Search Results (293)

Groundwater-potential assessment in karst aquifers is complicated by pronounced spatial heterogeneity driven by structural permeability, lithological variability, recharge redistribution, and unresolved subsurface conduit connectivity. Although machine-learning approaches have improved regional groundwater mapping, most existing models provide only deterministic predictions and offer limited information on predictive uncertainty and hydrogeological reliability. To address this limitation, we propose CT-TreeFlow. This probabilistic groundwater assessment framework goes beyond conventional machine-learning models by explicitly learning the full conditional probability distribution of groundwater favourability rather than a single deterministic estimate. The framework integrates sparse probabilistic environmental routing, conditional density estimation, hydrogeologically constrained pseudo-absence generation, geographically structured spatial validation, and explainability-driven interpretation within a unified modelling architecture, enabling simultaneous groundwater prediction, uncertainty quantification, and hydrogeological interpretation. The framework was applied to the Zagros karst system in Khuzestan Province, Iran, using remote-sensing-derived environmental predictors, Copernicus DEM-based morphometric variables, geological–structural datasets, and hydroclimatic indicators. Performance was evaluated against LightGBM and XGBoost using GroupKFold spatial cross-validation. CT-TreeFlow achieved a mean RMSE of 2.737 and a mean R² of 0.852, while also providing spatially explicit uncertainty estimates and probabilistic prediction intervals. Explainability analyses identified fracture density, lithology, drainage organisation, and terrain-controlled recharge conditions as the dominant controls on groundwater favourability. Predicted high-favourability zones showed strong spatial correspondence with major carbonate formations and independent spring–cave inventories, supporting the hydrogeological plausibility of the mapped patterns. These results demonstrate that probabilistic modelling can provide more reliable and physically interpretable groundwater assessments than deterministic approaches in structurally complex karst environments. CT-TreeFlow offers a transferable framework for uncertainty-aware groundwater exploration and regional hydrogeological decision support in heterogeneous aquifer systems. Full article

The dynamics of methane (CH₄), nitrous oxide (N₂O) and carbon dioxide (CO₂) in groundwater have rarely been investigated. As dissolved gases they may be transported to distant sites and, hence, to the atmosphere. Crumps Cave (CC) is located on a perched aquifer in south-central Kentucky. Water was sampled at a waterfall within the cave located 15 m below the surface, at two adjacent surface wells 15 m and 50 m deep, providing samples from the epikarst and regional aquifer, respectively. Dissolved gases and geochemistry parameters were analyzed for seasonal changes across three years of weekly monitoring (2015–2017) using Kruskal–Wallis H tests and Bonferroni-corrected pairwise comparisons. Dissolved CO₂ concentrations are mainly controlled by percolation through the epikarst, influenced by soil respiration, and vary with rainfall and seasonal temperature fluctuations. CH₄ showed a site-dependent pattern: concentrations were significantly elevated in warm seasons at the shallow and deep wells, where anaerobic conditions and agriculturally derived organic matter promote methanogenesis; no seasonal variation was detected at the cave site, where oxic conditions limit CH₄ year-round. N₂O was significantly elevated in cold seasons at all three sites, driven by cold-season denitrification of agriculturally derived nitrates. N₂O did not differ between sites, indicating seasonal temperature-driven denitrification as the primary control rather than site hydrology, with cold-season denitrification of agriculturally derived nitrates from fertilizer application. Indirect gas emissions are characteristic of karst systems and may be transported or stored in aquifers through complex interactions of groundwater recharge, microbial activity, and seasonal land-use variability. Full article

Karst terrains hold vital global groundwater reserves, underpinning regional water security and ecological stability. To elucidate groundwater hydrochemical patterns and formation mechanisms in Wuhan’s karst zone, this study adopted the Gibbs model, correlation analysis, principal component analysis and positive matrix factorization to explore water–rock interactions, hydrochemical origins, element migration, hydrogeochemical facies and genetic processes. The results show that water in both confined porous loose rock aquifers (CPLRAs) and karst fissure carbonate rock aquifers (KFCRAs) is mainly of HCO₃–Ca and HCO₃·SO₄–Ca types. Carbonate dissolution dominates hydrochemical evolution, with Ca²⁺, Mg²⁺, and HCO₃⁻ as major ions. Natural water–rock interactions control the ionic characteristics of both groundwater types. Silicate weathering exerts a greater influence on water in the KFCRA, while water in the CPLRA has more complex ion sources. Anthropogenic activities contribute 17.52% and 17.61% to their hydrochemical variations, suggesting moderate human influence. Water in the CPLRA is mainly affected by domestic sewage and soil organic nitrogen, locally superimposed with industrial and mining disturbances. Water in the KFCRA is primarily influenced by agricultural pollution, with minor domestic sewage input. These findings provide a scientific basis for sustainable development, protection, and targeted pollution control of groundwater resources in the Wuhan karst area, and offer a reference for hydrochemical studies in comparable karst regions. Full article

