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.

The Yarlung Zangbo River is a river of rich water resources, but its upper reach runoffs are impacted significantly by climate changes, glacier and frozen soil degradation. This paper develops a distributed eco hydrological model (GBEHM) coupled with cryospheric process modeling to simulate the runoff changes in its upper basin, focusing on an analysis of the variation trends of the hydrological elements in the study area and a quantitative assessment of the impact of climate changes. The results show that from 1981 to 2010, the annual runoff and evapotranspiration in this basin experienced a significant increase, and precipitation increase contributed most to the runoff increase. This period saw a 7% decrease in the permafrost area, a 30.6 cm/10a increase in the thickness of the permafrost active layer, and a 7.3 cm/10a decrease in the annual maximum freezing depth of seasonal frozen ground. The water reserves in the glaciers were decreased significantly at a rate of 1 billion m3/a, while their melting runoff increased at 2.7 mm/a.

As the core equipment in high-head hydropower harvesting, the impulse turbine has the advantages of higher operating head and wider high-efficiency range compared with reaction turbines. However, features of flow fields within a Pelton turbine are open, unsteady and multi-phase. Complex flow characteristics affected by multiple flow transitions and component-matching of the Pelton turbine may exert influence on its performance and utilizing efficiency of high-head hydropower. Recent advances in impulse turbine technologies have been accelerated by the rapid development of visual test technology, computational fluid dynamics (CFD), finite element method (FEM), and artificial intelligence optimization design algorithm. The technology of impulse turbines developed late but rapid in China, summarizing by three stages from scratch, independent design to optimization, and breakthrough progresses have been accomplished especially in the past 20 years. Based on a comprehensive synthesis of researches from both national and international impulse turbines researching field, this paper focuses on the main progresses of impulse turbine technology in the past 20 years, and reviews the recent advances in the research of internal flow characteristics, sediment abrasion, failure analysis, and optimization design theories. Several issues with performance analysis and optimization design are discussed, and the development trend of impulse turbine technology is summarized and prospected.

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.

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.

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.

To systematically analyze the variations in the Qinghai Lake stage under climate change and the related influence mechanism, this paper presents a quantitative analysis of the spatiotemporal changes in the hydrological and meteorological factors of the lake basin, using a combination of time series analysis, a geostatistical interpolation method, and correlation analysis based on the data collected at the observation stations over the basin during 1956-2020. We use the global climate model to estimate the varying trends of precipitation and lake stage under different scenarios. The results show that during the past 50 years, the stage and surface area of the lake presented a general trend of decreasing first and then increasing, while an increasing trend occurred in runoff, precipitation and temperature, and a decreasing trend in evaporation. The lake stage was affected significantly by runoff, precipitation, temperature and evaporation; the correlation coefficients were as high as 0.95, 0.92, 0.88 and 0.81, respectively. We project that from 2021 to 2040, the lake stage will take a rising trend, showing an annual average rising rates of 0.218, 0.187, 0.125 and 0.132 m/a under four different scenarios. Quantitative analysis of the lake behaviors under climate change in this study lays a basis for future studies on the water cycling mechanism of plateau lakes.

Machine learning and other related technologies find increasing applications to extracting manual operation experiences from massive data in the practice of reservoir regulation. However, reservoir operation schemes solely based on machine learning fail to describe reservoir operation with enough accuracy, resulting in outliers in calculation results and a lack of operational experience. This paper constructs a deep learning model guided by the physical mechanism for reservoir operation, taking the water balance constraint, monotonicity constraint, and boundary constraint as the penalty terms of loss functions; data enhancement is used to include the factor of rare flood operations in the data sets of model training and verification. Results show this model is effective in simulating reservoir decisions for conventional operations and rare flood operations. It better satisfies the water balance equation, reduces negative flows effectively, and improves high flow simulation accuracies in comparison with the benchmark model, thus promoting the realization of intelligent reservoir operation.

