Search Results (355)

Coastal reclaimed polders with shallow saline groundwater support intensive greenhouse horticulture but require timely diagnosis of root-zone water and salinity conditions. This study developed a compact Internet-of-Things (IoT) monitoring system based on the M5Stamp Pico microcontroller to acquire SDI-12 soil-sensor data, buffer records locally, and transfer them to a low-cost cloud dashboard. Outside-greenhouse validation showed high operational reliability, with a missing observation rate of only 0.9%, and acceptable agreement with a reference TDR100 for both volumetric water content (θ) and bulk electrical conductivity (EC_b). The system was then applied to ridge-position monitoring in a commercial pepper greenhouse on a coastal reclaimed polder. The ridge records captured depth-dependent infiltration and salinity redistribution under drip irrigation, together with contrasting responses between the cultivated layer and shallow groundwater. Potential-based interpretation indicated that the monitored ridge root zone was often not strongly limited by matric potential, whereas osmotic potential derived from pore-water salinity showed reduced water availability even when the soil remained relatively wet. These results demonstrate that continuous real-time monitoring at the ridge position can support diagnosis of root-zone stress and provide useful information for irrigation and fertigation management in salt-affected greenhouse soils. Full article

In Morocco, snow constitutes a crucial freshwater resource, particularly in the Atlas Mountains, where seasonal snowpack significantly contributes to surface water availability, groundwater recharge, and down-stream water supply. However, snow monitoring in these regions remains challenging due to the scarcity and uneven distribution of ground-based snow depth measurements, especially at high altitudes. This lack of observations limits the accurate assessment of snowpack dynamics and hampers hydrological modeling and water resource management. In this study, we assessed the performance of an empirical approach to estimate snow depth from satellite-derived fractional snow cover (FSC) obtained from MODIS observations. Five empirical FSC snow depth models, including linear and nonlinear exponential formulations, are developed and applied across multiple regions of the Moroccan Atlas Mountains. Model coefficients are calibrated independently for each region using three complementary optimization techniques, nonlinear least squares regression, genetic algorithms, and simulated annealing. Model skill was evaluated during calibration and validation using the Kling–Gupta Efficiency (KGE), Pearson correlation coefficient (R), and absolute error metrics (RMSE and MAE). Results show substantial performance differences across formulations and regions. The most flexible exponential model achieved highest efficiency (KGE up to 0.87; R > 0.85) and 0.26 cm (MAE) under moderate snow conditions. Linear formulations exhibited limited robustness, whereas exponential models better captured snow depth dynamics, particularly in high-altitude areas with deep and persistent snowpacks. These results highlight the potential of FSC-based empirical modeling as a practical and operational solution for snow depth estimation in data-scarce mountainous regions of Morocco. Full article

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

Given the challenges posed by high groundwater levels, thick sand layers, and strong permeability in water-rich sandy strata, cut-off walls often fail to fully isolate the hydraulic connection between the inside and outside of a foundation pit. As a result, dewatering inside the pit—especially from confined aquifers—can cause significant external groundwater drawdown and subsequent ground settlement. Using a deep excavation conducted in Xiamen as a case study, this study developed a two-dimensional hydro-mechanical coupled finite element model to systematically investigate the effects of various dewatering scenarios and soil permeability coefficients on surface settlement around the pit, and to reveal settlement patterns induced by dewatering and excavation in such strata. Field monitoring data were incorporated to validate the numerical model, ensuring accuracy and reliability. Key findings include the following: (1) Dewatering contributes to over 76% of the total settlement at each stage, with confined drawdown being the dominant factor, implying that dewatering optimization should take priority over controlling excavation rate. (2) Under confined dewatering, the settlement influence zone extends beyond 80 m, far exceeding the extension caused by excavation alone; thus, monitoring and protection ranges must be adjusted dynamically. (3) The horizontal permeability of sand shows a nonlinear positive correlation with settlement, and this sensitivity grows with depth, highlighting the need for accurate permeability determination and stricter controls in deep excavations within water-rich sand layers. From an engineering perspective, these findings underscore the importance of prioritizing confined aquifer dewatering management, dynamically expanding settlement monitoring zones, and rigorously characterizing permeability profiles to mitigate excessive ground settlement and protect adjacent infrastructure. Full article