This study presents the development and field testing of a Pressurized Driving-Down Air Multilevel Sampler (PDA-MLS), an integrated groundwater sampling device designed for depth-discrete sampling in boreholes affected by floating non-aqueous phase liquids (NAPLs). Conventional sampling methods—such as low-flow pumps, bailers, and packer-isolated systems—often fail under these conditions due to limited accessibility, cross-contamination, or disturbance of the water column. The proposed system addresses these limitations through a controlled pressurized-gas actuation mechanism that transfers groundwater from multiple PTFE-membrane chambers installed at discrete depths. This configuration enables low-disturbance sampling below floating contaminant layers. The use of chemically inert materials (stainless steel and PTFE) minimizes sampling artifacts and ensures compatibility with volatile organic compound (VOC) analyses. A simplified hydraulic conceptual framework describing inflow, outflow, and pressure-driven displacement was developed to support purge-duration estimation and operational parameter definition. The device was tested in a 90 m deep fractured limestone aquifer contaminated by tetrachloroethylene (PCE), where floating hydrocarbons limited the applicability of conventional sampling techniques. Field testing showed stable discharge conditions (~145–160 mL/min), repeatable sampling cycles, and successful collection of depth-discrete groundwater samples under the investigated site conditions. No evidence of sampler-related hydrocarbon entrainment was observed in the collected samples within the analytical detection limits of the adopted laboratory methods. To the authors’ knowledge, the PDA-MLS represents one of the few groundwater sampling systems specifically designed to combine low-disturbance multilevel sampling with operation in wells affected by floating NAPL. These features make it a promising tool for environmental monitoring, high-resolution characterization of fractured aquifers, and long-term assessment of contaminated sites. Full article

Shallow coal resources are being gradually depleted, which has led to an increase in mining depth. However, the safe extraction of deep coal seams is increasingly threatened by limestone water hazards. When vertical hydraulic channels such as karst collapse columns (KCCs) develop in limestone strata, high-pressure water may flow into the mine, potentially causing substantial casualties and property losses. In this study, the 1613A stope of the Zhangji coal mine was investigated through comprehensive detection, grouting treatment, and prevention effect evaluation. A numerical model was established to simulate the dynamic changes in groundwater levels within the limestone aquifers throughout the process. The results reveal that a KCC is developed beneath the C₃³ stratum, exhibiting an oval shape with a length of 53 m and a width of 35 m in plan view. A combination of surface and underground methods, including exploration, treatment, verification, and reinforcement, has sealed the hydraulic pathway connected to the Ordovician limestone, thereby eliminating the threat of floor water inrush. These findings are of significant value for the application and dissemination of advanced regional control technologies for water hazards in coal mines. Full article

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

The carbonate massifs of the southern Italian Apennines host extensive karst aquifers, which represent the principal drinking water resources. This study focuses on the Dragone Plain polje, a vast closed karst depression located in the main recharge sector of the Terminio–Tuoro carbonate massif. The polje drains a ~55 km² endorheic catchment and may be flooded during the cold and wet season, forming a temporary lake. We employed continuous hydroclimatic time series (rainfall, groundwater level, spring discharge, and river level) together with sparse Sentinel-2 true color satellite images for the period 2020–2024 to analyze the flooding process in the polje and its hydraulic connection with the saturated zone of the karst aquifer. Results indicate that lake formation depends on the balance among soil moisture, rainfall intensity, and runoff development, which were modeled on a daily scale. Daily recharge was also estimated and compared with groundwater level time series from the deep karst aquifer. The modeling was integrated with cross-correlation analysis of the time series, providing insights into the propagation of precipitation pulses through the hydrogeological system. This case study represents an important example for understanding the relationship between karst polje hydrological functioning and climate in a Mediterranean area. Full article

Karst aquifers present unique challenges for groundwater level prediction due to their dual-porosity structures and highly nonlinear hydrological responses. This study systematically evaluates nine machine learning and deep learning models (RF, XGBoost, LSTM, CNN, Transformer, N-BEATS, CNN-LSTM, Seq2Seq-LSTM, and Attention-Seq2Seq-LSTM) for rainfall-driven groundwater level forecasting in the Maocun subterranean river catchment, Guilin, Guangxi, China. Two years of hourly high-frequency data from three monitoring sites representing distinct hydrogeological zones (recharge, flow, and discharge) were employed within a multidimensional evaluation framework integrating single-step accuracy, multi-step stability, and computational efficiency. Results indicate that the Transformer achieved the highest single-step prediction accuracy, attaining the lowest RMSE (0.130–0.606 m) and highest R² (0.813–0.965) across all three sites. CNN-LSTM offered the best balance between predictive performance and computational cost, requiring an average training time of only 27.97 s and 28.0 convergence epochs. N-BEATS demonstrated superior long-term stability in 12-steps-ahead forecasting, achieving R² = 0.914 at ZK1, outperforming all other architectures. More fundamentally, hydrogeological complexity exerted a dominant control on predictive skill that systematically outweighed differences arising from model architecture. All models yielded R² below 0.813 at the geologically complex ZK2 site, whereas R² exceeded 0.950 across all models at ZK1, indicating that aquifer complexity, rather than algorithm selection, constitutes the primary constraint on prediction feasibility. This study presents the first application of N-BEATS to karst groundwater level forecasting and proposes a replicable multidimensional evaluation framework, providing a scientific reference for intelligent modelling of complex karst systems. Full article

