Search Results (189)

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article

Southwest China, with typical karst, is one of the 36 biodiversity hotspots in the world, facing extreme ecological fragility due to thin soils, limited water retention, and high bedrock exposure. This fragility intensifies under climate change and human pressures, threatening regional sustainable development. Ecological strategic areas (ESAs) are critical safeguards for ecosystem resilience, yet their spatiotemporal dynamics and driving mechanisms remain poorly quantified. To address this gap, this study constructed a multidimensional ecological health assessment framework (pattern integrity–process efficiency–function diversity). By integrating Sen’s slope, a correlated Mann–Kendall (CMK) test, the Hurst index, and fuzzy C-means clustering, we systematically evaluated ecological health trends and identified ESA differentiation patterns for 2000–2024. Orthogonal partial least squares structural equation modeling (OPLS-SEM) quantified driving factor intensities and pathways. The results revealed that ecological health improved overall but exhibited significant spatial disparity: persistently high in southern Guangdong and most of Yunnan, and persistently low in the Sichuan Basin and eastern Hubei, with 41.47% of counties showing declining/slightly declining trends. ESAs were concentrated in the southwest/southeast, whereas high-EHI ESAs increased while low-EHI ESAs declined. Additionally, the natural environmental and human interference impacts decreased, while unique geographic factors (notably the rock exposure rate, with persistently significant negative effects) increased. This long-term, multidimensional assessment provides a scientific foundation for targeted conservation and sustainable development strategies in fragile karst ecosystems. Full article

The deep carbonate reservoirs in the Tarim Basin, Xinjiang, China, are widely developed with multi-scale complex reservoir spaces such as fractures, pores, and karst caves under the coupling of abnormal high pressure, diagenesis, karst, and tectonics and have strong heterogeneity. Among them, fracture–cavity carbonate reservoirs are one of the main reservoir types. Revealing the petrophysical characteristics of fracture–cavity carbonate reservoirs can provide a theoretical basis for the log interpretation and geophysical prediction of deep reservoirs, which holds significant implications for deep hydrocarbon exploration and production. In this study, based on the mineral composition and complex pore structure of carbonate rocks in the Tarim Basin, we comprehensively applied classical petrophysical models, including Voigt–Reuss–Hill, DEM (Differential Effective Medium), Hudson, Wood, and Gassmann, to establish a fracture–cavity petrophysical model tailored to the target block. This model effectively characterizes the complex pore structure of deep carbonate rocks and addresses the applicability limitations of conventional models in heterogeneous reservoirs. The discrepancies between the model-predicted elastic moduli, longitudinal and shear wave velocities (Vp and Vs), and laboratory measurements are within 4%, validating the model’s reliability. Petrophysical template analysis demonstrates that P-wave impedance (Ip) and the Vp/Vs ratio increase with water saturation but decrease with fracture density. A higher fracture density amplifies the fluid effect on the elastic properties of reservoir samples. The Vp/Vs ratio is more sensitive to pore fluids than to fractures, whereas Ip is more sensitive to fracture density. Regions with higher fracture and pore development exhibit greater hydrocarbon storage potential. Therefore, this petrophysical model and its quantitative templates can provide theoretical and technical support for predicting geological sweet spots in deep carbonate reservoirs. Full article

