Search Results (173)

Groundwater is a critical yet understudied resource in Peru, where surface water has traditionally dominated national assessments. This study provides the first country-scale analysis of groundwater storage (GWS) variability in Peru from 2003 to 2023 using satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We used the GRACE Data Assimilation-Data Mass Modeling (GRACE-DA-DM GLV3.0) dataset at 0.25° resolution to estimate annual GWS trends and evaluated the influence of El Niño–Southern Oscillation (ENSO) events and anthropogenic extraction, supported by in situ well data from six major aquifers. Results show a sustained GWS decline of 30–40% in coastal and Andean regions, especially in Lima, Ica, Arequipa, and Tacna, while the Amazon basin remained stable. Strong correlation (r = 0.95) between GRACE data and well records validate the findings. Annual precipitation analysis from 2003 to 2023, disaggregated by climatic zone, revealed nearly stable trends. Coastal El Niño events (2017 and 2023) triggered episodic recharge in the northern and central coastal regions, yet these were insufficient to reverse the sustained groundwater depletion. This research provides significant contributions to understanding the spatiotemporal dynamics of groundwater in Peru through the use of satellite gravimetry data with unprecedented spatial resolution. The findings reveal a sustained decline in GWS across key regions and underscore the urgent need to implement integrated water management strategies—such as artificial recharge, optimized irrigation, and satellite-based early warning systems—aimed at preserving the sustainability of the country’s groundwater resources. Full article

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

This study aims to improve the accuracy and interpretability of deep groundwater level forecasting in Cangzhou, a typical overexploitation area in the North China Plain. To address the limitations of traditional models and existing machine learning approaches, we develop a Stacking ensemble learning framework that integrates meteorological, spatial, and anthropogenic variables, including lagged groundwater levels to reflect aquifer memory. The model combines six heterogeneous base learners with a meta-model to enhance prediction robustness. Performance evaluation shows that the ensemble model consistently outperforms individual models in accuracy, generalization, and spatial adaptability. Scenario-based simulations are further conducted to assess the effects of the South-to-North Water Diversion Project. Results indicate that the diversion project significantly mitigates groundwater depletion, with the most overexploited zones showing water level recovery of up to 17 m compared to the no-diversion scenario. Feature importance analysis confirms that lagged water levels and pumping volumes are dominant predictors, aligning with groundwater system dynamics. These findings demonstrate the effectiveness of ensemble learning in modeling complex groundwater behavior and provide a practical tool for water resource regulation. The proposed framework is adaptable to other groundwater-stressed regions and supports dynamic policy design for sustainable groundwater management. Full article

Mine water treatment and emissions have become important factors that restrict the comprehensive benefits of coal enterprises and local economic development, and the use of the deep well recharge method can address the specific conditions of mine surge water. This paper takes the actual situation of coal mine water treatment as an example and innovatively carries out dynamic tests for the Ordovician limestone aquifers deep in the mine. Intermittent reinjection test shows that under the same reinjection time, the water level recovery rate during the intermittent period is fast at first and then slow. Moreover, the recovery speed of the water level buried depth slows down with the increase in the reinjection time, which reveals the characteristics of the water level rising rapidly and recovering quickly during the reinjection of the reservoir. The average formation water absorption index is 420.81 m³/h·MPa. The water level buried depth of the long-term reinjection test showed three stages (rapid rise, slow rise, and stable stages), and the water level buried depth was raised to 1.52 m at its highest. Monitoring data from the surrounding 5 km area showed that reinjection did not affect aquifer water levels, verifying the excellent storage capacity of the deep Ordovician fissure-karst aquifer. The variability of well loss under pumping and injection conditions was comparatively analyzed, and the well loss produced by the recharge test was 4.06 times higher than that of the pumping test, which provided theoretical support for the calculation of hydrogeological parameters to eliminate the influence of well loss. This study deepens the understanding of Ordovician limestone aquifers in deep mine water, providing a reference for cheap mine water treatment and sustainable groundwater management in similar mine areas. Full article

