Based on the fifth generation atmospheric reanalysis dataset ERA5 of the European Centre for Medium Range Weather Forecasts (ECMWF), we define two types of hot droughts or compound drought-hot events－simultaneous occurrences of meteorological drought/agricultural drought and high temperature. We examine the evolution of such an event occurred in the Yangtze River basin in the summer of 2022, and evaluate the variations in its several characteristics such as duration and spatial coverage. The results show that this hot drought began in June, became most severe in August, and weakened in September; its spatial scale varied significantly, starting from the middle and lower reaches, gradually expanding to the whole basin, and reducing to the middle and lower reaches by September. And compared with typical events in historical periods, its characteristic values were the largest. We find a significant increase in the characteristic values of the two types of compound drought-hot events in July and August from 1979 to 2022. The results deepen our understanding of the hot droughts and extreme events in a river basin and can be useful for coping with extremes under global warming.

Unsafe behaviors of construction workers are the key factor leading to safety problems in the construction process of water conservancy project. Most construction sites adopt on-site safety inspection, wearable equipment, real-time monitoring, and other methods to identify unsafe behaviors, but such methods are time-consuming, laborious, expensive with low information level, and unfavorable to the timely discovery and early warning of dangerous behaviors. This paper presents a new method of unsafe behavior identification suitable for large scenes of water conservancy projects based on computer vision and deep learning. First, an improved method of YOLOv5 is developed to solve the problem of missing and wrong detection of small objects in construction workers and machinery in large scenes, and a multi-object object detection model of construction workers and machinery is constructed. Then, based on the target detection model, recognition methods are suggested for each of the routine unsafe behaviors, such as being close to the static danger area, dynamic construction machinery, and not wearing safety helmet, etc. Engineering application verifies that our identification method strengthens the means and intensity of construction site control and improves effectively the level of water conservancy engineering construction safety and intelligent control.

For dam deformation, some of the previous single-point models did not consider the spatial correlation of dam monitoring data and met difficulties in describing its overall response characteristics; The traditional regression models neglect the nonlinear relationship between the environmental and deformation quantities, resulting in poor prediction accuracy. To improve the prediction, this paper develops a predictive model based on an empirical modal decomposition of monitoring data by using an adaptive noise-complete set, or the technique of wavelet packet noise reduction. This model is combined with feature selection through an elastic network for the deformation factor under spatial correlation, considers the cross validation of the effectiveness of feature factors, and adopts the sparrow search algorithm extreme gradient to enhance computational efficiency. We examine the optimal factor set considering spatial correlation based on the deformation data measured at the Jinping arch dam. Comparison of the MSE and RMSE parameters of several models verifies the high accuracy and generalizability of our new method, which is useful for analysis of dam deformation patterns.

The low-carbon transformation of electricity has an overall strategic significance for China to achieve the goal of carbon neutrality. This paper presents an analysis and predictions of the power growth process and phased development goals in China in the next 40 years, on the basis of summarizing the developing process of low-carbon electricity in the past 10 years, and taking population, urbanization, economic aggregate, and economic structure as the boundary conditions and driving forces. We examine the influence of a variety of factors on the carbon emission level－such as fossil power proportion, terminal power consumption proportion, nuclear power development scale, and Carbon Capture, Utilization, and Storage (CCUS)－with the constraints of system security and supply-demand balance. We estimate that the total carbon emission from electricity will reach its peak around 2035, 6 - 6.5 billion tons, and then be reduced year by year down to a level below 1 billion tons in 2060. With the help of CCUS technology, zero electricity emissions can be achieved. Finally, in view of the long-term coexistence of demand growth and low-carbon transformation, we suggest that future power development be based on the premise of safe supply, giving priority to the development of renewable energy power, multi-energy development, and a wider range of multi-energy complementary coordinated development and complementary operation.

