Search Results (66)

Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between natural processes and human activities often limit the effectiveness of conventional prediction methods. To address this, a hybrid CNN-LSTM deep learning model is constructed. This model is designed to extract multivariate coupled features and capture temporal dependencies from multi-variable time series data, while simultaneously simulating the nonlinear and delayed responses of aquifers to groundwater abstraction. Specifically, the convolutional neural network (CNN) component extracts the multivariate coupled features of hydro-meteorological driving factors, and the long short-term memory (LSTM) network component models the temporal dependencies in groundwater level fluctuations. This integrated architecture comprehensively represents the combined effects of natural recharge–discharge processes and anthropogenic pumping on the groundwater system. Utilizing monitoring data from 2021 to 2024, the model was trained and tested using a rolling time-series validation strategy. Its performance was benchmarked against traditional models, including the autoregressive integrated moving average (ARIMA) model, recurrent neural network (RNN), and standalone LSTM. The results show that the CNN-LSTM model delivers superior performance across diverse hydrogeological conditions: at the upstream well AJC-7, which is dominated by natural recharge and discharge, the Nash–Sutcliffe efficiency (NSE) coefficient reached 0.922; at the downstream well AJC-21, which is subject to intensive pumping, the model maintained a robust NSE of 0.787, significantly outperforming the benchmark models. Further sensitivity analysis reveals an asymmetric response of the model’s predictions to uncertainties in pumping data, highlighting the role of key hydrogeological processes such as delayed drainage from the vadose zone. This study not only confirms the strong applicability of the hybrid deep learning model for groundwater level prediction in data-scarce arid regions but also provides a novel analytical pathway and mechanistic insight into the nonlinear behavior of aquifer systems under significant human influence. Full article

The sustainable use and management of groundwater resources is a challenging issue due to population growth and climate change. Accurate quantification of groundwater recharge is a basic requirement for effective groundwater resource management, yet it is still lacking in many areas around the world. The study was designed to estimate recharge to groundwater from natural rainfall in the Gilgel Gibe and Dhidhessa catchments in southwestern Ethiopia, employing the water table fluctuation (WTF) and chloride mass balance (CMB) techniques. These methods are being applied for the first time in the study area and have not previously been used in these catchments. Given the region’s data scarcity, a community-based data collection program was implemented and supplemented with additional field measurements and secondary data sources. Groundwater level, spring discharge, and rainfall were monitored over the 2022/2023 hydrological year. Groundwater level fluctuations were found to be influenced by topography and rainfall patterns, reaching 8.2 m in amplitude in the upstream part of the catchments. Chloride concentrations were determined in groundwater samples collected from hand-dug wells and springs, and rainwater was also collected. Rainwater exhibited a mean chloride concentration of 2.46 mg/L, while groundwater chloride concentrations ranged from 3 mg/L to 36.99 mg/L. The estimated recharge rates varied spatially, ranging from 170 to 850 mm/year using the CMB method (11% to 55% of annual rainfall, mean recharge rate of 454 mm/year) and from 76 to 796 mm/year using the WTF method (4% to 43% of annual rainfall, mean recharge rate of 439 mm/year). Notably, recharge estimates were lowest downstream in the lowland areas and highest upstream in the highland regions. Rainfall amount, local lithology, and topography were identified as major influences on groundwater recharge across the study area. Both CMB and WTF methods were deemed applicable in the volcanic aquifers, provided that all the respective assumptions are followed. This study significantly contributes to the groundwater dataset for the region, in addition to recharge estimation and the research conclusions, emphasizing the importance of long-term monitoring and time series analysis of chloride data to reduce uncertainties. The work serves as a valuable reference for researchers, policymakers, and regional water resource managers. Full article

