The rainfall erosivity factor (R-factor) is an important parameter in the universal soil loss equation (USLE), the revised universal soil lo
The rainfall erosivity factor (R-factor) is an important parameter in the universal soil loss equation (USLE), the revised universal soil loss equation (RUSLE) and several other soil erosion prediction models. It is necessary to choose suitable methods to map the R-factor at the basin and regional scales to effectively apply erosion prediction models and establish precise soil and water conservation measures. When rain gauges are sparsely and unevenly distributed on high and steep terrain, it is often difficult to obtain ideal results from traditional spatial interpolation methods. To optimize the interpolation method for the R-factor in mountainous areas and explore the impact of the number and distribution pattern of rain gauges on the interpolation results, the Longchuan River Basin in the Hengduan Mountain region in Southwest China was selected as the study area. Four methods, namely, inverse distance weighting (IDW), ordinary kriging (OK), global regression kriging (GRK) and geographically weighted regression kriging (GWRK), were selected to conduct a comparative analysis under various scenarios of gauge number and distribution. Compared with traditional univariate methods, GRK and GWRK, which incorporate elevation, yield more accurate and detailed results, with GWRK exhibiting the best performance. The accuracy and value ranges of the interpolation results are influenced by the coupling of the number and distribution of gauges. With fewer gauges, the impact of the distribution pattern is more obvious. This study provides insights into optimizing R-factor mapping under future climate scenarios, enabling precise soil erosion risk assessment and targeted prevention in mountainous regions.
