Search Results (343)

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

The establishment and management of nature reserves play a crucial role in protecting biodiversity and supporting sustainable agriculture. This study focuses on 2538 nature reserves in 22 provinces, 5 autonomous regions and 4 municipalities directly under the central government in mainland China. Integrating GIS spatial statistics, imbalance index, and geodetector models, we reveal critical insights: (1) Pronounced spatial inequity is observed, where a small number of eastern provinces dominate the total reserve count, highlighting significant regional disparities in ecological resource allocation. The sparse kernel density in western regions, indicating sparse reserve coverage. The Standard Deviation Ellipse highlights directional dispersion and human-ecological conflicts in high-density zones. (2) Key sustainability indicators driving reserve distribution include: total water resources, water resources per capita, forest area. (3) The spatial distribution of China’s nature reserves, along with factors such as altitude, river distribution, and transportation infrastructure, plays a crucial role in their development. This research provides theoretical support for the scientific planning and policy-making of nature reserves in China and offers practical guidance for optimizing and adjusting sustainable agricultural development. The study emphasizes the vital functions of nature reserves in maintaining ecosystem balance, enhancing regional climate resilience, and serving as biodiversity reservoirs. This research offers strategic insights for integrating nature reserve spatial planning with sustainable agricultural development policies, providing a scientific basis for optimizing the eco-agricultural interface in China. Full article

Wide-range electric submersible centrifugal pumps (ESPs) are critical for offshore oilfields but suffer from narrow high-efficiency ranges and frictional losses under dynamic reservoir conditions. This study introduces bio-inspired dimple-type non-smooth surfaces on impeller blades to enhance hydraulic performance. A combined numerical-experimental approach was employed: a 3D CFD model with the k-ω turbulence model analyzed oil–water flow (1:9 ratio) to identify optimal dimple placement, while parametric studies tested diameters (0.6–1.2 mm). Experimental validation used 3D-printed prototypes. Results revealed that dimples on the pressure surface trailing edge reduced boundary layer separation, achieving a 12.98% head gain and 8.55% efficiency improvement at 150 m³/d in simulations, with experimental tests showing an 11.5% head increase and 4.6% efficiency gain at 130 m³/d. The optimal dimple diameter (0.9 mm, 2% of blade chord) balanced performance and manufacturability, demonstrating that bio-inspired surfaces improve ESP efficiency. This work provides practical guidelines for deploying drag reduction technologies in petroleum engineering, with a future focus on wear resistance in abrasive flows. Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

The study presents a scenario analysis of the long-term dynamics of the water level of Lake Balkhash, one of the largest closed lakes in Central Asia, taking into account climate change according to CMIP6 scenarios (SSP2-4.5 and SSP5-8.5) and socio-economic factors of water use. Based on historical data (1947–2021) and a water balance model, the contribution of surface runoff, precipitation and evaporation to the formation of the lake’s hydrological regime was assessed. It was established that the main source of water resources for the lake is the flow of the Ile River, which feeds the western part of the reservoir. The eastern part is characterized by extremely limited water inflow, while evaporation remains the main element of water consumption, having increased significantly in recent decades due to rising air temperatures. Increasing intra-seasonal and interannual fluctuations in water levels have been recorded: The amplitude of short-term fluctuations reached 0.7–0.8 m, which exceeds previously characteristic values. The results of water balance modeling up to 2050 show a trend towards a 30% reduction in surface inflow and an increase in evaporation by 25% compared to the 1981–2010 climate norm, which highlights the high sensitivity of the lake’s hydrological regime to climatic and anthropogenic influences. The results obtained justify the need for the comprehensive and adaptive management of water resources in the Balkhash Lake basin, taking into account the transboundary nature of water use and changing climatic conditions. Full article

