Flood control operation coupled with risk assessment for cascade reservoirs

Water resources utilization, social economy development, and ecological environment protection are key factors in regional sustainable development. Scientific evaluation of regional coordinated development status and diagnosis of regional uncoordinated development constraints will improve the management level of decision-makers. At present, most developing countries have the problem of unbalanced regional development caused by the one-sided pursuit of a certain system. Taking 14 prefecture-level cities in Hunan Province as cases, this paper analyzed the spatial and temporal distribution characteristics of the carrying capacity level of the water resources system, the development level of the social economy system and the protection level of the ecological environment system in each evaluation unit based on entropy weight method and order parameter analysis. Based on the theory of coordinated development, a calculation model of a coordinated development degree was constructed, and the corresponding evaluation criteria were formulated. The spatial and temporal distribution characteristics of a coordinated development degree in each research unit were analyzed and evaluated. The results showed that the average coordinated development degree of Hunan Province from 2004 to 2016 evolved from “Light disorder recession” to “Nearly disorder recession”, then to “Reluctance coordinated development”. Restricted by different systems, the coordinated development degree in each research unit presented spatial and temporal differences. According to different development stages and the characteristics of different regions, corresponding development strategies can be formulated to provide the guidance for coordinated the development of regions.

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“…Indicators with a large relative change degree have a large weight. This method is widely used in various fields [34,35,36].…”

Section: Methodsmentioning

confidence: 99%

Study on Spatial and Temporal Distribution Characteristics of Coordinated Development Degree among Regional Water Resources, Social Economy, and Ecological Environment Systems

Wang

Dong

et al. 2019

IJERPH

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“…Flooding is probably one of the most devastating natural disasters and has caused severe consequences regarding loss of life and property (Collet et al., 2018; Meng et al., 2019; Zhu et al., 2017). As a typical non‐engineering measure, reservoir operation is of great importance for flood control (Jenkins et al., 2017; Pan et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Guo,

Xu,

et al. 2024

Water Resources Research

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Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

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“…Scientific management and reasonable reservoir operation are also important means to ensure the Yellow River's stability. Only by combining engineering and non-engineering measures scientifically can disaster prevention and mitigation be more effective [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

Guo

Chen

et al. 2023

Sustainability

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The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

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Flood control operation coupled with risk assessment for cascade reservoirs

Cited by 32 publications

References 33 publications

Study on Spatial and Temporal Distribution Characteristics of Coordinated Development Degree among Regional Water Resources, Social Economy, and Ecological Environment Systems

Study on Spatial and Temporal Distribution Characteristics of Coordinated Development Degree among Regional Water Resources, Social Economy, and Ecological Environment Systems

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

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