It is extremely difficult to carry out fine management and control of the full life cycle of water conservancy objects due to their large quantites, a wide range of types, a wide spatial distribution, complex operation environments, and many interrelated factors. To solve this problem, a fundamental approach is to combine a certain water conservancy big data technology characterized by association analysis with a professional mechanism model characterized by causality, integration and fusion, and efficient process; to analyze the massive and multi-source data intelligently; and to proactivily present useful highly-visualized results to management decision-makers. This paper provides a review on the concept of water conservancy big data and its technology system, along with an overview on the research status quo of natural law analysis, situation research and judgment, trend prediction, and decision optimization for water conservancy. We find that the future trend in big data technology development is toward scenario-based demands, management integration, analysis intelligence, service platforms, and guarantee systems. In water conservancy big data application, data are fundamental, analysis is the core, and the ultimate goal is to improve the efficiency of water treatment by using big data technology. Thus, we should deeply tap the real demands of water conservancy business management; integrate the panoramic data in the fields of water disasters, water resources, water environment, water ecology, and water engineering; comprehensively lay out the research on basic theories and core technologies, so as to accelerate the deep integration of big data technology with water conservancy and support the complete transformation and upgrade of water governance in China.

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.

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.

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 total number, maximum height and engineering scale of concrete dams in China rank the first in the world, and their long-term safety is a focus of public attention. Deformation is a comprehensive performance of concrete dam structures, especially the long-term deformation, a key index for evaluation of the structure behaviors, health status, and their evolution of a dam in long-term service. This paper summarizes the state of arts in creep calculation models for dam concrete and foundation rock and in the methods and models for safety monitoring and early warning of high concrete dam long-term deformation, based on analysis of the latest construction of concrete dams in China and concrete dam failures caused by long-term dam deformation. To ensure the safety of concrete dams in long-term service, future studies should focus on three issues: evolution of structure performances under the coupling effect of multi-factors, long-term structure deformation behaviors under the coupling effect of multi-fields, and evaluation of performance improvement under reinforcement measures.

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.

Current hydropower development in the lower Jinsha River encounters great technical and management challenges, including complicated and harsh natural conditions, rapid changes in social environments, diversified interests, and trans-regional multi sub-projects. This requires further improvement on the existing capability of large-scale project construction for forecast, analysis and control of the projects to minimize their uncertainties brought about by human factors, changing environments and construction conditions, so that the target of six managements of construction projects can be better achieved. To this end, a concept of intelligent dam was formulated and introduced into the construction management of Xiluodu ultra-high arch dam, and intelligent construction technologies were developed and applied to typical dam construction procedures. Based on these applications, this paper describes a theory of intelligent management of large-scale construction projects that was formulated by combining current project management trends with extensive applications of information technology. We also present a technical framework of this new type of management that applies a closed-loop intelligent control principle developed in this study and featured with comprehensive sense, accurate analysis, and real-time control. This framework have five key components: starting from a 3D GIS/DIM structure and multi-source data, targeting at the capability of creating life-long asset value, dynamic simulation and real-time control of actual working states as its core technology, a TGPMS/iDam system as a platform for comprehensive task integration, and N-dimensional enhanced collaborative management as its guideline. The new management framework and theory presented in this paper would be helpful in shaping and upgrading traditional construction project management to a new level characterized by informatization, digitalization, and intellectualization to achieve better project construction in quality, safety and efficiency.

The achievement of carbon peaking and carbon neutrality goals mainly relies on innovation in clean energy projects such as hydropower, wind power, photovoltaic and pumped storage projects. Most of the previous studies focus on general scientific and technological management issues such as innovation strategies, management systems, and resource allocation, but lack a holistic understanding of the innovative characteristics of clean energy projects and an analysis of the existing empirical research on different kinds of clean energy project innovation. This paper presents a new system of innovation indexes for these clean energy projects, and reveals the status of innovation in their development through an industry survey. We also identify the main problems in the innovation and their causes, and suggest its future direction and scope. The results have both theoretical and practical implications for clean energy project innovation.

