Climate change is closely related to energy-power supply and demand. In the summer of 2022, the upper reaches of the Yangtze River experienced three worst cases: the highest temperature, longest continuous hot days, and lowest rainfall in the same period in history. This resulted in a daily power shortage of 17 million kW and 370 million kW?h in Sichuan, a major hydropower province, and imposed a significant impact on its social and economic development and people’s livelihood. To guarantee energy and power safety, it is of great significance to establish power planning mechanism and some countermeasures for power supply guarantee in extreme weather. This paper presents an analysis on the impacts of extreme weather in the 2022 summer on the power supply guarantee in Sichuan, and examines the shortcomings of previous electric power development. We suggest certain countermeasures for the period of power transformation－such as water-wind-solar-thermal energy complementarity, and a coordinated development of power supply and power grid.

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.

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.

To maximize power generation benefit, this study explores reservoir impounding strategies adapted to extreme low-flow conditions in the case of the lower Jinsha River and the Three Gorges cascade reservoirs, and presents a new reservoir impounding scheme by combining the impounding rule of benefit maximization and the water supply requirement of the Three Gorges Reservoir. We also examine alternative reservoir impounding schemes aimed at different water allocation principles that meet the targets of reservoir stages or the control boundaries. Typical extreme low-flow scenarios are simulated numerically for operation and benefit analysis. From the results, we demonstrate a critical condition of natural inflow for full filling of the cascade reservoirs－the natural inflow is above the level at the frequency of 97% from August through early September, and above the level at the frequency of 90% from mid-September through November. Under extreme low-flow conditions, the cascade reservoirs will be able to perfectly fulfill the water supply requirement of the Three Gorges Reservoir if they adopt a certain joint water supply dispatching. In the case of a full filling impossible, their power generation benefit can be maximized by impounding preferentially those in the lower Jinsha River, such as the Baihetan Reservoir first and then the Xiluodu Reservoir as recommended by this study.

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.

Uncertainty in each forecasting component, such as driving data, hydrological simulation, and early warning method, imposes an impact on the uncertainty of flash flood risk early warning, but its mechanism is not well understood yet and the theory and method for a quantitative combination analysis are lacking. This study develops a coupled uncertainty analysis method for flash flood early warning based on hydrological model simulations, formulates a dynamic critical rainfall index, and applies both to an analysis of the lower reaches of the Min River in Fujian Province. The results show the uncertainties caused by precipitation input, runoff simulation, and critical rainfall quantification account for 36%, 24% and 40% of the total uncertainty, respectively. Under a coupled uncertainty analysis, the average probabilities of flash flood disasters at risk levels IV, III, II and I are 58%, 65%, 79% and 81%, respectively. This new analysis method is of great significance in practical flash flood warming and disaster prevention.

To improve the accuracy of runoff forecast, we construct combined prediction models by integrating gradient lifting tree regression (GBDT), back propagation algorithm (BP), and with differential evolution Grey Wolf algorithm (HGWO) support vector regression (SVR) algorithm optimized using the variational mode decomposition (VMD) and the extreme-point symmetric mode decomposition (ESMD), and apply them to the monthly runoff series measured at the Tangnaihai station and Lanzhou station of the Yellow River. The results show the combined model VMD-HGWO-SVR gives the best predictions compared with other models. Its average absolute error in predicting monthly runoff at the two stations is decreased by 53.38%, 14.27% and 6.8% compared with ESMD-HGWO-SVR, VMD-BP and VMD-GBDT, respectively. On average, its root-mean-square error is decreased by 53.66%, 22.0% and 11.54%, average relative error by 54.92%, 12.0% and 3.67%; its Nash efficiency coefficient is increased by 17.09%, 3.26% and 1.36%, respectively. This verifies our new method achieves satisfactory effects in predicting monthly runoff time series.

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.

The Qinghai-Tibet Plateau is a region impacted most dramatically by climate change. The trends of changes in the river runoff and their causes have attracted much attention. In this study, the Mann-Kendall trend test and the Pettitt test are used to detect the runoff variations from 1980 to 2016 at 11 hydrological stations in the Tibetan Autonomous Region, and the climate elasticity method based on the Choudhury-Yang equation is used to quantify the effects of precipitation, potential evaporation, and underlying surface changes on annual runoff changes. The results show that the annual runoff in the study area presents an insignificant upward trend, and has an abrupt point around 1997. The sensitivity of the runoff to climate change and underlying surface change is the lowest in the lower reaches of Yarlung Zangbo River. Precipitation is the main factor responsible for annual runoff change and its increase leads to a runoff increase in all basins, but this impact exhibits an obvious regional difference with a spike in the Lhasa River basin.

