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<p>Soil erosion represents a significant global environmental challenge, posing threats to agricultural sustainability and ecosystem services. Consequently, identifying zones susceptible to erosion is essential for effective land-use planning and mitigation strategies. This study sought to delineate areas prone to soil erosion within the Kaljani River Basin, West Bengal, India, by integrating the Analytical Hierarchy Process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Revised Universal Soil Loss Equation (RUSLE). Fourteen geo-environmental factors, including elevation, slope, aspect, lithology, profile curvature, plan curvature, lineament density, land use and land cover (LULC), rainfall, distance from the river (DtR), stream power index (SPI), soil moisture, compound topographic index (CTI), and normalized difference vegetation index (NDVI), were incorporated into the AHP-TOPSIS model to assess erosion susceptibility. The RUSLE model estimated soil loss based on five key factors: rainfall erosivity (R), soil erodibility (K), topographic factor (LS), support practice (P) and crop management (C). The resultant susceptibility map categorized erosion intensity into five classes, revealing that 2.88% of the area experienced very high erosion, 20.64% high and 37.52% moderate. The RUSLE model further identified that 15.22% of the region (166.83 hectares) was at risk of moderate to extremely severe erosion. Model validation using the receiver operating characteristic (ROC) curve, supplemented by a Global Positioning System (GPS) survey of 25 observation sites and a soil erosion map derived from RUSLE, yielded an area under the curve (AUC) value of 0.858, indicating 85.80% prediction accuracy. These findings underscore the effectiveness of integrating AHP-TOPSIS and RUSLE for erosion susceptibility assessment and its applicability to similar geo-environmental conditions. This study emphasizes the need for targeted soil conservation strategies and proactive land-use planning to mitigate erosion risks and ensure long-term sustainability in the Kaljani River Basin and beyond.</p>

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Mapping soil erosion susceptibility by integrating AHP-TOPSIS and RUSLE model: a case study of the Kaljani River Basin, West Bengal

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Pritam Saha,
  • Sushanta Das,
  • Alok Sarkar,
  • Shasanka Kumar Gayen

摘要

Soil erosion represents a significant global environmental challenge, posing threats to agricultural sustainability and ecosystem services. Consequently, identifying zones susceptible to erosion is essential for effective land-use planning and mitigation strategies. This study sought to delineate areas prone to soil erosion within the Kaljani River Basin, West Bengal, India, by integrating the Analytical Hierarchy Process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Revised Universal Soil Loss Equation (RUSLE). Fourteen geo-environmental factors, including elevation, slope, aspect, lithology, profile curvature, plan curvature, lineament density, land use and land cover (LULC), rainfall, distance from the river (DtR), stream power index (SPI), soil moisture, compound topographic index (CTI), and normalized difference vegetation index (NDVI), were incorporated into the AHP-TOPSIS model to assess erosion susceptibility. The RUSLE model estimated soil loss based on five key factors: rainfall erosivity (R), soil erodibility (K), topographic factor (LS), support practice (P) and crop management (C). The resultant susceptibility map categorized erosion intensity into five classes, revealing that 2.88% of the area experienced very high erosion, 20.64% high and 37.52% moderate. The RUSLE model further identified that 15.22% of the region (166.83 hectares) was at risk of moderate to extremely severe erosion. Model validation using the receiver operating characteristic (ROC) curve, supplemented by a Global Positioning System (GPS) survey of 25 observation sites and a soil erosion map derived from RUSLE, yielded an area under the curve (AUC) value of 0.858, indicating 85.80% prediction accuracy. These findings underscore the effectiveness of integrating AHP-TOPSIS and RUSLE for erosion susceptibility assessment and its applicability to similar geo-environmental conditions. This study emphasizes the need for targeted soil conservation strategies and proactive land-use planning to mitigate erosion risks and ensure long-term sustainability in the Kaljani River Basin and beyond.