In many African nations, including Nigeria, groundwater remains the most readily available source of clean water. However, finding and developing these resources in heterogeneous terrain, such as the Federal Capital Territory (FCT), Abuja, is challenging due to the uneven distribution of the aquifers and complex geological settings. Using a GIS-based machine learning approach that incorporates surface and subsurface hydrogeological parameters, this study defines groundwater potential zones (GWPZ). Nine conditioning factors were derived from remote sensing, geophysical and climatic datasets. Aquifer thickness, depth to bedrock, geology, rainfall, slope, LULC, lineament density, drainage density and distance from river were among these variables. Three machine learning models: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Random Forest (RF) were trained and validated using 2410 borehole records (productive and abortive). Hold-out validation (80:20), 10-fold cross-validation, ROC-AUC, and confusion matrix were used to assess each model’s performance. The ensemble models outperformed the SVM, achieving higher predictive accuracy and better generalisation (XGBoost: 0.89, RF: 0.88 and SVM: 0.87). The generated maps categorised the study area into five GWPZs: very high, high, moderate, low and very low. These findings provide a scientific foundation for groundwater exploration and sustainable water resource management in the study area. Full article

Water scarcity is a critical constraint to sustainable agricultural production in arid and semi-arid regions. This study evaluated the effectiveness of subirrigation controlled drainage (SCD) systems in improving water use efficiency, soil conditions, and productivity of Egyptian clover (Trifolium alexandrinum L.) over two consecutive growing seasons (2022–2024). Three drainage treatments were investigated: subirrigation controlled drainage with water table depths of 0.4 m (SCD-0.4) and 0.8 m (SCD-0.8), and conventional free drainage at 1.2 m (SFD-1.2). The results demonstrated that SCD significantly reduced irrigation water requirements, achieving water savings of up to 27% under SCD-0.4 compared with conventional drainage. The shallow water table enhanced groundwater contribution to crop evapotranspiration, reaching over 40%, which improved soil moisture availability and reduced soil water depletion. Consequently, SCD-0.4 increased fresh and dry biomass yields by approximately 18% and significantly improved water productivity and irrigation water productivity. However, controlled drainage led to increased soil salinity due to reduced leaching, particularly in upper soil layers. Economic analysis revealed that SCD-0.4 achieved the highest net returns and water use profitability. Overall, controlled drainage at shallow depths represents an effective strategy to enhance water productivity, crop yield, and economic efficiency, although long-term salinity management must be considered for sustainable implementation. Full article

Driven by global warming, increasing extreme precipitation events (EPEs) threaten low-lying coastal ecosystems. This study focused on the contemporary Yellow River Delta and established a continuous framework linking extreme precipitation, groundwater, and vegetation, based on long-term extreme precipitation changes during 1960–2022 and vegetation dynamics during 2001–2022. Using regional precipitation records, groundwater observations from 16 monitoring wells, and five-day kernel normalized difference vegetation index (kNDVI) data, we compared two EPEs that exceeded the 99th-percentile wet-day precipitation threshold and had complete precipitation–groundwater–vegetation observations. Our findings reveal that: (1) extreme precipitation was intensified in the study area, with an R99p trend of 19.1 mm/10 a; (2) vegetation disturbance was stronger and more persistent after the 2019 Lekima event, with a mean post-event kNDVI anomaly of −12.8%, whereas the 2022 Chaba event produced a weaker, later, and more spatially limited negative response; (3) groundwater response was also stronger in 2019, as the proportion of wells with above-surface water levels reached 43.8%, compared with 12.5% in 2022, indicating more extensive and longer-lasting inundation; (4) the shallowest post-event groundwater depth was significantly negatively correlated with kNDVI anomalies (r = 0.579, p < 0.001), and during the 2019 event, the kNDVI fell below about −17% when surface inundation lasted for 6 days. These results indicate that groundwater is a key hydrological link connecting extreme precipitation and vegetation response. This study provides new evidence for the identification and adaptive management of ecological risks in low-lying coastal deltas. Full article