As a critical link connecting groundwater and vegetation, the vadose zone’s lithological structural heterogeneity directly influences soil water distribution and vegetation growth. A comprehensive understanding of the ecological effects of the vadose zone can provide scientific evidence for groundwater ecological protection and natural vegetation conservation in arid regions. This study, taking the Minqin Basin in the lower reaches of China’s Shiyang River as a case, reveals the constraining effects of vadose zone lithological structures on vegetation water supply, root development, and water use strategies through integrated analysis, field investigations, and numerical simulations. The findings highlight the critical ecological role of the vadose zone. This role primarily manifests through two mechanisms: regulating capillary water rise and controlling water-holding capacity. They directly impact soil water supply efficiency, alter the spatiotemporal distribution of water deficit in the root zone, and drive vegetation to develop adaptive root growth patterns and stratified water use strategies, ultimately leading to different growth statuses of natural vegetation. During groundwater level fluctuations, fine-grained lithologies in the vadose zone exhibit stronger capillary water response rates, while multi-layered lithological structures (e.g., “fine-over-coarse” configurations) demonstrate pronounced delayed water release effects. Their effective water-holding capacities continue to exert ecological effects, significantly enhancing vegetation drought resilience. Full article

The main goal of our work was to evaluate approaches for modeling lateral outflow from shallow unconfined aquifers in a one-dimensional model of vertical variably-saturated flow. The HYDRUS-1D model was modified by implementing formulas representing lateral flow in an aquifer, with linear or quadratic drainage functions describing the relationship between groundwater head and flux. The results obtained by the modified HYDRUS-1D model were compared to the reference simulations with HydroGeoSphere (HGS), with explicit representation of 2D flow in unsaturated and saturated zones in a vertical cross-section of a strip aquifer, including evapotranspiration and plant water uptake. Four series of simulations were conducted for sand and loamy sand soil profiles with deep (6 m) and shallow (2 m) water tables. The results indicate that both linear and quadratic drainage functions can effectively capture groundwater table fluctuations and soil water dynamics. HYDRUS-1D demonstrates notable accuracy in simulating transient fluctuations but shows higher variability near the surface. The study concludes that both quadratic and linear drainage boundary conditions can effectively represent horizontal aquifer flow in 1D models, enhancing the ability of such models to simulate groundwater table fluctuations. Full article

Heavy metal contamination in groundwater has emerged as a significant environmental issue, driven by rapid industrialization and intensified human activities, particularly in southern China. Heavy metal pollution in groundwater often presents complex spatial patterns and multiple sources; understanding the spatial heterogeneity and controlling factors of heavy metals is crucial for pollution prevention and water resource management in industrial regions. This study applied spatial autocorrelation analysis and self-organizing maps (SOM) coupled with K-means clustering to investigate the spatial distribution and key influencing factors of nine heavy metals (Cr, Fe, Mn, Ni, Cu, Zn, As, Ba, and Pb) in a typical industrial area in southern China. Heavy metals show significant spatial heterogeneity in concentrations. Cr, Mn, Fe, and Cu form local hotspots near urban and peripheral zones; Ni and As present downstream enrichment along the river pathway with longitudinal increase trends; Zn, Ba, and Pb exhibit a fluctuating pattern from west to east in the piedmont region. Local Moran’s I analysis further revealed spatial clustering in the northwest, riverine zones, and coastal outlet areas, providing insight into potential source regions. SOM clustering identified three types of groundwater: Cluster 1 (characterized by Cr, Mn, Fe, and Ni) is primarily influenced by industrial pollution and present spatially scattered distribution; Cluster 2 (dominated by As, NO₃⁻, Ca²⁺, and K⁺) is associated with domestic sewage and distributes following river flow; Cluster 3 (enriched in Zn, Ba, Pb, and NO₃⁻) is shaped by agricultural activities and natural mineral dissolution, with a lateral distribution along the piedmont zone. The findings of this study provide a scientific foundation for groundwater pollution prevention and environmental management in industrialized areas. Full article

