Search Results (539)

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

The internal architecture of deep-water channels is highly complex. Previous research has primarily emphasized the sedimentary processes governing channel migration, yet the linkage between sediment-source mechanisms and migration patterns—particularly their vertical evolution—remains insufficiently understood. Drawing on 3D seismic data, well logs, and core analyses, this study delineates the channel architecture within the deep-water succession of the Niger Delta Basin. Furthermore, by correlating high-frequency sea-level fluctuations with the formation timing of structural units, we explore how sea-level changes influence the spatial distribution and evolutionary dynamics of submarine fan systems. This study investigated the bottom-up evolution of two channel-lobe systems—the East Channel System (ECS) and West Channel System (WCS) within the stratigraphic succession, identifying two principal channel migration styles: expansive migration and downstream migration. In the ECS, migration was primarily characterized by a combination of downstream and expansive patterns. In contrast, the WCS displayed intermittent downstream migration, accompanied by some irregular migration. Correlation of sea-level variation curves with corresponding core photographs indicates that the ECS developed during a fourth-order sea-level. Its lower lobe and upper channel intervals each correspond to two complete five-stage sea-level cycles. In this system, debris flows and high-density turbidity currents produced stronger lateral erosion and channel migration, giving rise to the expansive migration style. Conversely, the WCS formed during a four-stage sea-level rise, with its lobe and channel sections likewise corresponding to two complete five-stage sea-level cycles. Here, sedimentation dominated by high- and low-density turbidity currents promoted enhanced erosion and migration along the flow direction, resulting in the predominance of downstream migration patterns. The ECS and WCS together constitute a complete three-tiered stratigraphic sequence representing two lobe–channel systems. This configuration deviates to some extent from the conventional understanding of the spatial distribution of debris flows, lobate channels, main channels, and deep-sea mud deposits. Consequently, during intervals of frequent sea-level fluctuation, deep-water sedimentary components within the continental slope region can partially record the signals of fourth- and even fifth-order sea-level variations, facilitated by a stable tectonic framework and favorable sediment preservation conditions. These findings offer valuable insights for reconstructing regional sedimentary processes and interpreting sea-level evolution. Full article

Ultra-deep fractured tight sandstone reservoirs are key targets for natural gas development, where gas flow is controlled by pore structure, capillary forces, and water saturation. Using the ultra-deep tight sandstones from the Tarim Basin as study object, this paper investigates the gas flow behavior in matrix and fractured cores under high-temperature, high-pressure, and various water saturation conditions. The controlling factors of gas flow are investigated through scanning electron microscopy, casting thin-section, and high-pressure mercury intrusion measurements. The results show that increasing the water saturation can significantly reduce the permeability. The permeability of matrix and fractured cores decreases by 71.15% and 79.67%, respectively, when water saturation reaches 50%. The gas slippage is negligible, but the effect of gas threshold pressure is significant, which is primarily controlled by the pore structure and water saturation. The threshold pressure gradient of gas flow ranges from 0.0004 to 0.8762 MPa/cm, with the matrix cores exhibiting values approximately 13.21 times higher than the fractured cores. The water phase preferentially occupies the larger pores, forcing gas flow to rely on the finer pores. The pores with a maximum radius of 0.21 μm require 0.66 MPa of driving pressure for gas, whereas pores with a median radius of 0.033 μm require 4.18 MPa. The fracture networks can significantly reduce the lower limit for gas flow, serving as the key flow channels for the efficient development of ultra-deep tight sandstone gas. These findings not only reveal the gas flow mechanisms under water invasion but also provide theoretical and practical guidance for enhancing gas recovery from ultra-deep tight sandstone reservoirs. Full article

