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.

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.

This article reviews the historical development of dam construction, summarizes the developing trend and key technologies of intelligent construction of dams, sorts out the relationship between the key issues in intelligent construction and intelligent control, and presents a basic control theory for intelligent construction of dams. The concept and definition of intelligent control and its characteristics, theoretical structure and elements are elaborated in detail, and "intelligent decision-making plus automatic control" is clearly defined as its two core elements. On this basis, an intelligent control system for dam construction－featured with the functions of autonomous perception and cognitive information, intelligent organization planning and decision-making tasks, and automatic control of executive agencies to accomplish goals－is constructed; its design concept, component elements, module characteristics, and application levels are explained. This intelligent control theory lays a theoretical basis for solving a variety of dam construction problems: structural service state control, full life cycle safety performance evaluation, construction risk prediction, early warning during dam construction, and cost control. It is also essential to realize the intelligent construction goal of high quality, high efficiency, safety, economy and greenness.

China is the country that has built the largest number of dams in the world, with more than 23000 large dams of over 15 m that accounts for 40.6% of the global total. Against the backdrop of international disapproval of water dams, clarification of why China needs such a huge number of dams matters most. This study reviews the historical development of dam construction, clarifies the reasons for this huge-scale construction in China through examining its national conditions, and looks into the future of Chinese dams. The results show that China owning the largest number of dams is a natural outcome of its population, natural geography, climate, water situation, and other national conditions, and also for the purpose of meeting the major national needs－flood control, disaster reduction, agricultural irrigation, urban water supply, hydropower generation and so on. Water dams have enormous social, economic, and ecological benefits. To satisfy the future needs of the national strategy, the existing number of dams in China is not excessive, but deficient. China needs to further build and improve its dam system from a national strategic prospective of ecological civilization construction and high-quality development. The results cast light on scientific, reasonable, and fair understanding and evaluation of water dams.

To implement the dual-carbon strategy, energy is the main battlefield and electricity the main force; developing a new power system with new energy resources as the main body is the only feasible path to achieving carbon neutrality. This paper examines the emission reduction targets, electricity composition, and structure of the power industry under the dual-carbon strategy, and demonstrates that accelerating the construction of conventional hydropower stations and pumped storage power stations is an important basis for solving problems and challenges such as the reliability and long-term regulation capabilities of the new system. Based on the hydropower resources endowment and the development status, we present a strategic idea of strengthening the coordinated development of hydroelectric power and new energy, and redefine the development path of building clean energy bases and energy storage plants and accelerating the construction of pumped-storage power stations and cascaded conventional hydropower reservoirs. This new positioning of hydroelectric power in the new system not only provides basic electricity but plays its capacity function. Thus, we suggest certain policy should be formulated to speed up the construction of clean energy bases and energy storage plants. Our findings could help the revision and compilation of the planning schemes and regulations.

Controlling the flood limit water levels of a reservoir by stages is an effective approach to raising flood water utilization rates. In this study, by dividing flood season into different stages using the circular distribution method, we determine benefit index sets and risk index sets for a staged control of the limit water levels, and construct a multi-objective collaborative decision-making model that can coordinate conflicts between risk and benefit. Then, we calculate a utility function for each index set, determine a payoff function and a reachable utility set, and solve for the ideal Nash negotiation solution using a nonlinear programming method. And by the theory of pattern recognition, an optimal scheme can be determined; a certain preference for risk or benefit can be realized using the current situation point as the basis for benefit-risk negotiation decision and then adjusting this point. In a case study of the Xianghongdian Reservoir, we adopt a condition of risk and benefit valued equally, and obtain the optimal flood control levels of 126.5 m and 127.0 m for pre-flood and post-flood stages, respectively. It shows that by raising the risk at the corresponding risk status point, a risk preference in pre-flood stage can be realized, resulting in a greater value of the risk payoff function and thus a lower optimal limit water level. Similarly, by raising the benefit at this point, a preference for benefit in post-flood season can be realized with a greater value of the benefit payoff function and a higher optimal limit water level.

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.

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.

Water use efficiency (WUE) of vegetation reflects the amount of its dry matter through consuming per unit amount of water, a comprehensive indicator for assessing its growth conditions. However, contributions of multiple forcing factors to WUE are unclear due to the complicated influencing mechanism. Combining a structural equation model (SEM) with the artificial neural network (ANN), this paper develops a hybrid SEM-ANN model for analysis of the direct and indirect influences of WUE multiple factors to achieve an improvement on the simulations. It determines the structural relationship among the factors and their degrees of influence by using SEM, and then constructs the topology of ANN. The results show that in the Hotan region, various vegetation types have different WUE factors at different levels. We divide them into direct factors and intermediate variables that impact WUE indirectly－with the former including temperature (T), precipitation (P), vapor pressure deficit (VPD), and wind speed (WS); the latter including an enhanced vegetation index (EVI) for grassland and cropland and a standardized precipitation evapotranspiration index (SPEI) for shrub land and evergreen needle leaved forest. The SEM-optimized structure of ANN fits better, and the SEM-ANN model has high explanatory capacity and higher accuracy in the ecosystem’s environmental control and simulations of WUE, thus providing a theoretical basis and simulation method that can improve efficient water use and predict future WUE responses to climate changes in Xinjiang.

