Cascade hydropower development disrupts the continuity of natural river water temperature, leading to a significant cumulative effect on the temperature and a series of ecological effects. This study examines the water temperature along the lower Jinsha River and in the Three Gorges reservoir area , and reveals its spatial and temporal variations on different scales before and after dam construction and the cumulative effects. The impact of water temperature changes on fish spawning is also discussed. The results show that after the Xiluodu and Xiangjiaba dams were constructed, the annual mean water temperature difference along the lower Jinsha becomes smaller; along the river, the annual highest temperature at the hydrological stations shows a decreasing trend, while the annual lowest is significantly elevated, especially in January and December. After the construction, the annual temperature variation is reduced, and the time period featuring water temperature distribution has a trend of ‘convergence’－the days of water temperature distribution changed from M-type to V-type. An examination on the cumulative effect indicator finds that after dam construction, a significant time crowding effect occurs－a lag time in the extreme water temperature, water temperature delayed up to 1 - 2 months, the fish of 48% and 44% affected to a high degree at the Panzhihua section and the section downstream of Xiangjiaba dam respectively－thereby affecting severely the fish spawning and reproduction in the downstream and the fish protection sections. The study demonstrates the cumulative effects of water temperature impacted by cascade dam construction and its impact on fish spawning, laying a basis to enhance the role of temperature changes caused by large-scale cascade dams and the downstream ecological restoration.

The deformation and stability analysis of high steep slopes is a key technical problem in the construction and operation of hydropower projects. Over the past two decades, China has built a large number of large-scale water conservancy and hydropower projects. Many key technical problems of high dams and large reservoirs have been solved successfully, and remarkable progress has been achieved in the life cycle performance evolution and safety control of high, steep slopes of reservoirs. This paper takes the performance evaluation of high steep slopes in the southwest hydropower projects as the main research line, and focuses on the deformation and stability evolution of high steep slopes. We examine the research progress in determining the influencing factors of stability and failure modes of high steep slopes, stability evaluation and deformation analysis methods, seepage analysis, and safety control. The latest researches are discussed in detail on the strict three-dimensional limit equilibrium method, modified Hoek-Bray wedge method, rigid body spring method, parameter inversion method based on monitoring data, and slope seepage analysis. We also discuss the academic thinking and technical route and certain future challenges to the life-cycle deformation and stability evolution analysis of high steep slopes in hydropower engineering.

The hybrid pumped storage-wind-photovoltaic multi-energy complementary system has broad application prospects. However, its capacity design needs to characterize the complex relationship between the water volume and electric power, and its economic evaluation should consider the rules of electricity markets. This paper describes a new two-stage optimization framework for optimizing operation and capacity decision. First, a consistent assumption for the target gross output is presented; and a double-objective operation optimization model is developed. Then, a discrete decision space is obtained through optimization based on a large number of medium and long-term operation cases. Finally, the scheme with the maximized net present value (NPV) is selected. Application in a case study of the clean energy base in the upper Yellow River gives the conclusion as follows. New energy capacities corresponding to high, medium and low acceptance degrees of load loss risks are 3.2-3.9 times, 2.4-3.0 times, and 1.6-2.1 times that of the mixed pumping and storage capacity, respectively. The peak to valley ratios of the system's monthly electricity delivery range from 1.36 to 1.45, indicating the power sources in the system are well complementary on the medium and long time scales.

The efficiency of water energy utilization of a conventional fixed-speed small hydropower unit is often very low when its operation deviates from the design conditions. However, by examining the comprehensive characteristic curves of the turbine, a law that for a unit under the optimal guide vane opening and a certain optimal unit speed achieves the highest efficiency of water energy utilization has been found. And, a variable-speed control strategy was proposed to achieve such an efficiency, particularly for the condition of significant water head variations. This paper presents a mathematical model for simulations of a variable-speed small hydropower unit, incorporating the operation of the turbine, governor, generator and converter. A closed-loop variable-speed control system based on this new strategy was designed. Numerical simulation results indicate that compared to the fixed-speed operation scheme, the new scheme achieves a 2.39% increase in power output at the water head of 28 m. Field tests at the Zangtanqiao hydropower station verify that the small hydropower unit is successful in operating under its optimal guide vane opening and optimal rotational speed, and that the new control strategy improves power generation efficiency across high, medium, and low head conditions. The new strategy is easy to apply in the industry of small hydropower and of great significance to the real hydropower projects.