Construction and operation of reservoirs, dams, channels and other hydraulic projects altered the flow regime of rivers and their morphology evolution, and caused other problems including degradation in the ecological functions and reduction in the species diversity of rivers. This paper reviews the research on river environmental flow and its development in recent three decades, points out its three developing stages, and examines the characteristic of each stage. Facing with changing environments, further studies of river environmental flow should pay more attention to the integrity of ecosystem, modeling its mechanism, and the applicability in practice of ecological operation. In such studies, climate change, spatial heterogeneity, and correlation between ecological and social systems would become the frontiers and major focuses.

In solving models for optimal power generation of reservoir operation in a plan of regulating power generation by water supply, traditional dynamic programming are easy to fall into a dimension disaster; while modern intelligent algorithms using random search are inefficient and easy to fall into a local optimum. To overcome these shortages, this paper develops a floater algorithm, a fast solution method for reservoir dispatching model, based on the characteristic that the water consumption rate of power generation is relatively low at a high operating reservoir stage. This method directly finds the optimal solution by keeping the reservoir stage as high as possible during operation, while satisfying the requirements of guaranteed output and minimum generation discharge. A case study and comparison with the results of discrete differential dynamic programming (DDDP) shows this algorithm has the advantages of achieving high power generation and fast computation (two times as fast as DDDP) for general situations. Thus, our floater algorithm improves the optimization results and solving efficiency for reservoir operation, helping formulate a regulating-power-generation-by-water-supply operation plan and rapid decision-making.

The fusion of mechanism and data is crucial to accurate and efficient assessment of the dam life cycle state and reservoir regulation. This paper discusses the major problems in dam construction and the development of fusion models, and suggests three types of structure for a mechanism-data-driven model－series, parallel and hybrid－along with a brief description of its basic characteristics and applicability. Then, the application and applicability are demonstrated in detail through an example of temperature field analysis for a concrete arch dam. Results show that this fusion model is more accurate and stronger in analysis, prediction and generalization of dam construction and operation, and it is applicable to complicated dynamical-evolving data. Compared with the traditional model, all the three model structures are advantageous. The mechanism-data-driven model provides a new perspective and paradigm for solving the problems of parameter inversion, monitoring and analysis, and strategy optimization in the stages of dam construction, operation and maintenance.

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.

Driven by the carbon peaking and carbon neutrality goals, new forms of energy generation such as wind power and solar power have developed rapidly. To solve the problems of intermittency and volatility in the power system, it is necessary to build matched energy storage facilities. In this paper, the gravity energy storage type of taking water as the medium is defined as Water Energy Storage. A comprehensive comparison is made between different forms of energy storage available at present and in the future, from the aspects of technical principle, economy, environmental impact, and operation safety. The results show Water Energy Storage is the best form of energy storage for supporting new energy development and New Power Systems in the next period of time. This paper also explores the planning idea, regulation calculation, evaluation method, development modes, and other key technologies and relevant policies for pumped storage, hydropower expansion and cascade reservoir energy storage, and takes an outlook for their development in the future. The study would help to plan and design new energy development and New Power Systems.

This paper first reviews the advancements in dam construction and development in hydraulic engineering, and summarizes the conceptualization of digital dam and its related research achievements. Then, we present an interpretation of the difference between the two concepts of digital dam and smart dam, based on a brief discussion on several disadvantages of digital dam, including insufficient depth in intelligent analysis of massive data and relatively low fusing level of site monitoring, simulation analysis and intelligent control. Smart dam is a new concept that is based on the digital dam conceptualization but emphasizes a specific form of dam development, i.e. adoption of new-generation information technologies as a basic means, such as Internet of things, intellectual technology, cloud computing, and big data. This concept also means a specific intelligent system of dam management and operation that is dynamic, refined, and perceivable in analysis and control. Besides, a smart dam often has distinctive features such as integrity, interoperability, fusion-expansibility, autonomy, and robustness. Third, we describe a basic framework of smart dams in detail, including a real-time information perception module, an interconnected real-time transmission module, an intelligent real-time analysis module, and an intelligent real-time management decision system, focusing on analysis of the progress and superiority of smart dams over digital dams in independent information collection, smart reconstruction analysis, intelligent decision, and integrated visualization. Finally, we explore key directions of smart dam research in fundamental theory, key technology, and management and operation system.