Accurate dynamic reserve estimation is essential for effective reservoir development, particularly in fractured vuggy carbonate reservoirs characterized by complex pore structures, multiple spatial scales, and pronounced heterogeneity. Traditional reserve evaluation methods often struggle to account for the coupled behavior of pores, fractures, and vugs, leading to limited reliability. In this study, a modified material balance equation is proposed that explicitly considers the contributions of matrix pores, fractures, and vugs, as well as the influence of varying aquifer multiples. To validate the model, physical experiments were conducted using cores with different fracture–vug configurations under five distinct aquifer multiples. A field case analysis was also performed using production data from representative wells in the Fuman Oilfield. The results demonstrate that the proposed model achieves a fitting accuracy exceeding 94%, effectively capturing the dynamics of fractured vuggy systems with active water drive. The model enables quantitative evaluation of single-well reserves and aquifer multiples, providing a reliable basis for estimating effective recoverable reserves. Furthermore, by comparing simulated formation pressures (excluding aquifer effects) with actual static pressures, the contribution of external aquifer support to reservoir energy can be quantitatively assessed. This approach offers a practical and robust framework for reserve estimation, pressure diagnosis, and development strategy optimization in strongly water-driven fractured vuggy reservoirs. Full article

Predicting water losses and final storage in reservoirs has become increasingly relevant in the efficient control and optimization of water provided to agriculture, livestock, industry, and domestic consumption, aiming to mitigate the risks associated with flash floods and water crises. This research aims to develop a new Machine Learning (ML) algorithm based on a water balance model to simulate the outflow in the Cuchoquesera reservoir in the Ayacucho region. The method uses TensorFlow (TF), a powerful interface for graphing and time series forecasting, for data analysis of hydrometeorological parameters (HMP), inflow (QE_obs), and outflow (QS_obs) of the reservoir. The ML water balance model is fed, trained, and calibrated with daily HMP, QE_obs, and QS_obs data from the Sunilla station. The results provide monthly forecasts of the simulated outflow (QS_sim), which are validated with QS_obs values, with significant validation indicators: NSE (0.87), NSE-Ln (0.83), Pearson (0.94), R² (0.87), RMSE (0.24), Bias (0.99), RVB (0.01), NPE (0.01), and PBIAS (0.14), with QS_obs being slightly higher than QS_sim. Therefore, it is important to highlight that water losses due to evaporation and infiltration increased significantly between 2019 and 2023. Full article

Complex carbonate reservoirs feature large-scale karst cavern structures, exhibiting complex pore and bottom water energy distributions, which increase the difficulty of reservoir development and require targeted research. This paper proposes a new method for dynamic reserves calculation in these reservoirs based on the Differential Processing Method (DPM) and aimed at optimizing the development of complex reservoirs. The AD22 unit of the Tarim Oilfield in Xinjiang is taken as the research object, and this reservoir features complex karst and fault characteristics, which traditional reserves calculation methods cannot effectively capture due to its complex heterogeneous distribution. This study constructs a refined reservoir numerical model through 3D geological modeling and impedance inversion techniques, calculates dynamic reserves using the DPM, and compares the result with traditional material balance and production data analysis methods. The results indicate that the DPM has an advantage in estimating the petrophysical parameters and reserve utilization in such reservoirs. The error between the constructed reservoir numerical model and the actual reservoir development historical data is only 2.04%, demonstrating a good reference value. The model shows that more than 60% of the recoverable reserves in the target unit are located in areas shallower than 160 m underground, while the current development degree is only 12.6%. The model shows that the recovery rate is low in the strong bottom water energy areas of the unit, while the recovery potential is high in the weak bottom water areas. Therefore, a differentiated development strategy based on varying bottom water energy is required to enhance development efficiency. The model indicates that this strategy can improve the comprehensive development benefits of the reservoir by 81.66% over the existing baseline, demonstrating significant potential. This study provides new ideas and methods for dynamic reserve estimation and development strategy optimization for complex carbonate reservoirs, verifies the effectiveness of the DPM in evaluating the development of complex bottom water energy reservoirs, and offers data references for related research and field applications. Full article

Water injection is the most widely used secondary recovery method, but its low viscosity limits sweep efficiency in heterogeneous carbonate reservoirs, especially when displacing heavy crude oils. Polymer flooding overcomes this by increasing the viscosity of the injected fluid and improving the mobility ratio. In this work, we compare three biopolymers (i.e., Xanthan Gum, Scleroglucan, and Guar Gum) using a core flood test on Indiana Limestone with 16–19% porosity and 180–220 mD permeability at 60 °C and 30,905 mg/L of salinity. We injected solutions at 100–1500 ppm and 0.5–6 cm³/min to measure the Resistance Factor (RF), Residual Resistance Factor (RRF), in situ viscosity, and relative injectivity. All polymers behaved as pseudoplastic fluids with no shear thickening. The RF rose from ~1.1 in the dilute regime to 5–16 in the semi-dilute regime, and the RRF spanned 1.2–5.8, indicating moderate, reversible permeability impairment. In-site viscosity reached up to eight times that of brine, while relative injectivity remained 0.5. Xanthan Gum delivered the highest viscosity boost and strongest shear thinning, Scleroglucan offered a balance of stable viscosity and a moderate RF, and Guar Gum gave predictable but lower viscosity enhancement. These results establish practical guidelines for selecting polymer types, concentration, and flow rate in reservoir-condition polymer flood designs. Full article