The exhaust air pressurization process of a pump-turbine during the shift from this condition is explored to address the issue of instability that is easy to generate in pumped storage units during phase transition from power generation to power generation. Unsteady multiphase flows in a prototype pump-turbine are simulated using the SST k-w model and VOF model. The simulations demonstrate the transient variation trends in the unit's active power, exhaust rate, vaneless zone pressure, and the relationship of the evolution of internal flows in the pump-turbine versus its external characteristics. They also reveal the dynamic distribution of air and liquid phases in the unit in exhaust pressurization process. The study demonstrates that in this process, the unit's active power absorption from the grid and the exhaust rate decreases gradually after their initial rapid decreases, its pressurization effect is evident, and its pressure pulsation frequencies in the vaneless zone are higher. In the vaneless zone, a high-speed water ring forms gradually driven by the inflow coming from tail water, so that air-liquid movements are chaotic and the air is prone to flowing backward into the draft tube, which has a significant impact on the unit's stability. The findings in this study are useful for improving the stability and safety of pumped storage units.

A general segmentation model for dam concrete surface cracks in images often faces a shortage of training data due to the high cost of manual annotation, resulting in insufficient accuracy in its results. This paper presents an automatic annotation and segmentation algorithm that integrates image feature extraction and deep learning techniques. The algorithm first adopts a strategy for combining binarization and edge detection to annotate unlabeled crack defects automatically, and constructs a large-scale dataset of 19,101 crack masks. Then, a hybrid model for combining Swin-Transformer and Unet (Swin-Unet) is designed by introducing the hierarchical attention mechanism of Swin-Transformer into the Unet architecture. Finally, the model is validated through experiments and result analyses on the self-constructed datasets. The results show this Swin-Unet model achieves the highest crack classification accuracy (100%) and a segmentation IoU of 93.1% or 7.5% improvement over the Unet segmentation model (85.6%). This indicates the introduction of the Swin-Transformer architecture enhances the model's capability of associating global and local features, significantly improving the crack defect segmentation accuracy. Besides, an analysis of the minimum enclosing rectangle of cracks reveals significant clustering in both the direction and shape distribution of cracks, deepening our understanding of the mechanisms of crack formation and useful for predicting crack propagation direction.

As global climate change intensifies and extreme events occur more frequently, the safe and stable operation of hydro-wind-solar multi-energy complementary systems faces great challenges, necessitating a rational evaluation of the resilience of these systems under extreme event disturbances. This study first elaborates on the complementary characteristics of hydropower, wind, and solar energy, and examines the types of disturbances confronted by these integrated systems. Then, a new concept of resilience for a complementary system is discussed, and a three-stage conceptual model for resilience is developed. Based on this framework, we select monthly, seasonal, and annual timescales and specify power output thresholds for the complementary system, and formulate an evaluation index and methodology for resilience assessment based on power output loss. Finally, to evaluate the resilience, a case study is conducted on a clean energy base in the upper Yellow River. The assessment results validate the effectiveness of the evaluation index and methodology. Notably, hydropower energy demonstrates superior resilience compared to wind and solar power, and the system’s overall resilience is enhanced significantly through utilizing the complementary nature of hydropower, wind, and solar energy. This study helps decision-making for resource allocation, scheduling strategies, and safe operation of integrated hydro-wind-solar energy systems.

High-altitude wind energy is an important potential resource in the future. This paper presents a statistical analysis of the wind characteristics in China at different above ground levels below 4000 m, based on the ERA5 wind data from the European Centre for Medium-Range Weather Forecasts and digital elevation data, focusing on the spatiotemporal distributions of wind speeds and the frequencies of typical wind speeds. The results show a complicated spatial pattern of wind speeds over China, i.e., higher wind speeds in the northeastern and plateau regions, and lower wind speeds in the southern and northwestern regions. At low levels such as 100 m, coastal winds are high, while inland winds are low. Seasonal variations in wind feature higher speeds in spring and winter and the lowest in summer. The highest wind speeds occur typically in January and December, while the lowest typically in July and August. Wind power density is generally higher in the northeast, southwest and coastal regions. At low levels, the regions that feature relatively high frequencies of extreme high wind speeds appear in Inner Mongolia and the northeastern provinces; at high levels, they appear in Tibet and Qinghai. This paper also gives the maps and variation patterns of wind resources at different above ground levels, which are helpful for the planning and design of airborne wind power projects.

