Search Results (613)

Urban shallow geothermal systems are increasingly adopted for low-carbon heating and cooling, yet their performance and environmental impact depend on vertical heat transfer processes that are often simplified, particularly across the unsaturated zone that links the urban surface and groundwater. This study quantifies the buffering role of the unsaturated zone and assesses how its explicit representation affects predicted geothermal thermal impacts in an urban alluvial aquifer. We combine multi-depth temperature observations from instrumented piezometers and thermocouple arrays in the Zaragoza alluvial aquifer (NE Spain) with a three-dimensional transient groundwater-flow and heat-transport model implemented in FEFLOW. Model performance was evaluated by comparing simulated temperature profiles against field observations at −2 m, −5 m, and the water table, yielding root mean square errors (RMSE) of 1.24 °C, 0.58 °C, and 0.42 °C, respectively. Scenario simulations show strong damping and phase delay of seasonal signals through the unsaturated zone and indicate that surface heat exchange controls shallow thermal amplitudes (up to approximately 10 °C at approximately 1 m). Simplified configurations that neglect the unsaturated zone and/or surface heat transfer bias impact assessments by increasing simulated aquifer warming (up to 1 °C at the end of summer injection periods) and altering plume intensity and geometry (plume extents on the order of 80 m laterally in the analyzed configuration). These results underline that urban geothermal assessments require field-constrained representations of unsaturated-zone heat transfer and realistic surface boundary conditions to support sustainable subsurface energy planning. Full article

Understanding the behavior of light non-aqueous phase liquids (LNAPLs) in urban subsurface environments is essential to developing effective pollution control strategies, designing remediation systems, and managing waste and resources sustainably. Oil leakage from urban industrial facilities, underground pipelines, and fueling systems often leads to contamination that is challenging to characterize due to complex soil structures, limited access beneath densely built infrastructure, and dynamic groundwater conditions. In this study, we integrate a large-scale soil tank experiment with multiphase flow simulations to elucidate LNAPL distribution mechanisms under fluctuating groundwater conditions. A 2.4-m-by-2.4-m-by-0.6-m soil tank was used to visualize oil movement with high-resolution multispectral imaging, enabling a quantitative evaluation of saturation distribution over time. The results showed that a rapid rise in groundwater can trap 60–70% of the high-saturation LNAPL below the water table. In contrast, a subsequent slow rise leaves 10–20% residual saturation within pore spaces. These results suggest that vertical redistribution caused by groundwater oscillation significantly increases residual contamination, which cannot be evaluated using static groundwater assumptions. Comparisons with a commonly used NAPL simulator revealed that conventional models overestimate lateral spreading and underestimate trapped residual oil, thus highlighting the need for improved constitutive models and numerical schemes that can capture sharp saturation fronts. These results emphasize that an accurate assessment of LNAPL contamination in urban settings requires an explicit consideration of groundwater fluctuation and dynamic multiphase interactions. Insights from this study support rational monitoring network design, reduce uncertainty in remediation planning, and contribute to sustainable urban environmental management by improving risk evaluation and preventing the long-term spread of pollution. Full article

This study investigates the seismic response of double- and triple-buried steel pipeline systems using finite-element modeling in RS2, with particular emphasis on soil–pipeline interaction and symmetry-breaking behavior under pseudo-static seismic loading. Although the pipeline systems are initially symmetric in geometry, material properties, and boundary conditions, the analysis demonstrates that directional seismic excitation induces quantitatively measurable asymmetric responses in shear force, displacement, and spacing due to nonlinear soil–pipeline interaction. Five parametric scenarios were examined, including burial depth (1–5 m), pipeline diameter (8–56 in.), groundwater table (1.4–20 m), peak ground acceleration (0.1–0.6 g), and soil type. The results show that maximum shear forces increase with burial depth and diameter, reaching approximately 15–17 kN in clayey soils at a PGA of 0.4 g, whereas sandy and heterogeneous soils produce lower shear forces (≈12–14 kN). Horizontal displacements are strongly governed by groundwater and PGA, increasing from about 1.2–1.8 m in dry or deep groundwater conditions to more than 2.8 m for shallow groundwater and exceeding 5 m at PGA = 0.6 g. Triple-pipeline systems exhibit higher shear demand due to confinement effects, with the middle pipeline often developing the largest shear force, while the pipeline facing the seismic load consistently experiences the greatest displacement. This study makes two primary contributions. First, it demonstrates that initially symmetric multilined buried pipeline systems exhibit systematic, quantifiable symmetry-breaking behavior under directional seismic loading, manifested as unequal shear forces, displacements, and interaction effects among adjacent pipelines. Second, it presents an integrated multi-parameter coupling analysis that simultaneously accounts for burial depth, pipeline diameter, groundwater level, soil type, and peak ground acceleration, revealing interaction mechanisms that cannot be captured through single-parameter or single-pipeline assessments. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

