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Dam failure can cause extraordinary flooding. Water and wastewater treatment plants, electricity generation facilities, bridges, highways, population centers, and other dams often lie downstream of a dam. If poorly maintained, aging dams can be overtopped by extreme rainfall events, and long-term loss of critical infrastructure services for many communities and regions may result. Such events are now more likely as the climate changes. How should we prioritize dams that need immediate attention and retrofit or remove them? Losses from a dam failure could exceed the infrastructure's replacement costs, through cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classification frameworks in the US and many other countries do not formally characterize "hazard hotspots" considering these impacts. Given that there are over 90,000 dams at different stages of repair, maintenance, and budgetary constraints, a systematic approach is needed to rank their potential failure hazard and allocate resources for risk mitigation. In this paper, we synthesize an approach for preliminary ranking of the priority areas of concern. The approach is scalable over many regions for rapid assessment of magnitude and exposure of a dam failure. The estimation of the consequences of a dam failure, including its financial losses, as well as affected critical infrastructures and population, is explored using publicly available dam break and consequence tools as well as national infrastructure data sets. [ABSTRACT FROM AUTHOR]

The efficient operation of a multipurpose reservoir requires information on high and low flows. However, analyses of inflows for high flows and for low flows are typically done independently. In this paper, we considered the joint dependence of the low flow on the preceding high flow volume and duration for the wet season in the Three Gorges region of the Yangtze River Basin in China. High flow volume and duration were found to have a strong association with the annual minimum 7‐day flow in Cuntan, Wanxian, and Yichang stations. Furthermore, we identified the Arctic Oscillation, Pacific Decadal Oscillation, and snow cover in the Tibetan Plateau to have statistically significant teleconnections with the annual minimum 7‐day flow. Bayesian models that consider a different level of pooling of the site by site regressions were then developed for the annual minimum 7‐day flow conditional on the climate indices and high flow volume (or duration). The full pooling model performed best, suggesting that a homogeneous regional response is best identified given the global climate predictors. Statistics such as the deviance information criterion and reduction of error, coefficient of efficiency, and coverage rate under cross validation indicate the good performance of the model. Snow cover in the western Tibetan Plateau and high flow volume were identified as the most influential factors of the annual minimum 7‐day flow through their impact on water storage in the basin. Recent simulations since June 2003, when the Three Gorges Dam operation started, were used to analyse the effect of dam operation on the annual minimum 7‐day flow. A comparison of observations and predictions during the post‐dam period demonstrated that the dam operation effectively modifies the annual minimum 7‐day flow period to have higher flows. [ABSTRACT FROM AUTHOR]

The Three Gorges Dam is the world's largest capacity hydropower station located in the Hubei province along the Yangtze River in China, which began operations in 2003. The dam also functions to store and regulate the downstream releases of water in order to provide flood control and navigational support in addition to hydropower generation. Flow regulation is particularly important for alleviating the impacts of low‐ and high‐flow events during the summer rainy season (June, July, and August). The impact of dam operations on summer flows is the focus of this work. Naturalized flows are modelled using a canonical correlation analysis and covariates of subbasin‐scale precipitation resulting in good model skill with an average correlation of 0.92. The model is then used to estimate natural flows in the period after dam operation. A comparison between modelled and gauged streamflow post 2003 is made and the impact of the dam on downstream flow is assessed. Streamflow variability is found to be strongly related to rainfall variability. An analysis of regional streamflow variability across the Yangtze River Basin showed a mode of spatially negatively correlated variability between the upper and lower basin areas. The Three Gorges Dam likely mitigated the occurrence of high‐flow events at Yichang station located near the dam. However, the high flow at the remaining stations in the lower reach is not noticeably alleviated due to the diminishing influence of the dam on distant downstream flows and the impact of the lakes downstream of the dam that act to attenuate flows. Three types of flow regime changes between naturalized and observed flows were defined and used to assess the changes in the occurrence of high‐ and low‐flow events resulting from dam operations. [ABSTRACT FROM AUTHOR]

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions. [ABSTRACT FROM AUTHOR]

Storage and controlled distribution of water have been key elements of a human strategy to overcome the space and time variability of water, which have been marked by catastrophic droughts and floods throughout the course of civilization. In the United States, the peak of dam building occurred in the mid-20th century with knowledge limited to the scientific understanding and hydrologic records of the time. Ecological impacts were considered differently than current legislative and regulatory controls would potentially dictate. Additionally, future costs such as maintenance or removal beyond the economic design life were not fully considered. The converging risks associated with aging water storage infrastructure and uncertainty in climate in addition to the continuing need for water storage, flood protection, and hydropower result in a pressing need to address the state of dam infrastructure across the nation. Decisions regarding the future of dams in the United States may, in turn, influence regional water futures through groundwater outcomes, economic productivity, migration, and urban growth. We advocate for a comprehensive national water assessment and a formal analysis of the role dams play in our water future. We emphasize the urgent need for environmentally and economically sound strategies to integrate surface and groundwater storage infrastructure in local, regional, and national water planning considerations. A research agenda is proposed to assess dam failure impacts and the design, operation, and need for dams considering both paleo and future climate, utilization of groundwater resources, and the changing societal values toward the environment. [ABSTRACT FROM AUTHOR]

This narrative review paper explores the diverse applications of artificial intelligence (AI) in the field of dam engineering. Authored by research engineers specializing in civil engineering and data science, and reviewed by experienced dam engineers and AI experts, the paper aims to provide a comprehensive overview of the subject. The paper is structured into three parts. Part 1 offers a concise introduction to AI, covering its historical background, major categories, and current state of development. In Part 2, the focus shifts to the specific applications of AI in hydropower and dam engineering. This section delves into the utilization of AI for predictive modeling, real-time monitoring, optimization, and planning and design. Notable case studies and examples of machine learning techniques applied to predictive models and numerical simulations in the context of dams and levees are also discussed. Part 3 explores the current and emerging technologies being implemented in the industry, including automated decision-making systems and the use of AI-powered drones for efficient dam inspection. Additionally, the section sheds light on the challenges that must be addressed to fully integrate AI into dam engineering practices. This paper targets dam engineers, AI experts, and individuals interested in the intersection of these fields. Its primary contribution lies in providing a comprehensive and up-to-date review of the applications, challenges, and potential future developments of AI in dam engineering. Over 3300 articles on the intersection of dam engineering and AI were identified, and approximately 550 articles (about 17%) were further processed for inclusion in this study. Moreover, it emphasizes the need for robust data management, algorithmic transparency, and ethical considerations in the implementation of AI systems. [ABSTRACT FROM AUTHOR]