Ensuring the security of agricultural systems is essential for achieving national food security and sustainable development. Given that agricultural systems are inherently complex and composed of coupled subsystems—such as water, land, and energy—a comprehensive and multidimensional assessment of system security is necessary. This study focuses on Northeast China, a major food-producing region, and introduces the concept of agricultural system coupling security, defined as the integrated performance of an agricultural system in terms of resource adequacy, internal coordination, and adaptive resilience under external stress. To operationalize this concept, a coupling security evaluation framework is constructed based on three key dimensions: reliability (Rel), coordination (Cor), and resilience (Res). An Agricultural System Coupling Security Index (AS-CSI) is developed using the entropy weight method, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, while obstacle factor diagnosis is employed to identify key constraints. Furthermore, bivariate and trivariate Copula models are used to estimate joint risk probabilities. The results show that from 2001 to 2022, the AS-CSI in Northeast China increased from 0.38 to 0.62, indicating a transition from insecurity to relative security. Among the provinces, Jilin exhibited the highest CSI due to balanced performance across all Rel-Cor-Res dimensions, while Liaoning experienced lower Rel, hindering its overall security level. Five indicators, including area under soil erosion control, reservoir storage capacity per capita, pesticide application amount, rural electricity consumption per capita, and proportion of agricultural water use, were identified as critical threats to regional agricultural system security. Copula-based risk analysis revealed that the probability of Rel–Cor reaching the relatively secure threshold (0.8) was the highest at 0.7643, and the probabilities for Rel–Res and Cor–Res to reach the same threshold were lower, at 0.7164 and 0.7318, respectively. The probability of Rel–Cor-Res reaching the relatively secure threshold (0.8) exceeds 0.54, with Jilin exhibiting the highest probability at 0.5538. This study provides valuable insights for transitioning from static assessments to dynamic risk identification and offers a scientific basis for enhancing regional sustainability and economic resilience in agricultural systems. Full article

The planned construction of a steam–gas unit at the Adamów Power Plant raises questions about the potential hydrological impact on the neighboring Natura 2000 protected areas, particularly the Middle Warta Valley (PLB300002) and the Jeziorsko Reservoir (PLB100002). These ecosystems play a key role in protecting bird habitats and biodiversity, and any changes in water management can affect their condition. This paper presents a detailed hydrological analysis of the Warta River and Jeziorsko Reservoir for 2018–2022, with a focus on low-flow periods. The Peak Over Threshold (POT) method and Q_70% threshold were used to identify the frequency, length, and seasonality of low-flow periods in three water gauge profiles: Uniejów, Koło, and Sławsk. The longest recorded low-flow episode lasted 167 days. The permissible water intake for the investment (up to 0.8 m³∙s^–1) is in accordance with the applicable permits and is used mainly for cooling purposes. Calculations indicate that under maximum intake conditions, the water level reduction in the Jeziorsko Reservoir would be between 1.7 and 2.0 mm∙day^–1, depending on the current level of filling. Such changes do not disrupt the natural functions of the reservoir under typical conditions, although during prolonged droughts, they can pose a threat to protected areas. An analysis of the impact of periodic water overflow into the Kiełbaska Duża River indicates its negligible effect on water levels in the reservoir and flows in the Warta River. The results underscore the need for the integrated management of water and power resources, considering the increasing variability in hydrological conditions. Ensuring a balance between industrial needs and environmental protection is key to minimizing the potential impact of investments and implementing sustainable development principles. Full article

