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<p><?tk 2?>Ground water is critical in supporting agricultural, domestic and industrial demands in mountainous and water scarce areas. Nevertheless, it is difficult to detect the groundwater potential zones (GPZs) of structurally complicated terrains of Himalayas because of the insufficiency of the hydrogeological data and time delays in recharge conditions. This paper seeks to map the recharge areas on the groundwater of the Kinnaur District in Himachal Pradesh, India, by applying a multi-criteria decision-making model based on GIS. To create a groundwater recharge potential map as well as to take into consideration the temporal variation, 9 hydrogeologically significant parameters including rainfall, slope, curvature, drainage density, land use/land cover, lineament density, soil, aspect, and topographic wetness index were examined and weighted with the help of the Analytic Hierarchy Process (AHP). The study incorporates nine critical criteria—rainfall, curvature, slope, drainage density, land use/land cover, lineament density, soil, aspect, and topographic wetness index—to analyze and map the spatial distribution of GPZs. Through combining such criteria through AHP in a GIS framework, the research is able to consider the aspect of time variation, not to mention multi-criteria features that are crucial in proper GPSZ identification. The groundwater fluctuation zone of the study area has been categorized into five different classes: Very Low (24%), Low (31%), Medium (21%), High (18%), and Very High (6%). The delineated map was tested with the Receiver Operating Characteristic (ROC) technique, and the map was found to have a good correlation with the water bodies (AUC = 71.7%). This will give a complete picture of the groundwater recharge dynamics, and this will be of great help on how to use ground water sustainably. The resulting map of the generated ground water recharge can be a solid decision-support guide in the exploration of the ground water resource, its prioritization, and sustainable management of mountainous areas with limited data.</p>

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AHP integrated geospatial application for identifying groundwater recharge zones accounting for temporal variations through multi-criterion weighted overlay analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Kanwarpreet Singh,
  • Aashish Sharma,
  • Abhishek Sharma,
  • Rupesh Gupta,
  • Abhijit Bhowmik,
  • Amenjor Senagah,
  • Nadhir Al-Ansari,
  • Gaurav Juneja,
  • Mohamed A. Mattar

摘要

Ground water is critical in supporting agricultural, domestic and industrial demands in mountainous and water scarce areas. Nevertheless, it is difficult to detect the groundwater potential zones (GPZs) of structurally complicated terrains of Himalayas because of the insufficiency of the hydrogeological data and time delays in recharge conditions. This paper seeks to map the recharge areas on the groundwater of the Kinnaur District in Himachal Pradesh, India, by applying a multi-criteria decision-making model based on GIS. To create a groundwater recharge potential map as well as to take into consideration the temporal variation, 9 hydrogeologically significant parameters including rainfall, slope, curvature, drainage density, land use/land cover, lineament density, soil, aspect, and topographic wetness index were examined and weighted with the help of the Analytic Hierarchy Process (AHP). The study incorporates nine critical criteria—rainfall, curvature, slope, drainage density, land use/land cover, lineament density, soil, aspect, and topographic wetness index—to analyze and map the spatial distribution of GPZs. Through combining such criteria through AHP in a GIS framework, the research is able to consider the aspect of time variation, not to mention multi-criteria features that are crucial in proper GPSZ identification. The groundwater fluctuation zone of the study area has been categorized into five different classes: Very Low (24%), Low (31%), Medium (21%), High (18%), and Very High (6%). The delineated map was tested with the Receiver Operating Characteristic (ROC) technique, and the map was found to have a good correlation with the water bodies (AUC = 71.7%). This will give a complete picture of the groundwater recharge dynamics, and this will be of great help on how to use ground water sustainably. The resulting map of the generated ground water recharge can be a solid decision-support guide in the exploration of the ground water resource, its prioritization, and sustainable management of mountainous areas with limited data.