The section from Gaocun (GC) to Taochengpu (TCP) of the lower Yellow River (LYR) is a typical wide-floodplain transitional reach, featured with a complex channel-floodplain system and severe development into a secondary perched channel. This study conducts a detailed investigation of the relationship between spatiotemporal water-sediment variations and geomorphological evolution observed in this reach, using its historical bathymetric data and the cross-section method. The results show that during the operation of Xiaolangdi reservoir, the main channel was continuously scoured, causing narrower and deeper main channels, a decrease in river cross-sectional geomorphic coefficient, and an increase in the bankfull area mostly by more than 200%. In recent years, however, these trends have been significantly weakened－the main channel was slightly widened horizontally and deepened vertically between GC and Sunkou (SK), while mainly deepened vertically between SK and TCP. In the GC-TCP reach, the channel-floodplain system has gradually switched from sedimentation to erosion patterns, with the resulted erosion rate of roughly 0.07 m/a of its main channel and 0.002 - 0.008 m/a of its floodplains; its cumulative erosion volume during 2000 - 2016 was around 2.251×108 m3. The lateral slope of floodplains increased in time, and its spatial distribution was concentrated between 0.5‰ and 2‰.

Under outflow conditions, flows in a side inlet/outlet are in the diffusion mode and produce large velocity fluctuations in the trash rack section. In this work, a test facility for typical inlet/outlet flows is used for an experimental study with flow velocity measured using a laser doppler velocimetry and an acoustic doppler velocimetry. The results show large velocity fluctuations occur at the cross section of the trash rack installed near the exit of the diffuser, with the fluctuating components reaching up to 1.8 times of the mean velocity and an average turbulence intensity of 0.724. The probability density of the fluctuations basically follows a normal distribution. Turbulent intensity shows a streamwise trend of first increasing and then decreasing along the flow starting from the diffuser inlet, through the diffusion section, adjustment section, and anti-vortex section, and to the reservoir. Velocity fluctuations are small in the upstream tunnel section and become relatively large in the diffusion and adjustment sections. Large velocity fluctuations at the trash rack cross section are explained. The results provide a theoretical basis for optimization of the inlet/outlet parameters and analysis of the vibration damage to trash racks.

Constructing a high-accuracy deformation prediction model of dam structure is of great significance for dam risk assessment and formulation of preventive measures. Previous dam deformation prediction models lack an effective explanation of the time-lag characteristics, and ignore an influence analysis and evaluation of the deformation characteristic factors in model construction, thereby lowering prediction accuracy. This paper presents a Gated Recurrent Unit (GRU) architecture combined with a temporal attention mechanism to overcome these problems. First, a Kalman filter is used to denoise the original dam deformation data series and remove its outliers; then, Random Forest (RF) is used to analyze and evaluate the importance of different deformation characteristic factors, and pick out key model input factors. Finally, to consider the dam deformation lag, a time attention mechanism is used to further improve the attention of the GRU model to the time-dimension dynamic features and to enhance its adaptive learning capability to time-dimension information. This, through visualizing time attention, can further improve the interpretability of a prediction model for the dam deformation in the hidden state stage. The results of engineering case studies show our model, of higher prediction accuracy and strong explanatory power for hidden state levels, can reveal the long-term effects of temperature and water level factors on dam deformation. Thus, it is a new effective method for dam safety monitoring.

Human-water relationship is complex and involves many elements. It is important to find an effective approach for harmonious regulation of the existing conflicts on the basis of the mechanism hidden in the relationship. Using a case analysis of the Yellow River basin, this paper elucidates the complexity of human-water relationship from three aspects, complexity in the human system and the water system, complexity in their coupling, and complexity in their interaction. We examine the interaction process in human-water relationship, and reveal its mechanism based on the interaction of these two components; then, we summarize the issues in the existing human-water relationship observed in the case basin, and present an new idea － using harmonious regulation to improve such relationship. The results of two case studies, “Yellow River water distribution” and “harmony equilibrium between water resources and economy-society development”, show that facing such a complex relationship involved in this basin, we can adopt the theory and method of harmonious control to achieve an optimized water distribution plan that improves the level of its regional harmony and its human-water relationship, thus proving the approach is valid and useful.

