Assessment of Soil Erosion and Sediment Yield Using GIS-Based RUSLE Modeling- A Case Study of Musi Sub-Basin, Telangana, India

Soil loss, also known as erosion, is an irreversible natural phenomenon that affects the topsoil of the Earth’s surface. It reduces soil fertility and water availability, and initiates geohazards, leading to negative environmental consequences. A research study was conducted in part of the Musi River sub-basin, a tributary of the Krishna River basin in India, which is undergoing a lot of changes due to anthropogenic factors. The novelty of this study lies in the integration of the RUSLE (Revised Universal Soil Loss Equation) model with advanced Geographical Information System (GIS) techniques to evaluate soil erosion and sediment yield in the basin. Leveraging the capabilities of the Google Earth Engine platform, the study employs the CART (Classification and Regression Trees) machine learning algorithm to generate the LULC (Land Use Land Cover) map, crucial for accurate C factor estimation. This innovative approach improves the precision of erosion modeling by seamlessly integrating GIS, machine learning, and remote sensing technologies. The analysis reveals that the LULC map has a total accuracy of 89.6% and a kappa coefficient of 0.86. The analysis also shows that the agriculture class dominates the research area with 51.4%. The results reveal that 95.6% of the research area has very low soil erosion of 0-1 ton/ha/ year, and 60.8% of the area has low sediment yield of 0-1 ton.ha-1.y-1. As the study area consists of major towns and cities, and the agricultural area is being converted to open plots (barren lands for developmental activities), erosion may increase in the future. The findings of this study may be used by managers and legislators to suggest soil conservation laws to expedite development projects.