The design rule curves are used to simulate reservoir operation to verify the design values and obtain a benchmark for comparative study. Joint and multi-objective optimal operation models are developed for the lower Jinsha-Three Gorges-Gezhouba cascade reservoirs. We solve the optimal model and its Pareto frontier using a parameterization-simulation-optimization framework based on the Gaussian radial basis functions and a Borg many-objective evolutionary algorithm. Comparative study reveals that the design values of annual hydropower generation are reasonable and reliable, and the joint operation of cascade reservoirs must consider the backwater effect of downstream reservoirs. Compared to the rule curve scheme, the joint (optimal) operation scheme reduces water spillage by 20.38% (30.44%) and increases annual hydropower generation by 4.01% (5.45%), with significant increases in the generation by the reservoirs of Xiluodu, Xiangjiaba, and Gezhouba. Generally, a conflict between hydropower generation and impoundment efficiency occurs, and the Three Gorges Reservoir will be faced with a challenge in dry years if it starts impoundment on the tenth of September. We suggest that the Three Gorges Reservoir should optimize further its flood-limited water level and consider an earlier start of impoundment.

The characteristics of the water ring in a pump turbine are closely related to its stable operation under phase control conditions and the cooling of its clearance flow channel. Based on the discharge path of cooling water through the water ring drain pipe, this paper develops a full-passage 3-D model of the flow in a pump turbine including the local gaps, and examines the unsteady 3-D flows through VOF gas-liquid two-phase flow modeling. We focus on the drain valve’s two conditions of normal operation and failure, and have achieved the numerical simulations of pressure variations that agree well with the field measurements at a hydropower station. The results reveal the internal flow characteristics of the pump turbine during its phasing operation. The main runner channels are occupied fully by air, and its flow tracing shows formation of significant vortices; in the passage of bladeless areas, gas-liquid mixture flows are observed, and the liquid near the inner wall of the fixed guide vanes rotates at a higspeed leaving circular traces; the lower ring cavity features an upward helical flow of gas-liquid mixture. In the mode of the drain valve open, the thicker water ring in the bladeless area can be discharged into the tail pipe under its control to ensure a dynamic balance between water ring drainage and cooling water supply. If the drain valve fails, cooling water in the lower labyrinth will flow into the bladeless area, unable to enter the tail pipe, so that the water ring in this area grows in thickness and rubs the rotor due to its over-thickness and the water-filled clearance between the lower ring and the bottom ring of the rotor.

As the city expands, the river shoreline is constantly being encroached upon, and the river's flow capacity and water environment are damaged severely. Therefore, efficient methods for monitoring complicated river courses are urgently needed. This paper precents a new method for collecting images and data of a river and its banks using drones and enhancing data with the Generative Adversarial Network. We identify and locate five kinds of typical foreign bodies on the river, based on the YOLOv5 algorithm and the coordinate transformation localization algorithm. The target recognition algorithm of this model introduces the attention mechanism into the backbone network and uses EIOU-Focal Loss as its loss function to improve YOLOv5 in detection accuracy and convergence speed. The results show that data enhancement improves the model’s target recognition and raises the mean Average Precision (mAP) by 9.9%. The ablation experiment results verify that this model has the highest detection accuracy, with its maximum mAP of 0.96 or an increase of 11.6% relative to the one before improvement. Its positioning results show the real average error of the algorithm target object is not greater than 3m, which means a high accuracy. Application of the improved model to the Minjiang River section in Fujian has verified its higher accuracy in detecting target objects and its significance for related research.