Waterlogging and secondary salinization are major drivers of land degradation in irrigated dryland regions, undermining soil productivity and long-term sustainability. Pakistan’s Indus Basin Irrigation System (IBIS), one of the world’s largest irrigation networks, supports national food security over approximately 16.7 million hectares (Mha). However, large-scale canal irrigation, combined with flat topography, monsoonal recharge, and inefficient water management, has disrupted groundwater balance, leading to persistent shallow water tables and widespread land degradation. Currently, nearly one-third of the irrigated area is affected by groundwater depths of less than 3 m. This review synthesizes six decades of waterlogging development and management in the IBIS, analyzing the evolution of drainage infrastructure, salinity control strategies, groundwater exploitation, and institutional reforms within a land sustainability perspective. Although large-scale interventions—including 61 Salinity Control and Reclamation Projects (SCARPs) and major outfall systems—initially reclaimed substantial areas, long-term performance has been constrained by governance fragmentation, inadequate operation and maintenance, and environmentally problematic effluent disposal. The Indus Basin experience underscores the need to move beyond infrastructure-centered solutions towards more integrated land–water governance and adaptive management to enhance land system resilience in irrigated regions facing growing climatic and resource pressures. Full article

Overwintering peat fires are increasingly reported in the boreal regions, where they persist underground through winter and reignite in spring, intensifying greenhouse gas emissions and landscape degradation. This study investigates the conditions that enable peat fires to survive freezing and snow cover, and presents practical methods for their winter detection and suppression. We combined satellite data, UAV-based thermal imaging, time-lapse photography, and ground measurements of temperature, groundwater depth, and peat moisture to identify active overwintering hotspots. Our results show that these fires persist primarily where groundwater levels remain below 60 cm, particularly under tree roots, compacted soil, or elevated terrain that limits moisture recharge. UAV thermal imaging proved the most reliable detection tool, identifying 98% of hotspots. We developed and successfully applied a winter extinguishing method that involves mechanical disruption and dispersion of smoldering peat over frozen ground, allowing rapid cooling without re-ignition. These findings clarify the mechanisms sustaining overwintering fires and provide an effective approach for their mitigation, contributing to reduced emissions and improved management of boreal peatlands vulnerable to climate change. Full article

Climate change is increasingly affecting groundwater resources in the Carpathian Basin, while rising temperatures are likely to increase irrigation demand and pressure on aquifers. We assessed climate- and pumping-driven impacts on the Nyírség recharge–discharge system (north-eastern Hungary) by combining shallow groundwater monitoring (1970–2022) with hydroclimate indicators from CHIRPS precipitation and ERA5-Land air temperature and snow depth (1981–2024). Using these datasets, we developed and calibrated a MODFLOW groundwater-flow model for representative wet (2010) and dry (2022) conditions, incorporating permitted abstraction and scenario-based estimates of unregistered pumping. We then ran scenario simulations to evaluate mid-century (2050) conditions and managed aquifer recharge (MAR) options. Precipitation exhibits strong interannual variability, but the region shows marked warming and a pronounced decline in snow storage, implying reduced cold-season buffering and higher evaporative demand. Simulations reproduce the observed post-2010 decline in shallow groundwater, with the largest decreases in higher-elevation recharge areas, whereas increased pumping mainly intensifies localized drawdown near major well fields. Scenario results indicate that climate-driven reductions in recharge dominate basin-scale declines by 2050, while MAR provides primarily local benefits; direct subsurface injection performs best among the tested options. These findings support practical groundwater management by prioritizing measurable and enforceable abstraction (including unregistered withdrawals), demand-side irrigation efficiency and adaptive caps in recharge areas, and targeted subsurface MAR where source water and infrastructure are available. Full article

