Search Results (206)

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir (TGR) experiences seasonal submergence and exposure, resulting in soil structure degradation and intensified erosion. This study investigated how flooding duration affects root development and the erosion resistance of root–soil complexes in the WLFZ of the TGR. Two representative herbaceous species were chosen for this study: Xanthium sibiricum, an annual with a taproot system, and Cynodon dactylon, a perennial with a fibrous root system. Root traits, soil erodibility K-value, shear strength, and soil texture were measured from plant and soil samples collected at different flooding durations (145–175 m elevations). Our results showed that prolonged flooding significantly suppressed root growth, particularly in the 145–155 m zone, where root length density and root tips were markedly reduced (p < 0.05). Soil erodibility increased with flooding duration, with erodibility K-values ranging from 0.050 ± 0.002 to 0.062 ± 0.001 t·hm²·h/(MJ·mm·hm²), while shear strength declined correspondingly. Textural shifts from silty loam to silt were observed at zones experiencing extended flooding, contributing to aggregate instability and decreased internal friction angles. Notably, Cynodon dactylon demonstrated superior soil reinforcement capacity compared to Xanthium sibiricum, with its root volume and surface area significantly correlated with reduced K-values (p < 0.01) and enhanced shear strength (p < 0.001), enabling it to better prevent bank erosion under flooding conditions. These findings underscore the importance of root morphological traits in maintaining soil stability under hydrological stress and highlight the potential of perennial fibrous-rooted species for vegetation-based erosion control in fine-textured riparian zones. This study provides a theoretical basis and practical reference for ecological restoration in the WLFZ of the TGR and similar environments. Full article

Soil structural instability in reservoir riparian zones, induced by water level fluctuations, threatens sustainable land use by accelerating land degradation. This study examined the impact of water-level variations on soil aggregate composition and stability based on key indicators, including water-stable aggregate content (WSAC), mean weight diameter (MWD), and geometric mean diameter (GMD). The Savinov dry sieving, Yoder wet sieving, and Le Bissonnais (LB) methods were employed for analysis. Results indicated that, with decreasing water levels and increasing soil layer, aggregates larger than 5 mm decreased, while aggregates smaller than 0.25 mm increased. Rising water levels and increasing soil layer corresponded to reductions in soil stability indicators (MWD, GMD, and WSAC), highlighting a trend toward soil structural instability. The LB method revealed the lowest aggregate stability under rapid wetting and the highest under slow wetting conditions. Correlation analysis showed that soil organic matter positively correlated with the relative mechanical breakdown index (RMI) (p < 0.05) and negatively correlated with the relative slaking index (RSI), whereas soil pH was negatively correlated with both RMI and RSI (p < 0.05). Comparative analysis of aggregate stability methods demonstrated that results from the dry sieving method closely resembled those from the SW treatment of the LB method, whereas the wet sieving method closely aligned with the FW (Fast Wetting) treatment of the LB method. The Le Bissonnais method not only reflected the outcomes of dry and wet sieving methods but also effectively distinguished the mechanisms of aggregate breakdown. The study concluded that prolonged flooding intensified aggregate dispersion, with mechanical breakdown influenced by water levels and soil layer. Dispersion and mechanical breakdown represent primary mechanisms of soil aggregate instability, further exacerbated by fluctuating water levels. By elucidating degradation mechanisms, this research provides actionable insights for preserving soil health, safeguarding water resources, and promoting sustainable agricultural in ecologically vulnerable reservoir regions of the Yangtze River Basin. Full article

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 10¹⁰ kW·h and 4.19 × 10¹⁰ kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation. 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

Cyanobacterial blooms and aquatic vegetation dynamics are critical indicators of freshwater ecosystem health, increasingly shaped by climate change, nutrient enrichment, and ecological restoration efforts. Here, we present an automated monitoring system optimized for small- and medium-sized lakes. This system integrates phenology-based algorithms with Sentinel-2 MSI imagery, leveraging the AI Earth (AIE) platform developed by Alibaba DAMO Academy. Applied to monitor 12 ecologically sensitive lakes and reservoirs in Jiangsu Province, China, the system enables multi-year tracking of spatiotemporal changes from 2019 to 2024. A clear north-south gradient in cyanobacterial bloom intensity was observed, with southern lakes exhibiting higher bloom levels. Although bloom intensity decreased in lakes such as Changdang, Yangcheng, and Dianshan, Ge Lake displayed fluctuating patterns. In contrast, ecological restoration efforts in Cheng and Yuandang Lakes led to substantial increases in bloom intensity in 2024, with affected areas reaching 33.16% and 33.11%, respectively. Although bloom intensity remained low in northern lakes, increases were recorded in Hongze, Gaoyou, and Luoma Lakes after 2023, particularly in Hongze Lake, where bloom coverage surged to 3.29% in 2024. Aquatic vegetation dynamics displayed contrasting trends. In southern lakes—particularly Cheng, Dianshan, Yuandang, and Changdang Lakes—vegetation coverage significantly increased, with Changdang Lake reaching 44.56% in 2024. In contrast, northern lakes, including Gaoyou, Luoma, and Hongze, experienced a long-term decline in vegetation coverage. By 2024, compared to 2019, coverage in Gaoyou, Luoma, and Hongze Lakes decreased by 11.28%, 16.02%, and 47.32%, respectively. These declines are likely linked to increased grazing pressure following fishing bans, which may have disrupted vegetation dynamics and reduced their ability to suppress cyanobacterial blooms. These findings provide quantitative evidence supporting adaptive lake restoration strategies and underscore the effectiveness of satellite-based phenological monitoring in assessing freshwater ecosystem health. Full article

