Search Results (30)

Accurate reservoir inflow forecasting is essential for sustainable watershed management and low-carbon hydropower operation. Traditional fixed-weight ensemble models lack adaptability under non-stationary hydrological conditions, limiting their reliability in reservoir operation. This study proposes a Context-Aware Localized Weighting Ensemble (CALWE) framework for reservoir inflow forecasting. The framework constructs a predictive response space from heterogeneous model outputs, enabling context identification based on similarities in model prediction behaviors. A localized weighting strategy is then employed to adaptively determine model contributions across contexts. The framework was evaluated using daily reservoir inflow data from Xiaowan Hydropower Station in the Lancang River Basin and monthly inflow data from Xiluodu Hydropower Station in the Jinsha River Basin. Results demonstrate that CALWE outperforms individual models and conventional ensemble approaches in both cases. Compared with the best-performing individual benchmark model for each basin (i.e., SVR for Xiaowan and XGBoost for Xiluodu), CALWE achieved a relative RMSE reduction of 4.93% and an absolute NSE improvement of 0.007 for daily inflow forecasting at Xiaowan, while achieving a relative RMSE reduction of 5.32% and an absolute NSE improvement of 0.027 for monthly inflow forecasting at Xiluodu. SHAP analysis revealed scale-dependent feature contributions, with daily forecasts dominated by antecedent inflow information and monthly forecasts influenced by meteorological, land surface, and hydrological factors. These findings demonstrate that CALWE captures context-dependent inflow responses while providing interpretable insights into model predictions, thereby supporting sustainable watershed management and reservoir operation. Full article

In the daily operation of hydropower stations, the tailwater level is a fundamental parameter for calculating hydropower output, and it is essential for reservoir operation and management. Therefore, this study explores the influencing factors of tailwater level prediction, including the reservoir’s own downstream water level and the headwater level of the downstream reservoir as the characteristic factors. A Stacking ensemble model, using Ridge, Random Forest, Light Gradient Boosting Machine, and Support Vector Regression models as base learners and Ridge as the meta-learner, is established to predict the tailwater level of the Xiluodu Reservoir. We analyze results from the stacked ensemble model and the single model among different stacked combination models and across different quarters for each model. The tailwater level derived from the Stacking ensemble model is found to be in closer agreement with the measured water level than that obtained via interpolation. The model proposed in this study delivers superior predictive performance compared to the four baseline models, with the average absolute error achieving a maximum reduction of 38%. This confirms the effectiveness of the stacking ensemble strategy in predicting tailwater levels, thereby providing accurate boundary conditions for reservoir scheduling calculations. Full article

Reservoir water level fluctuations can reactivate landslides and cause severe losses. This study examines the Niulanjiang landslide, reactivated by the impoundment of the Xiluodu Hydropower Station in Southwest China, using field investigations, in situ displacement monitoring, and direct shear tests on soil–rock mixtures. The results show that the land-slide experienced a progressive failure process, evolving from long-term shear creep in the sliding zone to localized abrupt creep and finally to overall fracture sliding. The loose soil–rock mixture provided the structural basis for instability, whereas reservoir water level fluctuation was the dominant trigger. Rising water levels increased shear stress and promoted seepage-induced weakening, causing local failure of the sliding surface and gradual formation of a shear outlet. Laboratory tests indicate that rock block content and moisture content strongly affect mechanical behavior: higher rock block content enhances shear dilatancy and strain softening, while higher moisture content promotes shear contraction, plastic deformation, and linear reductions in cohesion and internal friction angle. The failure mechanism involves coupled strength degradation and increased seepage force. Initial instability occurred in the middle slope under hydrostatic–hydrodynamic pressure, then propagated rearward and forward, reducing front resistance and driving overall sliding toward the Niulanjiang River. These findings support early warning and mitigation of similar reservoir-induced landslides. Full article

