Groundwater depletion poses a serious threat to water security in arid regions worldwide, risking sustainable water resources and agricultural stability. This study examines groundwater dynamics and water resource sustainability in the arid Barind Tract of Northwest Bangladesh using integrated remote sensing techniques and logistic regression modeling. It employed three key indices to assess water resource vulnerability: Normalized Difference Vegetation Index, Topsoil Grain Size Index, and Aridity Index, integrating them through logistic regression to evaluate desertification susceptibility and water sustainability. The regression model boasts an ROC value of 96.22% and R² of 0.3893, indicating good classification performance with acceptable class variance. Results show that 82.66% of the area faces significant water resource challenges, with 6.27% (103.26 km²) at very high risk, 10.80% (177.89 km²) at high risk, and 28.17% (464.05 km²) at moderate risk. The northern regions, especially Porsha, Gomastapur, and Nachole Upazillas, are the most vulnerable to water depletion. The study recommends sustainable water management strategies, including surface water use through floating pontoons and rubber dams, emphasizing the urgent need for integrated water resource management to ensure long-term water security. Additionally, the research analyzed soil-vegetation feedback using rain use efficiency and found a negative loop in highly desertification-prone areas like Porsha and Nachole, indicating the need for regulation-based cropping practices and improved water governance in zones at risk of desertification to reduce crop-water redundancy. This research offers valuable insights for water resource planning and management in arid regions, supporting sustainable water governance and locally-led adaptation strategies for water-stressed environments.

Groundwater depletion in arid regions poses a significant threat to agricultural sustainability and rural livelihoods. This study employs geospatial analysis and economic modeling to address groundwater depletion in the arid Barind region of Northwest Bangladesh, where 84% of the rural population depends on agriculture. Using remote sensing and GIS, we developed an elevation map revealing areas up to 60 m above sea level, exacerbating evaporation and aquifer dryness. Field data collected through Participatory Rural Appraisal tools showed farmers exhibiting “ignorant myopic” behavior, prioritizing short-term profits over resource conservation. To address this, an Increasing Block Tariff (IBT) water pricing model was developed, dividing water usage into three blocks based on irrigation hours: 1–275 h, 276–550 h, and 551+ h. The proposed IBT model significantly increases water prices across the three blocks: 117 BDT/hour for the first block (from current 100–110 BDT/hour), 120 BDT/hour for the second block, and 138 BDT/hour for the third block. A demand function (y = −0.1178x + 241.8) was formulated to evaluate the model’s impact. The results show potential reductions in groundwater consumption: 59 h in the first block, 26 h in the second block, and 158 h in the third block. These reductions align with the principles of integrated water resource management (IWRM): social equity, economic efficiency, and environmental integration. The model incorporates economic externalities (e.g., well lifting costs) and environmental externalities (e.g., crop pattern shifts), with total costs reaching 92,709,049 BDT for environmental factors. This research provides a framework for sustainable groundwater management in arid regions, potentially reducing overextraction while maintaining agricultural productivity. The proposed IBT model offers a locally driven solution to balance resource conservation with the livelihood needs of farming communities in the Barind tract. By combining remote sensing, GIS, and economic modeling, this research provides a framework for sustainable groundwater management in arid regions, demonstrating the power of geospatial technologies in addressing complex water resource challenges.