Ali Elgendy

Groundwater recharge constrains sustainable aquifer use yet cannot be directly measured across entire basins. Recharge is often represented as a single recharge-potential surface, which can conflate favorable recharge conditions with the subsequent aquifer response. Here, we present a dual-indicator framework that explicitly separates climatic recharge opportunity from groundwater response. A climatic water-balance proxy (R_WB) was used to represent recharge opportunity, whereas median annual groundwater-level fluctuation (MedAF) provided an independent observation-based indicator of regional aquifer response. Machine-learning models were used to emulate the spatial organization of R_WB and to classify MedAF using climatic, soil-hydraulic, terrain, hydrogeomorphic, and land-cover predictors, with rigorous spatial validation. Applied to the Dél-Békés sector of the Maros alluvial fan (Hungary), both models achieved areas of applicability exceeding 95% of the basin, although observational support for MedAF varied with proximity to the 57 monitoring wells. Despite their physical connection, the two indicators showed limited spatial correspondence, agreeing across only 35.7% of the area. SHAP attribution for the R_WB emulator emphasized soil-hydraulic, hydrogeomorphic, and land-cover predictors, whereas the MedAF classifier was associated more strongly with climatic and topographic predictors. Applicability-constrained screening identified 100 candidate managed aquifer recharge (MAR) sites covering 18.03 km². By separating recharge opportunity from groundwater response, the framework provides a potentially transferable strategy for regional groundwater assessment and first-stage MAR screening, helping prioritize areas for subsequent site-specific investigation in heterogeneous alluvial aquifers.

Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter wheat yield prediction in Hungary. Using EnMAP images from February and May 2023, along with ground truth yield data from four fields, we derived 10 distinct vegetation indices. Random Forest, Gradient Boosting, and Multilayer Perceptron algorithms were employed, and model performance was evaluated using Mean Absolute Error (MAE) and Coefficient of Determination (R²) values. The results consistently demonstrated that integrating multi-temporal data significantly enhanced predictive accuracy, with the MLP model achieving an R² of 0.79 and an MAE of 0.27, notably outperforming single-date predictions. Shortwave infrared (SWIR) indices were particularly critical for early-season yield estimations. This research highlights the substantial potential of hyperspectral data and advanced machine learning techniques in precision agriculture, emphasizing the promising role of future missions such as CHIME in further refining and expanding yield estimation capabilities.

Groundwater serves as a lifeline in Egypt’s hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (δ¹⁸O and δ²H), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and isotopic signatures distinguish meteoric water from paleowater sources, revealing significant mixing and recharge processes. The findings indicate that the Quaternary aquifer is increasingly influenced by upward leakage from the Nubian aquifer, facilitated by deep-seated faults. Between 2014 and 2021, water levels in the Quaternary aquifer declined by up to 14 m due to over-extraction, particularly in agricultural zones. To enhance predictive capabilities, a Support Vector Machine (SVM) model was developed to estimate δ¹⁸O values using multiple hydrochemical indicators, achieving strong performance (R² = 0.92, MSE = 2.89). SHapley Additive exPlanations (SHAP) analysis identified Mg²⁺, HCO₃⁻, and SO₄²⁻ as dominant factors influencing isotope variation. This integrated approach represents a novel application of explainable machine learning in hydrogeology and offers a scalable, cost-effective tool for assessing groundwater systems in arid regions. The study contributes directly to national water security goals and supports the global Sustainable Development Goal 6 (SDG 6) for clean water and sanitation.