Spatial Modeling of Soil Erosion Risk and Its Relevance for Conservation Planning in the Ramis River Basin

Abstract

Water erosion is a core issue that threatens the ecological integrity of the highland ecosystems in the Andes Mountains and the agricultural sustainability of the Ramis River basin. This study uses the Revised Universal Soil Loss Equation (RUSLE), which integrates five factors, rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P), to estimate the spatial distribution of potential water erosion rates in this basin. The results show that the very low and low erosion classes together cover 73.21% of the basin, while the high, very high, and extreme erosion classes account for 17.29% of the total area. Among these, the extreme erosion class, with an annual erosion volume exceeding 250 tons per hectare, covers 8.07% of the basin, equivalent to 1190.13 square kilometers. This extreme erosion is concentrated in steep headwater areas and five sub-basins including Cuenca Grande. Comparative model verification shows that the Ordinary Least Squares (OLS) model only identifies a positive correlation between slope gradient and potential soil loss, with an extremely low explanatory power (R2 = 0.045). Its residuals exhibit significant spatial autocorrelation (Moran’s I = 0.204, p < 0.001). In contrast, the Geographically Weighted Regression (GWR) model greatly improves the model fit (R2 = 0.359, RMSE = 148.288) and eliminates the spatial autocorrelation of residuals, proving that the slope-erosion relationship has spatial non-stationarity. Sensitivity analysis shows that the C factor has the highest sensitivity (0.980), followed by the LS factor (0.626). Based on these findings, this study proposes that cover and management measures such as vegetation restoration should be prioritized in high-risk headwater sub-basins. It should be noted that the values estimated in this study are potential soil loss amounts, rather than actually measured erosion values.