Since the Paris Agreement was signed, it has become a broad consensus of the international community to promote global carbon neutrality. Green energy development is the key to achieving carbon neutrality; Hydropower, as a highly flexible renewable energy, will play an important role in the transformation of the global energy structure. The current hydropower development situation is different across the world, due to the difference in economic development levels, hydropower potentials, and the degree of development. And the development of global hydropower faces both opportunities and challenges under the current complex context of global energy transition, climate change, environmental policy impact, and geopolitical conflicts. In recent years, China is leading the development of hydropower and has made remarkable progress. Therefore, the global hydropower industry needs to speak up and take active measures to formulate development strategies from various aspects, deepen the energy revolution, drive the sustainable development of hydropower globally, and push it to continue to play a role as the backbone of tomorrow's novel energy system for a smooth realization of carbon neutrality.

To improve the hydraulic turbine regulation system with dead zones and mechanical delay, a control method based on the auto-coupling PID control theory is studied. First, we examine a delay turbine regulation system with dead-time links which delay, dead-time nonlinear factors, uncertainties and external disturbances are defined as the total disturbances; by introducing virtual controls, it is equally mapped to a double closed-loop system with an outer loop second-order linear disturbance system and an inner loop first-order linear disturbance system. Then, using the speed factor, we design an auto-coupling PD controller for the outer ring and an auto-coupling PI controller for the inner ring. Finally, the robust stability and anti-disturbance robustness of the closed-loop control system are analyzed in the complex frequency domain. The simulation results show that under different working conditions, the designed controllers gain better anti-disturbance robustness, and their dynamic quality and steady-state performance are improved significantly.

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.

Hydrological drought is jointly affected by climate change and human activities. Revealing the evolution characteristics of hydrological drought and its driving factors in the changing environment will contribute to improving the capacities of drought control and drought resistance. Based on the long time series of meteorological and hydrological data of 1960-2020 from the Upper Yangtze River Basin (UYRB), the present study first generates naturalized runoff time series using multi-model ensemble simulation method, and uses the reconstructed natural runoff to calculate the traditional standardized runoff index (SRIr) for characterizing the hydrological drought under natural conditions. Then, we simulate the time-dependent standardized runoff index (SRIt) using the generalized additive model for location, scale and shape (GAMLSS) with time as the covariate to represent the hydrological drought under non-stationary environment. Finally, the impacts of climate change and human activities on hydrological drought are distinguished quantitatively by comparative analysis of SRIr and SRIt series. The results show the overall trend of the hydrological drought evolution in this basin under natural conditions is intensified and has been further aggravated by human activities. The dominant factors of its hydrological drought evolution present obvious temporal and spatial differences: on the annual scale, climate change is the dominant factor in the basins of the Jinsha River, Tuo River and Wu River, while human activities are dominant in the Min River, Jialing River, and the whole upper Yangtze basin. Dominant factors of hydrological drought evolution on the seasonal scale are not completely consistent with those on the annual scale.

This paper develops a new ecological flow forecasting method based on deep learning and a conceptual hydrological model with application to the Jiaojiang River basin in Zhejiang Province to improve the forecast accuracy of ecological flow early warning and the efficiency of ecological operation of water conservancy projects. This method calculates the ecological flow and warning threshold using the hydrological method, and screens model forecast factors through the principal component analysis. The results reveal the check values of most suitable ecological flows are 2.89 m3/s and 1.92 m3/s at the Baizhiao and Shaduan stations, respectively. We use precipitation and evaporation as input factors and the grid search method for optimal parameters searching, and have achieved a 100% qualified rate of the ecological flow warning level forecasts in all the years by using the eXtreme Gradient Boosting (XGBoost) algorithm. Our coupling prediction model based on XGBoost and the Xin’anjiang model can well complete the ecological flow early warning prediction and reservoir ecological flow regulation, laying a basis of decision-making for protection and supervision of water resources in rivers and lakes.

