Mapping groundwater discharge potential from Earth observation data using machine learning: Evidence from an arid basin

Groundwater stands as a vital buffer against the growing impacts of climate change, especially in arid and semi-arid regions where surface water is ephemeral and rainfall patterns are becoming increasingly erratic. Understanding how recharge zones respond to climatic variability is crucial for ensuring long-term water security. This study provides a basin-scale assessment of groundwater discharge potential by integrating field measurements, geospatial predictors and supervised machine-learning techniques. A dataset of 239 boreholes with measured discharge (L s^⁻¹) from 2015-2025 was compiled to identify high-potential sites. Sixteen conditioning factors representing topography, hydrology, climate, vegetation, land use and structural characteristics were generated from remote-sensing products, DEM-derived indices and thematic datasets. After evaluating multicollinearity through correlation analysis and variance inflation factors, four single classifiers,Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and k-Nearest Neighbours (KNN), were developed, along with several hybrid ensembles and a four-model stacking configuration.

All models reveal a consistent spatial pattern. Favourable zones trace a continuous corridor along the main Drâa Valley, from the upstream sectors around Aguim and Taznakht through Ouarzazate and Agdz to the Zagora plains, with frequent extensions across adjacent piedmonts and alluvial surfaces. Around half of the basin is classified as favourable (class 1), underscoring the central geomorphological role of this valley system in concentrating infiltration and sustaining groundwater discharge. Among the single models, LightGBM shows the strongest performance (accuracy = 0.941; ROC_AUC = 0.985; LogLoss = 0.166; Brier score = 0.046). The four-model ensemble achieves an accuracy of 0.943 and an MCC of 0.885, with very low probability errors in independent validation. Elevation, soil moisture, drainage density, precipitation and NDWI are consistently identified as the most influential predictors. Overall, the proposed framework offers a robust decision support tool for guiding drilling, managed aquifer recharge and the protection of key groundwater corridors in one of Morocco’s most water stressed regions.