In this study, we selected a series of pothole wetlands to investigate their nucleation, evolution, and recent anthropogenic degradation in the Alcores Depression (AD), southern Iberian Peninsula, where over 100 closed watersheds containing shallow, ephemeral water bodies up to 2 hm² have been identified. We surveyed the regional geological framework, utilized digital elevation models (DEMs), orthophotos, and aerial images since 1956. Moreover, we analyzed precipitation and temperature data in Seville from 1900 to 2024, collected hydrometeorological data since 1990 and modelled the water level evolution from 2002 to 2025 in a representative pothole in the area. Our observations indicate a flooded surface reduction by more than 90% from the 1950s to 2025. Climatic data reveal an increase in annual mean temperatures since 1960 and a sharp decline in annual precipitation since 2000. The AD’s inception due to tectonic isolation during the Quaternary favoured the formation of pothole wetlands in the floodplain. The reduction in the hydroperiod and wetland degradation was primarily due to agricultural expansion since 1950, which followed an increase in groundwater extraction and altered the original topography. Recently, decreased precipitation has exponentially accelerated the degradation and even the complete disappearance of many potholes. This study underscores the fragility of small wetlands in the Mediterranean basin and the critical role of human management in their preservation. Restoring these ecosystems could be a highly effective nature-based solution, especially in semi-arid climates like southern Spain. These prairie potholes are crucial for enhancing groundwater recharge, which is vital for maintaining water availability in regions with limited precipitation. By facilitating rainwater infiltration into the aquifer, recharge potholes increase groundwater levels. Additionally, they capture and store run-off during heavy rainfall, reducing the risk of flooding and soil erosion. Beyond their hydrological functions, these wetlands provide habitats that support biodiversity and promote ecological resilience, reinforcing the need for their protection and recovery. Full article

Climate change will have a significant impact on saltwater intrusion in coastal aquifers between now and 2150. Global sea levels are predicted to rise somewhere between 0.5 and 1.8 m. To mitigate sea level rise, coastal aquifers will require intensive management to avoid inland migration of seawater that could impact water supplies. In addition to reducing pumping of freshwater, the construction and operation of salinity barriers will be required in many locations. Eleven types of salinity barriers were investigated, including physical barriers (curtain wall and grout curtains), infiltration canals filled with freshwater paralleling the coastline, injection of freshwater (treated surface water or wastewater), pumping or abstraction barriers, mixed injection and abstraction barriers, combined abstraction, desalination, and recharge (ADR), ADR hybrid barriers using various water sources including desalinated water and treated wastewater, compressed air barriers, aquifer storage and recovery dual use systems, biofilm barriers, and clay swelling or dispersion barriers. Feasibility of the use of each salinity barrier type was evaluated within the context of the most recent projections of sea level changes. Key factors used in the evaluation included local hydrogeology, land surface slope, water use, the rate of sea level rise, technical feasibility (operational track record), and economics. Full article

Relative permeability is a critical parameter governing multiphase fluid flow through porous media, significantly impacting recovery efficiency and CO₂ sequestration potential in geological reservoirs. Accurately evaluating relative permeability in heterogeneous reservoirs remains challenging due to spatially variable porosity and permeability distributions. This study presents a novel intelligent prediction approach for evaluating water-CO₂ relative permeability in heterogeneous porous media by integrating fluid properties, heterogeneity characteristics, and relative permeability measurements from uniform porous media. We established a comprehensive training dataset through systematic micromodel experiments that captured various heterogeneity patterns and fluid conditions. Using this dataset, we developed an Artificial Neural Network (ANN) model that achieved exceptional accuracy with a Mean Squared Error below 0.0025. The model was then applied to predict relative permeability in heterogeneous reservoirs using site-specific relative permeability data obtained from core experiments as input parameters. To validate our approach, we incorporated the predicted relative permeability values into Computer Modelling Group (CMG) reservoir simulations of CO₂ sequestration in saline aquifers. The simulation results demonstrated strong agreement with published literature, confirming the model’s predictive capability. This work provides a practical, efficient, and reliable methodology for predicting relative permeability in heterogeneous reservoirs, addressing a significant challenge in reservoir characterization and flow modeling. Full article

