Evaluation of soil loss estimation using the RUSLE model and SCS-CN method in hillslope mining areas

Abstract

Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses the Revised Universal Soil Loss Equation (RUSLE) and SCS-CN (Soil Conservation Service - Curve Number) process to estimate in Kiruburu and Meghahatuburu mining sites areas. The geospatial model of annual average soil loss rate was determined by integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity and runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management(C) and conservation practice (P) factors were calculated to determine their effects on annual soil erosion in the study area. The coefficient of determination (r2) was 0.834, which indicates a strong correlation of soil loss with runoff and rainfall. Sub -watersheds 5,9,10 and 2 experienced high level of highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell) to determine the critical soil loss areas (Sub-watershed 9 and 5). Total soil erosion area was classified into five class, slight (10,025 ha), moderate (3125 ha), high (973 ha), very high (260 ha) and severe (53 ha). The resulting map shows greatest soil erosion of >40 t h-1 y-1 (severe) through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the verification of soil erosion results.

Keywords

• Soil erosion
• Runoff
• RUSLE
• SVM
• Remote sensing
• GIS