Assessment and Mapping of Gully Erosion Susceptibility Using Geospatial and Multi Criteria Decision Analysis for Selected Watersheds of Bale Highland, Oromia, Southeast Ethiopia

Soil erosion, particularly gully erosion, is a major form of land degradation threatening agricultural productivity, ecosystem stability, and rural livelihoods in the Ethiopian highlands. The aim of this study was to assess the spatial variability and relative influence of gully erosion conditioning factors and to develop a gully erosion susceptibility map for prioritizing sub-watersheds for soil and water conservation interventions in the Bale Highlands. Multisource spatial datasets, including Digital Elevation Models, land use/land cover, soil properties, rainfall, and GPSbased gully inventory data, were integrated within a GIS framework. Key terrain and hydrological indices, including the Stream Power Index and Topographic Wetness Index, were derived to represent erosion-related processes. Ten gully erosion conditioning factors were identified and weighted using the Analytic Hierarchy Process based on expert judgment and pairwise comparison. A GIS-based weighted overlay analysis was applied to produce a susceptibility map classified into five classes: very low, low, moderate, high, and very high. Results show that rainfall (25.9%) and land use/land cover (17.6%) are the most influential factors, followed by slope (11.9%) and TWI (11.2%). High and very high susceptibility zones are mainly concentrated in steep cultivated areas and hydrologically convergent zones along drainage networks. Model validation indicates that 80% of observed gully locations fall within moderate to very high susceptibility classes, with an R² of 0.75, demonstrating good predictive performance. The study confirms that GIS and MCDA (AHP) integration is an effective approach for gully erosion susceptibility mapping and provides useful information for targeted land management and conservation planning in the Ethiopian highlands. Further studies should incorporate advanced techniques such as machine learning to improve the accuracy of gully erosion prediction and enhance monitoring of gully dynamics.