Arsenic (As) contamination in groundwater represents a major global public health threat, particularly in alluvial aquifer systems where redox-sensitive geochemical processes facilitate the mobilization of naturally occurring trace elements. This study investigates groundwater quality, particularly focusing on the origin of arsenic contamination in shallow and deep alluvial aquifers of the Lower Sakarya River Basin, which are crucial for drinking, domestic, and agricultural uses. Groundwater samples were collected from 34 wells—7 tapping the shallow aquifer (<60 m) and 27 tapping the deep aquifer (>60 m)—during wet and dry seasons for the hydrogeochemical characterization of groundwater. Environmental isotope analysis (δ¹⁸O, δ²H, ³H) was conducted to characterize origin and groundwater residence times, and the possible hydraulic connection between shallow and deep alluvial aquifers. Mineralogical and geochemical characterization of the sediment core samples were carried out using X-ray diffraction and acid digestion analyses to identify mineralogical sources of As and other metals. Pearson correlation coefficient analyses were also applied to the results of the chemical analyses to determine the origin of metal enrichments observed in the groundwater, as well as related geochemical processes. The results reveal that 33–41% of deep groundwater samples contain arsenic concentrations exceeding the WHO and Turkish drinking water standard of 10 µg/L, with maximum values reaching 373 µg/L. Manganese concentrations exceeded the 50 µg/L limit in up to 44% of deep aquifer samples, reaching 1230 µg/L. On the other hand, iron concentrations were consistently low, remaining below the detection limit in nearly all samples. The co-occurrence of As and Mn above their maximum contaminant levels was observed in 30–33% of the wells, exhibiting extremely low sulfate concentrations (0.2–2 mg/L), notably low dissolved oxygen concentration (1.45–3.3 mg/L) alongside high bicarbonate concentrations (450–1429 mg/L), indicating localized varying reducing conditions in the deep alluvial aquifer. The correlations between molybdenum and As (rdry = 0.46, rwet = 0.64) also indicate reducing conditions, where Mo typically mobilizes with As. Arsenic concentrations also showed significant correlations with bicarbonate (HCO₃⁻) (rdry = 0.66, rwet = 0.80), indicating that alkaline or reducing conditions are promoting arsenic mobilization from aquifer materials. All these correlations between elements indicate that coexistence of As with Mn above their MCLs in deep alluvial aquifer groundwater result from reductive dissolution of Mn/Fe(?) oxides, which are primary arsenic hosts, thereby releasing arsenic into groundwater under reducing conditions. In contrast, the shallow aquifer system—although affected by elevated nitrate, sulfate, and chloride levels from agricultural and domestic sources—exhibited consistently low arsenic concentrations below the maximum contaminant level. Seasonal redox fluctuations in the shallow zone influence manganese concentrations, but the aquifer’s more dynamic recharge regime and oxic conditions suppress widespread As mobilization. Mineralogical analysis identified that serpentinite, schist, and other ophiolitic/metamorphic detritus transported by river processes into basin sediments were identified as the main natural sources of arsenic and manganese in groundwater of deep alluvium aquifer. Full article

Complex feedback mechanisms and interdependencies exist among the water–energy–food–ecosystems (WEFE) nexus. In water-scarce regions, fluctuations in the supply or demand of any single subsystem can destabilize the others, with water shortages intensifying conflicts among food production, energy consumption, and ecological sustainability. Balancing the synergies and trade-offs within the WEFE system is therefore essential for achieving sustainable development. This study adopts the natural–social water cycle as the core process and develops a coupled simulation model of the WEFE (CSM-WEFE) system, integrating food production, ecological water replenishment, and energy consumption associated with water supply and use. Based on three performance indices—reliability, coupling coordination degree, and equilibrium—a coordinated sustainable development index (CSD) is constructed to quantify the performance of WEFE system under different scenarios. An integrated evaluation framework combining the CSM-WEFE and the CSD index is then proposed to assess the sustainability of WEFE systems. The framework is applied to the Beijing–Tianjin–Hebei (BTH) region, a representative water-scarce area in China. Results reveal that the current balance between water supply and socio-economic demand in the BTH region relies heavily on excessive groundwater extraction and the appropriation of ecological water resources. Pursuing food security goals further exacerbates groundwater overexploitation and ecological degradation, thereby undermining system coordination. In contrast, limiting groundwater use improves ecological conditions but increases regional water scarcity and reduces food self-sufficiency. Even with the full operation of the South-to-North Water Diversion Project (Middle Route), the region still experiences a 16.4% water shortage. By integrating the CSM-WEFE model with the CSD evaluation approach, the proposed framework not only provides a robust tool for assessing WEFE system sustainability but also offers practical guidance for alleviating water shortages, enhancing food security, and improving ecological health in water-scarce regions. Full article