Traditional reservoir flood control operations in China have long relied on a fixed flood-limited water level (FLWL), which frequently results in the underutilization of water resources during flood seasons. Dynamic FLWL regulation and joint reservoir operation have emerged as core strategies to optimize floodwater resource utilization while ensuring flood control safety. However, these approaches typically treat the flood control storage capacity of individual reservoirs as fixed constraints, failing to consider the potential for reallocating this capacity within a cascade reservoir system. This study explores the concept of “equivalent utilization of flood control storage capacity” among cascade reservoirs. Focusing on the four major reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) in the lower reaches of the Jinsha River, a methodology for analyzing the equivalent index of their flood control storage capacity is established. The core of this methodology involves a two-round scheduling simulation under various design flood scenarios. The first round of simulation adheres to standard operating rules, while the second round allows upstream reservoirs to retain additional flood volume—with downstream reservoirs correspondingly reducing their outflow—on the premise that downstream safety targets are satisfied. The equivalent index is defined as the ratio of the reduced storage capacity utilized downstream to the additional storage capacity utilized upstream. Nine design flood scenarios (covering three typical years with 1%, 2%, and 5% exceedance probabilities) for flood control in the Sichuan–Chongqing reach were analyzed, with the tightly coupled Wudongde–Baihetan and Xiluodu–Xiangjiaba reservoir pairs treated as two integrated units. The results indicate that the equivalent indices between these two reservoir groups range from 0.96 to 0.999, demonstrating near-perfect functional interchangeability of their flood control storage capacities for the specified research objective. For practical engineering application, a value of 0.96 is recommended as the lower-bound equivalent index. This study provides a methodological framework and specific index to support the dynamic, coordinated, and more efficient utilization of flood control storage capacity in large-scale cascade reservoir systems. Full article

Hydropower is a cornerstone of global renewable energy; however, reservoir sedimentation directly undermines its benefits and operational lifespan. A critical, often overlooked, aspect of sedimentation is the compaction of fine-grained deposits, which introduces systematic discrepancies between standard siltation calculation methods. This study addresses this gap by developing a machine learning-based model to quantify sediment compaction and correct siltation estimates using the Xiluodu Hydropower Station on the Jinsha River, China, as a case study from 2014 to 2020. Based on hydrological, sediment, and fixed-section monitoring data, we applied five machine learning algorithms (Linear Regression, Neural Network, Random Forest, Gradient Boosting, and Support Vector Regression) to establish a relationship between the compaction thickness and the following key predictors: Year, Cumulative Sediment Thickness, Annual Sediment Thickness, and Distance to the Dam. The results demonstrate that the Neural Network (NN) model significantly outperforms traditional models, effectively capturing complex, nonlinear compaction dynamics with strong predictive accuracy (test R² = 0.766, RMSE = 0.047 m) and no significant overfitting. SHAP analysis revealed the dominant influences of consolidation time (years) and overburden stress (Cumulative Sediment Thickness), linking the model’s predictions to fundamental geotechnical principles. Applying the NN model to correct for the cross-sectional volume method markedly improved its consistency with the independent sediment transport method, reducing the average relative difference from −33.7% to −6.5% (2016–2020). This study provides the first quantitative, continuous (198 km, 221 sections) assessment of reservoir-scale sediment compaction, confirming its widespread existence and demonstrating its critical role in the long-standing methodological discrepancies. Our study transformed compaction from an acknowledged phenomenon into a quantifiable correction, offering a novel, data-driven framework to enhance the accuracy of reservoir sedimentation assessments globally. Full article

Reservoir bank collapse is a critical geological hazard during the operation of large-scale water conservancy projects, controlled by unique hydrodynamic mechanisms induced by reservoir impoundment, and differs significantly from ordinary landslides. Traditional risk assessment methods, however, often struggle to achieve effective integration between macro-regional zoning and micro-mechanical analysis. Against this limitation, this study proposes a GIS-integrated multi-scale risk screening framework to achieve the preliminary integration of qualitative regional evaluation and quantitative site-specific analysis. Compared with traditional multi-scale studies, the innovations of this research are as follows: (1) a customized GIS component was developed to realize semi-automatic profile extraction from high-resolution DEMs and batch Bishop stability calculations, overcoming the bottleneck of spatializing micro-models over large areas; (2) a “bottom-up” dynamic feedback mechanism was established, utilizing the quantitative safety factor from site-specific evaluations as an explicit indicator for the conservative screening correction of the macro-regional risk map. Applied to the Xiluodu Reservoir, this framework illustrates a potential multi-scale approach for cross-scale risk screening driven by physical–mechanical mechanisms. This provides both a global perspective and a localized physical basis, offering a strategic screening tool for reservoir management. By linking failure mechanisms directly to spatial impacts, the framework provides a plausible conservative feedback rule for risk-informed decision-making in complex reservoir settings. Full article