Aimed at the goal of safety, high quality, high efficiency, economy and greenness in the construction of high concrete dams, this paper combines data-driven and mechanism-driven features and describes a digital concrete technology of integrating information and mechanism. And differences in the previous digital concrete theories used in China and abroad are discussed, and digital twins of concrete are developed. Then, based on the architecture of digital concrete, we discuss its composition and energizing technology from three aspects: intelligent analysis database, aggregate generation and packing, and hydration dynamic characteristics. Recent advancements in the research of its energizing technology are reviewed. We discuss its applications to data rescue, combined dam construction technology with high percentage fly ash concrete, and low heat cement dam technology, through examining the high concrete dams of Xiluodu, Xiangjiaba and Wudongde in the lower Jinsha River. Finally, its technical difficulties and application prospects are summarized. The digital concrete presented in this paper is a representation of concrete materials in another dimension. It can be used for cross-scale analysis based on the evolution mechanism of structural performance from the perspectives of nano-, micro-, meso- and macro-scales, and provides a reliable data support and decision basis for intelligent dam construction from the material aspect.

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.

To explore the effect of deep learning algorithms combined with the multi-source data fusion method in watershed runoff prediction, a bidirectional Long Short-Term Memory (LSTM) neural network model and a data fusion algorithm of the ensemble Kalman filter are combined to construct runoff prediction models for five watersheds in the upper Hanjiang River. These models are verified using long-series hydrometeorological datasets from the study area and atmospheric circulation factor datasets. The results show that in the same prediction period, the models improve the prediction indexes and better capture the extreme values of runoff series in comparison with the traditional LSTM model. After the data fusion algorithm is used to join the atmospheric circulation factor datasets, the evaluation indexes of different watersheds can be further improved, and their time variations are more stable with a longer forecasting period. These prediction models are effective in improving deep learning-based runoff predictions.

Combining and bundling the wind and photovoltaic (PV) power with a hydropower system is an effective way to promote the consumption of new energy, but it creates a great challenge to the traditional operation modes of pure hydropower system. This paper presents a systematic study on the operation modes of cascade hydropower stations in long-term, one-day ahead and real-time scales, considering the large-scale access of wind and PV power; and evaluates the benefits and risks of complementary operation via examining a case of the hydro-wind-PV power system in the Yalong River basin of China. The results show that in the complementary mode, the water level at large hydropower stations should be lowered more and in advance to increase power generation in dry season and reduce abandoned water volume in flood season. In this mode, the peak regulation ability of the system can be improved, and the daily output process of hydropower should be adjusted to alleviate the competition of export channels. Cascade hydropower can compensate and adjust the unstable wind and PV power at one day ahead and in real-time scales, significantly improving power transmission quality and power supply reliability.

The construction and operation of hydropower plants are faced with a large body of multi-source heterogeneous structured and unstructured text data that are difficult to manage and reuse effectively. Aiming at the issue, we apply ontology-based knowledge modeling to knowledge management and knowledge service for hydropower plant operation and maintenance (HPOM), and define an ontology-based knowledge representation model. Then, we construct ontology knowledge representation examples in detail and an ontology knowledge base for the three typical business fields－HPOM, fault warning, and emergency plans. Based on this, the ontology-driven visualization of knowledge retrieval, prediction and warning, and emergency drill, as well as its HPOM application are realized. For this ontology-based construction method and the key technologies of the HPOM knowledge base, their feasibility and effectiveness are demonstrated and verified through practical engineering cases, so as to improve the knowledge management of hydropower plants and its application level.

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.