Accurate river runoff prediction plays a vital role in water resource management, agricultural scheduling, disaster prevention, and climate adaptation. To address three long-standing challenges in multi-basin hydrological modeling—the insufficient nonlinear expressiveness of recurrent structures, underestimation of extreme high-flow events caused by sample imbalance, and weak cross-basin generalization—this study proposes a hybrid forecasting framework, KAN–WLSTM, that integrates physical priors with deep learning. Specifically, (i) the KAN replaces linear layers to achieve nonlinear mapping consistent with hydrological mechanisms; (ii) a WMSE loss is adopted to emphasize high-flow samples; (iii) Granger causality analysis is applied for causality-driven input selection; and (iv) Optuna is used to perform Bayesian-based adaptive hyperparameter optimization. Multi-scale experiments based on the CAMELS-GB dataset show that a 14-day lag window yields the best performance, with an average MSE = 1.77 ${({\mathrm{m}}^3/\mathrm{s})}^2$ and NSE of 0.81 across nine representative catchments. Comparative results indicate that the proposed model achieves the best or near-best scores in most metrics, outperforming traditional LSTM by 6.8% in MSE and 2.7% in NSE, while reducing peak discharge errors by up to 18%. In large-sample evaluations across 161 catchments, the KAN–WLSTM model attains an average and median NSE of 0.770 and 0.827, respectively, with the smallest variance and ranked first among all models, demonstrating outstanding robustness and generalization under diverse hydro-climatic conditions. Full article

Karst collapse columns (KCCs) represent key concealed hazard-inducing factors that threaten the safety of coal mines in North China. To clarify their primary controlling geological factors and evolutionary processes, this study focuses on KCCs in the Hancheng Mining Area, situated on the southeastern margin of the Ordos Basin, China. A comprehensive methodological approach—integrating field surveys, petrographic and mineralogical identification, geochemical analysis, and structural interpretation—was employed to investigate the dominant factors controlling KCC development and their evolutionary mechanisms. The results indicate the following: (1) Thick-bedded dolomites of the 5th Member of the Majiagou Formation (Middle Ordovician Series) serve as the material foundation for karstification. These dolomites were deposited in an oxidized shallow-water tidal flat setting, which endowed them with favorable lithological properties for subsequent dissolution. (2) NE-SW trending erosional grooves within the paleogeomorphology of the Ordovician top surface functioned as preferential flow paths for karst water, channeling fluid movement and intensifying localized dissolution. (3) Multi-phase tectonic activities, particularly extensional deformation during the Himalayan orogeny, created the necessary stress conditions to trigger cave collapse. (4) KCCs undergo a multi-stage formation and evolution process: Starting with the Majiagou Formation’s 5th Member dolomites as the primary lithology, initial modification occurred via Caledonian weathering–crust karstification. Subsequently, compressional tectonism during the Yanshanian orogeny generated void spaces that facilitated deep-seated dissolution. Rapid uplift in the Paleogene exacerbated vertical dissolution, leading to extensive cavity development, which ultimately collapsed under the extensional tectonic regime of the Neogene. This study provides theoretical support for predicting and mitigating sudden water inrushes caused by KCCs in the Hancheng Mining Area. Furthermore, it offers novel insights and a scientific basis for advancing understanding of the developmental mechanisms of North China-type KCCs. Full article

The geochemical characteristics of reinjection fluids play a crucial role in controlling water–rock interactions and the long-term stability of geothermal reservoirs. This study aims to evaluate how different fluid chemistries affect mineral dissolution–precipitation behavior and ion migration during geothermal reinjection. Five types of reinjection water—including geothermal source water (i.e., formation water from the reservoir), primary and secondary treated waters, and their mixtures—were reacted with carbonate rocks from the Wumishan Formation of the Xiong’an New Area, North China Basin, under reservoir-like conditions (70 °C, 17 MPa). A combination of batch experiments, inverse modeling using PHREEQC, and one-dimensional reactive transport simulations was employed. Results show that fluid pH, ionic strength, and saturation state significantly influence reaction pathways. Alkaline-treated waters enhanced silicate dissolution, increasing Na⁺, K⁺, and Si concentrations, while source water and its mixtures promoted carbonate precipitation, increasing the risk of clogging. Simulations revealed that the early injection stage is the most reactive, with rapid ion front advancement and strong mineral transformations. Reaction-controlled ions such as Ca²⁺ and SO₄²⁻ formed enrichment zones, while conservative ions like Na⁺ and Cl⁻ propagated more uniformly. Moderate alkaline regulation was found to mitigate carbonate scaling and improve silicate reactivity, thereby reducing permeability loss. This integrated approach provides mechanistic understanding and practical guidance for reinjection fluid design in medium-to-deep geothermal systems. Full article

