Dams are built for different purposes, but failure of a dam could result in extreme disasters such as floods. The Grand Ethiopian Renaissance Dam (GERD) was mainly built for power generation, but concerns about its safety, location and site conditions have led the downstream countries to investigate the GERD’s stability. This paper aims to investigate the impact of the failure of the dam on the downstream countries using the Hydrologic Engineering Center River Analysis System (HEC-RAS). Outflow hydrographs and flood inundation maps were provided through a hypothetical dam break scenario. An unsteady flow hydrodynamic routing with a 2D model was used to simulate the failure of the dam. A sensitivity analysis study of the output’s findings against breach parameters was also performed. The breach outflow discharge increases as the breach dimension increases. However, the peak discharge decreases with increasing breach formation time. Moreover, to prepare emergency action plans, it is important to predict the inundation depth, levels, arrival of flood waves, flood coverage area and water velocity. Furthermore, the results showed that Khartoum would turn into lakes within about 10 days and flood water depth would exceed 11 m at some locations in residential areas. Moreover, the flood waves overtopped the Roseires, Sennar and Merowe dams by 11, 7 and 20 m, respectively. In addition, the level of Lake Nasser would reach 188 m above sea level and the Aswan High Dam might be in great danger.

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments.

Effective estimation of water quality parameters is essential for the sustainability of the coastal ecosystem. This research examines the potential use of Sentinel-2 Satellite images with machine learning models to measure the concentrations of Cholorophyll-a (Chl-a) and Total Suspended Solids (TSS) in the coastal area of Abu Dhabi. Google Earth Engine (GEE) was utilized to obtain Sentinel-2 Level-2A surface reflectance values, which are collocated with the in situ data. Field measurements were obtained from various locations, with 365 and 196 available samples for Chl-a and TSS, respectively. The former had 165 collocated points, whereas the latter had only 77 points. For feature engineering, two strategies were compared: spectral indices from the literature and Principal Component Analysis (PCA) with raw bands. Four machine learning algorithms were examined to find the optimal model for each parameter by using 5-fold cross-validated hyperparameter tuning. The selected models are Random Forest Regression (RFR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Partial Least Squares (PLS) Regression. For Chl-a, the analysis showed that a general model was limited by localized bloom events near coastal outlets. Creating a specialized “Ambient-Conditions” model by excluding these outliers greatly improved performance. The optimal Chl-a model (XGB with PCA on six bands) achieved the highest accuracy with Test R ² = 0.7 and Test RMSE of 1.62 µg/L, representing an 80% improvement in precision compared to the general model trained on the full dataset (Test R² = 0.65, RMSE = 8.21 µg/L). PCA + Random Forest (on 10 bands) was the optimal model for TSS, with R ² = 0.61, despite the small dataset size. The results demonstrated that merging machine learning and remote sensing is effective for retrieving Chl-a and TSS in challenging marine waters.