To address the challenges in predicting roof water hazards in weakly cemented strata of Northwest China, this study pioneers an integrated CNN-BiLSTM model for aquifer water abundance prediction. Focusing on the 1301E working face in the Yili No. 1 Coal Mine, we employed kriging interpolation to process sparse hydrological datasets (mean relative error: 8.7%), identifying five dominant controlling factors—aquifer burial depth, hydraulic conductivity, core recovery rate, sandstone–mudstone interbedded layer count, and sandstone equivalent thickness. The proposed bidirectional architecture synergizes CNN-based spatial feature extraction with BiLSTM-driven nonlinear temporal modeling, optimized via Bayesian algorithms to determine hyperparameters (32-channel convolutional kernels and 64-unit BiLSTM hidden layers). This framework achieves the comprehensive characterization of multifactorial synergistic effects. The experimental results demonstrate: (1) that the test set root mean square error (1.57 × 10⁻³) shows 65.3% and 85.9% reductions compared to the GA-BP and standalone CNN models, respectively; (2) that the coefficient of determination (R² = 0.9966) significantly outperforms the conventional fuzzy analytic hierarchy process (FAHP, error: 0.071 L/(s·m)) and BP-based neural networks; (3) that water abundance zoning reveals predominantly weak water-rich zones (q = 0.05–0.1 L/(s·m)), with 93.3% spatial consistency between predictions and pumping test data. Full article

This study presents an innovative approach for repurposing depleted clastic hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, integrating numerical heat transport modeling and machine learning optimization. A detailed hydrogeological model of the Békési Formation was built using historical well logs, core analyses, and production data. Heat transport simulations using MODFLOW/MT3DMS revealed optimal dual-well spacing and injection strategies, achieving peak injection temperatures around 94.9 °C and thermal recovery efficiencies ranging from 81.05% initially to 88.82% after multiple operational cycles, reflecting an efficiency improvement of approximately 8.5%. A Random Forest model trained on simulation outputs predicted thermal recovery performance with high accuracy (R² ≈ 0.87) for candidate wells beyond the original modeling domain, demonstrating computational efficiency gains exceeding 90% compared to conventional simulations. The proposed data-driven methodology significantly accelerates optimal site selection and operational planning, offering substantial economic and environmental benefits and providing a scalable template for similar geothermal energy storage initiatives in other clastic sedimentary basins. Full article

Agroforestry systems (AFSs) have been recognized for their ecological potential, yet quantitative assessments of their hydrological functions in semi-arid regions remain limited. This study evaluates soil moisture retention and potential aquifer recharge in two agroforestry systems compared to a traditional rainfed maize system in the semi-desert region of Celaya, Mexico, where aquifer depletion is a growing concern. Field measurements during the 2022 rainy season included precipitation, soil moisture at multiple depths, and soil physical properties across seven vegetation covers. The results show significantly higher moisture content, improved uniformity, and enhanced recharge potential under tree species such as Bursera graveolens and Lysiloma divaricatum. These effects are attributed to vegetation cover, organic matter input, and reduced evaporation. This study provides empirical evidence supporting the integration of AFSs into regional water management strategies, offering a nature-based solution for aquifer recovery and climate adaptation in arid landscapes. Full article

There are serious ecological and environmental risks associated with groundwater level decline, particularly in areas with little in situ monitoring. In order to monitor and assess the resilience and dependability of groundwater storage, this paper proposes a solid methodology that combines data from land surface models and satellite gravimetry. In particular, the GRACE Groundwater Drought Index (GGDI) is used to analyze the estimated groundwater storage anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). Aquifer resilience, or the likelihood of recovery after stress, and aquifer reliability, or the long-term probability of remaining in a satisfactory state, are calculated using the core method. The two main components of the methodology are (a) calculating GWSA by subtracting the surface and soil moisture components from GLDAS, total water storage from GRACE, and comparing the results to in situ groundwater level data; and (b) standardizing GWSA time series to calculate GGDI and then estimating aquifer resilience and reliability based on predetermined threshold criteria. Using this framework, we validate GRACE-derived GWSA with in situ observations in eight sub-basins of the Chao Phraya River (CPR) basin, obtaining Pearson correlation coefficients greater than 0.82. With all sub-basins displaying values below 35%, the results raise significant questions about resilience and dependability. This method offers a framework that can be applied to assessments of groundwater sustainability worldwide. Full article