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based HEC-RAS simulation outputs, including extreme events, were incorporated as additional inputs. The Seomjin River Basin in South Korea, which recently experienced severe flooding, was selected as the study area. Hydrological data from 2010 to 2023 were utilized, with 2023 reserved for model testing. Forecasts were generated for four lead times (3, 6, 12, and 24 h), consistent with the operational flood forecasting system of the Ministry of Environment, South Korea. Using only observed data, the model achieved high accuracy at upstream sites, such as Imsil-gun (Iljung-ri, R² = 0.92, RMSE = 0.27 m) and Gokseong (Geumgok Bridge, R² = 0.91, RMSE = 0.35 m), for a 6-h lead time. However, performance was lower at Gurye-gun (Songjeong-ri, R² = 0.72, RMSE = 1.48 m) due to the complex influence of two dams. Incorporating enhanced inputs significantly improved predictions at Gurye-gun (R² = 0.91, RMSE = 1.17 m at 3 h). Overall, models using only observed data performed better at upstream sites, while enhanced inputs were more effective in downstream or multi-dam regions. The 6-h lead time yielded the highest overall accuracy, highlighting the potential of this approach to improve real-time dam operations and flood risk management. Full article

The Baijianshan deposit is the sole skarn Zn-Cu polymetallic deposit in the Xiaoshitouquan ore field, Xinjiang, China. Its ore genesis remains controversial, which hinders understanding of the relationship between skarn-type Zn-Cu and adjacent epithermal Ag-Cu-Pb-Zn mineralization and consequently impedes further regional exploration. LA-ICP-MS U-Pb dating on zircons from the granite and granite porphyry from the mining area yielded ages of 311 ± 1.7 Ma and 312 ± 1.6 Ma, respectively. The corresponding zircon ε_Hf(t) values and T_DM ages are 8.7–9.9 and 624–555 Ma for the granite, and 7.2–9.9 and 673–552 Ma for the granite porphyry. These granites are metaluminous, high-K calc-alkaline I-type granites, with high LREE/HREE ratios (4.92–9.03) and pronounced negative Eu anomalies. They are enriched in K, Th, U, Zr, and Hf, with significant depletions in Sr, P, and Ti. Combined geological and geochemical evidence indicate that these Late Carboniferous granites were derived from the juvenile crustal and formed in subduction-related back basin. Two-phase aqueous inclusions in the ore-bearing quartz and calcite have homogenization temperatures ranging from 117 to 207 °C and 112 to 160 °C, respectively, with the salinities in the ranges of 0.18~7.17 and 0.53~5.26 wt% NaCl eq. The S and Pb isotopic compositions of sulfides in the ores indicate that the ore-forming metals were sourced from the medium-acidic magmatite. The δ¹⁸O_H2O and δD_H2O values of hydrothermal fluids range from −6.97% to −5.84% and −106.8% to −99.6%, respectively, suggesting that the ore-forming fluids originated from the mixing of magmatic and meteoric water. Fluid mixing and corresponding conductive cooling were identified as the principal mechanism triggering the metallic mineral precipitation. The Baijianshan skarn Zn-Cu polymetallic deposit shares contemporaneous magmatic-mineralization ages and analogous material sources with the epithermal polymetallic deposits in the Xiaoshitouquan ore field, collectively constituting a unified skarn-epithermal metallogenic system. This hypothesis indicates that the deep parts of the epithermal deposits within the Yamansu volcanic rocks possess potential for exploring the porphyry-skarn-type deposits. Full article

