Search Results (921)

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

Hidden disaster-causing factor investigation is a fundamental task for safety production in mines. Water hazards in karst metal underground mines are characterized by complex disaster-forming mechanisms, strong suddenness, and high risk, while traditional assessment methods are prone to expert subjective bias and cannot meet the demand for precise prevention and control. This study proposes an improved fuzzy comprehensive evaluation model by integrating the analytic hierarchy process (AHP) and normal distribution-based expert confidence weighting. A three-level assessment index system consisting of 3 first-level indicators and 11 s-level indicators is established for karst metal mine water hazard risk. The normal distribution function is used to quantify expert confidence weights so as to reduce subjective deviation. A three-level fuzzy comprehensive evaluation is performed to achieve quantitative risk grading, and the model robustness is verified through sensitivity analysis. Furthermore, three-dimensional geological modeling and seepage–stress coupling numerical simulation are conducted using COMSOL 6.0 software to validate the reliability of assessment results. The Mao’erling Gold Mine in Hunan Province is taken as a case study. The evaluation yields a comprehensive membership vector of (0.103, 0.130, 0.184, 0.351, 0.232), which is strongly consistent with numerical simulation results and field water inrush records. The results demonstrate that the improved model features strong objectivity and favorable robustness, and can provide a scientific basis for water hazard investigation, risk assessment, and prevention engineering in karst metal underground mines. Full article

This study investigated spring water in the core area and buffer zone of the Shibing Dolomite Karst World Heritage Site using one-year monthly monitoring, hydrochemistry, nitrate dual isotopes, and the MixSIAR model. The buffer zone spring exhibits shallow fissure-conduit flow with rapid hydrological response, anthropogenic nitrate dominance (>62%), nitrification as the main process, and limited denitrification. Its nitrate concentration shows seasonal peaks. In contrast, the core area spring is recharged by deep fissure water, with natural nitrate sources (>80%), stable nitrate levels (5–7.4 mg/L), and potential local denitrification. Nitrogen export in the buffer zone increases 4.5 times in the rainy season (NO₃⁻ accounting for 93% of TN). The core area shows higher TN export flux per unit area (3.34 vs. 0.4 g/m²/a) and greater DON proportion. Nitrogen export far exceeds that from rocky desertified areas, suggesting that dissolved nitrogen leaching drives karst rocky desertification evolution. Full article

Despite extensive research on soil organic carbon in karst regions, the synergistic changes in multiple carbon fractions and their stabilization mechanisms across a complete rocky desertification gradient remain poorly understood. To clarify how soil carbon pools and their drivers change during karst rocky desertification, we selected Kaiyuan City, Yunnan Province, China, as the study area. Total carbon (TC), soil organic carbon (SOC), and their related fractions, including particulate organic carbon (POC), mineral-associated organic carbon (MAOC), iron-bound organic carbon (Fe-OC), calcium-bound organic carbon (Ca-OC), and soil carbon isotopic composition (δ¹³C), were analyzed under different degrees of rocky desertification. SOC and TC followed a nonlinear pattern: increasing from no to potential desertification, decreasing at light and moderate stages, and rising again at the severe stage, indicating a phased response rather than a monotonic decline. POC was lowest under no rocky desertification and increased significantly after desertification occurred, reaching its maximum at the severe stage. MAOC peaked at the potential stage. With increasing rocky desertification severity, POC/SOC increased from no to moderate stages and then slightly decreased, whereas MAOC/SOC generally decreased. Fe-OC and Ca-OC were lowest under no desertification and increased after desertification occurred, pointing to enhanced mineral protection. Soil δ¹³C values under moderate and severe desertification were higher than under no, potential, and light desertification, implying intensified decomposition and a relative increase in C₄ plants. Mean weight diameter (MWD) and geometric mean diameter (GMD) did not differ significantly among rocky desertification stages (p > 0.05). In contrast, fractal dimension (FD) differed significantly only between the light and moderate stages (p < 0.05). Correlation and redundancy analyses showed that soil water content, bulk density, and porosity were the key factors driving variation in SOC and its fractions. These findings provide both a theoretical basis and practical guidance for soil restoration and ecological management in karst faulted basins affected by rocky desertification. Full article

