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.

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.

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.

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.

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.

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.

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.

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 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.

The Yarlung Zangbo River (YZR) basin is located in a cold plateau area. Understanding the spatiotemporal characteristics of its snow cover is of great significance to the construction of projects and the prevention and control of snow disasters. In this study, the characteristics of snow depth in this basin are examined using the Mann-Kendall trend analysis and Pearson correlation analysis, based on the snow cover depth data set of China (1979-2021) and the monthly precipitation and temperature data set of China. The results show the snow cover depth presents a spatial trend of higher at two ends and lower in the middle, with a deep snow cover over the river sources in the west and over the northeastern margin. The snow cover is distributed mostly at an elevation of 4000-5200 m with a slope milder than 35°, and the deepest snow is mostly located around the elevation of 4850 m. During 1979-2021, the annual average snow depth decreased at a rate of 0.032 cm/a, taking its lowest value in the year of 2017. In recent years, the snow depth has gradually rebounded to the multi-year average level. The annual mean temperature is increasing at a rate of about 0.024 ℃/a, and it has a significant negative correlation with snow cover depth.

Construction information such as the rolling quality and progress of roller compacted concrete (RCC) dam surface is transmitted in feedback mostly in traditional ways such as reports and pictures, which is not conducive to timely detection of construction quality defects and overall grasp of construction progress. This paper presents a solution method of fixed camera three-dimensional registration based on the Tsai two-step calibration method to realize the fixed augmented reality (AR) feedback of construction quality. And a mobile AR feedback expression method is developed for concrete placing progress information by using hybrid registration. We use the relational database and building information modeling (BIM) to interrelate construction quality information and construction progress information, so that a complementary added value of construction information is achieved. Through system development and engineering application, we show this coordinate mapping method has an error of no more than 0.5 m in the placing area and an average response time of 5.06 s, meeting the requirement of field construction. The construction progress feedback module based on mobile AR has an average frame rate of 29.55 for mobile phone use, and 96.17% of the time for the frame rate greater than 27, meeting the fluency requirement of field construction. Thus, our AR-based method provides on-site managers with multi-view construction information expression and feedback channels.

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.

When a concentrated leakage passage exists in the body of a dam, the passage will exchange water with its surrounding porous media driven by pressure difference. Such exchange may lead to an increase in dam discharge, but it is often ignored in traditional seepage field analysis. This study develops a sand tank model to simulate the presence of concentrated seepage channels in a dam, and examines the mechanism of water exchange and the water discharges from the concentrated leakage passage and the porous medium. A new formula for calculating the water exchange coefficient is then suggested. Results show that the amount of water exchange is proportional to passage diameter (D), porous medium permeability coefficient (Km), and hydraulic gradient (J). Orthogonal tests are adopted to analyze the influence degree of each factor on the water exchange capacity, revealing D is most influential, followed by J and Km. The results demonstrate the importance of the channel-medium exchange in seepage calculation, and would lay a basis for seepage field analysis of concentrated seepage channels in the dam.

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.

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.

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.

Previous multi-index bank-collapse prediction models are built on a river bank stability evaluation method that relies on the analytic hierarchy developed in recent years. As a key factor affecting the stability of river banks, determining the threshold values of hydrodynamic factors responding to different collapse risk levels is often empirical and lacks theoretical, systematic or practical research support. In this study, the thresholds of the key hydrodynamic factors－such as dominant discharge, flow velocity, water level variation, and channel diversion ratio change rate－are determined by integrating data statistics, theoretical derivation, numerical model calculation, and the previous results in literature. Determination of the dominant discharge threshold of a river and its water level variation threshold takes into account river-tide interactions in its tidal reach; a flow velocity factor is introduced in a formula for calculating riverbed stability. For a branched reach, the threshold of the diversion ratio change rate is calculated to distinguish its major branch channel from the minor ones, and we conclude the bank of the major branch is unstable at the diversion ratio change rate greater than 8%. The results support the development and application of an analytical bank-collapse-prediction hierarchy model for the lower Yangtze, and help prevent bank collapse disasters.

For load rejection in a pumped storage power station, noise interference usually makes it difficult to extract pressure pulsation information accurately from pressure signals at its volute inlet. This paper presents a joint reduction method that combines the variational mode decomposition (VMD) and the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN). This method first decomposes a pressure signal using VMD and reconstructs its components based on mutual information (MI) to reduce the permutation entropy (PE). Then, it decomposes the reconstructed signal using CEEMDAN and superimposes the components, so as to obtain the modified pressure data sequence that features a permutation entropy close to that of the simulation signal. Engineering case studies show that our new processing method is quite accurate in decomposing pressure signals measured at the volute inlet, and its use of mutual information improves the accuracy of component reconstruction using correlation coefficients. The results would promote accurate extraction and analysis of pressure pulsation in future.

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.

This study examines the impact of hydropower development on river habitat connectivity, and develops a connectivity optimization path coupling connectivity and benefits, through a case study of the Dadu River basin. We create a database for the 135 hydropower stations in the basin through data collection and remote sensing image identification, and quantify river habitat connectivity using the River Connectivity Index (RCI). The results show that 1) the RCI of this basin is 16.68 at present and will decrease to 4.2 when all the planned power stations are completed in the future, and that the spatial distribution of RCI shows a decreasing trend from upstream to downstream. 2) Hydropower stations located in the middle of the river network have a greater impact than those located in its headwater region or near its tributary. 3) The impact of different hydropower development plans on the connectivity varies significantly, and the connectivity can be improved effectively through reasonable planning while ensuring the development benefits. This study would help river habitat connectivity restoration and watershed strategic planning.