The increasing urban flooding threatens urban safety and sustainable development, and further improvement in the accuracy of urban flood risk simulation and assessment are essential to protect people's lives and properties. In this study, a flood simulation model is developed for the Shenzhen River basin based on a Cellular Automata flood simulation model, and inundation depths are simulated for the design rainfall conditions of this basin. Using the Hazard-Vulnerability risk assessment framework, we consider its different rainfall scenarios and assign weights to its different urban flood risks, through a game theory-based combination of weights determined by the analytic hierarchy process and the Criteria Importance Though Intercrieria Correlation methods. Then, its urban flood risks are evaluated and predicted. The results show typical temporal and spatial differences occur over the high-risk areas of urban flooding in the basin. With an increasing rainfall recurrence period, the ratio of the risky area increases, such as from 0.20% of a 2-year rainfall event to 0.82% of a 100-year event; the ratio of the medium-low risk area decreases, such as from 67.4% to 64.7% in terms of the two events. The results of this study can be visualized to show the distribution of flood risks over the basin, helping improve its flood control system and enhance the urban flood prevention capability and resilience of the city.

As the scale of wind and photovoltaic energy integration increases, it is more difficult to satisfy the needs of large-scale new energy integration by only using hydropower and other peak-shaving resources on the power source side. To address this issue, we develop a short-term optimization model for hydro-wind-solar power complementary scheduling considering participation of the price-based demand response (PDR), which adjusts optimally the next-day load curve using PDR on the load side. And this model is decomposed into two sub-models of single target optimization, and solved in stages to reduce solution difficulty. Finally, it is applied to the system of the Wudongde hydropower station and the wind and photovoltaic power stations in Kunming and Yuxi. The results show that PDR has an obvious peak-load shifting effect on the load curve, so it not only can improve the source-load matching, but also can enhance the accommodation capacity of wind and solar power and increase the system’s total power generation. This demonstrates the effectiveness of the model and a new idea for the large-scale accommodation of new energy in the power grid.

Previous simulation methods of the splitting diffusion of self-expanding polymer materials in soils are not complete yet. This paper develops a new, preliminary simulation method for simulating the splitting compaction effect of polymer on soil. This method uses the extended finite element method (XFEM) to solve numerically the regional soil medium, and adopts a modified Cam-clay model to describe the mechanical characteristics of the soil. And it calculates slurry expansion pressure acting on the surfaces of a soil fracture iteratively using the relationship of polymer density versus confining pressure. We realize numerical solutions of the process of crack propagation in the soil, and verify them against experimental data. Example analysis shows that the method is applicable and effective in the simulation of the soil’s crack initiation process, propagation direction, and grout vein thickness variation under the expansive force, and it gives the distribution characteristics of deformation modulus, void ratio, stress field, and density field over the soil zone around a crack, thus laying a basis for further study of the splitting compaction mechanism of expansive polymer grouting material in soil.

Previous zonal deformation prediction models lack the capability of capturing spatial differences in the trend, periodic and fluctuating components of dam deformation. This paper describes an improved zonal deformation prediction model to solve this problem. First, we adopt a variational mode decomposition algorithm to split dam displacements into trend, periodic and fluctuating components, and determine the representative environmental and load factors using hierarchical clustering of the principal components, so that these factors can be decomposed into the trend, low- and high-frequency components according to their physical meanings. Then, an optimized dynamic time warping algorithm based on a shape-based distance is used to divide the displacement components at the measured points into different deformation zones; for these zones, a sequence of their centroids is calculated to capture shared characteristics. The zonal data sets of the centroid sequences and their strongly related components of the dominant influencing factors can be established. Finally, we construct an improved zonal deformation prediction models using three machine learning algorithms－random forest, least squares support vector machine, and boosted regression tree－and an improved hydrostatic-thermal-time model. These improved models are verified against the measurements of Xiluodu super-high arch dam. The verification shows satisfactory results in accuracy and well explains the spatiotemporal correlation and differences in the trend, periodic and fluctuating components of dam displacements.

Accurate deformation prediction is of great significance to safe operation and long-term maintenance of dams, but previous methods have low prediction accuracy and lack sufficient information extraction from monitoring data. This paper constructs a relationship of dam deformation components versus their influencing factors through variational mode decomposition on the deformation series, and constructs Long Short-Term Memory neural networks with different structural parameters. Then, we develop a dam deformation analysis model that can realize optimal modeling through integrating the Grey Wolf Optimizer algorithm, the Minimum Redundancy Maximum Relevance method, and other strategies to improve its accuracy from three aspects－front-end decomposition, information extraction, and time series prediction. A case study shows that compared with the conventional monitoring model, this new model is more accurate in the simulations of dam deformation time variations and better in generalization performance, thus useful for dam deformation safety analysis.

A deterministic model of reservoir optimal operation is usually difficult to be directly applied to practical operation. To improve applicability, we develop a new joint optimal operation model for the Yalong River cascade reservoirs and use the Gaussian radial basis function (RBF) to extract operation rules, and then calibrate the rule and verify its fitting errors. The results show the annual power generation of the cascade is increased by 98.32 billion kW?h or an increase of 8.73% compared with its design value. The new operation processes optimized by the model for the Lianghekou, Jinping I and Ertan reservoirs follow certain common patterns. The determination coefficient of Gaussian RBFs is larger than 90%, and it can fit the operation rules effectively. This achieves a simulated power generation greater than the design value by 51.27 billion kW?h (+5.67%).

