Search Results (2,383)

This study aims to characterize rainfall-triggered phenomena, including floods, landslides, and urban flooding, defined as damaging hydrogeological events (DHEs), through the integration of the scientific literature and historical documentary sources, and to analyze their rainfall-triggering conditions. The analysis focuses on a sector of the Ionian coast of Calabria (southern Italy) in the period 1925–2025. The identified DHEs were organized into 463 damage records (DRs), enabling a municipal-scale analysis at monthly temporal resolutions. To characterize the rainfall conditions associated with DHEs, we identified a rainfall indicator (R), defined as the ratio between the monthly rainfall observed during a DHE and the corresponding long-term climatological average rainfall. Results show that DHEs occur more frequently during autumn (46%) and winter (41%) and are mainly associated with moderate (1< R < 2) to strong rainfall anomalies (R > 3). Summer events, although limited in number, are often (43%) associated with very strong rainfall anomalies (R > 3). Spatial analysis highlights a heterogeneous distribution of DHEs in the study area, with some municipalities showing a greater occurrence of multiple phenomena. Landslides are the most frequent phenomenon, occurring in 29% of cases in combination with other processes and across a wide range of precipitation conditions. Floods are most often (over 60%) associated with moderate to strong anomalies, while urban flooding exhibits intermediate behavior. Stronger-rainfall-anomaly conditions are generally associated with DHE impacts with wider spatial extents. The study suggests that the proposed indicator may provide a useful framework for the first-order characterization of rainfall conditions associated with DHEs in contexts characterized by the limited availability of long-term data or in similar climatic areas. Full article

Intensive extraction in shallow coal seam groups poses a severe threat to regional hydrogeological stability. This study investigates the evolutionary laws of water-conducting fracture zone (WCFZ) height and phreatic level response at the Wanli No. 1 Mine. Although limited to a two-dimensional physical model and a single-case study, the research integrates field monitoring with similarity simulations to evaluate the efficacy of gangue grouting backfilling (GGB). The results reveal a significant superposition effect in dual-seam mining, where cumulative disturbances trigger the reactivation of upper-seam fractures, causing the WCFZ to penetrate the surface (170 m)—a phenomenon absent in single-seam mining. Scientifically, this work identifies a dual-threshold effect for ecological and structural preservation. While an equivalent filling rate (η) of 35% is sufficient to maintain the ecological water level in single-seam mining, dual-seam extraction requires a minimum η of 65% to restrict phreatic drawdown within the 1.5 m ecological threshold. Notably, while the laboratory model suggests a higher mechanical safety limit of η = 80% to prevent fracture propagation, the 65% threshold provides a balance between backfilling efficiency and environmental protection. The primary scientific contribution of this study is the quantification of the coupling relationship between overburden mechanical stability and long-term ecological functions. By shifting the overburden failure mode from “surface-penetrating fracturing” to “controlled bending subsidence,” this research provides a robust theoretical foundation for decoupling mining intensity from hydrogeological degradation in fragile multi-seam environments. Full article

Ningbo City is endowed with abundant mineral water resources. Investigating their chemical characteristics and formation mechanisms is essential for understanding hydrochemical evolution and supporting sustainable resource utilization. Based on hydrochemical data from 12 drinking natural mineral water sources in Ningbo City, this study investigates the hydrochemical features and genesis of mineral water by integrating statistical analysis, hydrochemical diagrams, ionic ratios, and mineral equilibrium modeling. The results indicate that metasilicic acid (as H₂SiO₃) and strontium (Sr) are the principal characteristic components of the drinking natural mineral water in Ningbo City, with concentrations of 32.87–60.8 mg/L and 0.05–4.59 mg/L, respectively. The mineral waters are neutral to slightly alkaline and weakly mineralized, with the pH values ranging from 6.70 to 8.16, and total dissolved solids (TDS) contents of 76.8–767.2 mg/L. The predominant hydrochemical facies are HCO₃-Ca-Na, HCO₃-Ca, HCO₃-Na-Ca. Their chemical composition is mainly governed by rock weathering, whilst also being influenced by cation exchange and mineral dissolution–precipitation equilibrium. H₂SiO₃ is mainly derived from the weathering and hydrolysis of silicate minerals such as plagioclase. Sr enrichment is associated with the dissolution of Sr-bearing silicate minerals and certain sulphate minerals, as well as prolonged water–rock interaction. The Sr- and Si-rich aquifers provide the material basis for the enrichment of Sr and H₂SiO₃ in groundwater. Structural fractures and weathering fractures provide transport pathways and storage spaces for groundwater, facilitating the migration and enrichment of these characteristic components. The mechanism of mineral water emergence can be summarized as of the tectonic fracture-controlled circulation-leaching type. Full article

