Integrating GIS, Multi-Criteria Decision Analysis and Google Earth Engine for National-Scale Groundwater Potential Assessment in Zimbabwe

<title>Abstract</title> <p>Reliable groundwater assessment remains a major challenge in data-scarce regions underlain by crystalline basement aquifers, where limited hydrogeological information restricts sustainable water resource planning. This study developed and validated a national geospatial framework for groundwater potential assessment in Zimbabwe by integrating Geographic Information Systems (GIS), remote sensing, the Analytical Hierarchy Process (AHP), and Google Earth Engine (GEE). Eleven hydrogeological conditioning factors representing topographic, geological, hydrological, climatic, vegetation, and land surface characteristics were integrated within a weighted overlay multi-criteria decision analysis framework to delineate Groundwater Potential Zones (GWPZs). The model was independently validated using a national borehole database comprising 18,617 records spanning Zimbabwe's seven hydrological catchments, while temporal groundwater dynamics were assessed using GRACE terrestrial water storage anomalies and CHIRPS rainfall data processed within GEE. The AHP identified geology (24.09%), lineament density (20.46%), rainfall (16.76%), and drainage density (16.16%) as the dominant controls on groundwater occurrence. The national GWPZ map classified 22.7% of Zimbabwe as High to Very High groundwater potential and 28.2% as Low to Very Low. Validation demonstrated strong agreement between predicted groundwater potential and observed borehole productivity (Pearson r = 0.78, p &lt; 0.05), with average borehole yields ranging from 7.2 L s⁻¹ in Very High zones to 0.6 L s⁻¹ in Very Low zones. Temporal analysis revealed a strong positive relationship between rainfall and terrestrial water storage anomalies (R² = 0.78), with a recharge lag of approximately one to two months. The integrated framework provides a transparent, reproducible, and scalable geospatial decision-support tool for groundwater exploration, borehole siting, and climate-resilient water resource management in data-scarce crystalline basement aquifer systems.</p>