Groundwater is a critical natural and strategic economic resource, and the accurate prediction of groundwater depth dynamics is essential for the rational development and utilization of water resources. However, under the combined influence of climate variability, human activities, and complex hydrogeological conditions, groundwater level time series exhibit strong nonlinear and non-stationary characteristics, posing great challenges to the accurate prediction of groundwater level dynamics. Most existing prediction models rely on sufficient hydro-meteorological and exploitation data that are difficult to obtain in water-scarce regions, or fail to effectively decouple the multi-scale features of non-stationary groundwater level signals, resulting in limited prediction accuracy and insufficient generalization ability. To address these research gaps, this study takes Zhengzhou, a typical water-deficient city in the Yellow River Basin, as the study area, and proposes a hybrid deep learning framework combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) neural network for predicting shallow and intermediate-deep groundwater level changes. Kolmogorov–Arnold Networks (KANs) and Gated Recurrent Units (GRUs) are selected as benchmark models to verify the superior performance of the proposed framework. In this framework, the non-stationary groundwater level signal is adaptively decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics via VMD. An independent LSTM model is constructed for each IMF to capture its unique temporal variation pattern, and the final groundwater level prediction is obtained by linearly reconstructing the predicted results of all IMFs. The results show that the coefficient of determination (R²) of the VMD-LSTM model exceeds 0.90 for all monitoring datasets, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). It significantly outperforms the benchmark models in handling nonlinear and non-stationary time series features. Using only historical groundwater level data as input, the proposed framework effectively overcomes the limitation of insufficient driving variables in data-scarce regions and fully explores the multi-scale evolution of groundwater dynamics through the synergistic effect of multi-scale decomposition and deep learning. The method presented in this study provides a novel and reliable technical approach for groundwater level prediction in water-deficient and data-limited areas, and also offers scientific support for the rational management and sustainable utilization of regional groundwater resources. Future research will incorporate driving factors such as meteorology and exploitation to further improve the model’s ability to capture abrupt changes in groundwater level dynamics. Full article

Urban flooding is prevalent in low-lying, coastal regions, where subtle topographic variation, shallow groundwater, and impervious surfaces govern inundation dynamics. This study evaluates urban flood susceptibility across Miami-Dade County by integrating flood-conditioning factors, including elevation, slope, rainfall, land use/land cover, distance to roads and open water, stream power index (SPI), topographic wetness index (TWI), groundwater depth, and flow accumulation within an Analytical Hierarchy Process (AHP)-based weighted overlay framework. The AHP-derived weights demonstrated strong consistency (consistency ratio = 0.022) and were applied to reclassify each conditioning factor into five flood susceptibility classes—very low to very high. The model performance was evaluated using the Federal Emergency Management Agency (FEMA) flood zone, and the findings demonstrated that the AHP-based framework effectively differentiates flood susceptibility at a fine urban scale, achieving strong predictive performance; area under the Curve (AUC) = 0.85. The results also reveal pronounced spatial variability in flood susceptibility, with northeastern urbanized areas, particularly in Hialeah, Miami Gardens, Miami Lakes, and Downtown Miami, exhibiting higher susceptibility compared to the northwestern Everglades region. Overall, this study presents a robust urban flood susceptibility framework that supports improved flood risk assessment and decision-making in complex urban coastal environments. Full article