This study aims to explore a leakage-aware and explainable machine learning framework for predicting tunnel water inrush quantity (WIQ) under small-sample and high-heterogeneity geological conditions. A project-level dataset was compiled at a fixed spatial granularity of 30 m per excavation segment by integrating forward prospecting outputs, construction-face observations, and geological reports, and six hydrogeological–structural indicators were used to predict the water inflow rate in cubic meters per hour. To overcome data scarcity and improve generalization, a tabular generative adversarial network (GAN) was introduced to augment the training distribution while preserving marginal statistics and inter-variable dependence, and a swarm-intelligence optimizer was employed to tune a Categorical Boosting (CatBoost) regressor for stable performance. In addition, six mainstream tree-based learners were benchmarked under a unified protocol, and model transparency was ensured through a multi-level interpretability suite combining SHapley Additive exPlanations (SHAP) attribution, partial dependence with individual conditional expectation (ICE) diagnostics, and interaction surfaces. Results show that, under the present fixed split, training-set augmentation was associated with improved performance for the evaluated baseline learners, and the proposed hybrid model achieved encouraging hold-out accuracy. However, because the dataset contains only 55 real samples and the test set contains only 11 real samples, the reported performance should be interpreted as an initial project-specific indication rather than robust evidence of generalizable reliability. Interpretability analyses further identify lithologic and reflector-related factors as dominant drivers, and reveal nonlinear response patterns and interaction-sensitive high-risk regions. Overall, the proposed framework shows potential to improve predictive performance and engineering interpretability for the studied project, and may provide a useful reference for drainage and reinforcement planning. Further confirmation through repeated data splitting, additional samples, and external validation is still needed before broader application. Full article

Due to its unique hydrogeological conditions, the Southwest Karst Area (SKA) in China experiences droughts far more frequently than non-karst regions. Exploring the distribution patterns and driving factors of different drought types is crucial for enhancing the region’s disaster prevention and mitigation capabilities and effectively addressing climate change risks. Using meteorological data from 1979 to 2023 in the SKA—including precipitation, temperature, humidity, potential evapotranspiration, and soil moisture—this study employed Copula theory to construct the Standardized Temperature Deficit Index (SDTI), the Standardized Humidity–Temperature Deficit Index (SDHTI), and the Standardized Atmosphere–Soil Index (SASI). Based on these indices and run theory, this study revealed the spatial distribution characteristics of different drought types (general, atmospheric, and composite) in terms of intensity, frequency, severity, and duration. Furthermore, the Mann–Kendall test and random forest analysis were applied to investigate drought trends and primary driving factors. The results indicate that droughts in the SKA exhibit significant regional characteristics and an overall worsening trend. Among them, droughts in karst-developed regions are generally more severe, though their manifestations vary across areas: compound droughts are particularly severe on the western Sichuan Plateau but relatively mild in Guangxi. In contrast, atmospheric droughts are more pronounced in Guangxi. Regarding trends, the rate of drought intensification was relatively moderate in Guangxi and the western Sichuan Plateau but more pronounced in other regions, with the maximum increase reaching 0.59. However, this upward trend is not statistically significant. Additionally, drought in karst areas was characterized by high frequency and intensity but shorter duration and lower severity, whereas the opposite was true in non-karst areas. Random forest analysis revealed that temperature is the primary driver of SDTI (2.60), while relative humidity and temperature have significant impacts on SDHTI (3.21 and 2.42, respectively). Soil moisture and temperature contribute most significantly to SASI (2.08 and 1.48, respectively). These findings provide important insights to guide the rational allocation of regional water resources and optimize agricultural management strategies. Full article