Karst mountain areas, as complex geological systems formed by carbonate rock development, possess unique three-dimensional spatial structures and hydrogeological processes that fundamentally influence regional ecosystem evolution, land resource assessment, and sustainable development strategy formulation. In recent years, through the implementation of systematic ecological restoration projects, the ecological degradation of karst mountain areas in Southwest China has been significantly curbed. However, the research on the fine-grained land use mapping and quantitative characterization of spatial heterogeneity in karst mountain areas is still insufficient. This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. Hence, this paper proposes a novel methodology for land use mapping in karst mountain areas using very high resolution (VHR) remote sensing (RS) images. The innovation of this method lies in the introduction of strategies of geographical zoning and stratified object extraction. The former divides the complex mountain areas into manageable subregions to provide computational units and introduces a priori data for providing constraint boundaries, while the latter implements a processing mechanism with a deep learning (DL) of hierarchical semantic boundary-guided network (HBGNet) for different geographic objects of building, water, cropland, orchard, forest-grassland, and other land use features. Guanling and Zhenfeng counties in the Huajiang section of the Beipanjiang River Basin, China, are selected to conduct the experimental validation. The proposed method achieved notable accuracy metrics with an overall accuracy (OA) of 0.815 and a mean intersection over union (mIoU) of 0.688. Comparative analysis demonstrated the superior performance of advanced DL networks when augmented with priori knowledge in geographical zoning and stratified object extraction. The approach provides a robust mapping framework for generating fine-grained land use data in karst landscapes, which is beneficial for supporting academic research, governmental analysis, and related applications. Full article

Amid declining fish diversity and human pressures in freshwater ecosystems, robust basin-scale assessments are vital for effective fisheries management. This study collated nearly four decades of fishery yields from the Pearl and Yangtze Rivers to identify conservation priorities in the Pearl River Basin. It introduced a novel cumulative effect indicator based on zeta diversity—a biodiversity pattern metric—integrated with cumulative effects analysis for management decision-making. The research employed a multi-site generalized dissimilarity model to examine the non-linear relationships between fish species composition (ζ_n) and human pressures, environmental factors, and geospatial variations across elevation gradients. The cumulative effect indicator, reflecting responses to anthropogenic stress when assessing ζ₂ (related to β diversity), helped evaluate basins for conservation or restoration needs based on their unique or homogenized biotic communities. The results suggest that ζ diversity in low-elevation sub-basins has a stronger filtering effect on ζ by human pressures than in mid- to high-elevation sub-basins, where community aggregation is more random. The impact varied with diversity aspects (nestedness vs. turnover) and zeta order. A negative correlation between cumulative effects and community uniqueness validated the novel cumulative effect indicator’s effectiveness for guiding restoration in the Pearl River Delta, potential fishing bans, and karst conservation. This approach offers a theoretical basis for prioritizing areas for freshwater fish diversity conservation and fishing restrictions in the Pearl River Basin. Full article

This study systematically unravels the hydrochemical evolution mechanisms and driving forces in multi-aquifer systems of Qingdao, a coastal economic hub. Integrated hydrochemical analysis of porous, fissured, and karst water, combined with PHREEQC modeling and Positive Matrix Factorization (PMF), deciphers water–rock interactions and anthropogenic perturbations. Groundwater exhibits weak alkalinity (pH 7.2–8.4), with porous aquifers showing markedly higher TDS (161.1–8203.5 mg/L) than fissured (147.7–1224.8 mg/L) and karst systems (361.1–4551.5 mg/L). Spatial heterogeneity reveals progressive hydrochemical transitions (HCO₃-Ca → SO₄-Ca·Mg → Cl-Na) in porous aquifers across the Dagu River Basin. While carbonate (calcite) and silicate weathering govern natural hydrochemistry, evaporite dissolution and seawater intrusion drive severe groundwater salinization in the western Pingdu City and the Dagu River Estuary (localized TDS up to 8203.5 mg/L). PMF source apportionment identifies acid deposition-enhanced dissolution of carbonate/silicate minerals, with nitrate contamination predominantly sourced from agricultural runoff and domestic sewage. Landfill leachate exerts pronounced impacts in Laixi and adjacent regions. This study offering actionable strategies for salinity mitigation and contaminant source regulation, thereby providing a scientific framework for sustainable groundwater management in rapidly urbanizing coastal zones. Full article