Understanding gas–water two-phase flow mechanisms in deep tight gas reservoirs is critical for improving production performance and mitigating water invasion. However, the effects of pore-throat-fracture multiscale structures on gas–water flow remain inadequately understood, particularly under high-temperature and high-pressure conditions (HT/HP). In this study, we developed visualizable multiscale throat-pore and throat-pore-fracture physical nanofluidic chip models (feature sizes 500 nm–100 μm) parameterized with Keshen block geological data in the Tarim Basin. We then established an HT/HP nanofluidic platform (rated to 240 °C, 120 MPa; operated at 100 °C, 100 MPa) and, using optical microscopy, directly visualized spontaneous water imbibition and gas–water displacement in the throat-pore and throat-pore-fracture nanofluidic chips and quantified fluid saturation, front velocity, and threshold pressure gradients. The results revealed that the spontaneous imbibition process follows a three-stage evolution controlled by capillarity, gas compression, and pore-scale heterogeneity. Nanoscale throats and microscale pores exhibit good connectivity, facilitating rapid imbibition without significant scale-induced resistance. In contrast, 100 μm fractures create preferential flow paths, leading to enhanced micro-scale water locking and faster gas–water equilibrium. The matrix gas displacement threshold gradient remains below 0.3 MPa/cm, with the cross-scale Jamin effect—rather than capillarity—dominating displacement resistance. At higher pressure gradients (~1 MPa/cm), water is efficiently expelled to low saturations via nanoscale throat networks. This work provides an experimental platform for visualizing gas–water flow in multiscale porous media under ultra-high temperature and pressure conditions and offers mechanistic insights to guide gas injection strategies and water management in deep tight gas reservoirs. Full article

This study focuses on the middle Eocene lacustrine shales of the Lower Member 2 of the Liushagang Formation (L–LS2) in the Weixi’nan Depression of the Beibu Gulf Basin. Employing an integrated approach that combines core observation, thin-section analysis, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and geochemical proxies, we systematically characterize the lithofacies, sedimentary processes, and paleoenvironmental evolution. Six distinct lithofacies were identified: clay-rich mudstone, calcium-bearing mudstone, clay-rich siltstone, siliceous siltstone, ankerite-bearing sandstone, and siliceous sandstone. Based on depositional processes and structural features, these are grouped into three lithofacies assemblages: interbedded lithofacies assemblage, laminated lithofacies assemblage, and matrix lithofacies assemblage. Vertical facies distribution shows that the interbedded lithofacies assemblage dominates the lower L–LS2, reflecting active faulting, volcanism, a low lake level, prevalent gravity flows, and episodic oxidative conditions. The laminated lithofacies assemblage dominates the middle section and results from the combined influence of chemical and mechanical deposition, indicating fluctuating climate conditions that affected water depth, salinity, and redox dynamics. The upper section is characterized by matrix lithofacies assemblage, representing a stable, deep water, anoxic environment with low energy suspension settling. We propose a depositional model in which tectonics and climate jointly control lacustrine shale deposition. During the middle Eocene, intensified tectonic activity expanded accommodation space and increased clastic input, while climate fluctuations influenced chemical weathering, nutrient supply, and salinity. Together, these factors drove lake deepening and variability, affecting sedimentary energy and redox conditions. This study not only clarifies the sedimentary evolution of L–LS2 but also provides a critical geological framework for lacustrine shale oil exploration. Full article

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions. Full article

Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution historical and downscaled future climate data. TerraClimate observations (1985–2014) and bias-corrected CMIP6 projections (2030–2050) under SSP2-4.5 and SSP5-8.5 scenarios were utilized to develop and evaluate the models. Among the tested ML algorithms, Random Forest (RF) demonstrated the best trade-off between accuracy and interpretability and was selected for feature importance analysis. The top-ranked predictors—precipitation, solar radiation, and maximum temperature—were used to train a Long Short-Term Memory (LSTM) network. The LSTM outperformed all ML models, achieving high predictive skill (R² = 0.766, CC = 0.880, RMSE = 0.885). Scenario-based projections revealed increasing drought severity and variability under SSP5-8.5, with mean PDSI values dropping below −3 after 2040 and deepening toward −4 by 2049. The high-emission scenario also exhibited broader uncertainty bands and amplified interannual anomalies. These findings highlight the value of hybrid AI–climate modeling approaches in capturing complex drought dynamics and supporting anticipatory water resource planning in vulnerable dryland environments. Full article