The spot market of power is an important part of the modern power system, which plays a key role in restoring the properties of power commodity, finding its prices, and optimizing its resource allocation. Hydropower is often affected by the randomness of river runoff, which results in prominent contradiction between the high water and low water and great output uncertainty. Operation of a reservoir is usually limited by its comprehensive utilization, the power stations of a cascade system are mutually connected closely, and information sharing between upstream and downstream multi-agent power stations is difficult. In addition, external transmission channels are lacking and section blocking is severe. Thus, hydropower participation in the electricity spot market is facing many challenges. This paper summarizes the research status quo in China and abroad, focusing on the mathematical models and algorithms previously developed and the pros and cans of the existing methods. We also discuss the trends of hydropower spot market development in the future, including the key scientific and technological problems involved in "quantity price" declared in the spot market: prediction of market clearing prices, optimization of power generation capacity, bidding strategy, and the decision support system of hydropower spot quotation. This would help enrich the theory and method of power spot market and improve the production and operation of hydropower enterprises.

Deformation monitoring data of a rockfill dam are a time series that can be mined using a time series prediction model for analysis of its variation trend. This paper presents a new method for rockfill dam deformation prediction. First, we use a seasonal-trend decomposition procedure based on loess (STL) to decompose the deformation monitoring data of a rockfill dam into three parts: secular trend, seasonal variation, and irregular variation. Then, an empirical mode decomposition (EMD) method is used to stabilize the irregular variation. Finally, we adopt a long short-term memory (LSTM) technique to predict the decomposed sequences and a Bayesian optimization method to optimize the parameters. To evaluate the accuracy of this method, we numerically simulate the Shuibuya concrete faced rockfill dam for different training time, prediction time, and numbers of outliers; and compare it with other time series prediction models. The results show our new method is more accurate and applicable to evaluating rockfill dam performance.

Dam deformation behavior is a consequence of long-term interaction of many factors, and its evolution pattern usually involves two dimensions: time and space. However, previous intelligent modeling of dam deformation lacks a comprehensive consideration of time and space variations, and a large amount of spatiotemporal information needs to be further excavated from the prototype observation data. This paper develops a dynamic monitoring model for dam deformation with spatiotemporal coupling correlation characteristics from two view angles: time-series correlation for a single measurement point, and spatial correlation of multiple measurement points. This model takes the gated recurrent unit (GRU) neural networks as core layers to model and learn the inherent time-varying patterns in a historical deformation data series, and constructs the features of spatial variations through iterative extraction of effective deformation factors. It uses a soft attention mechanism to improve the probability weight allocation rule of the GRU hidden states, thus achieving adaptive learning of key information. Its effectiveness is verified in a case study of the Fengman concrete gravity dam. The results show that this monitoring model can accurately simulate the dynamic deformation evolution of a dam, and are more accurate in extrapolation prediction than conventional monitoring models.

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.

A systematic quantification of the thresholds for the propagation from meteorological drought to different levels of hydrological drought is useful in guiding the early warning and delicacy management of hydrological drought. In this paper, a standardized precipitation index and a standardized runoff index are used to characterize meteorological drought and hydrological drought, respectively, for the Qinhe River basin of Loess Plateau; the run theory is applied to identify drought events, and merge or reject drought events to match both types of drought events. Then, Bayesian networks combined with Copula functions are used to construct models to solve for the propagation thresholds. Results show that when the level of hydrological drought is raised, the threshold is elevated and the intensity of a meteorological drought is weakened in the propagation. For the Qinhe River basin, the duration thresholds of meteorological droughts are 12.8, 21.8 and 30.9 months for triggering moderate, severe and extreme hydrological droughts, respectively, corresponding to the intensity thresholds of 14.2, 22.4, and 30.0, respectively, and its drought tolerance ability is related to the basin meteorological and underlying surface conditions.

There are some issues in the degree and stage of hydropower development in China, such as different statements, vague comparative concepts, unclear data sources and statistical caliber, etc. This paper presents a systematic comparison of the hydropower development degrees in China and other key countries and regions to judge accurately the development stage and global level of China's hydropower, based on the international authoritative statistical data of hydropower resources and development status. The technical exploitable hydropower in China reaches 2740 TWh·a-1, accounting for 44.3% of the theoretical exploitable total, indicating her abundant hydropower resources and good development conditions. From the comparison of key countries and regions, the hydropower development degree in China has reached 48.2%, only second to the United States among the top ten in terms of the resources and installed capacity of hydropower, meaning 4.6% higher than the average level of developed countries. Although this overall degree is relatively high, the degree of some rivers in Southwest China is only 16%, and the proportion of pumped-storage power stations with good regulation performance is only 1.4% of the installed power capacity. Problems exist such as unbalanced regional development and weak pumping and storage function. Under the new situation, China should focus on promoting the development of conventional hydropower in key areas of the southwest basin, and accelerate large-scale construction of pumped-storage power stations.