Projections of Future Droughts in Morocco: Key Insights from Bias-Corrected Med-CORDEX Simulations in the Haouz Region

Earth Systems and Environment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Язык: Английский

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A novel approach to analyze the impact of groundwater drought on the perennial environment and hyporheic zone of surface water bodies

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132668 - 132668

Опубликована: Янв. 1, 2025

Язык: Английский

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A Versatile Workflow for Building 3D Hydrogeological Models Combining Subsurface and Groundwater Flow Modelling: A Case Study from Southern Sardinia (Italy)

Water, Год журнала: 2025, Номер 17(1), С. 126 - 126

Опубликована: Янв. 5, 2025

This research project aims to develop a basin-scaled 3D hydrogeological model by using Petrel E&P (Petrel 2021©) as the basis for numerical groundwater flow developed with “ModelMuse”. A relevant aspect of is use 2021© geologic modelling tools in field applied hydrogeology improve details both and models, their predictive capabilities. The study area located South Sardinia (Campidano Plain), where previous studies were available. was digitising interpreting facies available borehole logs; grid subsequently created, including main surfaces performing geostatistical based on grain size percentages. Afterwards, an empiric formula, achieved from tests laboratory analyses, distribution obtain preliminary hydraulic conductivity values, calibrated during simulations. These simulations, under various head scenarios, established boundary conditions values needed determine balance area. probabilistic approach has produced highly detailed able adequately represent natural phenomena anthropic stresses places underground.

Язык: Английский

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Predicting Soil Salinity Based on Soil/Water Extracts in a Semi-Arid Region of Morocco

Soil Systems, Год журнала: 2025, Номер 9(1), С. 3 - 3

Опубликована: Янв. 8, 2025

Soil salinity is a major constraint to soil health and crop productivity, especially in arid semi-arid regions. The most accurate measurement of considered be the electrical conductivity saturated extracts (ECe). Because this method labor-intensive, it unsuitable for routine analysis large sampling campaigns. This study aimed identify best models estimate based on ECe relation rapid (EC) soil/water (referred as S:W henceforward) extracts. We evaluated relationship between ECS:W extract ratios (1:1, 1:2, 1:5) salt-affected soils from Sehb El Masjoune region Morocco. 0.5 235 dS/m, determined by method. A total 125 samples, topsoil (0–15 cm) subsoil (15–30 with mainly fine medium textures, were analyzed using linear, logarithmic, second-order polynomial regression models. included all samples or grouped according texture (fine, medium) specific textural classes. mean values 2.6, 3.1, 7.9 times greater than EC 1:1, 1:5 extracts, respectively. Polynomial had predictive accuracy, R2 = 0.98, lowest root square error 10.6 10.7 dS/m 1:2. could represent non-linear relationships indicators, 80–170 range, where other typically underestimate salinity. These results confirm that advanced techniques are suitable predicting region. site-specific outperformed previously published models, because they consider spatial variability heterogeneity area explicitly. confirms importance calibrating local environmental conditions. Consequently, we can undertake assessments hundreds simple, extraction direct indicator extrapolate model. Our approach enables widespread needed land-use planning, irrigation management, selection landscapes.

Язык: Английский

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Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102249 - 102249

Опубликована: Фев. 18, 2025

Язык: Английский

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Hydrogeological Parameters Identification in the Qingtongxia Irrigation Area Using Canal Stage Fluctuations

Water, Год журнала: 2025, Номер 17(6), С. 861 - 861

Опубликована: Март 17, 2025

Accurate characterization of aquifer hydrogeological parameters is critical for sustainable groundwater resource management. Traditional methods such as pumping tests often assume homogeneity and require substantial resources, limiting their applicability large-scale heterogeneous systems. This study proposes a novel approach to estimate the spatial distribution hydraulic conductivity (T) specific storage (Ss) in Qingtongxia Irrigation Area, utilizing canal stage fluctuations natural stimuli. By analyzing high-frequency level responses from monitoring wells during irrigation channel operations, we employed Sequential Linear Estimator (SLE) method combined with tomography invert parameters. The results demonstrate that inverted aligns well lithological variations historical data, showing higher values southern alluvial fan lower northern plains. SLE effectively captured heterogeneity, RMSE correlation coefficients between test inversion improving 1.81 0.76 after excluding outliers. work highlights potential stimuli (e.g., irrigation-induced fluctuations) basin-scale parameter estimation, offering cost-effective alternative traditional methods. findings provide valuable insights modeling management arid regions intensive

Язык: Английский

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An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction

Water Resources Research, Год журнала: 2025, Номер 61(6)

Опубликована: Июнь 1, 2025

Abstract Reliable groundwater level (GWL) prediction is essential for sustainable water resources management. Despite recent advances in machine learning (ML) methods GWL prediction, further improvements may be made uncertainty quantification and model interpretability. This study proposes Bayesian TimesNet (BTimesNet), a novel deep probabilistic explainable prediction. BTimesNet transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. A framework using Stein Variational Gradient Descent implemented to quantify predictive uncertainties. For interpretability, SHapely Additive exPlanations (SHAP) utilized predictor contributions. The efficacy multi‐step‐ahead evaluated monthly collected from 19 monitoring wells across three hydroclimatic regions U.S., compared against widely used long short‐term memory (LSTM) Autoformer models. Results show that consistently outperforms LSTM Autoformer, providing more accurate predictions up 4 months ahead. SHAP analysis reveals historical GWLs are most informative features, with meteorological predictors making secondary BTimesNet's superior performance stems its ability extract both short‐ long‐term features. approach represents valuable advancement risk‐informed decision‐making, critical lead proactive ecosystem management agricultural irrigation planning. Its data‐driven nature also facilitates broader applications hydrological environmental domains.

Язык: Английский

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Groundwater vulnerability and, risk assessment of seawater intrusion for the development of a strategy plan towards sustainability: Case of the Souss-Massa coastal area, Morocco

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 57, С. 102128 - 102128

Опубликована: Дек. 20, 2024

Язык: Английский

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Investigating the role of ENSO in groundwater temporal variability across Abu Dhabi Emirate, United Arab Emirates using machine learning algorithms

Groundwater for Sustainable Development, Год журнала: 2024, Номер 28, С. 101389 - 101389

Опубликована: Дек. 3, 2024

Язык: Английский

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Strategic Imputation of Groundwater Data Using Machine Learning: Insights from Diverse Aquifers in the Chao-Phraya River Basin

Groundwater for Sustainable Development, Год журнала: 2024, Номер unknown, С. 101394 - 101394

Опубликована: Дек. 1, 2024

Язык: Английский

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