Soil Erosion Assessment Using the RUSLE Model and Geospatial Techniques (Remote Sensing and GIS) in Kalyani River Watershed of Uttar Pradesh, India

Akash Pal, Mukesh Kumar, Shakti Survanshi, Neeraj Kumar, Krishan Tyagi, Jagadeesh Menon, Prabhat Singh and Deepak Lal

Asian Journal of Agricultural Extension, Economics & Sociology, 2025, vol. 43, issue 1, 13

Abstract: Soil erosion significantly impacts environmental sustainability, agriculture, and water quality. This study examines soil erosion in the Kalyani River within the Nindoora and Fatehpur blocks of Barabanki District, Uttar Pradesh, India, where seasonal fluctuations and steep banks exacerbate the issue. The region experiences severe soil degradation due to uncontrolled land use, deforestation, over-cultivation, overgrazing, and biomass exploitation driven by population growth. To address this, GIS and Remote Sensing technologies were utilized, employing the Revised Universal Soil Loss Equation (RUSLE) model to identify erosion-prone areas. The RUSLE model involves calculating parameters such as the runoff-rainfall erosivity factor (R), soil erodibility factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). Layer-wise thematic maps of each factor were generated using a GIS platform, incorporating various data sources and preparation methods. The study's results indicate that value of K factor is found to be 0.025 indicates that the soil is relatively resistant to erosion. Higher LS factor values are scattered across the area, especially near the Kalyani River. The southeastern regions show higher C factor values, indicating less effective soil cover and management against erosion. It has also been estimated that 90% of the Kalyani River watershed faces low soil erosion risk (0–10 ton/ha/yr), while 0.20% primarily near riverbanks experiences high to very high erosion risk (10–40 ton/ha/yr). Sandy and sandy loam soils near riverbanks, exacerbated by seasonal water level fluctuations and steep slopes, are highly prone to erosion. The RUSLE-based GIS approach allowed for the precise identification of erosion hotspots, facilitating the development of targeted soil conservation strategies to mitigate soil degradation and promote sustainable land management.

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Date: 2025
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