Coastal aquifers are more exposed to pollution and salinity than other hydrogeological systems due to their proximity to the sea, increasing groundwater withdrawals, and climate change. The aim of this study is not only to evaluate and compare the vulnerability of coastal aquifers using the DRASTIC and GALDIT models but also to investigate effects of different weighting methods on the results of vulnerability zoning. The spatio-temporal vulnerability assessment was conducted for coastal aquifers in Hormozgan Province in Iran over a 15-year period (2010–2024). After collecting information layers required for both models, vulnerability maps were calculated for three consecutive five-year periods using three weighting methods: (a) normal weighting, (b) Shannon entropy, and (c) particle swarm optimization (PSO) algorithm. The results indicate that the coastal areas of the western part of the province have the highest vulnerability in both models, and the intensity and extent of high-risk zones have increased in recent periods. Comparison of weighting methods revealed that normal weighting provided a conservative and uniform distribution, while the entropy method, due to its reliance on statistical dispersion of data in some areas, led to a hyperbole of the risk. In contrast, the PSO algorithm provided the most accurate and realistic results compared to classical fixed-weight and entropy-based vulnerability maps, as it was able to identify critical areas with higher spatial concentration and hydrogeological coherence. The combined results of DRASTIC and GALDIT demonstrated that parts of the coastal aquifers of Hormozgan are simultaneously in a critical state in terms of inherent vulnerability and salinity potential. The findings of this study can be used as a scientific basis for sustainable management of groundwater resources, withdrawal control, and protection climate adaptation planning in coastal areas. Full article

Lake Vrana on Cres Island (northern Adriatic Sea) is a rare hydrogeological system consisting of a large freshwater body located within a karst cryptodepression with its bottom below sea level and surface above it. This study investigates long-term hydroclimatological changes using daily records of lake water level (1978–2024), water temperature (1979–2024), precipitation, and air temperature (1981–2024). Linear regression, the Mann–Kendall trend test, Sen’s slope estimator, and day-to-day variability metrics were applied to quantify long-term trends and system responses. A multi-index approach was used to enable a robust assessment of drought dynamics in this unique karst system: the Standardized Precipitation Index (SPI), representing meteorological conditions based on precipitation; the Standardized Hydrological Index (SHI), reflecting hydrological response derived from lake levels; and the New Drought Index (NDI), integrating precipitation and temperature to account for evapotranspiration effects. Results indicate a statistically significant decline in lake water levels (−4.5 to −5.2 cm yr⁻¹), while precipitation shows no significant trend. In contrast, both air and water temperatures exhibit a significant increase (~0.5 °C per decade) and are strongly correlated (R² = 0.767). The lake demonstrates pronounced thermal inertia and delayed response to atmospheric forcing. Day-to-day analysis reveals increasing variability in water temperature and decreasing variability in air temperature, suggesting changes in system energy dynamics. Drought indices (SHI and NDI) show significant negative trends, whereas SPI does not, indicating that drought intensification is primarily driven by rising temperatures and enhanced evapotranspiration rather than precipitation deficits. These findings demonstrate that Lake Vrana acts as a sensitive integrator of climatic forcing. Full article

Groundwater is the primary source of livestock watering across the arid pasturelands of western Kazakhstan, yet no systematic field hydrochemical assessment has been published for this region in over 40 years. This study presents the first systematic field-based hydrochemical characterisation of groundwater sources used for pasture livestock watering in the West Kazakhstan Region and Aktobe Region, filling a critical data gap that has persisted since the Soviet era. Specifically, it characterises the hydrochemistry, water quality, and infrastructure condition of groundwater sources, and evaluates the groundwater resource potential against current and projected livestock water demand. A total of 139 groundwater samples were collected along 11,182 km of field routes during May–July 2025, and analysed for 25 physicochemical parameters; hydrochemical classification was performed using AquaChem 11, and spatial analysis was conducted in ArcGIS 10.8. The groundwater chemistry distribution is bimodal: fresh bicarbonate-calcium-magnesium waters (TDS < 3.0 g/L) constitute approximately 80% of samples, while highly mineralised chloride-sulphate-sodium waters (TDS up to 9.91 g/L) occur in salt-dome-influenced discharge zones. Nitrate concentrations exceeded 50 mg/L in 23–36% of samples, with maxima of 635 mg/L, reflecting intensive anthropogenic contamination near livestock facilities. Predictive exploitable fresh groundwater resources exceed current livestock demand by a factor of 162. The principal constraint on pasture water supply is not resource scarcity but the non-operational status of 51–75% of inspected watering infrastructure, a legacy of post-Soviet institutional collapse that requires urgent rehabilitation. Full article