The ensemble Kalman filter approach has been used to correct the state variable in hydrological models. Difficulties of its application include how to select the state variable for correction, whether or not to synchronize parameter correction with the state variable, and how to set up the filter algorithm's hyperparameters. To address these issues, we take the calibrated GR5J model for the Qijiang River basin as a prototype tool to assimilate observed streamflows and correct model state variables using feedback correction. We use synthesis experiments and rolling forecast tests to examine the impacts of state variable selection, model parameter disruption, and hyperparameter optimization of the filter algorithm on forecast accuracy. The results suggest that while the biased initial state could be specified, the ensemble Kalman filter does raise forecast accuracy; otherwise, a better way is to fix the runoff generation variable and the flow confluence variable simultaneously to avoid overcorrection on model states. In the case of biased model parameters, it is best to identify the parameter first and then adjust the state variable. Increasing the ensemble members and warm-up periods generally improve correction accuracy, but the impacts of model noises and observation noises on the correction accuracy are non-monotonic. The filter algorithm is superior to the warm-up method, though its forecast accuracy decreases with an increasing forecast period. The findings would help apply the state correction method to operational forecasting.

For concrete dam construction, traditional computer vision target recognition methods are difficult to meet the requirements for intelligent detection in complex construction sites, as it involves narrow spaces, continuous process transitions, and various other elements such as personnel, machinery, and environment (human-machine-environment or HME). These elements often lead to occlusions, dense overlaps, and variations in size and orientation. This paper describes a new method, YOLOv5-SS, for recognition of the multiple elements in the HME scenarios of such construction. By integrating a CBAM attention module, this method improves the performance of the object detector and enhances its sensitivity to HME elements of different sizes and positions. And, it incorporates the weighted bidirectional feature pyramid network (BiFPN) to enable the object detector to focus on key image information related to real-time HME elements. To validate the recognition capability of this method, a dataset based on image information from a concrete arch dam construction site is used. Comparison of YOLOv5-SS with the YOLOv5 and Faster R-CNN models demonstrates it effectively improves the efficiency and accuracy of target detection in concrete dam construction scenarios.

Basalt fiber fly ash concrete conforms to the concept of green and economically viable development. To improve the reliability of its practical application in engineering, this paper presents a study on the characterization of its pore structure, frost resistance by the formation of pores, pore physical phase distribution, and fractal dimension, through the hydration process of fly ash and cement under the conditions of different basalt fiber lengths (6, 12, and 18 mm) in single mixing and blending scenarios. The results show that after 300 freeze-thaw cycles, the degree of freeze-thaw damage to fly ash concrete from high to low is unadulterated basalt fiber, single-adulterated basalt fiber, and mixed-adulterated basalt fiber. From pore structure characterization, basalt fiber admixture refines the pore space, that is, the maximum pore diameter and porosity both are reduced. From pore phase distribution and fractal dimension, the pore space of single admixture is mainly concentrated around the fiber, so that the internal pore space forms a linear concentrated distribution, causing the concrete to be prone to frost stress concentration. In the blending case, pore concentration is reduced significantly, and the small pore dispersion effect is conducive to reducing the role of frost stress. The fly ash component improves the pore structure through delaying the hydration micro-filling effect, and the fibers’ bridging effect prevents radial cracks in the pore space from further development, thus improving the frost resistance of concrete.

The multi-energy complementary system of wind, solar and hydropower has altered the operating conditions of hydropower stations, making the traditional hydropower dispatch rules no longer applicable. We develop a decision tree scheduling rule extraction method based on automatic hyperparameter optimization to examine such a system in the upper reaches of the Yellow River. It extracts a set of scheduling rules for multi-energy complementary systems, while considering new energy consumption and river ecological protection. The results show a competitive relationship exists between the power generation of this system and its river ecological protection goal. And the reservoir inflow is the most important predictor in consideration of its comprehensive power generation benefits. In all three accuracy evaluations, the best is the decision tree scheduling rule extraction model based on automatic hyperparameter optimization, indicating it achieves a significant improvement in accuracy. The research findings help the operation and scheduling of multi-energy complementary systems aimed at river ecological protection and power generation.

