Groundwater storage is influenced by many geo-environmental factors. Most of these factors are prepared in the form of categorical data. The present study utilized raster satellite data instead of categorical data and a Random Forest machine learning model to identify groundwater potential zones at the downstream parts of Wadi Yalamlam, western Saudi Arabia. Eighteen groundwater-influenced variables are prepared in continuous raster format from ASTER GDEM, TRMM, and SPOT-5 satellite data. The Random Forest (RF) model is trained using (70%) of the target variable and validated using the rest (30%). The accuracy, sensitivity, and F1-score are all generated to evaluate the model performance. SPOT band 3, band 4, and the rainfall variables are the most important for groundwater potential mapping contributing 11%, 7%, and 8% during the prediction stage. The GDEM elevation variable contributed 6% and the slope variable scored 1%. The main conclusions of the study are: (1) The RF machine learning algorithm successfully identified three groundwater potential zones with an accuracy of 96%. (2) The high, moderate, and low potential groundwater zones covered 11.5%, 59.9%, and 28.6% of the study area respectively. (3) Majority of high and moderate zones lie within the pumping rate range between 10 and 20 m³/day. (4) The approach developed in this study can be applied to any other wadis having the same conditions to help authorities and decision-makers in planning and development projects.

A flash flood is one of the hazardous phenomena, especially in dry regions. In Saudi Arabia, Jazan Province experiences data scarcity especially historical hydrological data and a lack of studies related to flash flood analysis and the rainfall and runoff interrelationship. Thus, this research aims to study the rainfall-runoff inter-relation, predict flash floods, and to map the risk areas in Jazan Province by the geological, geomorphological, and hydrogeological characteristics along with digital elevation model (DEM), watershed modeling system (WMS) and HEC-HMS models. Jazan Province encompasses 25 drainage basins, receiving a considerable amount of rainfall (ranging from 100 to 500 mm) (August, October-November, March) which intermittently cause strong and destructive flash floods. The DEM was used for delineating the catchment (drainage basins) parameters. Physiographic parameters of the catchments have been analyzed for mapping the hazard degree of the flash flood strength. Further, basins with high hazard degrees of flash floods were selected to assess rainfall-runoff inter-relation using the HEC-HMS models, GIS, and morphometric parameters. Forty %, 8%, 52% of the study area are high, medium, and low hazard degrees of flash floods, respectively. The groundwater recharge in the study area was calculated through the integration of satellite image analysis, SWAT and GIS techniques and it ranges from 0.002 mm/km²/year to 8 mm/km²/year with an average of 2.5 mm/km²/year. Rainfall-runoff inter-relation of study basins, assessed based on the integration of WMS and HEC-HMS models, indicates that the resulting runoff volume ranges from 18.5 × 10⁶ m³ to 473.1 × 10⁶ m³ at a recurrence period of 5 and 100 years at rainfall events of 65 mm and 116.8 mm, respectively.