Fresh groundwater is essential for sustaining life and socio-economic development, particularly in regions with limited safe drinking water alternatives. However, contamination from natural and anthropogenic sources poses severe health and environmental risks. This research examines the health risks linked to groundwater quality in the agroeconomic region of Boudh district, Odisha, India, where residents depend on untreated groundwater due to limited access to alternative sources. A total of 82 groundwater samples were analyzed during pre- and post-monsoon of the year 2023 using multivariate statistical methods (PCA, correlation analysis) to determine pollutant sources and regulatory factors, while XRD was employed to characterize fluoride-bearing minerals in associated rock samples. Fluoride concentrations range from 0.14 to 4.6 mg/L, with 49% of samples exceeding the WHO limit of 1.5 mg/L, which raises significant health concerns. Nitrate levels fluctuate between 1.57 and 203.51 mg/L, primarily due to agricultural fertilizers. A health risk assessment (hazard quotient and hazard index) indicates that 63% of samples fall into the low-risk category, 21% into moderate-risk, and 16% into high-risk. Children (HI = 29.23) and infants (HI = 19.51) are at the greatest health risk, surpassing that of adult males (HI = 12.2) and females (HI = 11.2). Findings provide scientific evidence for policymakers to implement groundwater protection and remediation strategies. Immediate interventions, including water quality monitoring, defluoridation measures, and community awareness programs, are essential for ensuring long-term water security and public health. Full article

Natural radioactivity was measured and analyzed at the Nice Slope for over a month using radon daughters in order to trace groundwater movement from a coastal aquifer to a nearshore continental shelf. Such groundwater movement may have resulted in submarine groundwater discharge (SGD) and potentially sediment weakening and slope failure. The relationship among major hydrological parameters (precipitation, Var discharge, groundwater level, salinity and water origin) in the area is demonstrated in this study. Time series analyses also helped to detect tidal fluctuations in freshwater input, highlighting the crucial role SGD plays in the slope stability of the still failure-prone Nice Slope, parts of which collapsed in a tsunamigenic submarine landslide in 1979. Earlier deployments of the underwater mass spectrometer KATERINA showed that SGD is limited to the region of the 1979 landslide scar, suggesting that the spatially heterogenous lithologies do not support widespread groundwater charging. The calculated volumetric activities from groundwater tracing isotopes revealed peaks up to ca. 150 counts ²¹⁴Bi, which is similar to those measured at other prominent SGD sites along the Mediterranean shoreline. Therefore, this rare long-term radioisotope dataset is a valuable contribution to the collaborative research at the Nice Slope and may not remain restricted to the unconfined landslide scar but may charge permeable sub-bottom areas nearby. Hence, it has to be taken into account for further slope stability studies. Full article

