Assessment and management of soil erosion in the hilltop mining dominated catchment using GIS integrated RUSLE model

Abstract

The Saranda forest region, which is well known for its biodiversity in India, is now confronted by rapid socio-economic development, particularly the hilltop mining activities. Hilltop mining areas of this region have always been responsible for producing excessive soil erosion in the associated river basin. This erosion phenomenon becomes hazardous during the rainy season, thereby contributing to various environmental problems, and consequently, necessitating soil erosion control planning in the Saranda forest. Hence, this study aimed to estimate average annual soil erosion in the Saranda region in terms of the spatial distribution using the Geographic Information System (GIS) integrated Revised Universal Soil Loss Equation (RUSLE) model. The erosion was quantified at a spatial resolution of 10 m (pixel by pixel) using the GIS-based RUSLE inputs. This study also applies GIS integrated Analytic Hierarchy Process (AHP) model to identify the favorable zones for sediment deposition in the study area. On the basis of erosion severity, the entire study area is classified into six categories (very low to extreme). The study reveals that the Saranda forest's average annual soil erosion is 76 tons per hectare per year (t/ha/yr). Approximately 63% of the total area is categorized under very low to low erosion category, and the relevant area is mainly covered by forest land, whereas the mining region comprises less than 1% of the total study area with extremely high soil erosion (156 t/ha/yr) potential. As envisaged from the present study, the erosion-prone mining areas are located within a 1–5 km range of the adjacent Karo and Koina rivers, thereby, necessitating the erosion control strategy to avoid the possible threats. From this perspective, the study also investigates the favorable zones for sediment deposition using the GIS integrated AHP model to suggest the appropriate erosion control measures. Finally, the RUSLE and AHP models are combined on the GIS platform to identify the distressed catchment area. Moreover, 42% (41,060 ha) of the total area is disturbed due to the present mining activities, which involves 11 sub-watersheds, and their associated 50 micro-watersheds. From the context of watershed conservation, erosion control measures are also recommended. The methodology adopted in this study can be easily extended to any global mining-dominated catchment for sustainable conservation planning.