Scholarlink

<p>Flooding is a recurrent and destructive hazard in the Lower Gangetic Plains, severely affecting agriculture, infrastructure, and rural livelihoods. This study develops an integrated flood risk and agricultural vulnerability assessment framework for the Burhi Gandak Basin using the Analytic Hierarchy Process (AHP) combined with GIS-based spatial analysis and high-resolution land use/land cover (LULC) data. Six flood-conditioning parameters slope, elevation, Topographic Wetness Index (TWI), rainfall, Normalized Difference Vegetation Index (NDVI), and LULC were weighted through pairwise comparison and overlaid to generate a composite flood risk index. The basin was classified into five risk categories: very high, high, moderate, low, and very low. Results reveal pronounced spatial heterogeneity, with high (55.78%) and moderate (34.53%) risk zones covering more than 90% of the basin, while very high-risk hotspots account for 7.02%. Flood risk–LULC interaction analysis indicates extreme agricultural exposure over 99% of cropland lies within moderate to very high flood risk zones, with nearly 61% concentrated in high-risk areas. Sensitivity assessment shows nonlinear crop yield responses to flood duration, with severe losses beyond five days of inundation particularly during reproductive stages. While paddy exhibits partial tolerance to short-term flooding, non-rice crops demonstrate near-total failure under prolonged submergence. Adaptive capacity remains uneven, constrained by limited crop insurance, low mechanization, and institutional disparities affecting smallholders. Model validation using 168 flood inventory points produced an ROC–AUC of 0.95, confirming excellent predictive performance. The proposed framework is transferable and scalable to other flood-prone basins, offering a robust decision-support tool for climate-resilient agricultural planning and integrated flood risk management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Flood risk zonation and agricultural vulnerability assessment using AHP and land use analysis in the Burhi Gandak basin in Lower Gangetic Plains, India

  • 期刊论文

  • 发表日期：

    2026年5月26日

查看全文
  • Ambrish Kumar,
  • Vipin Kumar Mishra,
  • Uday Mandal,
  • Rajesh Kaushal,
  • Trisha Roy,
  • Sweta Garg,
  • M. Madhu

摘要

Flooding is a recurrent and destructive hazard in the Lower Gangetic Plains, severely affecting agriculture, infrastructure, and rural livelihoods. This study develops an integrated flood risk and agricultural vulnerability assessment framework for the Burhi Gandak Basin using the Analytic Hierarchy Process (AHP) combined with GIS-based spatial analysis and high-resolution land use/land cover (LULC) data. Six flood-conditioning parameters slope, elevation, Topographic Wetness Index (TWI), rainfall, Normalized Difference Vegetation Index (NDVI), and LULC were weighted through pairwise comparison and overlaid to generate a composite flood risk index. The basin was classified into five risk categories: very high, high, moderate, low, and very low. Results reveal pronounced spatial heterogeneity, with high (55.78%) and moderate (34.53%) risk zones covering more than 90% of the basin, while very high-risk hotspots account for 7.02%. Flood risk–LULC interaction analysis indicates extreme agricultural exposure over 99% of cropland lies within moderate to very high flood risk zones, with nearly 61% concentrated in high-risk areas. Sensitivity assessment shows nonlinear crop yield responses to flood duration, with severe losses beyond five days of inundation particularly during reproductive stages. While paddy exhibits partial tolerance to short-term flooding, non-rice crops demonstrate near-total failure under prolonged submergence. Adaptive capacity remains uneven, constrained by limited crop insurance, low mechanization, and institutional disparities affecting smallholders. Model validation using 168 flood inventory points produced an ROC–AUC of 0.95, confirming excellent predictive performance. The proposed framework is transferable and scalable to other flood-prone basins, offering a robust decision-support tool for climate-resilient agricultural planning and integrated flood risk management.