In this study, we analyzed the microearthquake seismicity in the Enguri area (Georgia) recorded between 2020 and 2023 using a newly installed seismic network developed within the DAMAST project. The high sensitivity of the network allowed the detection of even very small seismic events, enabling a detailed investigation of the temporal dynamics of local seismicity. Statistical analyses suggest that the seismic activity around the Enguri Dam is influenced by a combination of natural tectonic processes and subtle reservoir-induced stress changes. While the dam does not appear to exert strong seismic forcing, the observed ≈7-month delay between water level variations and seismicity may indicate a triggering effect. Localized stress variations and temporal clustering further support the hypothesis that water level fluctuations modulate seismic activity. Additionally, the mild persistence in interoccurrence times is consistent with a stress accumulation and delayed triggering mechanism associated with reservoir loading. Full article

Land use composition, water level fluctuations (WLFs), and biogeographical factors are recognized as key drivers of phytoplankton dynamics in reservoir ecosystems. This two-year study presents the first assessment of the combined effects of catchment land use, WLFs, and geographical distance on phytoplankton biomass and community composition across twelve Mediterranean reservoirs in Cyprus, which serve primarily for drinking water supply and irrigation. The results show that higher phytoplankton biomass was recorded in reservoirs whose catchments had >30% coverage by developed land (urban and agricultural), suggesting that increased anthropogenic pressures may lead to nutrient enrichment and elevated productivity. However, despite elevated biomass, no consistent spatial patterns were observed in phytoplankton community composition. The geographical distance between reservoirs had only a minor effect on species distribution, implying that other factors—such as water residence time or hydrological variability—play a more prominent role in shaping community structure. Phytoplankton biomass maxima were most often recorded during periods of elevated water levels and were typically dominated by Chlorophyta, Dinoflagellata, Bacillariophyta, and Charophyta. The pronounced temporal variability in species composition across all reservoirs points to a highly dynamic system, where environmental fluctuations strongly influence community assembly. This study provides the first comprehensive data on phytoplankton in Cyprus reservoirs, highlighting the importance of land use and hydrological regulation for water quality management in similar settings. Importantly, this baseline dataset can support the implementation of the Water Framework Directive (WFD) by contributing to the definition of ecological status classes, establishing reference conditions, and guiding future monitoring and assessment efforts. Expanding such datasets through coordinated, basin-wide monitoring initiatives is essential to improve our understanding of phytoplankton dynamics and their role in ecosystem functioning under the pressures of climate change and intensified land use in this Mediterranean “hot spot”. Full article

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

The water-level fluctuation zone (WLFZ) in the Three Gorges Reservoir (TGR) has attracted significant attention because of its pivotal role in shaping environmental processes. However, with the increasing water level, the effects of nitrogen and phosphorus release from submerged soil–plant systems in the WLFZ on the deterioration in water quality remain poorly understood. In this study, a simulation experiment was conducted involving submerged undisturbed soil columns that was submerged once a year at different elevations (150, 160, and 170 m) before reservoir impoundment in the WLFZ within the TGR area. The results revealed that the concentrations of various forms of nitrogen and phosphorus in the overlying water released after system submergence first decreased, then rapidly increased after 30 days, and reached equilibrium after 120 days of flooding. Particulate N accounted for approximately 70% of the total nitrogen (TN) released, while particulate P accounted for more than 90% of the total phosphorus (TP) released by soil–plant systems after submergence for 200 days, which may be related to soil erosion and plant decomposition. The amounts of N and P released were significantly negatively correlated with the initial mass of the soil–plant system, indicating that nutrient release by the system is more susceptible to submerged soil than to submerged plants. During the flooding period of the WLFZ in the TGR, the release loads of soil–plant systems into reservoir water were 159.83 kg N ha⁻¹ and 19.30 kg P ha⁻¹. These results suggest that soil and plants in the WLFZ of the TGR could be at risk for water-induced deterioration. Therefore, additional vegetation management might be implemented to alleviate water eutrophication in the TGR caused by submerged soil and plants in the WLFZ. Full article