The development of large-scale cascade hydropower complexes has improved the navigation conditions of mountainous rivers but creates unique “continuous dam zones,” presenting complex challenges for port site selection due to hydrological variability and geological risks. To address the lack of specialized evaluation tools for this specific context, this paper constructs a comprehensive evaluation indicator system tailored for mountainous reservoir areas. The proposed system explicitly integrates critical engineering and physical constraints—specifically fluctuating backwater zones, geological hazards, and dam-bypass mileage—alongside ecological and social requirements. The Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) are integrated using a Game Theory model to determine combined weights, and the Evaluation based on Distance from Average Solution (EDAS) model is applied to rank the alternatives. An empirical analysis of the Xiluodu Reservoir area on the Jinsha River demonstrates that operational efficiency, geological safety, and environmental feasibility constitute the critical decision-making factors. The results indicate that Option C (Majiaheba site) offers the optimal solution (ASi = 0.9695), effectively balancing engineering utility with environmental protection. Sensitivity analysis further validates the consistency and stability of this ranking under different decision-making scenarios. The findings provide quantitative decision support for project implementation and offer a replicable reference for infrastructure planning in similar complex mountainous river basins. Full article

Climate variability and growing competition for limited water resources have made the operation of cascade reservoirs increasingly complex. This study develops a comprehensive system-based multi-objective optimization and evaluation framework that simultaneously integrates five goals: power generation, water supply, ecological protection, navigation reliability, and flood control as a constraint. The framework employs the NSGA-III evolutionary algorithm to address the high-dimensional optimization problem and combines Analytic Hierarchy Process (AHP), Entropy Weight Method, and TOPSIS to integrate subjective expertise with objective data in the evaluation of alternatives. Applied to the lower Jinsha River cascade under wet, normal, and dry hydrological scenarios, the model reveals distinct conflicts between hydropower and ecological or navigational requirements, partial synergies between hydropower and water supply, and tension between ecological and supply demands. Hydrological variability alters these relationships, with wet years intensifying conflicts and dry years heightening supply and ecological pressures. Functional differentiation among reservoirs is also evident, with Baihetan and Xiluodu showing pronounced power–ecology tensions, while Xiangjiaba primarily supports supply and navigation. The study not only advances the theory of multi-objective decision-making in water resources systems but also offers actionable guidance for sustainable reservoir governance and regional development. Full article

The process of reservoir impoundment poses a significant threat to the stability of reservoir bank slopes, potentially triggering new landslides or reactivating ancient ones. Consequently, long-term and stable monitoring of surface deformation in reservoir areas is essential for ensuring safe reservoir operation. SBAS-InSAR technology—characterized by its high precision, multi-temporal capability, and wide spatial coverage—offers an effective means of comprehensively characterizing landslide deformation in such environments. In this study, SBAS-InSAR is applied to monitor landslides in the Xiluodu Reservoir area using Sentinel-1A imagery. Ascending and descending orbit data are jointly inverted to reconstruct the two-dimensional (2D) surface deformation time series. The deformation patterns and their spatiotemporal evolution are analyzed in conjunction with remote sensing imagery, topographic and geological data, and reservoir water level fluctuations. The integrated analysis identifies 10 and 12 significant deformation zones in the vertical and east–west directions, respectively—demonstrating improved detection accuracy compared to single-orbit approaches. Two representative landslides, the Mixiluo and Huanghua landslides, are selected for detailed investigation. Their toe deformation exhibits a pronounced response to both rainfall and reservoir water level variations. These findings provide valuable reference data and technical support for the early identification of reservoir bank landslides and the safe operation of reservoirs in this and similar engineering contexts. Full article

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions. Full article

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 evolution of the landslide erosion of bank slopes in reservoir regions is crucial for disaster prevention and mitigation in hydropower projects, and it is also an important topic to investigate the impact of anthropogenic activities on the environment. A dispute exists on the landslide erosion of bank slopes under reservoir impoundment. In this paper, we chose the Xiluodu reservoir as a typical case for analysis. The reservoir is located on the eastern Qinghai–Tibetan Plateau and ranks as the fourth largest hydropower station in the world. Firstly, we identified landslides using the multi-temporal image interpretation method before and after impoundment in the reservoir area, and established a landslide dataset. On this basis, we analyzed the spatiotemporal distribution of these landslides, and derived the landslide erosion rate based on the quantitative relation between landslide volume and area. The results showed that the landslide erosion rate increased sharply during the initial impoundment period and decreased exponentially. We then analyzed reservoir-induced landslides worldwide and found that most reservoir bank slopes tend to stabilize after about five years of impoundment, eventually even becoming more stable than pre-impoundment, regardless of the location, scale, water fluctuations, and geoenvironment. Thus, in the long term, reservoir construction tends to reduce erosion and contribute to bank stability. This study provides a preliminary answer to the controversial issue of the impact of reservoir construction on the natural environment. Full article