Wildfires severely disrupt soil nitrogen (N) cycling, yet the mechanisms driving this disruption in fragile karst forest ecosystems remain poorly understood. We investigated how wildfires affect soil N transformation dynamics and the microclimatic drivers of these dynamics in a karst forest. Using an in situ paired burned versus unburned plot design, we evaluated post-fire soil physicochemical properties, N fractions, and N-acquiring enzyme activities in the 0–10 cm soil layer. Wildfires significantly deteriorated the soil microenvironment, increasing mean soil temperature by 9.93% and bulk density by 36.66%, while sharply reducing soil water content, porosity, and saturated hydraulic conductivity. Consequently, the fires severely depleted total and organic soil N pools. Furthermore, N-acquiring enzymes (urease, protease, nitrate reductase, and nitrite reductase) initially declined in activity before gradually recovering. Notably, partial least squares structural equation modeling (PLS-SEM) revealed a fundamental shift in the drivers of nitrogen transformation. In unburned soil, abiotic climatic factors regulated N dynamics. After wildfire, enzyme-mediated biological processes controlled N dynamics, and these processes were constrained by altered soil physics. Restoring soil physical structure and stimulating enzymatic mineralization are therefore critical, rate-limiting steps for the recovery of soil N reservoirs in fire-prone karst landscapes. Full article

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics in China’s Yujiang River Basin using an integrated framework combining run theory, drought propagation analysis, and the partial least squares–structural equation model (PLS-SEM). We analyzed the 1-, 3-, 6-, and 12-month standardized precipitation index (SPI) and standardized streamflow index (SSI) at four hydrological stations during 1984–2014, together with meteorological factors, land cover indices, large-scale climate indices, areal precipitation, and naturalized streamflow. The results show that precipitation and streamflow exhibited slight declining tendencies with marked seasonal variability, and that drought durations of all severity levels generally decreased with increasing time scales. At the same time scale, SSI was more stable than SPI, and both indices tended to become more stable as the time scale increased. SPI-3 and SSI-1 were identified as the optimal time scales for monitoring meteorological and hydrological drought, respectively, providing a practical basis for drought identification and early warning in karst basins. Hydrological drought lagged meteorological drought by 1–3 months, indicating a measurable propagation time that is valuable for improving drought preparedness and water resources regulation. PLS-SEM further revealed that precipitation and streamflow were the dominant direct drivers of drought development, while land cover exerted a persistent negative effect, and climate-related factors mainly influenced drought indirectly. These findings enhance the understanding of drought propagation and multi-factor coupling mechanisms in karst basins and provide scientific support for regional drought monitoring and water resources management. Full article

Understanding how soil acidification and moisture jointly regulate carbon mineralization is particularly important in karst grasslands, where high carbonate content can interfere with CO₂-based measurements. In this study, a controlled incubation experiment was conducted using soils collected from a typical karst grassland in Guizhou Province, China. Two pH levels (4.5 and 6.5) and three moisture levels (30%, 40%, and 60% of field water-holding capacity, WHC) were applied in a full-factorial design following a pre-incubation step to minimize carbonate-derived CO₂ interference. Soil CO₂ efflux, emission rate, and cumulative mineralization were monitored over a 60-day incubation period. Both soil moisture and pH significantly affected carbon mineralization, with a clear interaction between the two factors (p < 0.05). CO₂ efflux peaked during the early incubation stage and declined thereafter, indicating rapid depletion of labile carbon substrates. Across both pH levels, increasing moisture consistently enhanced CO₂ efflux and cumulative mineralization. Under comparable moisture conditions, near-neutral soils (pH 6.5) exhibited higher mineralization rates than acidic soils (pH 4.5). The highest carbon mineralization was observed at 60% WHC under pH 6.5, whereas the lowest occurred at 30% WHC under pH 4.5. These results suggest that moisture availability regulates substrate diffusion and microbial activity, while soil acidification constrains microbial metabolism and enzyme function. Notably, the effect of pH became less pronounced under low moisture conditions, indicating that water limitation can override pH regulation. This study offers a methodological framework for quantifying carbon mineralization in carbonate-rich soils and underscores the necessity of accounting for both physical and chemical limiting factors, as well as the confounding influence of inherent carbonates. Nevertheless, given the exclusive use of a single soil type and controlled laboratory conditions, the findings constitute preliminary evidence and require validation under field conditions and across diverse soil types before broader generalization. Full article

