This paper presents a novel method for urban flooding risk assessment, incorporating social information that was not yet or less considered in previous studies. We have obtained flooding data from an urban storm water model and collected social information from webpages using the web crawler technology. Then, using these two types of information, we build an assessment index system of urban flooding and an exponential model, so as to achieve a comprehensive evaluation of urban flooding risk for the study area. Application to a study site, the Qingnishier region in Dalian, shows that for the 50- and 100-year return periods, the calculated areas of high-risk zones are 0.53 km2 and 1.24 km2, respectively, if only the flooding information is taken into account, while they become 1.12 km2 and 1.50 km2, respectively, if the social information is also included, revealing considerable increases in the latter case. Incorporating social information in the model will significantly raise the flooding risk level in strategic locations such as densely populated urban areas, traffic arteries, but it will lower the risk level in those unimportant areas, which indicates an improvement of the modeling.

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.

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.

Hydropower development in the lower Jinsha River is of great significance to carbon peaking and carbon neutrality goals. It is a significant contribution to the formation of a clean, low-carbon, safe and efficient energy system and the promotion of coordinated economic and social development in the western regions. Aimed at the challenges in hydropower construction to the "four high, three big, two sides and one strict" management, this paper summarizes the practical experience of hydropower development in this reach, and presents a comprehensive analysis of engineering construction risk and project entity construction management ability. The main results involve engineering construction management concepts, project management modes, green hydropower practices, and industry-college-institute collaborative innovation mechanism, etc. These practices are based on the effective solution of key problems and technical and management challenges, ensuring smooth construction and on-schedule power generation of the hydropower stations in the lower Jinsha River. Our research findings may also be applicable to development and construction of similar river basin projects.

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.

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.

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.

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.

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.

Through embedding a long short-term memory (LSTM) neural network in the encoder-decoder (ED) structure, this study constructs a LSTM-ED deep learning model and uses the Bayesian probabilistic forecasting processor to quantify flood forecast uncertainty. A probabilistic operational approach is developed, and the influence of rainfall forecast information on the probabilistic forecast performance is discussed. The new models are trained and validated using 6h rainfall and runoff series during 2010-2021 flood seasons in the interval basin between the Xiangjiaba reservoir and Three Gorges reservoir to forecast its floods for the forecast periods of 1 - 7 d. The results show the LSTM-ED model has a forecast accuracy higher than that of LSTM, achieving the Nash efficiency coefficients above 0.92 for the validation of 1 – 7 d forecast periods. The continuous ranking probability score values of the probabilistic forecasts are reduced by 26.8% - 32.7% relative to the mean absolute errors, effectively quantifying forecast uncertainties. The probabilistic forecasts could be further improved by considering rainfall forecast information so as to provide more reliable risk information for decision-making of reservoir scheduling.

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 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.

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.

The deformation prediction of a concrete dam is important to its safe operation. To solve the problem of low prediction accuracy of traditional analysis methods resulted from the difficulty in capturing the characteristics of long-term sequences, this paper uses a combination of Sparrow Search Algorithm (SSA) and the K-Harmonic Mean (KHM) algorithm to cluster the monitored values and capture the long-sequence features. Then, we use methods such as Complete Ensemble Empirical Mode Decomposition (CEEMDAN) to reduce the noise in the clustered data, and a long short-term memory (LSTM) model to predict long sequences. The analysis results show this clustering method has a better capability of identifying long-sequence features. It removes the redundant information from the sequence by cooperating with the CEEMDAN decomposition-based method, and enables the LSTM model to better capture the time-sequence characteristics of dam deformation, thus improving the prediction accuracy significantly. The proposed method is good in accuracy and adaptability and useful for dam deformation prediction.

The seepage monitoring model of rockfill dams is a key factor for quantitative analysis of seepage safety. Most of the traditional models adopt a statistical model or machine learning intelligent algorithm model separately, unable to effectively integrate the advantages of both. This paper presents an innovative integration of statistical models with multiple parallel intelligent algorithm prediction models in the framework of integrated learning, and uses the interpretability of statistical models and the high adaptability of fit of intelligent algorithms to improve the prediction accuracy of this integrated model. First, we fully consider the lag effect of seepage influence factors on the basis of the classical seepage statistical model, and improve the expression for the water level factor and the rainfall factor. Then, based on the integration principle of differential evolution adaptive Metropolis (DREAMZS), several advanced intelligent algorithms and improved statistical models in machine learning are integrated, and optimal weight coefficients are obtained for each model. Case analysis shows that in comparison with the single statistical model or the intelligent algorithm model, our integrated learning fusion model improves prediction accuracy significantly and can integrate effectively the advantages of a statistical model and multiple intelligent models, providing a new modeling method for dam seepage monitoring.

Protection measures of a long-distance water pipeline system are usually necessary to mitigate the water hammer effect since it can cause vibration and rupture. To optimize the water hammer protection measures and improve their cost and reliability, a multi-objective optimization method is developed based on the Random Forest (RF) algorithm and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). First, a hydraulic transient model based on the method of characteristics is used to obtain an expected sample set; based on this set, an RF prediction model is used to establish the relationships between the optimization variables and the optimization objectives. Then, a multi-objective optimization model is constructed and used to find the Pareto frontier solution set, by taking the highest water hammer pressure, the highest dimensionless reversal speed, and the lowest protection cost as its objective functions. This study shows our method can quickly generate optimized protection schemes that balance the requirements of different targets, thus significantly improving the design of water hammer protection for long pipeline systems.

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.