Identification of Potential Spring-Water Recharge Zones in a Springshed of Kalimpong District, Darjeeling Himalaya, India: A Geospatial Approach to Water Conservation
DOI:
https://doi.org/10.31305/rrijm.2026.v11.n08.019Keywords:
Kalimpong, springshed, spring-water recharge zone, groundwater recharge, weighted overlay, GIS, remote sensing, Himalayan water resourcesAbstract
Springs are natural groundwater discharge points that sustain river systems and provide essential water supplies to mountain communities. Across the Indian Himalayan Region, springs support domestic needs, livestock, irrigation, and other livelihood activities; however, many springs are increasingly vulnerable to declining and seasonally variable discharge. This study identifies potential Spring-Water Recharge Zones (SWRZs) within a springshed in Kalimpong District, Darjeeling Himalaya, using an integrated geospatial and multi-criteria approach. A set of thematic layers representing key controls on recharge—rainfall, elevation, slope, soil hydrologic group (HSG), lineament density, Normalized Difference Vegetation Index (NDVI), and land use/land cover (LULC)—was prepared from remotely sensed, GIS, field-survey, and secondary datasets. Thematic classes were ranked according to their relative recharge suitability, and parameter weights were assigned using expert judgement and published literature. The resulting standardized layers were integrated in a GIS-based weighted-overlay model to generate a spatially explicit SWRZ map, which was subsequently classified into priority zones using the Jenks natural breaks method. The analysis highlights areas where terrain, rainfall, vegetation, soil characteristics, structural discontinuities, and land cover collectively indicate greater potential for recharge interventions. The resulting prioritization framework provides a practical basis for springshed management, including the strategic planning of recharge measures, water-storage structures, and conservation activities. The workflow is also suitable for preliminary assessment in data-scarce Himalayan environments because it combines widely accessible geospatial datasets with a transparent and replicable decision framework.
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