Landslides are mass movements of rock, soil, or debris under the influence of gravity. These phenomena occur due to the loss of slope stability or imbalance of external loads. The intensity and consequences of landslides depend on various factors such as topography, geological structure, and precipitation regime. This study investigates the characteristics of rainfall-induced landslides in red basaltic soils on the basis of field investigations, geotechnical surveys, and slope stability modeling under anthropogenic triggers. The results indicate a close relationship between soil moisture and shear strength parameters, which significantly influence slope stability. A real-time observation system recorded groundwater level fluctuation in relation to surface runoff and precipitation rates. It is revealed that intense rainfall and low temperatures regulate soil moisture, resulting in a reduction of cohesion and shear strength parameters. These findings enhance the understanding of landslide mechanism in basaltic soil regions, which are highly sensitive to precipitation. The results also highlight that human activities play a significant role in triggering landslides. Therefore, a real-time monitoring system for rainfall, soil moisture, and groundwater is essential for early warning and supports the integration of smart technologies and Internet of Things (IoT) solutions in natural disaster management. Full article

Oil leakage during the processes of extraction, storage, and transportation poses a significant challenge due to the complex nature of pollution caused by frequent fluctuations in groundwater levels and variations in the water–oil interface. To effectively identify and monitor the position of the water–oil interface and displacement processes, geophysical methods have proven to be an efficient approach. This study utilizes electrical resistivity measurements to analyze changes in medium resistivity during water–oil displacement, enabling simulation of the spatial relationship between groundwater levels and petroleum contaminants based on resistivity characteristics and natural potential responses. After analysis, the following conclusions can be drawn: (1) During the water displacement process, when water forms a connected flow channel between sand and gravel, the resistivity decreases abruptly. Conversely, during oil displacement by water, when oil fills soil pores and creates a high-resistance conductive path, the resistivity increases abruptly. (2) Changes in resistivity are determined by the position of the water–oil interface. By observing characteristic changes in resistivity, it is possible to verify whether soil is undergoing water–oil displacement. (3) The direction of displacement significantly affects changes in resistivity for all three media involved due to gravity effects during water displacement by the oil process. (4) Resistance values during the water–oil displacement process are directly influenced by the size of sand particles used in experiments. Full article

Mercury is naturally present in soils at trace concentrations, but its cycle is increasingly disrupted by anthropogenic activities, which affect its distribution and behavior. Due to its toxic nature, mercury has become a significant focus in environmental and public health policies. Following the detection of mercury anomalies during groundwater quality monitoring at the Pays de Caux study site (France), a comprehensive multidisciplinary research effort was initiated. This included geological and hydrogeological studies aimed at tracking mercury concentrations in piezometric wells and identifying the sources of these anomalies. This study seeks to assess the groundwater quality and characteristics from ten hydrogeological wells. The evaluation will focus on key hydrogeological parameters, including pH, redox potential (Eh), suspended solids, and groundwater levels, as well as a detailed geochemical analysis of elements such as Hg, Fe, Mn, Zn, Pb, and Cu. The mobilization of mercury and other metallic traces elements is strongly governed by environmental factors. Hydrochemical analyses highlight the complex interplay of various parameters that influence the chemical forms and behavior of mercury in both soil and groundwater. The results from the piezometric measurement campaigns (Pz1 to Pz7) have provided crucial insights, enabling the development of hypotheses about mercury’s behavior in the chalk aquifer. It is hypothesized that impermeable areas may trap groundwater for extended periods, leading to the accumulation and abnormal concentration of mercury. This could cause mercury to be intermittently released, potentially affecting the surrounding environment. Mercury concentrations in groundwater are highly sensitive to pH and redox potential (Eh), with low pH and reducing conditions promoting mercury mobilization and the formation of toxic methylated species. The study suggests the chalk aquifer is generally in equilibrium with mercury, but fluctuations in mercury levels between Pz7 and Pz4 are likely due to the heterogeneity of the clay and geological factors such as mineral composition and fracturing. This research provides insights into mercury transfer in heterogeneous environments and emphasizes the need for continuous hydrogeological monitoring, including piezometer readings, to manage mercury dispersion in the aquifer. Full article

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

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems. Full article

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R² of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. Full article