Reproducing a real-time flow field is an important approach for examining hydrodynamic processes and helping find the laws underlying flow motions, and becomes a challenging hotspot in computer simulation studies. Application of flow visualization technology enables a more direct and complete information acquisition for end users. This study develops a B/S structure channel flow visualization platform with application to the flows in the Bei River navigation channel, focusing on the Image Based Flow Visualization (IBFV) technology. In this platform, integration of dynamic visualization with a cesium framework is achieved by adopting both the render to texture technology and the ping-pong technology－commonly used in general-purpose graphics processing; this viewpoint-based dynamic rendering method can be used to demonstrate effectively the details of layer-dependent flows. In the application, our new method generates a series of coherent images at a high operating rate of 60 frames per second. Thus, it would promote the application of flow field visualization technologies for far-end access through a certain Web-supported system.

Offshore Floating Photo Voltaic (FPV) is an effective way to deal with the contradiction between photovoltaic development and land resources. However, under the action of sea waves, the photovoltaic panels on a FPV structure always oscillate with wave motion, which makes their angles to the sun change constantly and imposes a great impact on power generation. In this paper, a formula is derived for calculating the radiation energy of the panels under regular sea waves, and the concept of time-equal-dip angle is summarized and introduced. Using the Bohai Bay conditions of regular waves at the same latitude, efficiency ratios are calculated for the irradiated energy between the offshore photovoltaic panels with different dip angles and the onshore ones with the best dip angles. From the calculations, we find that for the panels with different dip angles, variations in the radiation energy under sea waves follow basically the same trend－it decreases with the increase in the average dip angle. A recommended standard of the panel motion amplitude is given as a design criterion useful for estimating hydrodynamic responses in the development of FPV structure.

This paper presents a concrete crack detection method based on cascaded neural networks in complex environments, aiming at the problems of the traditional deep learning crack detection method in complex environments: low robustness, poor edge area identification accuracy, and large errors in damage quantification results. In this three-step method, first it uses the improved semantic segmentation model to preliminarily identify cracks in complex environments, and determines roughly a cracking area of interest in the image. Then, it optimizes the rough segmentation image using the mask based on pyramid pooling to accurately capture the context information of the crack edge. Finally, it calculates crack width using the image pixel resolution with the QR code targets and a crack width parameter acquisition algorithm. The test results show that compared with the traditional crack identification, this method improves significantly in the five evaluation indicators－precision rate, recall rate, accuracy rate, F1 score and intersection ratio－and achieves an overall detection accuracy of higher than 95%, thereby realizing the detection and quantitative analysis of concrete cracks in complex environments.

The ratio of the sediment carrying capacity (S*) of a river flow to its local sediment concentration (S) serves as a direct indicator of topographic change in riverbed. We have collected the field data of the upper Yangtze measured at its major hydrological stations in 2010-2020, and conducted an analysis of the ratio S*/S. The results show that with a few exceptions, the carrying capacity generally exceeded local sediment concentration, and S*/S presented a first-increase-then-decrease pattern of variation with flow discharge. This pattern is closely related to upstream dam operation and earthquakes－e.g., impoundment of the Xiluodu and Xiangjiaba reservoirs since 2013 led to an increase in S*/S at the same flow discharge, while a decrease in S*/S has been observed at the Xiaoheba and Fushun stations in recent years due to the gradually reducing effect of earthquakes. The findings in this study provide a new insight for understanding sediment transport and promoting channel regulation in mountainous rivers.

A machine learning-based method is developed based on the liquefaction-induced lateral displacement database of Youd et al., 2002, and applied to the simulation of some soil displacement cases in recent earthquakes. We collect the histories of these cases, and then predict them using the existing engineering experience methods to explore the applicability of the model, showing the Youd 2018 method has a good performance. To obtain an optimal machine learning model, this paper discusses the applicability of five models－BP neural network (BPNN), radial basis neural network (RBF), decision tree (DT), random forest (RF), and support vector machine (SVM). We find that the performance of RF is superior to the other machine learning methods. It has high computational efficiency and good data scalability, and can well reflect the characteristics of the data available to this study. Its prediction accuracy is increased by 18.17% relative to the Youd 2018 method. In addition, a sensitivity analysis is carried out of the influencing factors of liquefaction-induced lateral deformation simulation using the RF model.