Integrated GIS, statistical modeling, and CMIP6-based assessment of groundwater irrigation quality in north-central Bangladesh for sustainable management
This study employs an integrated approach combining Geographic Information Systems (GIS), multivariate statistical analysis, and CMIP6 climate scenarios to evaluate groundwater irrigation quality in Islampur Upazila, Bangladesh. Focusing on the Brahmaputra River floodplain—where 80 % of irrigation r...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025025654 |
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| Summary: | This study employs an integrated approach combining Geographic Information Systems (GIS), multivariate statistical analysis, and CMIP6 climate scenarios to evaluate groundwater irrigation quality in Islampur Upazila, Bangladesh. Focusing on the Brahmaputra River floodplain—where 80 % of irrigation relies on groundwater—the research addresses critical gaps in understanding spatiotemporal water quality variations. Water samples were gathered from 24 different sites across two seasonal periods—pre-monsoon (April 2024) and post-monsoon (October 2024)—and evaluated for 12 critical physicochemical properties. A novel Groundwater Irrigation Water Quality Index (IWQI) was formulated using principal component and factor analyses, incorporating broader parameters than traditional methods and dynamically adapting parameter selection based on seasonal hydrogeochemical conditions—a significant advancement over conventional fixed-parameter approaches. Unlike existing IWQIs that rely on static parameter sets derived from arid conditions, this study introduces the first seasonally-adaptive IWQI specifically designed for monsoon-influenced alluvial aquifers, incorporating phosphate and other region-specific parameters typically excluded from traditional assessments. Pre-monsoon results showed IWQI values between 50.49 and 79.72 (mean: 67.83), with 83.33 % of samples classified as ''moderate restriction.'' Post-monsoon conditions improved significantly (82.74–88.20, mean: 85.07), with 54.17 % classified as ''no restriction.'' Geospatially, the northwestern region consistently exhibited the poorest water quality across both seasons, while the north-central and northeastern areas demonstrated superior irrigation suitability (IWQI >70). Site S12 (near Islampur Paurashava) emerged as the most problematic location, showing the lowest pre-monsoon IWQI value (50.49) and representing the sole ''high restriction'' classification. Hierarchical cluster analysis identified three distinct hydrochemical clusters, reflecting natural geochemical processes and anthropogenic influences. Piper diagrams revealed a seasonal shift in water type from Ca-Mg-Cl to Ca-Mg-HCO₃, suggesting enhanced carbonate weathering following monsoonal recharge. Future climate projections under four shared socioeconomic pathways predict temperature increases (up to 4.46 °C by 2100) and altered precipitation patterns, potentially intensifying seasonal hydrological cycles and impacting groundwater quality. The study revealed that approximately 16.67 % of the study area requires targeted management interventions, particularly in the southwestern zones where intensive agricultural activities coincide with unfavorable topographic conditions. By integrating spatial analysis, statistical modeling, and climate projections, this research offers a comprehensive approach to support sustainable groundwater management and inform climate-resilient irrigation strategies in complex hydrogeological environments. |
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| ISSN: | 2590-1230 |