The daily operation of cascade hydropower stations induces periodic water level fluctuations (WLFs) that propagate as gravity waves, significantly affecting the hydrodynamics of reservoirs. Previous studies have mainly focused on the effects of individual stations, with little attention paid to the combined impacts of upstream and downstream operations. Taking the Wudongde Reservoir on the Jinsha River as a case study, we used a one-dimensional hydrodynamic model and cross-correlation analysis to simulate flow fluctuation patterns under joint daily operations. The results show that fluctuations from upstream stations attenuate rapidly in the reservoir, with greater attenuation during the dry season. Under joint operations, wave energy decayed exponentially near the reservoir tail and linearly in the main reservoir area, leading to a further reduction in the WLF amplitudes. The interactions between upstream- and downstream-propagating waves enhance energy dissipation. The wave type transitioned from kinematic to dynamic as the water depth increased. During the wet and dry seasons, the average wave velocities were approximately six and nine times higher, respectively, than those under natural conditions. Joint operations expand the range of potential slope instability but reduce the WLF rate compared to natural flows. These findings provide a scientific reference for optimising the daily operations of cascade hydropower stations and mitigating their ecological impacts. Full article

This study focuses on the upper section of the Zhujiang Formation in the Enping Sag of the Zhujiangkou Basin in the South China Sea, investigating the mechanisms by which astronomical orbital cycles drive paleoclimate, sea-level fluctuations, and sedimentary development. In this study, a cyclic stratigraphic analysis was performed using natural gamma-ray logging data and geochemical proxies (Chemical Index of Alteration (CIA); Al₂O₃ content) in combination with depositional noise modeling (DYNOT Dynamic Orbital Tuning Model and ρ1 noise factor). High-resolution time series analysis revealed three key findings: (1) a 15.98–19.09 Ma astronomical timescale was established through the identification of Milankovitch cycles including 405 kyr eccentricity, 100 kyr eccentricity, 40 kyr obliquity, and 20 kyr precession; (2) sea-level changes exhibited 405 kyr eccentricity-driven cyclicity, with high-eccentricity phases corresponding to warm-humid climates and transgressive mudstone deposition, and low-eccentricity phases reflecting arid conditions and regressive sandstone development; and (3) orbital-scale precession modulation regulated sediment source-to-sink processes through climate–sea level coupling. This work provides a quantitative framework for predicting astronomical cycle-controlled reservoirs, offering critical insights for deepwater hydrocarbon exploration in the Zhujiangkou Basin. Full article

This study aims to develop fluid–solid coupled similar materials to enhance the reliability of geotechnical model tests simulating reservoir slope stability under water-level fluctuations. Using an orthogonal experimental method, materials were prepared with quartz sand and barite as aggregates, cement and gypsum as binders, and water as the regulator. Tests on density, uniaxial and triaxial compressive strength, and flow properties determined the relationships between material properties and raw components. Uniaxial compressive strength tests under dry–wet cycles revealed that cement-to-binder ratio primarily influenced density, uniaxial compressive strength, cohesion, and hydraulic conductivity, while the binder-to-aggregate ratio affected elastic modulus and internal friction angle. Uniaxial compressive strength continuously degraded with cycles but at a decreasing rate. A water-damage resistance coefficient was defined to quantify degradation. Multiple linear regression analysis established a robust model for uniaxial compressive strength prediction, providing a theoretical basis for material proportioning. These findings improve the simulation accuracy in hydrologically active zones, with applications in designing stable reservoir slopes. Full article

The Chaoshan Depression, situated in the northern South China Sea, is a Mesozoic residual depression beneath the Cenozoic Pearl River Mouth Basin. Borehole LF35-1-1 has confirmed the existence of marine Jurassic layers rich in organic carbon within this depression. However, the understanding of petroleum geology in this area is limited due to the complex interplay of Mesozoic and Cenozoic tectonic activities and the poor quality of seismic imaging from previous surveys, which have obstructed insights into the characteristics of Mesozoic reservoirs and the processes of oil and gas accumulation. Recent quasi-3D seismic data have allowed for the identification of Mesozoic bioherms and carbonate platforms in the Middle Low Uplift of the Chaoshan Depression. This research employs integrated geophysical data (MCS, gravity, magnetic) and well data to explore the factors that influenced Middle Jurassic reef development and their implications for reservoir formation. The seismic reflection patterns of reefs and carbonate platforms are primarily characterized by high-amplitude discontinuous to chaotic reflections, with occasional blank reflections or weak, sub-parallel reflections, as well as significant high-velocity, high Bouguer gravity and low reduced-to-pole (RTP) magnetic anomalies. Atolls, stratiform reefs, and patch reefs are located on the local topographic highs of the platform. Three vertical evolutionary stages have been identified based on the size of atolls and fluctuations in relative sea level: initiation, growth, and submergence. The location of bioherms and carbonate platforms was influenced by paleotectonic topography, while their horizontal distribution was affected by variations in relative sea level. Furthermore, the reef limestone reservoirs from the upper member of the Middle Jurassic, combined with the mudstone source rocks from the Lower Jurassic and the lower section of the Middle Jurassic, as well as the bathyal mudstone caprocks from the lower part of the Late Jurassic, create highly favorable conditions for hydrocarbon accumulation. Full article