Concrete is a key basic material for the construction of hydraulic and hydropower projects in China. However, as a typical porous medium material, it is prone to ionic erosion, product corrosion, and matrix cracking in the environment of hydraulic engineering, leading to corroded reinforcement and reduced structural bearing capacity. At present, the existing concrete materials are difficult to satisfy the requirements by the design of long-life, high-quality projects in hydraulic and hydropower engineering in the western plateau or the southern coastal regions. This paper first summarizes the damage mechanism and challenges faced by traditional hydraulic concrete structural materials under severe environments, and then discusses in detail the long-life design method and performance enhancement mechanism of these concrete materials. Finally, we give an outlook on the future application and development of artificial intelligence in the long-life design and use of such materials, so as to provide new methods and new ideas for safe operation and maintenance and long-lasting service of the national major hydraulic and hydropower projects.

Establishing a stable, efficient, and intelligent renewable energy system is important for promoting energy transition. Based on the patents of renewable energy systems, this study sorts out the technology framework systematically and uses social network analysis to reveal the current status and dynamic characteristics of technology associations in China and abroad. The results show an increasing trend has occurred recently in the number of patents related to renewable energy generation, energy storage, multi-energy complementation, and smart grids. Technologies for renewable energy systems can be divided into three categories－energy acquisition and application, energy conversion, and energy system construction－covering 13 technology fields. Compared with other countries, the technology network density in China continues to rise; most of its technology associations are in two modes－“energy acquisition and utilization - energy conversion” and “energy conversion - energy systems construction”. The technology network density in Japan is relatively low, and the latter mode dominates its technology associations. The United States has a high level of technology network density in recent years, and most of its technology association modes are shifting from the former to the latter. These findings not only serve as empirical references for technology innovation and integration in renewable energy development, but also provide practical implications for collaboration between energy industries.

This paper studies the efficiency and influencing factors of interprovincial water use in China, and provides reference for the construction of a water-saving society. First, an improved data envelopment model is used to calculate water use efficiency in different regions from 2015 to 2020. Then, compared with the results of ordinary least square regression, quantile regression is used to explore the influence of various factors on water use efficiency at different grades. The results show the average water use efficiency in China fluctuated around 0.45, showing an inverted "U" shaped trend. Regional water use efficiency is eastern, central and western in descending order. Water use efficiency is impacted significantly by natural factors, economic development level, sustainable use level, scientific and technological progress, human literacy, and enterprise cost. Among them, the impact of water endowment is significant on the areas with low water efficiency, while it is not significant on the areas with medium or high efficiency, which is different from the "resource curse" hypothesis. Thus, there is still much room for improvement in China's water use efficiency, especially in the low efficiency areas.

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.

Sequential power generation simulations play a critical role in the capacity configuration of hydroelectric-thermal-wind-photovoltaic-storage multi-energy complementary systems. In practice, 8760-hour system operation are hard to simulate directly using an optimization model because of its large scale. In this paper, a new time-scale decomposing technique is developed to solve this problem and realize the accurate simulations of 8760-hour system operation. Based on this, a two-stage optimization model is constructed for capacity configuration of a grid-connected multi-energy complementary system that comprises thermal power, hydropower, wind, photovoltaic, pumped-storage, and electrochemical energy storage. This new model was applied to the Qinghai power grid and achieved an optimized configuration of the system’s capacity of wind, photovoltaic, and pumped-storage.

The measured deformation of a concrete faced rockfill dam is highly nonlinear and complicated, owing to a variety of influential factors and the collinearity among them. To improve the deformation prediction in dam analysis, this paper develops a deformation prediction model of concrete faced rockfill dams based on the factor fusion. First, we use the variational mode decomposition to decompose a deformation time series so as to effectively reduce its complexity and enhances feature extraction. Next, we employ the partial least square regression to reduce and fuse the influential factors of deformation, reducing the impact of multicollinearity between independent variables on model construction and enhancing model interpretability. Finally, we reconstruct and predict the subsequences using a one-dimensional convolutional network fused with a gated recurrent unit neural network. Analyses of certain real projects show our model greatly improves the efficiency and accuracy of deformation prediction for concrete faced rockfill dams, and is also useful for deformation monitoring and analysis of similar dams.