Magma-heated geothermal systems have garnered significant attention in academia due to their unique formation mechanisms and vast potential. This paper focuses on the Rehai, Ruidian, and Banglazhang geothermal fields in the Tengchong area. We present the element geochemistry and isotope compositions of hot springs, cold springs, and surface water to explore magmatic fluid input into geothermal systems and investigate the release of deep mantle-derived components. Based on our findings, we propose a conceptual model and theoretical framework for geothermal system genesis constrained by magmatic heat source influences. Results indicate that magma-heated geothermal systems coexist with three types of geothermal water: neutral chloride-rich water, acidic sulfate-rich water, and alkaline bicarbonate-rich water. The infusion of magmatic fluids into geothermal systems. The enrichment of trace elements in hot springs is jointly controlled by magmatic fluid input and host rock leaching. The magma chamber is the primary factor influencing the reservoir temperature. The parent geothermal fluid can be identified within the geothermal system. During circulation, the parent geothermal fluid undergoes three cooling processes: adiabatic cooling, conductive cooling, and mixing with cold water. We propose that the release of mantle-derived materials is a key factor in element enrichment within magma-heated geothermal systems, and mantle-derived components are more enriched in areas with active magma chambers. The findings of this study provide insights into magmatic fluid input into geothermal systems and highlight the critical role of the release of mantle-derived components in the formation of high-temperature geothermal resources. Full article

In the Rhône-Mediterranean-Corsica (RMC) basin (130,000 km², 14 million inhabitants), groundwater intended for human consumption has been monitored for decades. These data, stored in the SISE-EAUX database, were cross-referenced with information from the CORINE Land Cover (CLC) database, which describes human land use, in order to identify potential relationships between pollutant pressure and water quality at the basin scale, as well as the mechanisms specific to each geographical area. Data processing was carried out in three stages. The 27,741 water samples from 2825 abstraction points were assigned to the 224 groundwater bodies (GWBs), and average values for each physicochemical and bacteriological parameter were calculated for each GWB. At the same time, the percentage of surface area covered by each land use type was also extracted at the scale of each GWB. This information was subjected to statistical processing, first separately and then jointly, using principal component analysis (PCA) and hierarchical clustering of parameters. A redundancy in the information carried by the quality parameters, previously observed at the scale of administrative regions (four to five times smaller), is confirmed at this new analysis scale, paving the way for data consolidation and a more synthetic representation. Fecal contamination primarily concerns areas with crystalline lithology and, secondarily, a few karst sectors, with other livestock farming regions being less contaminated. Higher nitrate concentrations are observed in cereal-growing regions and areas of intensive row cropping, while metal concentrations are lower in the drier Mediterranean climate zone than under the more humid continental climate. Structuring factors, notably altitude and climate, emerge at the RMC basin analysis scale, which was not the case at the scale of administrative regions. These structuring factors influence land use, soil type, and hydrological regimes alike, which explains the correlations between the information contained in the CLC and SISE-EAUX databases. Full article

Inverse modeling for subsurface flow represents a fundamental scientific challenge in hydrogeology and geotechnical engineering, which seeks to reconstruct critical hydrogeological parameters from sparse observational constraints. The marked spatial heterogeneity of subsurface formations, combined with the prohibitively high costs of data acquisition, renders parameter inversion, especially with very sparse supervision, inherently ill-posed and susceptible to non-uniqueness and instability. Numerical simulation-based iterative inversion methods are computationally expensive and time-consuming. Purely data-driven approaches require extensive labeled data, whereas the existing physics-informed methods lack an explicit architecture-level information transfer channel between parameter and response fields. Under sparse supervision, this prevents hydraulic head observations from effectively constraining hydraulic conductivity identification, resulting in weak parameter identifiability. In this work, we propose a physics-coupled and message-transferred inverse modeling method for transient subsurface flow problems with very sparse supervision. Specifically, the static parameter field estimated by the inversion network is explicitly incorporated into the dynamic response prediction network, and the static inversion and dynamic prediction networks are physics-coupled by the governing equations in parallel. This method enables accurate hydraulic conductivity inversion under extremely limited supervision. Experiments on multiple parameter fields, label scales, and noise levels demonstrate accurate and stable inversion performance under very sparse supervision, with ensemble-based uncertainty analysis, further confirming the reliability of the proposed method. Full article

