Geospatial Decision Support System for Urban and Rural Aquifer Resilience: Integrating Remote Sensing-Based Rangeland Analysis With Groundwater Quality Assessment

Abstract

This study presents a comprehensive assessment of groundwater quality across the urban-rural continuum of Lahore and Sheikhupura districts in Pakistan, employing an innovative approach that integrates geospatial analysis, multicriteria decision analysis (MCDA), remote sensing-based land use/land cover (LULC) classification, and simulated treatment scenarios. Utilizing a Geostatistical analytical hierarchy process (AHP) within a Geographic Information System (GIS) framework, we developed a groundwater quality index to evaluate contamination levels across 68 sampling points, while incorporating rangeland distribution analysis through remote sensing techniques. The study area encompassed diverse land use patterns, ranging from densely populated urban centers to rural agricultural lands and rangeland ecosystems. Our analysis revealed significant spatial variations in groundwater quality, with urban areas in Lahore district, particularly Lahore Cantt and Model Town tehsils, exhibiting higher levels of contamination compared to rural and rangeland areas in Sheikhupura district. The integration of LULC analysis revealed that rangeland areas, which constitute approximately 15% of the study area, showed distinct groundwater quality patterns, with generally lower contamination levels but specific vulnerabilities to certain pollutants. To address these water quality issues, we simulated various treatment scenarios, including reductions in heavy metals, chemical parameters, and specific contaminants such as arsenic, total dissolved solids (TDS), and hardness. The results demonstrated that tertiary treatment approaches, especially those targeting TDS and hardness, yielded the most substantial improvements in water quality. A 65% reduction in TDS and a 45% decrease in hardness led to significant enhancements, with some locations showing index value reductions of over 40%. Notably, our arsenic-specific treatment scenario revealed that a 90% reduction in arsenic levels could be sufficient for most locations, offering a potentially cost-effective approach to addressing this critical contaminant. The integration of GIS-based AHP, MCDA techniques, and remote sensing analysis proved instrumental in identifying contamination hotspots, understanding land use impacts, and evaluating the effectiveness of various treatment scenarios. This study's outcomes provide valuable guidance for policymakers and water management authorities in developing targeted, location-specific strategies for sustainable groundwater management across urban, rural, and rangeland landscapes.