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. Full article

With the intensification of human activities, the water resource environment in the karst mountainous area of central Shandong has undergone significant changes, directly manifested in the cessation of karst spring flows and the occurrence of karst collapses within the spring basin in the Laiwu Basin. To support the scientific development and management of karst water, this study utilizes comprehensive analysis and deuterium-oxygen isotope test data from surveys and sampling of 20 typical karst springs conducted between 2016 and 2018. By integrating mathematical statistics, correlation analysis, and ion component ratio methods, the study analyzes the genesis, hydrochemical ion component sources, and controlling factors of typical karst springs in the Laiwu Basin. The results indicate that the genesis of karst springs in the Laiwu Basin is controlled by three factors: faults, rock masses, and lithology, and can be classified into four types: water resistance controlled by lithology, by faults, by basement, and by rock mass. The karst springs are generally weakly alkaline freshwater, with the main ion components being HCO₃⁻ and Ca²⁺, accounting for approximately 55.02% and 71.52% of the anion and cation components, respectively; about 50% of the sampling points have a hydrochemical type of HCO₃·SO₄-Ca·Mg. Stable isotope (δ¹⁸O and δD) results show that atmospheric precipitation is the primary recharge source for karst springs in the Laiwu Basin. There are varying degrees of evaporative fractionation and water–rock interaction during the groundwater flow process, resulting in significantly higher deuterium excess (d-excess) in the sampling points on the southern side of the basin compared to the northern side, indicating clear differentiation. The hydrochemical composition of the karst groundwater system is predominantly governed by water–rock interactions during flow processes and anthropogenic influences. Carbonate dissolution (primarily calcite) serves as the principal source of HCO₃⁻, SO₄²⁻, Ca²⁺, and Mg²⁺, while evaporite dissolution and reverse cation exchange contribute to the slight enrichment of Ca²⁺ and Mg²⁺ alongside depletion of Na⁺ and K⁺ in spring waters. Saturation indices (SI) reveal that spring waters are saturated with respect to gypsum, aragonite, calcite, and dolomite, but undersaturated for halite. The mixing of urban domestic sewage, agricultural planting activities, and the use of manure also contributes to the formation of Cl⁻ and NO₃⁻ ions in karst springs. Full article

Climate change and intensified human activities have increasingly threatened the sustainability of groundwater resources, especially in ecologically fragile karst regions. To address these challenges, this study proposes a karst spring discharge prediction model that integrates BiLSTM (Bidirectional Long Short-Term Memory) and the Transformer Encoder. The BiLSTM component captures both forward and backward information in spring discharge data, extracting trend-related features. The Transformer’s attention mechanism is employed to identify key precipitation factors influencing spring discharge. A patching preprocessing strategy divides monthly scale sequences into annual segments, reducing input length while enabling local modeling and global interaction. Experiments on Shentou Spring discharge show that the BiLSTM–Transformer Encoder outperforms other deep learning models across multiple evaluation metrics, with notable advantages in short-term forecasting. The patching strategy effectively reduces model parameters and improves efficiency. Attention visualization further confirms the model’s ability to capture critical hydrological drivers. This study not only provides a novel approach to sustainable water management in karst spring basins but also demonstrates an effective use of deep learning for long-term hydrological sustainability. Full article

In recent years, many spruce (Picea abies (L.) H. Karst., Pinaceae) forests have been severely affected by bark beetle (Ips typographus L., Coleoptera: Curculionidae) outbreaks in the Southern Alps, but their ecological impacts remain poorly studied. We analyzed the distribution, ecological, and floristic–vegetational characteristics of forests recently affected by the bark beetle in the upper basin of the Oglio River (Northern Italy) and developed a MaxEnt model to map forests with a bioclimate more prone to severe insect attacks in the coming decades. The results showed that the spruce forests affected by the bark beetle are located exclusively in the submountain and mountain belts (below 1600 m a.s.l.) and that 85% of them are found in areas with high annual solar radiation (>3500 MJ m⁻²). The predictive model for areas susceptible to severe bark beetle attacks proved highly accurate (AUC = 0.91) and was primarily defined by the mean temperature of the dry winter quarter (contribution: 80.1%), with values between −2.5 and 2.5 °C being particularly suitable for the pest. According to the model, more than 58% of the current spruce forests in the study area will exhibit high susceptibility (probability > 0.7) to severe bark beetle attacks by 2080. The floristic–vegetational and ecological analysis of plant communities of 11 bark beetle-affected areas indicated that more thermophilic and significantly different forest communities (in both floristic and physiognomic terms) are expected to develop compared to those of pre-disturbance. Furthermore, the high coverage of spruce snags/standing dead trees appears to accelerate plant succession, enabling the establishment of mature forest communities in a shorter time frame. Full article

