Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.