This study investigates gas injection gravity miscible flooding to enhance oil recovery in fractured-vuggy volatile oil reservoirs of the Fuman area, Tarim Basin. The Fuman 210 reservoir, containing light oil with high maturity, large column heights, and strong fracture control, provides favorable conditions for gravity-driven flooding. Laboratory tests show that natural gas and CO₂ achieve miscibility, while N₂ reaches near-miscibility. Mixed gas injection, especially at a natural gas to nitrogen ratio of 1:4, effectively lowers minimum miscibility pressure and enhances displacement efficiency. Full-diameter core experiments confirm that miscibility improves oil washing and expands the sweep volume. Based on these results, a stepped three-dimensional well network was designed, integrating shallow injection with deep production. Optimal parameters were determined: injection rates of 50,000–100,000 m³/day per well and stage-specific injection–production ratios (1.2–1.5 early, 1.0–1.2 middle, 0.8–1.0 late). Field pilots validated the method, maintaining stable production for seven years and achieving a recovery factor of 30.03%. By contrast, conventional development relies on depletion and limited water flooding, and dry gas injection yields only 12.6%. Thus, the proposed approach improves recovery by 17.4 percentage points. The novelty of this work lies in establishing the feasibility of mixed nitrogen–natural gas miscible flooding for ultra-deep fault-controlled carbonate reservoirs and introducing an innovative stepped well network model. These findings provide new technical guidance for large-scale application in similar reservoirs. Full article

Floating vertical−axis wind turbines present unique advantages for deep−water offshore deployments, but their basin model testing encounters significant challenges in aerodynamic load simulation due to Reynolds scaling effects. While Froude−scaled experiments accurately replicate hydrodynamic behaviors, the drastic reduction in Reynolds numbers at the model scale leads to substantial discrepancies in aerodynamic forces compared to full−scale conditions. This study proposed two methodologies to address these challenges. Fully physical model tests adopt a “physical wind field + rotor model + floating foundation” approach, realistically simulating aerodynamic loads during rotor rotation. Semi−physical model tests employ a “numerical wind field + rotor model + physical floating foundation” configuration, where theoretical aerodynamic loads are obtained through numerical calculations and then reproduced using controllable actuator structures. For fully physical model tests, a blade reconstruction framework integrated airfoil optimization, chord length adjustments, and twist angle modifications through Taylor expansion−based sensitivity analysis. The method achieved thrust coefficient similarity across the operational tip−speed ratio range. For semi−physical tests, a cruciform−arranged rotor system with eight dynamically controlled rotors and constrained thrust allocation algorithms enabled the simultaneous reproduction of periodic streamwise/crosswind thrusts and vertical−axis torque. Numerical case studies demonstrated that the system effectively simulates six−degree−of−freedom aerodynamic loads under turbulent conditions while maintaining thrust variation rates below 9.3% between adjacent time steps. These solutions addressed VAWTs’ distinct aerodynamic complexities, including azimuth−dependent Reynolds number fluctuations and multidirectional force coupling, which conventional methods fail to accommodate. The developed techniques enhanced the fidelity of floating VAWT basin tests, providing critical experimental validation tools for emerging offshore wind technologies. Full article

The submarine topography in the canyon area of the Qiongdongnan Basin is complex, with severe risks of shallow gas hazards threatening marine engineering safety. To accurately characterize seabed morphology and assess shallow gas risks, this study employed multi-source data fusion technology, integrating 3D seismic data, shipborne multibeam bathymetry data, and high-precision AUV topographic data from key areas to construct a refined seabed terrain inversion model. For the first time, the spatial distribution characteristics of complex geomorphological features such as scarps, mounds, fissures, faults, and mass transport deposits (MTDs) were systematically delineated. Based on attribute analysis of 3D seismic data and geostatistical methods, the enrichment intensity of shallow gas was quantified, its distribution patterns were systematically identified, and risk level evaluations were conducted. The results indicate: (1) multi-source data fusion significantly improved the resolution and accuracy of terrain inversion, revealing intricate geomorphological details in deep-water regions; and (2) seismic attribute analysis effectively delineated shallow gas enrichment zones, clarifying their spatial distribution patterns and risk levels. This study provides critical technical support for deep-water drilling platform site selection, submarine pipeline route optimization, and engineering geohazard prevention, offering significant practical implications for ensuring the safety of deep-water energy development in the South China Sea. Full article