In recent years, the Three Gorges reservoir has implemented drought-relief water replenishing operation several times in emergency response to drought events in the middle and lower Yangtze River. However, it is not yet clear how an increase in the discharge replenishment influences the water levels at the main cross sections downstream of the reservoir, and an index system has not been established yet for evaluating the drought-relief water replenishment benefits of a reservoir. This paper develops a water level simulation model for a river section based on long short-term memory neural networks, and simulates the time variations in water level at the typical sections of the study reaches. And an index system for the reservoir’s benefits evaluation is constructed considering comprehensively water supply, ecology, shipping, and power generation. The results show that under the reservoir’s three replenishing schemes of 500, 1000, and 2000 m3/s in August, 2022, the water level and discharge at the main control stations in the study reaches would be able to rise to a certain extent. The restored irrigation area would be 2.9 thousand ha, and the economic benefit of agricultural irrigation be 13.55 million yuan. The increased power generation benefits would be 14.00, 27.25, and 32.25 million yuan under the three schemes, respectively. The ecological and shipping benefits would not change considerably, because of only a small increase in water level and discharge. This study helps formulate drought-relief water replenishing plans for the Three Gorges reservoir.

Knowledge graphs can efficiently integrate the knowledge of a hydraulic project and advance digitalization significantly. However, traditional methods face challenges in cross-domain ontology construction, high annotation cost, and limited transferability. This study constructs an automated knowledge graph framework that leverages large language models (LLMs) for cross-domain intelligent hydraulic construction. The method has two parts: (1) constructing a shared ontology through terminology discovery, co-occurrence networks, and LLM reasoning to resolve cross-domain semantic inconsistencies; (2) extracting enhanced knowledge, combining prior knowledge, hybrid retrieval, dynamic prompting, and chain-of-thought reasoning to reduce LLM hallucinations. Numerical experiments show the shared ontology achieves structural consistency, with cross-domain knowledge extraction reaching an average F1 score of 84.5, outperforming conventional models. This validates the method's effectiveness in multi-subdomain knowledge integration with reduced annotation requirements.

To achieve the coordinated development of a water-energy-food system covering the Beijing-Tianjin-Hebei region under carbon neutrality, this paper constructs a three-dimensional water-energy-food-carbon security assessing model that considers reliability within individual systems, coordination between dual systems, and sustainability among multiple systems. We adopt an improved criteria importance through intercriteria correlation (CRITIC) method to assess the security level of this system in the 2000-2022 period, and examine the changes in system security caused by implementing certain carbon emission indicators. Its primary obstacle factors are identified for this region. The results indicate a rising trend in the coupling system’s security level in this period, with its average value rising from 0.354 to 0.535, while it decreased to 0.508 in 2022 thanks to the carbon neutrality policy. Since carbon neutrality implementation started in 2020, system security has been improved to a certain degree. Primary obstacle factors that affect the system’s security in this region are energy production elasticity coefficient, SO2 emission in exhaust gases, food yield fluctuation index, difference between food exports and imports, number of civilian vehicles, and proportion of hydroelectricity generation.

Uncertainties in wind and solar power outputs reduce their market competitiveness. Participation of cascade hydropower, wind, solar, and storage systems in energy and frequency regulation markets has become an effective means to achieve high-quality renewable energy supply. This study first develops a day-ahead bidding model and a real-time adjustment and frequency regulation response model for such hybrid systems that participate in these regulation markets. To address the uncertainty in wind and solar outputs, we develop a distributionally robust chance-constrained model, based on the Wasserstein distance fuzzy set for the systems' market participation. Then, a bi-objective optimization is made to maximize day-ahead market revenue and minimize real-time adjustment deviations. And a normalized normal constraint (NNC) method is used to obtain Pareto frontier solutions; the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to select the optimal solution from the Pareto front. Application to a case study of a river basin in southwestern China verifies our method and the models prove effective, rational, and applicable.

As key water resource projects, reservoirs and their efficient operation are crucial to the society. However, most reservoirs, domestic or international, have been formulating the operating rules based on historical statistical data, and the static rules for planning and design limit their capability of proactive responding to floods and droughts. Recent advancements in meteorological and hydrological forecasting have made forecast informed reservoir operation (FIRO) a research hotspot. This paper discusses the main factors that impact the accuracy of numerical weather forecasts, commonly used hydrological models, and rapidly developing artificial intelligence forecasting techniques. The review also covers the FIRO methods and their applications. Finally, we suggest the comprehensive use of various meteorological and hydrological forecasting products to achieve efficient water resource utilization－including global navigation satellite system (GNSS)-based precipitable water vapor, the development of FIRO methods focusing on the vapor-precipitation-runoff three lines of defense, and the construction of deep reinforcement learning operation models that account for the multi-blocking effects of cascade reservoirs.