Soil attribute data.

Soil erosion is one of the most widespread environmental issues globally, posing serious threats to ecosystems and land resources. This study employs precipitation, soil, digital elevation model, and land-use data from 2000 to 2020 to quantitatively analyze the spatiotemporal patterns of land-use change and soil erosion in the Kangding River Basin through GIS-based spatial analysis and the RUSLE (Revised Universal Soil Loss Equation) model, and to evaluate soil stability across the watershed. Furthermore, using Geographical Detector (including single-factor detection and dual-factor interaction detection) and the SHAP (SHapley Additive exPlanations) algorithm to analyze the optimal machine learning model enables the assessment of the contribution of each driving factor to soil erosion. The results revealed that: (1) From 2000 to 2020, the areas of woodland and water body exhibited a decreasing trend, while cropland and construction land expanded steadily.(2) The soil erosion modulus in the Kangding River Basin first increased and then declined during the study period, rising from 16.32 t·hm-2·a-1 in 2000 to a peak of 21.85 t·hm-2·a-1 in 2005, and subsequently decreasing to 11.16 t·hm-2·a-1 by 2020.(3) The predominant erosion intensity was slight and very slight, with an increase in erosion severity between 2000 and 2005, followed by a gradual decrease thereafter.(4) The results of the geographical detector and SHAP analysis indicate that slope, land use, and vegetation coverage were the three most influential driving factors affecting soil erosion in the basin. These findings provide a scientific basis for comprehensive watershed management and land use planning in the Kangding River Basin, offering important theoretical support for soil and water conservation in the region.