Taking the Guojiawan coal mine in the Shenfu Mining Area as a case study, five evaluation factors (aquifer thickness, brittle–plastic rock thickness ratio, core recovery rate, number of sandstone–mudstone interbeds, and fractal dimension of the faults) were selected as indicators to evaluate the water richness of the sandstone aquifer in the roof strata of the main coal seam. Accordingly, the weights of the water richness evaluation indicators, derived using the criteria importance through intercriteria correlation (CRITIC) evaluation method, were integrated with the computational procedures of the technique for order of preference by similarity to ideal solution (TOPSIS) evaluation method. The indicator weights and evaluation approaches were combined through different fusion strategies. Finally, based on the water richness zoning results for the study area, the advantages and disadvantages of the two fusion approaches, C-TOPSIS-a and C-TOPSIS-b, were compared. Comprehensive analysis was conducted to evaluate the rationality of the water richness zoning. The C-TOPSIS-b evaluation method achieved the optimal evaluation outcome. The water richness was classified into five grades: weak, relatively weak, moderate, relatively strong, and strong. Among these, the regions with weak to relatively weak, moderate, and strong to relatively strong water richness are primarily in the northern, central, southern, and southwestern parts, respectively. Full article

Groundwater levels were monitored before, during and after groundwater pumping to understand the impacts of groundwater extraction on Abercorn Spring, a recharge spring in the Great Artesian Basin (GAB) in southeast Queensland, Australia. We measured the wetted area of the spring during this time to understand if changes in hydrology affected the water available for vegetation communities. Sustained groundwater extraction >20 km upgradient of the spring resulted in (1) rapid drawdown of the source aquifer, causing a reduction in aquifer pressure; (2) a small decline (0.35 m) in water level at the spring; and (3) a significant change (p = 0.0001) in wetted area in winter. Recovery of water levels and wetted area of the mound spring took over three years after pumping ceased. Our study demonstrated that significant changes to the wetted area occurred with only a minimal drawdown at the springs. Abercorn Springs have a natural low variability in water level (<0.2 m), implying a stable and predictable biological community. This natural range is less than half the water level change that is currently considered for impact assessment in artesian springs in the Queensland section of the GAB, highlighting the need to incorporate updated information to inform future management of both recharge and discharge springs. In the case of Abercorn Springs, long-term monitoring and research have led to refinement of license conditions for groundwater extraction, thereby mitigating further impacts to the springs and demonstrating adaptive management. Full article

This research work investigates the Myki Municipality’s aquifer system in the mountainous region of Xanthi Prefecture, Northeast Greece, with regard to the area’s groundwater exploitation and management requirements for drinking water supply. During the period 2021–2023, the work involved (i) groundwater discharge measurements and groundwater sampling from forty-seven (47) springs and five (5) groundwater wells, followed by groundwater chemical analyses; (ii) appropriate analysis, elaboration, and presentation of the results obtained; and (iii) formulation of related proposals that would improve the conditions of the water supply in the study area. The study revealed that water shortage circumstances exist in the study area, which may be due to low aquifer capacity in some areas, deficient groundwater recovery facilities, and water losses in the water supply network. Full article

This study presents a precise and efficient methodology for evaluating the water-richness of the aquifer overlying the No. 3 coal seam in Hancheng Mine. A comprehensive assessment model was developed by integrating subjective and objective weighting through the sequential relationship analysis–entropy value method. This model facilitated the delineation of water-richness zones within the sandstone aquifer of the Shanxi Group associated with the No. 3 coal seam. Five key evaluation indices were selected based on the aquifer’s water-richness index: core recovery rate, thickness of water-rich sandstone, number of sand–mudstone interlayers, sandstone lithology coefficient, and the thickness ratio of brittle to plastic rock. Furthermore, an advanced evaluation model combining set pair analysis and variable fuzzy sets was established to assess the water-richness risk levels across the entire Hancheng mining area. The results reveal distinct spatial patterns in water-richness: the northeastern region exhibits strong water-richness, while the southwestern area is characterized by medium to weak water-richness over a broad expanse. Overall, the No. 3 coal seam in the Hancheng mining area is classified as having a medium risk level of water-richness. Full article