In this paper, the concept of symmetry is utilized to evaluate the distribution characteristics of flow fields—that is, flow fields with balanced displacement generally exhibit good spatial symmetry. In the late stage of water-flooding reservoir development, identifying flow field distribution and implementing targeted adjustments are crucial for improving development efficiency and enhancing oil recovery. This study establishes a quantitative evaluation index system integrating both static geological and dynamic production factors to comprehensively characterize flow field distribution in ultra-high water-cut reservoirs. The system incorporates residual oil potential abundance, water-flooding ratio, and water influx intensity as key indicators. A flow field classification method based on the K-Means clustering algorithm was proposed, with the Davies–Bouldin index applied to evaluate clustering validity. The approach was validated using the Egg model, where the flow field was effectively classified into four types: inefficient retention field, effective displacement field, dominant displacement field, and extreme displacement field. Adjustment measures were then applied based on classification results. The findings demonstrate that the proposed method weakens dominant displacement areas while expanding effective and inefficient displacement zones, leading to a 1.1 percentage point increase in recovery factor. This research provides a practical and quantitative tool for flow field diagnosis and adjustment, offering valuable technical guidance for managing ultra-high water-cut reservoirs. Full article

Wetland monitoring is a key means of protecting wetland ecosystems. In order to achieve continuous monitoring of wetlands and predict future patterns, this paper analyzes the spatiotemporal evolution characteristics of wetlands in the upper reaches of the Yellow River from 1990 to 2020, and uses the Patch Generation Land Use Simulation (PLUS) model to simulate the spatial distribution of wetlands from 2040 to 2060 under four scenarios: farmland protection (FPS), wetland protection (WPS), comprehensive protection (CPS) and natural development (NDS). The results show that the total area of wetlands in the upper reaches of the Yellow River is on the rise, increasing by 7.12% in 2020 compared with 1990. The changes in various types of wetlands are different: the areas of river and canals increased by 26.39% and 57.97%, respectively, paddy fields increased by 7.95%, lakes remained basically stable, and tidal flats decreased by 5.67%. The simulation results of the future spatial pattern of wetlands show that: under the FPS scenario, farmland and related land use will expand significantly, mainly through the development of beaches, dry land and unused land, while under the WPS scenario, wetlands will be strictly protected, the area of water resource features such as rivers, lakes and reservoirs will increase significantly, and land use changes will be more ecologically oriented. Compared with the CPS and NDS scenarios, the wetland protection and urbanization process in the upper reaches of the Yellow River can be balanced under the FPS and WPS scenarios. This study has important reference value for the protection and sustainable development of wetland ecosystems in the upper reaches of the Yellow River. Full article

Glaciers, often regarded as “frozen reservoirs”, play a crucial role in replenishing numerous rivers in arid regions, contributing to ecological balance and managing river flow. Recently, the rapid shrinkage of the glaciers in the East Tianshan Mountains has affected the water quantity in the Karez system. However, studies on glacier changes in this region are limited, and recent data are scarce. This study utilizes annual Landsat composite images from 1990 to 2022 obtained via the Google Earth Engine (GEE). It utilizes a ratio threshold approach in conjunction with visual analysis to gather the glacier dataset specific to the Turpan–Hami region. The Open Global Glacier Model (OGGM) is used to model the flowlines and mass balance of around 300 glaciers. The study analyzes the glacier change trends, distribution characteristics, and responses to climate factors in the Turpan–Hami region over the past 30 years. Additionally, future glacier changes through the end of the century are projected using CMIP6 climate data. The findings indicate that the following: (1) From 1990 to 2022, glaciers in the research area underwent considerable retreat. The total glacier area decreased from 204.04 ± 0.887 km² to 133.52 ± 0.742 km², a reduction of 70.52 km², representing a retreat rate of 34.56%. The number of glaciers also decreased from 304 in 1990 to 236 in 2022. The glacier length decreased by an average of 7.54 m·a⁻¹, with the average mass balance at −0.34 m w.e.·a⁻¹, indicating a long-term loss of glacier mass. (2) Future projections to 2100 indicate that under three climate scenarios, the area covered by glaciers could diminish by 89%, or 99%, or even vanish entirely. In the SSP585 scenario, glaciers are projected to nearly disappear by 2057. (3) Rising temperatures and solar radiation are the primary factors driving glacier retreat in the Turpan–Hami area. Especially under high emission scenarios, climate warming will accelerate the glacier retreat process. Full article