Sea-level rise (SLR) and regional precipitation pattern change cause island subsurface freshwater, typically shaped like a thin lens, to be at higher risk of contamination from seawater intrusion (SWI). Installing a cutoff wall is considered a feasible strategy for protecting coastal fresh groundwater from SWI. However, the performance of the cutoff wall in managing freshwater lens (FWL) development and mitigating SWI into island aquifers under SLR and aquifer recharge (RCH) fluctuations remains inadequately quantified. This study investigates how water table elevation (WTE), FWL depth, thickness, and SWI extent, measured by aquifer salt mass and freshwater volume, in an island aquifer equipped with cutoff walls, respond to SLR and RCH fluctuations. It focuses on a two-dimensional, variable-density island groundwater simulation model based on hydrogeological conditions of San Salvador Island, Bahamas. The results demonstrate that RCH critically influences cutoff wall effectiveness for FWL development and SWI mitigation, with higher RCH amplifying gains in WTE, FWL metrics, freshwater storage, and aquifer salt removal, but this influence diminishes with wall depth increasing. SLR elevates WTE in a stable manner associated with its magnitude but negligibly affects the cutoff wall performance in FWL enhancement and SWI mitigation. Under simultaneous SLR and RCH fluctuations, SLR can offset the WTE reduction caused by reduced RCH, but the joint effects of SLR and RCH on FWL metrics, freshwater storage and aquifer salt removal align with their individual impacts. Moreover, cutoff walls are more efficient in low-RCH settings, yielding greater relative improvements in FWL development and SWI mitigation per unit wall depth increase. Full article

Unconfined coastal karst aquifers are highly susceptible to contamination from anthropogenic activities, particularly in regions lacking adequate wastewater treatment. Their open hydrological structure facilitates the input and dispersion of contaminants from both point and non-point sources. Furthermore, groundwater exerts a significant influence on coastal water quality through submarine discharge that could impact vulnerable ecosystems like seagrasses, mangroves, and coral reefs. Seasonal hydrological variability—especially between dry and rainy periods—affects contaminant transport, with increased groundwater flux potentially enhancing spatial dispersion. Additionally, the balance between the contributions from the coastal karst aquifer and the hydrodynamics of the coastal zone determines the extent and degree of salinization occurring at the interface between these two systems, which in turn influences aquifer water quality. This study presents a five-year dataset of metal and nutrient concentrations measured during dry and rainy seasons in surface waters (0.5 m depth) from 24 cenotes within the Ring of Cenotes (RC), Yucatán Peninsula, Mexico. The RC functions as a preferential groundwater flow path from inland to the coast via underwater conduits and submarine groundwater discharge (SGD), transporting contaminants present in groundwater into highly vulnerable coastal ecosystems. While most parameters remained below regulatory thresholds, concentrations of total Al, Cr, Pb, and N-NH₃ exceeded limits established by NOM-127-SSA1-2021 at several sites measured within the RC. Spatial heterogeneity was observed across seasons and years, driven by groundwater flux dynamics, land use, and individual sinkhole characteristics. Notably, N-NH₃ concentrations were higher during the dry season, particularly near agricultural and peri-urban zones. These findings underscore the need for mandatory wastewater treatment and integrated coastal karstic aquifer management to protect the region’s sole freshwater resource and the vulnerable ecosystems in the coastal area. Full article

Electronic waste (e-waste) has emerged as one of the most critical environmental challenges of the twenty-first century. It encompasses a wide range of discarded electrical and electronic equipment, including information and communication technologies, household appliances, entertainment systems, and related components. While e-waste contains valuable recoverable materials, it also harbours hazardous substances such as toxic heavy metals, flame retardants, and persistent organic pollutants. Inadequate disposal practices, particularly open dumping and landfilling, result in the generation of toxic leachates that contaminate soil as well as surface and groundwater, posing severe threats to environmental integrity and public health. Evidence indicates that landfill leachates can infiltrate groundwater at considerable depths, exceeding permissible limits of heavy metals and metalloids and contributing to serious health disorders. Consequently, the implementation of effective e-waste management strategies and environmentally sound disposal practices is imperative to minimize its detrimental environmental and human health impacts. Microalgae systems can achieve up to 98% removal efficiency and up to five cycles reusability. In this paper, the drawbacks of the traditional methods for metal recovery from e-waste and the potential of microalgae were discussed. The downstream processing and metal extraction from microalgal biomass is critically discussed as well as strategies to support the circular economy. Full article