Modelling Soil Erosion in Un-Gauged Golole Catchment in Marsabit County, Kenya
Njiru, Gabriel Nyagah
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Soil erosion is a major form of land degradation worldwide with 60% of it attributed to human activities. Golole catchment in Marsabit County with undulating topography is prone to soil erosion and little has been done to avert the soil loss. This study modeled soil erosion between January 2016 and September 2018 for land management in Golole catchment. The Revised Universal Soil Loss Equation (RUSLE) constituting the main agents of soil erosion was modeled in a Geographical Information System (GIS) environment. The objective of this study was to simulate soil erosion for land management in the ungauged Golole catchment using RUSLE. RUSLE input digital data was processed in GIS software using algorithms to yield the catchment’s spatial soil erosion loss map. The catchment soil erosion map revealed spatial variation of the rate of soil erosion. The soil loss and risk areas of soil erosion within the catchment were not homogeneous. Golole catchment mean annual soil loss rate was calculated at 279 t/ha/yr that is above the recommended maximum allowable annual soil loss rate of 4 t/ha/yr. The catchment’s soil loss rates is described as high and severe representing 70% and 30% of landmass respectively. The model calibration and validation showed strong correlation between the observed and simulated soil losses. The correlation coefficient (r) was 0.97 while the NSE was 95%. The strong correlation is attributable to both observed and simulated input data being either for or from the study area. The model can be adopted in the study area catchment with improvement involving high resolution data covering three parameters: soil, slope length, land cover while rainfall would require more rainfall stations. This study recommends further research in (1) the forest reserve areas that showed the greatest rates of soil erosion menace to determine the underlying causes, and (2) to assess the temporal trends of the soil erosion hazard using high-resolution data.