Ashraf Mohamed Elmoustafa

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.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth-generation atmospheric reanalysis (ERA5) for the period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the optimal RegCM5 configuration over the UBNB. Results show that the model succeeds to capture the dominant spatiotemporal pattern of the precipitation, and the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) reduces the dominant wet bias in the precipitation simulation. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77, where deficiency in capturing the large-scale circulations, especially the low-level circulations. The research recommended to focus on dynamics advancement and exploring parameterization schemes that enhance the precipitation representation, such as the Planetary Boundary Layer (PBL) in Future.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth generation atmospheric reanalysis (ERA5) for a period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the most optimal RegCM5 configuration over the UBNB. Results show that the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) is the most effective parameterization to capture precipitation but reveals significant overestimation with an accepted wet bias of 66 %. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77. Sensitivity analysis suggests that the interplay between the model's hydrostatic configuration and vertical domain extent significantly influences precipitation outputs, where deficiency in capturing the large-scale circulations. The research recommended to focus on dynamics advancement, and exploring parameterization schemes enhancing the precipitation representation such as the Planetary Boundary Layer (PBL) in Future.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth generation atmospheric reanalysis (ERA5) for a period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the most optimal RegCM5 configuration over the UBNB. Results show that the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) is the most effective parameterization to capture precipitation but reveals significant overestimation with an accepted wet bias of 66 %. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77. Sensitivity analysis suggests that the interplay between the model's hydrostatic configuration and vertical domain extent significantly influences precipitation outputs, where deficiency in capturing the large-scale circulations. The research recommended to focus on dynamics advancement, and exploring parameterization schemes enhancing the precipitation representation such as the Planetary Boundary Layer (PBL) in Future.

The Upper Blue Nile Basin (UBNB), which contributes about 60% to the annual Nile flow, plays a critical role in the Nile water management. However, its complex terrain and climate create significant challenges for accurate regional climate simulations, which are essential for climate impact assessments. This study aims to address the challenges of climate simulation over the UBNB by enhancing the Regional Climate Model system (RegCM5) with its new non-hydrostatic dynamical core (MOLOCH) to simulate precipitation and temperature. The model is driven by ERA5 reanalysis for the period (2000–2009), and two scenarios are simulated using two different schemes of the Planetary Boundary Layer (PBL): Holtslag (Hol) and University of Washington (UW). The two scenarios, noted as (MOLOCH-Hol and MOLOCH-UW), are compared to the previously best-performing hydrostatic configuration. The MOLOCH-UW scenario showed the best precipitation performance relative to observations, with an accepted dry Bias% up to 22%, and a high annual cycle correlation >0.85. However, MOLOCH-Hol showed a very good performance only in the wet season with a wet bias of 4% and moderate correlation of ≈0.6. For temperature, MOLOCH-UW also outperformed, achieving the lowest cold/warm bias range of −2% to +3%, and high correlations of ≈0.9 through the year and the wet season. This study concluded that the MOLOCH-UW is the most reliable configuration for reproducing the climate variability over the UBNB. This developed configuration is a promising tool for the basin’s hydroclimate applications, such as dynamical downscaling of the seasonal forecasts and future climate change scenarios produced by global circulation models. Future improvements could be achieved through convective-permitting simulation at ≤4 km resolution, especially in the application of assessing the land use change impact.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth generation atmospheric reanalysis (ERA5) for a period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the most optimal RegCM5 configuration over the UBNB. Results show that the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) is the most effective parameterization to capture precipitation but reveals significant overestimation with an accepted wet bias of 66 %. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77. Sensitivity analysis suggests that the interplay between the model's hydrostatic configuration and vertical domain extent significantly influences precipitation outputs, where deficiency in capturing the large-scale circulations. The research recommended to focus on dynamics advancement, and exploring parameterization schemes enhancing the precipitation representation such as the Planetary Boundary Layer (PBL) in Future.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth generation atmospheric reanalysis (ERA5) for a period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the most optimal RegCM5 configuration over the UBNB. Results show that the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) is the most effective parameterization to capture precipitation but reveals significant overestimation with an accepted wet bias of 66 %. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77. Sensitivity analysis suggests that the interplay between the model's hydrostatic configuration and vertical domain extent significantly influences precipitation outputs, where deficiency in capturing the large-scale circulations. The research recommended to focus on dynamics advancement, and exploring parameterization schemes enhancing the precipitation representation such as the Planetary Boundary Layer (PBL) in Future.

Accurate simulation of precipitation over complex terrains such as the Upper Blue Nile Basin (UBNB) is essential for water resource management and climate impact assessments. The UBNB is characterized by complex terrain and convective precipitation systems that challenge the fine-scale climate simulation processes. This research aims to investigate the best precipitation parameterizations in the Regional Climate Model System (RegCM5) simulating different convective and large-scale schemes over the UBNB domain with 10 km resolution. The RegCM5 is driven by the fifth generation atmospheric reanalysis (ERA5) for a period of (2000–2009) using the hydrostatic dynamical core. The total precipitation simulations of the different calibration scenarios are assessed to select the most optimal RegCM5 configuration over the UBNB. Results show that the Emanuel scheme coupled with Nogherotto-Tompkins (NoTo) is the most effective parameterization to capture precipitation but reveals significant overestimation with an accepted wet bias of 66 %. The model highlights challenges in reproducing the UBNB's precipitation variability with a moderate to relatively good correlation of precipitation patterns from 0.46 to 0.77. Sensitivity analysis suggests that the interplay between the model's hydrostatic configuration and vertical domain extent significantly influences precipitation outputs, where deficiency in capturing the large-scale circulations. The research recommended to focus on dynamics advancement, and exploring parameterization schemes enhancing the precipitation representation such as the Planetary Boundary Layer (PBL) in Future.