The intelligent construction of dams, characterized by safety, high quality, efficiency, economy, and environmental friendliness, is a future development trend. Appropriately evaluating the degree of intelligence in dam construction is crucial to achieving organization-level intelligent construction. However, the current stage of dam construction is in transition from digitization to intelligence, and effective evaluation methods for the intelligence degree in engineering systems are lacking. This paper outlines the system characteristics and hierarchical divisions of intelligent dam construction, based on the theory of intelligent dam construction. We discuss the basic principles of evaluating the intelligence degree and develop a new model for evaluating it, based on a general framework of dam construction. A general model application procedure is also constructed. Finally, our method is applied to projects of typical high earth-rockfill dams and concrete arch dams, and their intelligence degrees are evaluated systematically from the perspectives of execution level, coordination level, and organization level, thus lying a basis for the design of intelligent construction systems in large-scale hydro projects.

To quantify water supply risk caused by runoff forecast uncertainty, this paper defines water supply risk based on the probability distribution of inflow uncertainty, and develops a water supply scheduling model for the Hanjiang-to-Weihe River Basin Water Diversion Project. Multi-scale water supply risk values with different forecasting errors are obtained. The critical forecasting error of risk escalation is clarified, and the influence of inflow uncertainty on the water supply risk of this cross-basin diversion project is revealed. The results demonstrate that for water supply risk, 15% is a threshold of the runoff forecast error; the relationship between forecast error and water supply risk follows a quadratic power function. The Sanhekou reservoir can activate its multi-year water regulation to mitigate water supply risk caused by forecast errors. The water supply risks are divided into three levels: light, medium and heavy, and the thresholds for risk level upgrading are determined to be 0.117 and 0.190, corresponding to forecasting errors of 24.0% and 32.7%, respectively. The results provide a decision-making basis for ensuring the water diversion safety of the project.

For the time series prediction issue of dam deformation, a spatiotemporal multi-dimensional input matrix of deformation is derived considering the correlation of deformation at multiple measuring points; an Informer-AD dam deformation prediction model is constructed that integrates multi-dimensional spatiotemporal information based on K-means clustering. We use the K-means clustering to partition rationally the deformation measuring points, then apply a panel data regression model to integrate the analysis of spatiotemporal dimensions and partition results. Finally, we develop an Informer-AD dam deformation prediction model to integrate multi-dimensional spatiotemporal information. This model is used to learn spatial feature sequences and integrate spatial features through a fully connected layer to output predicted dam deformation values. Its application to a concrete gravity dam shows that our prediction method, considering spatiotemporal correlation, can fully explore the relationship of the overall state of dam deformation versus the spatial distribution characteristics of measuring points. It better captures the spatiotemporal characteristics of deformation values and thus improves prediction accuracy, which implies that our model has a high accuracy and satisfactory applicability, useful for engineering application.

To study the durability of hydraulic concrete under the environment of freeze-thaw salt intrusion in the northwest region, we prepare concrete specimens with different fly ash dosages and conduct freeze-thaw cycling tests, using different concentrations of sodium sulfate solution as the medium. The tests clarify the specimens’ behaviors under different cycles－appearance, quality, compressive strength, and dynamic modulus of specimens. And a concrete lifespan prediction model is developed based on the XGBoost model, and it is evaluated and validated. The results indicate that as the number of freeze-thaw cycles increases, the quality, compressive strength, and dynamic modulus of concrete gradually decrease; The number of freeze-thaw cycles and the concentration of sodium sulfate solution are the key factors of concrete lifespan. The 8% solution causes the highest degree of damage, and the corresponding rate of concrete quality loss reaches 4.55% after 150 freeze-thaw cycles. The fly ash content has a certain impact on concrete durability; its optimal value is 10% and the resulted quality loss rate is 3.99% after 150 freeze-thaw cycles. The results show the XGBoost model has high accuracy and reliability in predicting concrete lifespan. This study would help the durability design and lifespan predictions of concrete structures.