The whole Mediterranean is suffering today because of climate changes, with projections of more severe impacts predicted for the coming decades. Egypt, on the southeastern flank of the Mediterranean Sea, is facing many challenges for water and food security, further exacerbated by the arid climate conditions. The Nile River represents the largest freshwater resource for the country, with a minor contribution coming from rainfall and from non-renewable groundwater aquifers. In more recent years, another important source is represented by non-conventional sources, such as treated wastewater reuse and desalination; these water resources are increasingly becoming valuable additional contributors to water availability. Moreover, although rainfall is scarce in Egypt, studies have shown that rainfall and flash floods can become an additional available source of water in the future. While presently rare, heavy rainfalls and flash floods are responsible for huge losses of lives and infrastructure especially in parts of the country, such as in the Sinai Peninsula. Despite the harsh climate, water from these events, when opportunely conveyed and treated, can represent a precious source of freshwater for small communities of Bedouins. In this work, rainfall climatology and flash flood events are presented, together with a discussion about the dynamics of some selected episodes and indications about future climate scenarios. Results can be used to evaluate the water harvesting potential in a region where water is scarce, also providing indications for improving the weather forecast. Basic information needed for identifying possible risks for population and infrastructures, when fed into hydrological models, could help to evaluate the flash flood water volumes at the outlets of the effective watershed(s). This valuable information will help policymakers and local governments to define strategies and measures for water harvesting and/or protection works.

This study develops a response-based hydrologic model for long-term (continuous) rainfall-runoff simulations over the catchment areas of big rivers. The model overcomes the typical difficulties in estimating infiltration and evapotranspiration parameters using a modified version of the Soil Conservation Service curve number SCS-CN method. In addition, the model simulates the surface and groundwater hydrograph components using the response unit-hydrograph approach instead of using a linear reservoir routing approach for routing surface and groundwater to the basin outlet. The unit-responses are Geographic Information Systems (GIS)-pre-calculated on a semi-distributed short-term basis and applied in the simulation in every time step. The unit responses are based on the time-area technique that can better simulate the real routing behavior of the basin. The model is less sensitive to groundwater infiltration parameters since groundwater is actually controlled by the surface component and not the opposite. For that reason, the model is called the SCHydro model (Surface Controlled Hydrologic model). The model is tested on the upper Blue Nile catchment area using 28 years daily river flow data set for calibration and validation. The results show that SCHydro model can simulate the long-term transforming behavior of the upper Blue Nile basin. Our initial assessment of the model indicates that the model is a promising tool for long-term river flow simulations, especially for long-term forecasting purposes due to its stability in performing the water balance.

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions.