Soil erosion and sediment yield assessment using RUSLE and Google Earth Engine framework in the Robit watershed, North-Eastern Ethiopia

Abstract

Accurate and timely assessment of soil erosion and sediment yield in watersheds is vital for sustainable land management and achieving the Sustainable Development Goals. This study estimates soil erosion and sediment yield dynamics in Ethiopia’s Robit watershed (1993–2023) by applying a robust Revised Universal Soil Loss Equation (RUSLE) within the Google Earth Engine (GEE) platform. The GEE-based workflow automates the integration of multi-source geospatial data (CHIRPS, SoilGrids, SRTM, and Landsat) to efficiently derive time-series RUSLE parameters. The findings reveal a significant increase in the mean annual soil erosion rate from 32.5 t ha−1 yr−1 in 1993 to 41 t ha−1 yr−1 in 2023, with sediment yield correspondingly rising from 3 to 5 t ha−1 yr−1. Bareland and grazingland emerged as the most erosion-prone land use and land cover (LULC) types. Among RUSLE parameters, rainfall erosivity (R-factor) was the most sensitive driver of erosion, followed by Slope Length and Steepness (LS factor) and the cover (C) factors. The analysis successfully prioritized five sub-watersheds, covering 10, 088 hectares, as extremely severe erosion hotspots requiring immediate conservation action. This study highlights a rapid, cost-effective, and scalable method for erosion monitoring. The GEE-RUSLE framework offers a robust, data-driven tool for informed land management, adaptable to other regions with local C and P factors calibration.