The northern Yucatán Peninsula hosts a complex karstic environment shaped by carbonate platform development and the Chicxulub impact event, making subsurface characterization crucial for geological and hydrogeological studies. This work aimed to resolve the shallow crustal structure and identify major tectonic features that influence karst processes and groundwater dynamics. We applied a rapid 3D gravity inversion method, linear back projection (LBP), to Bouguer anomaly data, combined with Euler deconvolution to map shallow and deep fault systems. The inversion produced a high-resolution density model down to 12.8 km depth, revealing key geological structures. Multilevel thresholding delineated significant low-density basins, notably the Chicxulub crater, as well as buried sedimentary basins. Euler solutions identified fault networks that coincide with areas of intense karstification, particularly in the eastern peninsula. Results highlight the interplay between impact-related tectonics and karst evolution, influencing groundwater flow paths and recharge zones. This study demonstrates the effectiveness of gravity inversion and Euler deconvolution for regional crustal imaging in carbonate platforms and emphasizes the need for further local-scale surveys to investigate coastal aquifer vulnerability and saltwater intrusion processes. Full article

Calcite in hydrocarbon reservoirs records abundant information about diagenetic fluids and environments. Understanding the formation mechanisms of calcite is crucial for predicting reservoir characteristics and hydrocarbon migration. This study identifies the types of authigenic calcite present in the Lower Paleozoic carbonate reservoirs of the Bohai Bay Basin through petrographic analysis, cathodoluminescence, and other experimental methods. By integrating electron probe microanalysis, in situ isotopic analysis, and fluid inclusion studies, we further constrain the source of the diagenetic fluids responsible for the authigenic calcite. The results show that there are at least three types of authigenic calcite in the Lower Paleozoic carbonate reservoirs of the Bohai Sea. Calcite cemented in the syn-depositional-to-early-diagenetic stage displays very weak cathodoluminescence, with δ¹³C and δ¹⁸O and paleo-salinity distributions similar to those of micritic calcite. These features suggest that the calcite was formed during burial heating by sedimentary fluids. Calcite filling fractures shows heterogeneous cathodoluminescence intensity, ranging from weak to strong, indicating multiple stages of cementation. The broad elemental variation and multiple cementation events suggest that the diagenetic fluid sources were diverse. Isotopic data show that samples with carbon isotope values greater than −2.9‰ likely formed through water–rock interaction with fluids retained within the strata, whereas samples exhibiting more negative δ¹³C were formed from a mixed-source supply of strata and mantle-derived fluids. Calcite that fills karst collapse pores exhibits alternating bright and dark cathodoluminescence, strong negative δ¹⁸O shifts, and variability in trace elements such as Mn, Fe, and Co. These characteristics indicate a mixed origin of diagenetic fluids derived from both meteoric freshwater and carbonate-dissolving fluids. Full article