Underground coal mining-induced subsidence threatens farmland resources and ecological sustainability in coal–grain overlapping regions with high groundwater tables, making concurrent mining and reclamation a critical management need. Previous studies have not systematically compared the integrated effects of mining sequence, extraction method, and panel optimization on subsidence control and reclamation efficiency in such regions. This study designed six mining schemes, integrating these three technical factors to investigate spatiotemporal subsidence evolution and the performance of deep digging–shallow filling reclamation. Findings reveal that mining design synergistically regulates short-to-mid-term subsidence: deep–thin seam-first skip mining eliminates initial severe subsidence damage, while shallow-thick seam-first sequential mining induces the most severe early-stage ecological disturbance. After a full extraction of both coal seams, long-term surface damage converges to 2374 ha (1509 ha severe damage), dictated by total extracted coal volume and inherent geological conditions. Reclamation efficiency depended on earthwork availability and terrain adaptability, with the optimal scheme achieving a reclamation rate of 65.00%. The findings identify mining strategies that balance subsidence mitigation and farmland restoration, providing actionable insights for sustainable mining in high-groundwater coal–grain overlapping regions. Full article

Rainfall–triggered clustered landslides pose severe risks to communities and infrastructure in mountainous regions. High–precision susceptibility assessment is essential for early warning and hazard mitigation. The traditional buffering method neglects physical slope stability mechanisms, leading to the misclassification of potentially unstable areas. To improve susceptibility model accuracy, we propose an improved non–landslide sampling strategy that integrates the physical–model TRIGRS (Transient Rainfall Infiltration and Grid–based Regional Slope–Stability Model) with 50 m buffering constraints. A hybrid physics–machine learning framework is used to evaluate the performance of landslide susceptibility assessment across four machine learning models, such as Multi–Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). Among the four models, the TRIGRS model integrated with MLP achieves the highest accuracy in susceptibility mapping. The improved non–landslide sampling strategy increased average Area Under the Curve (AUC) by 16.46% in random cross–validation and improved spatial generalization capability by 29% in spatial cross–validation, demonstrating its robustness in unseen areas. SHAP factor analysis further confirms rainfall, groundwater table, and human activity as the primary influencing factors, which aligns with physical mechanisms and improves model interpretability. Therefore, the proposed non–landslide sampling strategy coupled with the TRIGRS and MLP models outperforms traditional buffering method in evaluating regional landslide susceptibility, providing a more physically basis for geohazard risk assessment. Full article

Pile foundations are critical load-bearing components in bridge structures, particularly in soft, high-moisture soils susceptible to external disturbances. This study investigated the impact of large-scale soil excavation on the stability of adjacent pile foundations through comprehensive field monitoring of a newly constructed bridge during both the bridge construction and channel excavation phases. The close proximity of the excavation site to the pile caps facilitated a detailed assessment of soil–structure interaction. The results indicate that the pile axial force peaked at the pile head and decreased progressively with depth, consistent with the load transfer mechanism of friction piles. Notably, a distinct variation in axial force was observed at the bedrock interface, attributed to reduced relative displacement between the pile and the surrounding soil. Furthermore, channel water filling raised the local groundwater table, which increased the buoyancy and reduced negative skin friction, thereby decreasing the pile axial force. The study also highlighted the sensitivity of pile deformation in soft soil to unbalanced earth pressure. Asymmetric excavation and surface surcharge loading were identified as critical factors compromising pile stability and overall structural safety. These findings provide valuable insights for construction practices and offer effective strategies to mitigate adverse excavation effects, ensuring long-term structural stability. Full article

This study evaluates the accuracy of various Geographic Information System interpolation methods in predicting the stratified spatial distribution of organic pollutants (Benzene, Total Petroleum Hydrocarbons [TPH], and Methyl Tert-butyl Ether [MTBE]) in groundwater at a petrochemical-contaminated site. Given the limitations of traditional monitoring methods in predicting spatial distribution, this study focuses on the spatial computational prediction of volatile organic compound concentrations at a former petrochemical industrial site. Three interpolation methods—Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and Ordinary Kriging (OK)—were applied and evaluated. Prediction accuracy was assessed using leave-one-out cross-validation, with performance quantified through key metrics: Root Mean Square Error, Coefficient of Determination, and Spearman’s Rank Correlation Coefficient. Results demonstrate significant variations in optimal prediction methods depending on pollutant type and depth stratum. For pollutants predominantly enriched in shallow and middle layers (Benzene, TPH), OK yielded the highest accuracy and stability. Conversely, for predictions of pollutants primarily concentrated in deeper layers, RBF achieved superior performance. IDW consistently underperformed across all strata and pollutants. All interpolation methods generally exhibited systematic overestimation of pollutant concentrations (mean cross-validation error > 0). Through a hierarchical evaluation of the accuracy and interpolation effectiveness of these methods, this study develops a more accurate modeling framework to describe the composite groundwater contamination patterns at petrochemical sites. This study systematically evaluates the spatial prediction accuracy of various non-aqueous phase liquid species under differing groundwater-table depths, identifies the most robust interpolation method, and thereby provides a benchmark for enhancing predictive fidelity in subsurface contaminant mapping. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