The performance of hydraulic turbines, as the core mechanical equipment for developing hydropower, determines the development and utilization rate of hydropower. Driven by the progress of modern science and technology, hydraulic turbine technology has made a great progress in China, it has experienced the developing stages of introduction, absorption, digestion and recreation, and now has reached an internationally advanced level after a rapid development over recent two decades. This paper focuses on the key research advances made in the field of turbine technology in the past two decades, based on a comprehensive synthesis of research achievements in this field in China and abroad. The progress is summarized in three parts: hydrodynamic foundation of hydraulic turbines, optimal design theory of hydraulic turbine flow components, and development of new hydraulic turbines. This paper also discuss the problems in some of the research fields and present a prospect for the development trends in hydraulic turbine technology.

Back analysis of the mechanical parameters of surrounding rocks using a method without sufficient geotechnical tests, has been at the forefront in geotechnical engineering research. To obtain more reasonable and accurate surrounding rock parameters for a water diversion tunnel, an intelligent inversion model integrating multiple machine learning algorithms is developed, and the influence of the parameters on tunnel displacement is examined via sensitivity analysis. Parameters of 25 groups are designed using orthogonal experiment, and the displacements at the monitoring points are calculated through FLAC3D simulations. Then, based on these data, different algorithms are selected to construct an intelligent fusion model for calculations of elastic modulus, Poisson's ratio, cohesion, and internal friction angle. Finally, through a case study of the Yindajihuang water diversion tunnel in Qinghai, the influence of mechanical parameters of surrounding rocks on its displacements is analyzed. By back analysis using this model and FLAC3D forward calculations of the parameters, the settlements at the different positions are obtained with the errors of 5.01%, 3.21%, 3.87% and 4.17% in calculations of the crown settlement, bottom heave, and left and right spandrel displacements respectively relative to on-site measurements. These relative errors, smaller than those of the single models, indicate our intelligent inversion fusion model and analysis method are a significant improvement on surrounding rock parameter calculations.

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.

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

Based on the cusp-catastrophe model of nonlinear theory, this paper examines the influence of water flow conditions and the relative position of sediment particles on riverbed on the nonlinear dynamic characteristics of sediment transport, formulates the state variables and control variables of this model, and constructs a new cusp-catastrophe model for qualitative description of bedload transport. Through coordinate transformation and topological transformation, a nonlinear bedload transport rate formula is derived. This formula is verified against previous measurements and compared with other formulas of the same theory and the classical formulas. We explain the difference in calculations of low-intensity sediment transport using different formulas, and present an analysis on the cause of sudden changes in the calculation accuracy of high-intensity sediment transport. And the influence of incipient shear stress of bedload motions on the formula of sediment transport rate is discussed. It is verified that our nonlinear formula is reasonable with good applicability.

With popularization and application of the project general contracting model in the water conservancy and hydropower industry, the integrated design and construction model led by design has become an effective means of reducing costs and increasing efficiency, unified management, and scientific decision-making. This paper reviews the recent advances in the research of general contracting models, design models, construction models, and design and construction collaboration models for the water conservancy and hydropower industry in China and overseas; and clarifies the current pain points of this industry and its major bottlenecks in developing and implementing such integrated models. We investigate the key roles and concept of applying the informatization technology forefront, deepening technological innovation and practice, and adopting the top-down design and lean construction models in solving the existing problems of low design optimization efficiency in design and construction integration, cumbersome mutual feedback of design and construction information, and low level of intelligent construction. To further develop this concept, taking the digital twin technology as a basic application framework for the future, we expound key research directions for further research, and suggest how to promote the integrated design and construction in hydropower development and overcome the shortcomings in water conservancy informatization.