In karstic environments, water supply wells are vulnerable to rapid sediment transfer during intense rainfall events, often generating turbidity peaks that disrupt water-treatment operations. In Normandy (France), the high density of sinkholes and the complexity of transport processes in karsts complicate the identification and prioritization of sinkholes requiring mitigation to reduce sediment fluxes at water supply wells. This study aims to quantify the time-lagged impact of each sinkhole on turbidity peaks at a supply well using a cascade modeling approach that couples numerical surface erosion–runoff simulations with deep learning models representing hydrosedimentary responses through the karst network. Surface erosion–runoff was simulated using WaterSed. Hydroclimatic time series and WaterSed model outputs were used as inputs for our deep learning models. Several deep learning architectures were compared and optimized across multiple rounds to identify a best-performing model, which was then interpreted using interpretability methods. Interpretability analyses show that turbidity is primarily controlled by seasonal conditions and short-term rainfall accumulation, while multiple sinkholes contribute jointly to short time lags. Temporal attributions reveal rapid karst response followed by attenuation, consistent with reactive karst behavior. The contribution of each sinkhole to turbidity peaks allows us to identify the most important sinkholes requiring mitigation by stakeholders. Full article

Regional curtain grouting for water interception serves as a critical technique for achieving safe and efficient mining under complex hydrogeological conditions in deep mine deposits. This study focuses on the Cuihongshan Iron-Polymetallic Mine, where repeated incidents of water inrush and sand outbursts have occurred due to complex hydrogeological conditions. By identifying the water-conducting pathways and characterizing the spatial distribution of relative aquitards within the mining area, a precise hydrogeological model was established. On this basis, the engineering application and performance evaluation of horizontal curtain grouting were systematically investigated. Through field monitoring and multi-method joint analysis, the water-blocking effectiveness of the grouting technique was comprehensively assessed. The results demonstrate a significant sequential reduction in grout take per meter for primary, secondary, and tertiary grouting holes, indicating a clear cumulative grouting effect. The grout effectively filled karst fractures, forming a continuous and stable water-resisting curtain. The project successfully severed the hydraulic connection between the highly water-rich Quaternary aquifer and the mine workings, substantially reducing mine water inflow. This study provides important theoretical support and practical reference for water hazard control in similar deep metal mines. Full article

Tunnel construction in karst aquifers can substantially alter groundwater flow systems. In this study, a three-dimensional groundwater flow model based on MODFLOW-CFP was developed to simulate the Pingyanggai synclinal karst system in Chongqing, China, incorporating dynamic tunnel excavation and lining processes. Under natural conditions, groundwater recharge is approximately 4.8 × 10⁴ m³/d and is primarily balanced by discharge to the Yanmenkou and Miaolongtang underground rivers. Tunnel excavation introduced a new drainage outlet, generating an inflow of about 5.6 × 10⁴ m³/d. The two underground rivers exhibited contrasting responses to excavation. Discharge from the Yanmenkou underground river decreased by approximately 6 × 10³ m³/d (about 30%), indicating strong hydraulic connectivity with the tunnel, whereas the Miaolongtang underground river showed only minor changes. The simulated responses were qualitatively consistent with field observations during key excavation stages. These results demonstrate that tunnel excavation modifies not only the overall groundwater balance but also the internal redistribution of discharge pathways within the karst system, providing a quantitative basis for evaluating tunnel-induced hydrogeological impacts in complex karst environments. 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

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

Karst aquifers provide critical water resources in the Mediterranean region, yet climate change threatens their sustainability. This study integrates stable isotope analysis (δ²H, δ¹⁸O), hydrochemistry, and hydrological time series to characterize precipitation–groundwater dynamics in the Mt. Učka karst system (Croatia). Precipitation samples collected across an altitudinal gradient of approximately 1400 m and groundwater from three major groundwater sources were analyzed over a 2.5-year period. Precipitation exhibits pronounced isotopic variability with d-excess values indicating mixed Atlantic–Mediterranean moisture sources. Groundwater is primarily recharged by precipitation from the cold part of the hydrological year. It exhibits substantial attenuation of isotopic signals, which indicates extensive mixing processes but prevents quantitative estimation of mean residence time. Groundwater is predominantly recharged from elevations above 900 m a.s.l., with one spring showing evidence of higher-elevation recharge. Analysis confirms the system’s dual porosity: a rapid, conduit-dominated response indicates high vulnerability to surface contamination, while a sustained, matrix-dominated response provides greater buffering capacity. These findings highlight the vulnerability of karst systems to projected reductions in autumn precipitation, the critical recharge season, and demonstrate the necessity of multi-tracer approaches for comprehensive aquifer characterization. Full article