To address the challenges of identifying mine water inrush sources and the low efficiency of risk control under complex karst hydrogeological conditions in the Beiya Gold Mine, Yunnan, this study proposes an intelligent identification model integrating nonlinear feature extraction and intelligent parameter optimization. Utilizing 42 sets of measured water samples (comprising karst springs, surface water, and solution caves), a coupling identification model was constructed based on 11-dimensional features including hydrochemical indices and hydrogen–oxygen isotopes. The model employs Kernel Principal Component Analysis (KPCA) to extract discriminative low-dimensional features from nonlinear data, while the critical parameters of the Support Vector Machine (SVM) are optimized via an Improved Sparrow Search Algorithm (ISSA) to enhance generalization performance. The results demonstrate that the following: (1) the proposed model achieves an identification accuracy of 91.7% on the independent test set, significantly outperforming benchmark models such as RF and standard SVM; (2) three sets of comparative experiments indicate that the fusion of multi-source features yields superior identification performance compared to single-source inputs; and (3) SHAP (shapley additive explanation) interpretability analysis reveals that HCO₃⁻, Mg²⁺, Ca²⁺, and F⁻ are the core discriminative factors, with their contribution patterns aligning closely with the hydrogeochemical evolution mechanisms of the mining area. This model achieves a synergy between high-precision identification and mechanical interpretability, providing reliable technical support for water disaster prevention in karst mining areas. Full article

Under the context of global change, forest cover, as a critical component of terrestrial ecosystems, exerts a profound influence on regional ecological security and sustainable development through its spatiotemporal evolution. Current research on forest cover change primarily focuses on pattern description and single-factor driver analysis, with insufficient in-depth exploration of the interactions among multiple factors and their associated nonlinear mechanisms. To address this gap, this study focuses on the Wumeng Mountain area, a typical ecologically fragile karst region in Southwest China. By comprehensively employing methods such as Theil–Sen Median trend analysis, land use transfer matrix, standard deviation ellipse, and spatial autocorrelation analysis, this study systematically reveals the spatiotemporal evolution characteristics of forest cover from 1985 to 2024. On this basis, an integrated eXtreme Gradient Boosting–SHapley Additive exPlanations (XGBoost-SHAP) model is introduced to construct an indicator system comprising 16 driving variables, including elevation, slope, aspect, temperature, precipitation, soil type, soil pH, soil thickness, soil organic matter, soil moisture content, GDP, population, distance from water, distance from railway, distance from grade highway, and distance from government. This model quantifies the influence intensity of each driving factor on forest change. The main findings are as follows: (1) From 1985 to 2024, the forest cover rate in the Wumeng Mountain area significantly increased from 54.7% to 60.2%, exhibiting a “high-low-high” heterogeneous spatial distribution pattern along the northeast-southwest axis; (2) Forest increase primarily originated from the conversion of cropland and grassland, with contribution rates reaching 93.58% and 5.9%, respectively, indicating an overall trend of “increase in low-value areas and decrease in high-value areas”; (3) Forest cover change is driven by both natural and anthropogenic factors, with dominant driving factors exhibiting phased replacement over time. Overall, this is manifested as long-term stable constraints exerted by natural background factors, alongside strong disturbances from anthropogenic factors such as social-economic, and transportation-related activities. Natural factors remain the primary driving force behind changes in forest cover. The core findings of this study elucidate the complex driving factors of forest change in karst mountainous areas, thereby providing scientific support for the precise management of regional forest resources, the planning of ecological restoration projects, and the implementation of sustainable development strategies. Full article

