Integrating remote sensing and multi-criteria decision analysis for groundwater zoning in the Eastern Desert of Egypt

Abstract

Abstract The Eastern Desert of Egypt is a hyper-arid region where groundwater serves as the primary alternative to surface water for sustainable development. However, comprehensive assessments of groundwater potential across this vast and geologically complex region remain limited. This study addresses this gap by developing a spatial model for delineating groundwater potential zones (GWPZ) using an integrated approach that combines remote sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP). Seven key thematic layers, precipitation, lithology, slope, drainage density, soil type, land use/land cover (LULC), and lineament density, are selected, standardized, and weighted based on hydrogeological relevance. These layers are integrated through a weighted overlay analysis in a GIS environment. The resulting GWPZ map is initially classified into five categories: very high, high, moderate, low, and very low potential. The very high and very low potential zones have very small areas in the final output due to the region’s arid conditions and hydrogeological limitations. The remaining zones covered the study area as follows: high potential (7.7%), moderate (54.5%), and low potential (37.7%). Model reliability is assessed through two complementary validation approaches. First, 370 groundwater wells with available location data are spatially overlaid on the GWPZ map, showing limited overlap with high recharge zones, as most wells target deep fossil aquifers not influenced by present-day surface conditions. Second, a supplementary validation using three independent surface-derived indicators: Topographic Wetness Index, curvature, and lineament–stream intersection density, demonstrated strong agreement with the GWPZ output. The integration of these two validation methods confirms the robustness of the model for mapping shallow groundwater recharge potential in arid environments. This framework offers a scalable, data-driven approach to support groundwater exploration and strategic water resource planning in similar regions worldwide.