The drainage of groundwater in mountainous tunnel projects always leads to substantial decline of the regional water table, which may induce numerous environmental issues, such as spring depletion, surface subsidence, vegetation degradation, and impacts on local water supplies, especially in the enclosed karst aquifers of anticlines in the area, such as the Jura mountain type. A systematic hydrological monitoring was conducted during the excavation of the Wufu Tunnel in Chongqing, China. The monitoring data includes discharge rate and water level collected from tunnels, boreholes, coal mines, springs, and ponds, respectively. Hydrological responses of karst aquifers and surface water bodies to tunnel drainage and precipitation were investigated by statistical analysis, Mann–Kendall test, heat map, and wavelet analysis. Results show that the enclosed karst water system has strong hydraulic connections and good water storage conditions. Tunnel drainage is the dominant factor causing dynamic changes at monitoring points, while the influence of rainfall is relatively limited. Borehole water levels and coal mine drainage have a close correlation with tunnel inflow, while springs are influenced by both rainfall and tunnel drainage. Few pond monitoring points are related to rainfall. Tunnel drainage has transformed the regional groundwater dynamic conditions, causing local groundwater flow direction reversal and reconstructing the groundwater recharge-flow-discharge pattern. Full article

The observed shortage of water resources in the western and southern regions of the Russian Federation may soon affect the territory of the Republic of Bashkortostan. An increase in the share of groundwaters can help to solve this problem. To provide the population of the republic with water resources, the groundwater of magnesium–bicarbonate-type from the Kraka ophiolite massifs can be used. The massifs occur on the western slope of the Southers Urals. In this work we studied ultramafic rocks and their influence on the formation of the chemical composition of water. The research area is located in the northern part of the Zilair synclinorium, which occurs within the Central Ural megazone. In terms of hydrogeology, of particular importance to the territory of the synclinorium is the Zilair basin of fracture waters of the second order, which is part of the Uralian hydrogeologic folded zone. The ultramafic rocks from the studied area have consistently high CaO/Al₂O₃ ratios (0.4–1.6), which indicates the widespread development of parageneses with participation of clinopyroxene and a low degree of depletion of the primitive mantle source. Because of the complex geological structure of the area, water samples collected from both water points in the Kraka massifs, and the surrounding Early–Middle Paleozoic rocks were analyzed for major ions using a laboratory method to identify possible hydro-geochemical zoning. A statistical analysis was then conducted based on the obtained anion–cation composition data. From the viewpoint of the hydrolytic concept, the formation of the chemical composition of groundwater takes place due to the removal of Mg²⁺ from the rock-forming minerals of ultramafic rocks (olivine and pyroxene) and the supply of Na⁺, K⁺, Ca²⁺, and SO₄²⁻ Cl⁻ from atmospheric precipitations. The bicarbonate anion has a complex nature, where both biochemical processes in the soil and atmospheric precipitation play a significant role. Magnesium–bicarbonate-type of waters, due to low mineralization (to 1 g/L) and the majority of other geochemical parameters (pH of the medium, and content of Na, K, Ca, SO₄, and Cl), whose values that are within the limits set by the World Health Organization (WHO), can be used as drinking water. The increased values of total hardness (0.20–3.39 mmol/L) in accordance with the regulatory document SanPiN 1.2.3685–21, adopted by the Russian Federation, do not exceed the maximum permissible concentrations (up to 7.00 (10.00) mEq/L or 3.50 (5.00) mmol/L). The high magnesium content, in accordance with GOST (state standard) R 54316–2020, allows the magnesium–bicarbonate waters of the Kraka massifs to be classified as table mineral waters for the treatment of various diseases (including hypomagnesemia). Full article

Land subsidence caused by groundwater withdrawal remains a significant challenge in urbanized regions, requiring robust predictive models to manage its impact effectively. In this study, a set of coupled partial differential equations is formulated using Biot’s poroelasticity theory and Darcy’s law to model the hydro-mechanical behavior of a multi-aquifer system. The numerical models capture the coupled dynamics of fluid flow and subsurface deformation induced by groundwater table depression. Hydraulic head reductions, vertical compaction, and lateral deformation patterns over a 10-year pumping period are systematically examined. The results manifest that greater hydraulic gradients near geological discontinuities, such as bedrock steps, induce localized deformation and stress redistribution. While Terzaghi’s model effectively predicts vertical compaction in simple systems, Biot’s model accounts for lateral strain and coupled feedback mechanisms, providing a more comprehensive analyses and understanding of subsidence phenomena. This study highlights the importance of coupled hydro-mechanical modeling for accurately predicting land subsidence and offers insights into managing groundwater extraction in geologically complex regions. Full article

