Soil erosion characteristics in an earthquake-impacted mountainous basin based on RUSLE model: implications for geohazards

Purpose In this study, the revised universal soil loss equation (RUSLE) model was used to assess soil hydraulic erosion processes in the Baisha River Basin, and the relationship with landslides and debris flows was investigated. Materials and methods Here, soil erosion was simulated using the revised universal soil loss equation (RUSLE) in the Baisha River catchment, Southwest China, for the period of 2001–2020. Land use, rainfall, topography, NDVI, and soil erodibility data were used to calculate 20 years of soil erosion in the watershed. Hazard data of the watershed were used to qualitatively study the relationship between the RUSLE model and landslides and debris flows. The method of partial correlation analysis with the results of the RUSLE model was used to determine the dominant factors of post-earthquake recovery in the watershed. Results and discussions The results show that soil erosion in the watershed from 2001 to 2020 can be qualitatively divided into three phases: 2001–2007, 2008–2013, and 2014–2020. The first phase is characterized by a state of natural equilibrium without external disturbances, while the second phase is a period of instability produced by the earthquake. The third phase is a period of erosion slowdown and catchment recovery. The RUSLE model is comfirmed to be a reliable representation of the evolution of geohazards in mountainous watersheds, with a strong correlation between geohazard-prone areas and areas with high erosional rates. The post-disaster recovery process in the watershed was studied qualitatively through a partial correlation analysis, and the final results were consistent with other studies. Conclusions The effectiveness of the RUSLE model combined with actual data in qualitatively inferring the evolution of geohazards in the mountainous watershed is confirmed. These results are important references for studying the evolution of geological hazards in mountainous watersheds where data are limited.