Mapping Groundwater Potential Zones Using a Knowledge-Driven Approach and GIS Analysis

Despite the Sahara being one of the most arid regions on Earth, it has experienced rainfall conditions in the past and could hold plentiful groundwater resources. Thus, groundwater is one of the most precious water resources in this region, which suffers from water shortage due to the limited rainfall caused by climatic conditions. This article will assess the knowledge-driven techniques employed to develop a model to integrate the multicriteria derived from geologic, geomorphic, structural, seismic, hydrologic, and remotely sensed data. This model was tested on the defunct Kom Ombo area of Egypt’s Nile river basin in the eastern Sahara, which covers ~28,200 km2, to reveal the promising areas of groundwater resources. To optimize the output map, we updated the model by adding the automated depression resulting from a fill-difference approach and seismic activity layers combined with other evidential maps, including slope, topography, geology, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics, after assigning a weight for each using a Geographic Information System (GIS)-based knowledge-driven approach. The paleochannels and soil characteristics were visualized using Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) data. Several hydromorphic characteristics, sinks/depressions, and sub-basin characteristics were extracted using Shuttle Radar Topography Mission (SRTM) data. The results revealed that the assessed groundwater potential zones (GPZs) can be arranged into five distinctive groups, depending on their probability for groundwater, namely very low (6.56%), low (22.62%), moderate (30.75%), high (29.71%), and very high (10.34%). The downstream areas and Wadi Garara have very high recharge and storage potential. Interferometry Synthetic Aperture Radar (InSAR) coherence change detection (CCD) derived from Sentinel-1 data revealed a consistency between areas with high InSAR CCD (low change) that received a plausible amount of surface water and those with very low InSAR CCD values close to 0 (high change). Landsat data validated the areas that received runoff and are of high potentiality. The twenty-nine groundwater well locations overlaid on the GPZs, to assess the predicted model, indicated that about 86.17% of the wells were matched with very good to moderate potential zones.