We present and discuss time-lapse gravity variations recorded by a large-scale absolute gravity network operating in Central Italy. The network comprises four stations distributed across the Lazio, Umbria, and Abruzzo regions, areas affected by the significant seismic activity of 2009 and 2016–2017. From 2018 to 2023, six campaigns were carefully conducted using an FG5 absolute gravimeter. We detected significant gravity decreases around 2020 reaching between −15 and −20 μGal in three sites and approximately −37 μGal at the fourth. The Sentinel-1 time series of permanent scatterers (PS) allowed us to exclude significant contribution from vertical deformations to the observed gravity changes. We analyzed both ground-based data (rainfall gauges and well water levels) and satellite-based observations (the Gravity Recovery and Climate Experiment-Follow-On, GRACE-FO, mission) together with the Global Land Data Assimilation System (GLDAS) and precipitation models. The results reveal a significant decrease in the regional groundwater content from 2018 to the end of 2020, which coincides temporally with the observed gravity decrease. We show that the absolute gravity variation trends observed at all stations are consistent with regional-scale hydrological processes, pointing to a significant decrease in terrestrial water storage (TWS) during the same time interval. At L’Aquila (AQUI), the gravity anomaly is larger than expected from regional hydrological products alone, suggesting an additional local component possibly related to the hydrogeological response of the fractured karst system undergoing significant post-seismic activity. Full article

Underground water flow in karst areas and changing water levels due to extreme rain can lead to the creation of caverns and sinkhole hazards. Such is the historical experience of the Valaská village in central Slovakia. To better understand the current sinkhole threat in the village, we aim to detect shallow caverns using microgravimetry. Our broader objective is to examine the capabilities of the Growth inversion methodology to detect and characterize shallow cave space. In our study, we focus on the benefits and weak points of the Growth inversion approach, which is a free-geometry inversion method based on model exploration and growing source bodies. Since a sole gravimetric inversion produces ambiguous results, we pay attention to the role and setup of the several free user-adjustable inversion parameters of Growth. We examine tuning these parameters for the specific needs of shallow cavity detection. Valaská experienced sinkholes in 1964, 1968 and 2019. That of 1964 is known for a curious loss of a horse sunk into a karst chimney. Our gravimetric work shows that the sinkhole hazard at the exposed lot in Valaská is ongoing despite the mitigation construction measures. The Growth approach proved to be applicable and useful in microgravimetric identification of sinkhole threat and detection of shallow caverns in karst. Full article

