摘要：Water demand has been increasing considerably around the world, mostly since the start of the COVID-19 pandemic. It has caused many problems for water supply, especially in arid areas. Consequently, there is a need to assimilate lessons learned to ensure water security. In arid climates, evaluating the groundwater potential is critical, particularly because the only source of drinking water and irrigation for the community is groundwater. The objective of this report is to locate and identify probable groundwater basins in the upper Wadi Namous basin’s Ain Sefra area. GIS and RS were used to evaluate the parameters of morphometry and to demarcate groundwater potential zones by using eight different influencing factors, viz., geology, rainfall, height, slope, land cover, land use, and lineaments density are all factors to consider. The analytical hierarchical process (AHP) was used to give weightages to the factors, and definitions within each attribute were sorted in order of priority for groundwater potentiality. The major findings of the research were the creation of groundwater-potential zones in the watershed. The hydrogeological zone of the basin was assessed as follows: very poor (0.56%), poor (26.41%), moderate (44.72%), good (25.22%), and very good (3.1%). The groundwater recharge potential zones are concentrated in low cretaceous locations, according to analytical data. The groundwater potential regions were checked to field inventory data from 45 water locations to corroborate the findings. The qualitative findings and the groundwater inventory data agreed 77.78%, according to the cross-validation study. The produced groundwater potential map might substantially assist in the development of long-term management plans by enabling water planners and decision-makers to identify zones appropriate for the placement of productive wells and reducing investment losses caused by well drilling failures. The results of the study will also serve as a benchmark for further research and studies, such as hydrogeological modeling.