The reliable power supply is critical for the safe and steady operation of a power system. The reliable power support function of power supplies refers to the immediate full-load operation within its reliable capacity according to the load demand of a power system. This paper presents a calculation method of power-energy balance based on the reliable power support. The overall framework is to arrange the units on standby and in maintenance according to the power supply installation and system load demand. On this basis, the support capacity of each month (or week, or ten days) for various power sources was determined through power balance, and the annual 8760 h power-energy balance simulation was carried out. The simulation idea is to make room for wind and photovoltaic power as much as possible while meeting the minimum technical output or forced output of various power sources, so as to realize the overall power-energy balance at all time with the wind and photovoltaic power being absorbed as much as possible. This calculation method has been applied to an analysis of the Shanxi power grid with different scales of pumped storage plants, and the impact of wind power uncertainty on power-energy balance was evaluated. The results show the increase in the system’s pumped storage capacity will replace part of the thermal power capacity and increase the power for pumping, thus promoting the absorption of new energy sources. If the wind power committed too much as the power support role, in the case of wind power output is reduced by weather and other factors, the load demand could be difficult to be met even if the thermal power, hydropower and other kinds of power operate at full load, which suggests the harm to the safety and stability of power system.

The outputs of hydropower, wind power and photovoltaic in a hydro-wind-photovoltaic complementary energy system (HWPCES) are integrated into the power grid. The traditional method forecasts these three outputs separately and then sums them up as the system’s total power capacity, but such a method suffers from error accumulation and lacks consideration of spatiotemporal complementarity. To improve the forecasting accuracy, first we consider spatiotemporal correlation and complementarity, and select certain predictors from the teleconnection factors and power factors. Then, a point forecasting model and an interval forecasting model are constructed based on the Long Short-term Memory network and the Lower Upper Bound Estimation method. Finally, joint forecasting of the power outputs is achieved. This study selects the Ertan HWPCES as the case study. The results show that in the verification period of total power forecasting, its Nash-Sutcliffe efficiency coefficient reaches 0.908 by the joint forecasting method, or an increase of 0.016 compared to the accumulation method. The interval forecasting method achieves a reduction of 0.352 in the coverage width-based criterion. Our new method is useful for complementary operation of hydropower, wind power, and photovoltaic.

Effective control of the compaction quality of roller compacted concrete (RCC) is an important prerequisite for ensuring the safety and stable operation of dams. This paper formulates an improved stiffness index of RCC dam material, considering its lag phase angle, and presents a new method for measuring the lag phase angle. Then, we develop a comprehensive evaluation method of RCC compaction quality, using an XGBoost algorithm that couples this index, dam material properties, rolling parameters, and frequency domain compaction indexes. The results of case analysis show that the improved stiffness index can better characterize the compaction quality of dam materials. The comprehensive evaluation model has a relatively high accuracy, with a correlation coefficient of determination of 0.86 and an average relative error of 0.47% in compactness calculation against measurements, thus providing a new effective approach to the compaction quality control of RCC dam construction.

Reasonable ground motion parameters are a prerequisite for seismic hazard analysis of high dams. This study investigates scenario earthquakes and the corresponding site-related response spectra for the Xiluodu dam, based on the Code for seismic design of hydraulic structures of hydropower project (NB 35047—2015) with category A of seismic protection. First, the Yongshan area with the largest contribution to the exceedance probability of this dam is selected as a potential seismic area for earthquakes; then, the magnitude and epicenter distance of the scenario earthquakes are determined by the principle of the maximum occurrence probability. Finally, the site-related response spectrum is generated using the ground-motion prediction equation AS08. This study gives a basis for seismic analysis of the Xiluodu dam based on real dynamic working state, and helps seismic analysis of similar projects.

When the general segmentation model is applied to the apparent cracks in the dam face concrete, the network suffers the problem of depth increasing that leads to excessive model parameters and certain loss of effective crack features. To reduce network memory occupation and feature loss, this paper develops a lightweight crack segmentation method based on a convolutional neural network. The network adopts an encoding-decoding structure, and uses a depth-separable convolution module and a lightweight feature extraction module to construct a cascade encoder; it is equipped with a decoder to fuse cross-scale information in the second stage of the encoder and to reconstruct the pixel-level geometric information lost in feature extraction to improve the accuracy of network segmentation. The experimental results show the model size of the network trained on the crack dataset of dam face concrete is 10.8 MB or a size reduction of 90.8% from U-Net, with its PA of 73.3% and IoU of 85.4%. The results verify the network is feasible in dam face crack segmentation and useful for improving the efficiency of dam face detection and maintenance.