Snowmelt recharge critically affects water table fluctuations in cold-region unconfined aquifers, where it serves as a primary source of groundwater. This study investigates the temporal and spatial variations in water table responses to snowmelt events in the Oakes Aquifer, North Dakota. Climatic data, including winter snowfall and temperature, were collected from the North Dakota Agricultural Weather Network (NDAWN), as well as the National Weather Service (NWS) and National Oceanic and Atmospheric Administration (NOAA) stations. Observation well data (1991–2023) were analyzed, and Inverse Distance Weighting (IDW) interpolation in ArcGIS Pro 3.6 was used to generate continuous spatial maps of groundwater level rises during spring. Results indicate that snowmelt significantly drives water table fluctuations, with higher snowfall associated with larger rises. Spatial variability in responses reflects differences in soil permeability, and land cover, with high-permeability soils showing more pronounced increases. Temperature strongly influenced the magnitude of snowmelt-induced groundwater rise, with warmer winters generally associated with greater recharge, while colder periods limited infiltration, likely due to frozen soil effects. These findings underscore the role of snowmelt as a key recharge source in cold-region unconfined aquifers, with variations controlled by local hydrogeological and climatic conditions. Understanding these dynamics is critical for groundwater management, particularly under changing climate scenarios. Future studies should focus on long-term monitoring, climate modeling, and cross-regional comparisons to improve predictions of snowmelt-driven recharge. Full article

Groundwater depletion causes a critical risk for the sustainability of urban and agricultural resilience in semi-arid regions such as Tangshan city. This study deployed an integrated framework that combines understandable machine learning (Shapley Additive exPlanations (SHAP), Geographically Weighted Regression (GWR), spatial autocorrelation (Local Indicators of Spatial Association or LISA), and scenario-based recharge forecasting to evaluate the spatial drivers and patterns of groundwater stress and project potential future risks. Using spatial groundwater table data from 2022 and key environmental and anthropogenic variables such as evapotranspiration (ET), population, temperature, precipitation, and land use and land cover changes, an XGBoost (Extreme Gradient Boosting) regression model was trained to capture nonlinear spatial patterns. SHAP analysis found that ET and population density are prominent contributors to groundwater depletion in agricultural and urban zones. To capture spatial heterogeneity, GWR was utilized to estimate localized coefficients and construct a Vulnerability and Resilience Index (VRI) from normalized coefficients and residuals. LISA validated vulnerability zones and revealed transitional stress regions. Future risks are also projected using Coupled Model Intercomparison Project Phase 6 (CMIP6) climate data and land-use data to run recharge modeling from 2023 to 2049 for both representative concentration pathway (RCP) 4.5 and RCP 8.5. Results show that RCP 8.5 demonstrates highly unstable recharge with frequent negative episodes (ET > P), while RCP 4.5 shows relatively stable patterns of groundwater table. Furthermore, coupled with urban and agricultural expansion, RCP 8.5 intensifies depletion risks. This combined framework provides analytical understandings of spatial driver patterns and scenario-based risk assessments under climate and land use change. The findings of the study recommend priority zones for intervention and underline the importance of adaptive, scenario-sensitive groundwater governance in semi-arid, urbanizing regions. Full article

Groundwater is a vital freshwater resource for Pakistan, particularly in the rapidly urbanizing cities of Rawalpindi and Islamabad. However, rising demand, changing land use, and climate uncertainty pose significant risks to its long-term availability. This study employs the Analytic Hierarchy Process (AHP), Remote Sensing (RS), and Geographic Information System (GIS) to map groundwater potential zones (GWPZs). A total of eleven parameters, including Rainfall, slope, elevation, drainage density, soil type, water table depth, land use/land cover (LULC), and remote sensing indices (NDVI, MSI, TWI, and LST), were used for the identification of groundwater potential zones. The results showed that 51.96% of the study area is classified as having “moderate” groundwater potential, while 5.64% and 33.09% are categorized as “very high” and “high” potential zones, respectively. Conversely, 8.25% and 1.04% of the area are classified as “low” and “very low” zones, respectively. Parameters such as steep slopes, urbanization, and high land surface temperatures hinder recharge, whereas gentle slopes, vegetation, and shallow water tables enhance recharge potential. In semi-arid, urbanizing areas, the integrated AHP–GIS–RS techniques provide a reliable and cost-effective method for mapping GWPZs, offering essential decision support for sustainable water resource management. Full article