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In response to Bangladesh's severe water shortages, this research delves into the intricate dynamics of groundwater vulnerability. Integrating often-overlooked factors such as topography, meteorology, socio-economic conditions, and land use & geology, the study employs advanced Random Forest (RF) modeling. Rigorously analyzing 200 strategically chosen sample points, the research uncovers critical areas like Rajshahi, Nawabganj, Naogaon, and Dhaka, constituting 21% of the land, as highly vulnerable. In contrast, regions like Rangpur, Mymensingh, and Barisal, encompassing 31% of the area, exhibit lower vulnerability. Topographic factors, accounting for 45% of the vulnerability, including aspect, drainage density, and slope, significantly influence susceptibility. Socio-economic elements contribute 22%, particularly population density and industrial activities. The RF model achieves exceptional accuracy (over 90%), emphasizing the complexity of groundwater dynamics. By integrating geological, social, and economic aspects, the study provides actionable insights for nuanced and sustainable management strategies. This research not only unveils a highly accurate groundwater vulnerability map, enriching scientific understanding, but also offers a unique approach by incorporating often-overlooked variables and leveraging machine learning. These insights empower policymakers and urban planners to craft targeted and sustainable groundwater management strategies, ensuring a resilient water supply system for Bangladesh's growing population. Through this work, the aim is to significantly contribute to the scientific community while providing data-driven solutions for the nation's pressing water challenges.

Hard rock systems are typically characterized by undulated landscapes, short flow path, dense drainage network, and low aquifer storage. If located in water-limited environments (WLE), then typically have variable rainfall characteristics, frequent floods and droughts, and savannah type of vegetation capable to uptake groundwater. In such environments, surface–groundwater interactions are of primary importance. In this study, surface–groundwater interactions are analyzed focusing on spatiotemporal net recharge dependence on hydrotopes, where a hydrotope is conceptualized as spatial unit (or object), which at a given scale of assessment can be assumed as internally homogeneous (e.g. tree canopy or rock outcrop) but hydro(geo)logically different than the adjacent area. The study is carried out in the small (7.6 ha) Trabadillo study area (TSA) in Spain, representative for hard rocks of WLE using 3D-GSFLOW integrated hydrological model (IHM) at very fine grid (5x5m), extracted from coarser grid (100x100m) of Sardon 3D-GSFLOW model (Hassan et al., 2014, Journal of Hydrology 517, 390–410). The IHM was calibrated using: (i) 20-year head observation; (ii) 8-year soil moisture observations at 4 profiles, each at different hydrotope and each at 4 different depths; and (iii) MODIS satellite earth observation of evapotranspiration. Despite applying spatially uniform rainfall as forcing, surface–groundwater interactions were highly spatially variable, primarily, because of hydrotope-differences in rainfall interception loss and secondarily, because of differences in transpiration, in subsurface evaporation and in hydrological properties of subsurface. The high temporal variability of rainfall, ranging yearly from 310 to 925 mm, resulted in high temporal variability of all water fluxes, including groundwater fluxes; e.g. gross recharge (Rg) varied annually from 2 to 445 mm, net recharge (Rn), from −50 mm to + 89 mm, while the 20-year mean Rn was close to zero, because Rg was counterbalanced by groundwater exfiltration and less by groundwater evapotranspiration. The high temporal variability of groundwater fluxes and many dry years with largely negative Rn, in combination with low aquifer storage, emphasized TSA dependence on rainfall and its vulnerability to droughts. The hydrotope modeling concept applied at the very fine scale, emphasized also large Rn dependence on land cover type, especially on density and species composition of trees, invisible at coarser scale modeling. As such, the proposed hydrotope modelling approach can be applied as a tool to test whether in a given environment planting (or cutting) trees will result in increase or decrease of water resources.