Wetland ecosystems have been a central focus of ecological research for an quite some time. Nevertheless, the degradation of wetland riparian zones has markedly accelerated due to anthropogenic activities, climate change, and habitat heterogeneity. The objective of this paper is to investigate the differences in functional traits of riparian plants under changing wetland environments on a karst plateau, as well as to elucidate the adaptive strategies of wetland plants across different habitats. This study examines the Caohai Wetland located on the Guizhou karst plateau, selecting the leaves of four dominant plant species (Phragmites australis, Onopordum acanthium, Galium odoratum, Paspalum distichum) in the Caohai Wetland lakeshore zone and analyzes the influence of soil factors on the variation of plant functional traits within the wetland riparian zone. The results reveal that: (1) significant differences exist in the functional traits of dominant plants in the riparian zones of karst plateau wetlands, with complex interrelationships among these traits; (2) the coefficients of variation for magnesium (Mg) and calcium (Ca) in the soil are notably high (79.53% and 67.21%, respectively), whereas soil oxidation-reduction potential (ORP) exhibits the lowest coefficient of variation (4.36%)—furthermore, the convergent variation in specific leaf area (SLA) and leaf dry matter content (LDMC) directly reflects the strong environmental filtering imposed by this habitat—and (3) redundancy analysis (RDA) indicates that leaf length (LL), specific leaf area (SLA), leaf area (LA), and plant carbon content (PCC) are particularly sensitive to environmental changes, while soil calcium (Ca), total nitrogen (TN), water-dispersible clay (WDR), soil organic matter (SOM), soil moisture content (SPMC), and total potassium (TK) constitute the principal soil factors influencing plant adaptive strategies in karst plateau wetlands. In conclusion, this study demonstrates that adaptation to karst wetland habitats is mediated through trade-offs in the allocation of photosynthetic products and the regulation of carbon (C), nitrogen (N), and phosphorus (P) nutrient balances under calcium-enriched and phosphorus-limited conditions, thereby reflecting the response characteristics of functional traits in karst plateau wetland plants to environmental changes. 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

Tourism development in the Yucatán Peninsula has long been dominated by coastal mass tourism, resulting in environmental pressure and pronounced spatial imbalances. In response, increasing attention has been directed toward diversification strategies based on inland and nature-based attractions. Among these, cenotes—karst sinkholes connected to regional groundwater systems—have emerged as a distinctive tourism resource. This paper introduces the concept of cenotourism as a form of nature-based and geoculturally embedded tourism centred on cenotes and their associated karst environments. The analysis combines conceptual development with empirical evidence from a large-scale tourism survey conducted in Yucatán (n ≈ 2800). The findings suggest that cenotes constitute a meaningful component of tourists’ activity portfolios, with 24.6% of respondents declaring an intention to visit them. Cenotourism contributes to diversification and appears to support the redistribution of tourist flows toward inland areas, while simultaneously increasing exposure to highly sensitive groundwater systems. These results point to a clear sustainability trade-off, although its magnitude may vary depending on local governance conditions. While cenotourism may strengthen local economies and reduce pressure on coastal destinations, it also introduces risks related to groundwater contamination, cultural commodification, and uneven benefit distribution. Such outcomes depend strongly on governance conditions, including visitor management, environmental monitoring, and community participation. By conceptualizing cenotourism as an integrative framework linking tourism demand, environmental vulnerability, and governance processes, the study contributes to understanding tourism development in groundwater-dependent systems. The findings emphasize the need for context-specific management approaches and situate cenotourism within broader water-sensitive tourism planning. Full article

The multi-cycle high-rate injection and production operations in Underground Gas Storage (UGS) facilities converted from depleted fracture-pore carbonate gas reservoirs induce complex rock–fluid interactions that threaten long-term integrity and performance. This study experimentally investigates the petrophysical responses of the Xiangguosi (XGS) UGS carbonate reservoirs in China using multi-cycle stress sensitivity tests, fines migration experiments, and water evaporation–salt precipitation analyses. SEM observations distinguish the contributions of crack closure and matrix compression to permeability evolution. Results show a sharp contrast in mechanical damage: high-quality rocks present negligible permanent deformation (<8% Young’s modulus reduction), whereas poor-quality rocks suffer catastrophic deterioration (>60%). Fines migration exhibits a three-stage behavior under cyclic flow, with water saturation significantly aggravating permeability impairment. A critical salinity threshold (220,000 ppm) is identified for the transition between drying-enhanced storage and salt plugging. Permeability declines sharply despite a slight porosity increase due to selective salt clogging of key pore throats, revealing a clear porosity–permeability decoupling. Salt deposition under movable water conditions can reduce UGS capacity by up to 1.45%. Reservoir heterogeneity, microfractures, karst structures, and initial petrophysical properties dominate the storage and flow space evolution. This work provides a predictive framework for optimizing injection–production strategies and improving the performance of complex carbonate UGS. Full article