As a typical karst landform region, the Lijiang River Basin, located in Southwest China, is characterized by both soil erosion and ecological fragility. The transformation of land use, driven by long-term intensive human activities, has exacerbated the degradation of ecosystem services, threatening the region’s carbon sink function. To clarify the coupling mechanism between land use and land cover change (LUCC) and carbon storage, this paper integrates complex network theory with the PLUS-InVEST model framework. Based on land use data from five periods, i.e., 2001, 2006, 2011, 2016, and 2021, the key transformation types are identified, and the evolution of carbon storage from 2021 to 2041 is simulated under three scenarios, namely, inertial scenario, ecological protection scenario, and urban development scenario. The paper finds that (1) land use transformation in the basin exhibits spatial heterogeneity and network complexity, as evidenced by a significant negative correlation between the node clustering coefficient and the average path length, revealing that land type transitions possess small-world network characteristics. (2) The forested land experienced a net decrease of 196.73 km² from 2001 to 2021, driving a 3.03% decline in carbon storage. This highlights the inhibitory effect of unregulated urban expansion on carbon sink capacity. (3) Scenario simulations indicate that the carbon storage under the ecological protection scenario will be 1.0% higher than under the inertial scenario and 1.5% higher than under the urban development scenario. These suggest that restricting impervious land expansion and promoting forest and grassland restoration can enhance carbon sink capacity. Therefore, this paper provides a quantitative basis for optimizing territorial spatial planning and coordinating the “dual carbon” goals in karst regions. Full article

Despite the increasing adoption of machine learning and data-driven models for predicting regional groundwater potential (GWP), exploration geoscientists have recognized that these models still face various challenges in their predictive precision. For instance, the stochastic uncertainty associated with incomplete groundwater investigation inventories and the inherent non-transparency characteristic of machine learning models, which lack transparency regarding how input features influence outcomes, pose significant challenges. This research constructs a bagging-based learning framework that integrates Positive–Unlabeled samples (BPUL), along with ex-post interpretability, to map the GWP of the Lijiang River Basin in China, a renowned karst region. For this purpose, we first aggregated various topographic, hydrological, geological, meteorological, and land conditional factors. The training samples were enhanced with data from the subterranean stream investigated in the study area, in addition to conventional groundwater inventories such as wells, boreholes, and karst springs. We employed the BPUL algorithm with four different base learners—Logistic Regression (LR), k-nearest neighbor (KNN), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)—and model validation was conducted to map the GWP in karst regions. The findings indicate that all models exhibit satisfactory performance in GWP mapping, with the hybrid ensemble models (RF-BPUL and LightGBM-BPUL) achieving higher validation scores. The model interpretation of the aggregated SHAP values revealed the contribution patterns of various conditional factors to groundwater distribution in karst zones, emphasizing that lithology, the multiresolution index of valley bottom flatness (MRVBF), and the geochemical element calcium oxide (CaO) have the most significant impact on groundwater enrichment in karst zones. These findings offer new approaches and methodologies for the in-depth exploration and scientific prediction of groundwater potential. Full article

Rocky desertification is a significant ecological issue in the karst regions of Southwest China, severely affecting both the environment and local livelihoods. Accurate extraction and prediction of rocky desertification are critical for its prevention and control, playing a crucial role in advancing ecological civilization and sustainable land management. This study focuses on the Pearl River source area in Yunnan, analyzing dynamic changes in rocky desertification over eight periods from 1990 to 2023, using long-term remote sensing data and multi-source reference data. It also predicts the intensity and trends of rocky desertification for the next decade. The results indicate that: (1) Rocky desertification is widespread and severe in the study area; however, its further intensification has been effectively mitigated through long-term governance efforts. By 2023, an area of 14,896.19 km² of rocky desertification has been mitigated to varying extents, accounting for 55.77% of the total watershed area. Trend analysis suggests that, under current management conditions, rocky desertification will continue to decline and improve over time. (2) The overall development of rocky desertification in the basin is showing a positive trend, with deep-level rocky desertification gradually transitioning to shallow-level rocky desertification. In future scenarios, the extent of rocky desertification will continue to decrease. (3) The approach of integrating the Google Earth Engine with traditional remote sensing platforms for extracting rocky desertification information has proven to be both fast and efficient. This method retains high extraction accuracy while alleviating the data burden typically associated with exclusive use of local platforms, thereby enhancing processing efficiency. Full article