Modeling WEPP erodibility parameters in calcareous soils in northwest Iran. (March 2017) - Mirzaee, S.; Ghorbani-Dashtaki, S.; Mohammadi, J.; Asadzadeh, F.; Kerry, R.
Highlights: The objective was to determine the best models for predicting WEPP erodibility properties. Input data were soil properties and auxiliary data (terrain attributes and remote sensing data). The WEPP models performed poorly in comparison to the derived models. Artificial neural networks performed better than regression models. Predicting WEPP erodibility with soil properties and auxiliary data is recommended. Abstract: 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 R 2 and lowest RMSE values) for predicting K ib and K rb . 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.