Assessing soil erosion risk through geospatial analysis and magnetic susceptibility: A study in the Oued Ghiss dam watershed, Central Rif, Morocco

In northern Morocco, water erosion presents a critical risk for soil degradation and dam sedimentation, attributable to extreme climatic conditions, variegated topography, lithological vulnerabilities, and anthropogenic pressures. This study quantitatively and spatially evaluates soil erosion susceptibility within the catchment area of the Oued Ghiss dam in Central Rif, Morocco. Utilizing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and calibrated through magnetic susceptibility metrics, the study provides a detailed analysis of erosion factors. The model was applied using GIS to spatially analyze data on rainfall, soil type, topography, land cover, and management practices, with magnetic susceptibility measurements used to calibrate the model by identifying stable and erosion-prone areas. The resultant soil loss map reveals a mean erosion rate of 24 t/ha/year. Specifically, 37.28 % of the catchment area exhibits an erosion rate under 5 t/ha/year, 40.16 % between 5 and 10 t/ha/year, 22.77 % between 10 and 20 t/ha/year, and 0.86 % surpasses 20 t/ha/year. Magnetic susceptibility measurements across various soil profiles indicated that stable soils showed increased susceptibility towards the surface, while eroded soils displayed decreased susceptibility, with significant correlations found between magnetic susceptibility variations and the final risk map. This innovative calibration of the RUSLE model through magnetic susceptibility improves erosion vulnerability assessments and results in a more detailed soil loss map. The findings provide critical information for policymakers to develop targeted management plans for erosion-prone areas, particularly benefiting the Ghiss dam watershed, where these insights will aid in preventing dam siltation and enhancing water resource management. This confirms magnetic susceptibility's reliability in assessing soil erosion vulnerability and improving numerical models.