The Xiangjiaba and Xiluodu reservoirs, as important components of the large cascade reservoirs in the lower Jinsha River, and the interactive changes in sediment trapping amounts, the differences in sedimentation dynamics, and the potential mutual influence mechanisms among them are scientific issues worthy of attention. Based on the multiple observed data of thalweg elevation before and after the completion of the dam construction, this study calculated the average sedimentation rates of all 20 km segments of the above-mentioned reservoirs in different periods. Meanwhile, the local mean gradients between adjacent segments and the regional mean gradients from the segments to the dam in the corresponding periods were calculated. The results show that the maximum and average sedimentation rates of the Xiangjiaba Reservoir, which was built earliest and is located downstream, were as high as 19.62 m yr⁻¹ and 8.88 m yr⁻¹, respectively, in the first half year after the dam closure. After the completion of the Xiluodu Reservoir, an adjacent cascade reservoir upstream, the average sedimentation rate of the Xiangjiaba Reservoir in the following seven years dropped to 0.67 m yr⁻¹. The maximum and average sedimentation rates of the Xiluodu Reservoir were 9.07 m yr⁻¹ and 4.15 m yr⁻¹, respectively, within one year after the dam closure, and its average sedimentation rate in the following six years was 2.51 m yr⁻¹. The spatial variations of sedimentation rates in these two reservoirs follow different changing patterns. There is an obvious correlation between the change in mean gradient and the change in sedimentation rate. The sequence of dam construction, the relative positions of the reservoirs, the differences in sediment trapping amounts, and operation modes are the key factors controlling the changes in sedimentation rate and gradient in the reservoir area. This study reveals the interactive changes in sedimentation rates among cascade reservoirs and the response mechanism of river channel morphology, and has a guiding role for the formulation of effective measures for the sustainable utilization of cascade reservoirs. Full article

It is of great significance to identify the spatiotemporal stress distribution characteristics to ensure the safety of a super-high arch dam during the initial operation stage. Taking the 285.5 m-high Xiluodu Dam as an example, the spatiotemporal distribution characteristics were analyzed based on the five-year observation data after impoundment. Statistical and boosted-regression-tree-based prediction models for the dam stress were established. The boosted-regression-tree-based prediction model is more accurate than the statistical model. The monitoring indicators for the measuring points of focused locations were determined using the confidence interval estimation method. The results show that the dam was in a compression state, and the arching effect was obvious. The arch direction compressive stress gradually increased and stabilized, and the maximum appeared in the middle of the upstream face of the crown cantilever monolith. For the crown cantilever, the cantilever direction stress at the dam heel was significantly affected by the interior temperature recovery, and the arch direction stress in the middle of the upstream face was significantly affected by the reservoir water level. The measuring points of the focused locations with reliable observation data can be selected as a monitoring index to guide the initial operation. Full article

Hydropower is an important clean renewable energy that plays a key role in coping with issues such as global energy security, environmental protection, and climate change. In order to improve the optimal operation ability of hydropower reservoirs in the context of forecast runoff with limited accuracy and prediction period, there has been a growing interest in deriving operating rules of hydropower reservoirs. Reasonable operation decision is very important for safe operation of reservoirs and efficient utilization of water resources. Therefore, a novel method of operation rules derivation is proposed in this study. Optimal operation model of hydropower reservoir is established and support vector machine (SVM) is used to derive operation rules based on the optimal operation results. In order to improve the performance of SVM, the Henry gas solubility optimization (HGSO) is used to optimize its hyperparameters for the first time. Meanwhile, multiple strategies are applied to overcome the drawbacks of HGSO. The multi-verse optimizer (MVO) is used to enhance the exploration capability of basic HGSO. Quadratic interpolation (QI) is used to improve the exploitation ability of HGSO. In this study, the Xiluodu and Xiangjiaba hydropower reservoirs in the upper Yangtze River of China were selected as a case study. First, the improved HGSO called MVQIHGSO was tested on 23 classical benchmark functions. Then, it was employed to optimize hyperparameters of SVM model for deriving operation rules. The results and statistical studies indicate that the improved HGSO outperforms the comparison algorithms in exploration and exploitation. The obtained results imply that the novel method named MVQIHGSO-SVM can provide a new practical tool to deriving operation rules for hydropower reservoirs, which is conducive to the safe and efficient utilization of water resources. Full article