This study evaluated the spatial and temporal variability of rainfall erosivity (R factor) and its implications for soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated from 49 rain gauge stations and CHIRPS precipitation data using empirical equations-based on monthly and annual precipitation totals. Soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity. Between 2014 and 2024, annual R values ranged from approximately 3900 to more than 9000 MJ mm ha⁻¹ h⁻¹ yr⁻¹, with the lowest values recorded in 2014 and the highest in 2022. Although 2020 had the highest annual rainfall, 2022 showed the highest erosivity, indicating that rainfall intensity and temporal concentration were more important than total rainfall volume. Furthermore, the comparison of erosivity was estimated from ANA stations and derived from CHIRPS agreement for paired station-year observations (r = 0.7196), although CHIRPS slightly underestimated erosivity values (mean bias −5.74%). Estimated soil loss ranged from 0.60 to 274.17 Mg ha⁻¹ yr⁻¹, with the highest values occurring mainly in exposed soil and agricultural areas. These findings highlight the importance of rainfall temporal distribution in erosion risk and support the use of satellite-derived precipitation products for regional-scale erosion assessments in data-scarce tropical basins.

This study assessed the spatial and temporal variability of rainfall erosivity (R factor) and its implications for potential soil loss in the Velhas River Basin, Minas Gerais, Brazil. Rainfall erosivity was estimated using data from 48 rain gauge stations and precipitation derived from CHIRPS product, processed in a cloud-based environment Google Earth Engine. Between 2014 and 2024, annual R values exhibited high variability, ranging from 3,900 to more than 9,000 MJ mm ha⁻¹ h⁻¹ yr⁻¹, with peak values recorded in the wettest year (2022) and the lowest values in 2014. Potential soil loss was estimated using the RUSLE model for the years of minimum and maximum erosivity, yielding values between 0.60 and 274.17 Mg ha⁻¹ yr⁻¹. The highest soil losses occurred in areas of exposed soil and agricultural land, whereas forest formations exhibited lower rates even under high rainfall erosivity conditions. The comparison between observed and estimated datasets revealed strong spatial and statistical agreement according to the Pearson correlation coefficient (r ≈ 0.999), although CHIRPS slightly underestimated extreme values. These results demonstrate the strong potential for integrating observed and remote sensing data in hydrosedimentological analyses at the basin scale.