Soil erosion management at a large catchment scale using the RUSLE-GIS: the case of Masinga catchment, Kenya

Abstract

Kenya is one country suffering heavily from land degradation due to increasing anthropogenic pressure on its natural resources. As is common to many tropical countries, Kenya suffers from a lack of financial resources to research, monitor and model sources and outcomes of environmental degradation for large catchment domains. In order to evaluate viable management options, soil erosion modelling at the catchment scale needs to be undertaken. This paper presents a comprehensive methodology that integrates an erosion model, the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) for estimating soil erosion at Masinga catchment, which is a typical rural catchment in Kenya. The objective of the study was to map the spatial mean annual soil erosion for the Masinga catchment and identify the risk erosion areas. Current land use/cover and management practices and selected, feasible, future management practices were evaluated to determine their effects on average annual soil loss. The results can be used to advice the catchment stakeholders in prioritising the areas of immediate erosion mitigation. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil erosion and sediment delivery. It thus provides a useful and efficient tool for predicting long-term soil erosion potential and assessing erosion impacts of various cropping systems and conservation support practices.