This study is conducted to investigate the deformation evolution and collapse mechanism of the Rongxing gypsum mine by integrating multi-source monitoring data, including synthetic aperture radar (SAR), global navigation satellite system (GNSS), and microseismic observations. Long-term surface deformation from 2015 to 2025 is retrieved using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), while GNSS data (2021–2022) are used to capture rapid ground displacement during the collapse event. Microseismic monitoring provides insights into the evolution of subsurface fracturing processes. The results show that the pre-collapse stage is characterized by continuous and spatially heterogeneous subsidence. Prior to the collapse, microseismic activity is observed to exhibit clear precursory signals, including an increase in event number, a decrease in b-value, and accelerated cumulative energy release, suggesting that the transition from distributed microcrack development to large-scale fracture coalescence is occurring. The b-value, derived from the Gutenberg–Richter frequency–magnitude relationship, describes the relative proportion of small to large seismic events and reflects variations in the statistical distribution of event magnitudes. During the collapse stage, abrupt, large-magnitude subsidence is observed by GNSS. After the collapse, the deformation is found to enter a long-term adjustment phase characterized by the coexistence of subsidence and uplift, indicating that stress redistribution within the overburden is occurring. Based on these observations, a conceptual model is proposed to describe the progressive failure mechanism of the goaf, with four stages: slow subsidence, accelerated deformation, collapse, and post-collapse adjustment. This study demonstrates the effectiveness of integrating SBAS-InSAR, GNSS, and microseismic monitoring for understanding the full lifecycle of goaf collapse. It provides valuable insights for early warning of mining-induced geohazards. Full article

Fine-scale urban underground exploration is vital for geological safety and hydrogeological protection. In spring-rich cities like Jinan, shallow structures—such as sedimentary layers and fault systems—act as critical regulators of groundwater migration and spring formation. Yet, traditional seismic methods are often hindered by high costs and complexity. While Distributed Acoustic Sensing (DAS) offers a solution, its effectiveness is frequently limited by the poor coupling and coherent signal loss of existing cables in pipes. This study proposes an efficient alternative using mobile, unburied surface fiber-optic cables. Ten temporary DAS experiments were conducted along a 23 km line in Jinan, accompanied by nodal seismometers. Stable dispersion curves along the line can be extracted by subarray ambient noise interferometry with short-duration urban traffic noise DAS recording, and finally a high-resolution 2D S-wave velocity profile was mapped. The result shows that the profile has pronounced subsurface lateral heterogeneity, characterized by the alternation between two uplift zones and two grabens, which is highly consistent with H/V results from nodal seismometers. This confirms that mobile surface-cable DAS provides a rapid, reliable, and cost-effective imaging solution for characterizing complex urban subsurface structures, providing essential data for both geohazard assessment and the protection of groundwater transport pathways. Full article

In complex hydrogeological systems, such as multilayered aquifers in densely urbanized coastal areas, multi-parametric, multi-depth networks are required for discriminating between anthropogenic and natural signals. This study presents an evaluation protocol of a pre-existing piezometric network, composed of 66 piezometers, aimed at implementing a near real-time (NRTM) hydrogeochemical monitoring system in the Strait of Messina (Sicily, Italy) area. A rigorous selection process was conducted to determine the suitability of these sites for hosting permanent, above-ground instrumentation. After excluding 55 sites for logistical and administrative reasons, the remaining piezometers were evaluated through a multi-step protocol. Video inspections and vertical logs of temperature and electric conductivity were carried out to identify pipe integrity and screened sections. Water samples were collected, for the execution of geochemical and isotopic analyses, to distinguish between groundwater bodies and stagnant water or local infiltration. Finally, preliminary near real-time monitoring of water level and temperature assessed the response of the sites to hydrological cycles and tidal effects. A scoring system was applied to rank the sites, resulting in a priority list for the installation of the permanent monitoring network. The evaluation protocol was tested in the Strait of Messina, but it is based on a generical approach, independent of the specific setting of a study area, making it suitable for general applications worldwide. Full article

The DC resistivity method is extensively employed in metal mineral exploration, hydrogeology, and engineering geology owing to its cost-effectiveness and high precision. High-precision 3D forward modeling of DC resistivity is crucial for the efficient inversion of resistivity data to delineate the true subsurface resistivity distribution. This paper presents a three-dimensional DC resistivity forward modeling algorithm based on a high-order finite element method (FEM). Initially, the model domain is discretized using an unstructured mesh composed of arbitrary tetrahedral elements. Subsequently, isoparametric element transformation techniques are utilized to construct high-order nodal basis functions. Furthermore, a novel absorption boundary condition, leveraging real number and coordinate stretching techniques, is implemented; this condition is notably straightforward to implement. The resulting finite element system of equations is then solved using the parallel direct solver MUMPS. To validate the accuracy and efficacy of the proposed algorithm, forward calculations are performed on several typical geoelectric models. The results demonstrate that increasing the element order enhances the computational accuracy of the finite element numerical solution. Moreover, the proposed absorption boundary condition outperforms conventional Dirichlet boundary conditions without incurring additional computational cost. Full article