Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties

Xu, Bin; Huang, Xin; Mo, Ran; Zhong, Ping‐an; Lü, Qingwen; Zhang, Hanwen; Si, Wei; Xiao, Jianfeng; Sun, Yu

doi:10.1016/j.jhydrol.2021.126679

Cited by 21 publications

(8 citation statements)

References 34 publications

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“…The correlation between these variables means that uncertainty characterization and scenario generation should address the dependency characteristics through establishment of a joint probability distribution. The copula function (Xu, Huang, et al., 2021) is applied to modeling for its superiority in formulating a multivariate joint distribution by selecting arbitrary marginals and a dependence structure.…”

Section: Methodsmentioning

confidence: 99%

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

Sun

Huang

et al. 2022

Water Resources Research

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Hydropower, which has long been recognized as an effective and reliable source of renewable energy, accounted for 70% of total renewable energy production in 2016. In addition to provision of clean renewable energy, large hydropower systems are often operated on a long-term schedule with multiobjective requirements such as maintaining flood control, water supply, navigation, and ecological protection (Liu et al., 2020;Xu et al., 2015Xu et al., , 2020. In recent years, following the rapid development globally of intermittent renewable energy sources (e.g., wind and solar energy) to address degraded environmental quality and problems associated with climate change, operation of hydropower systems must now incorporate new tasks associated with integration and stabilization of the uncertain energy production by these intermittent renewable sources (Liu et al., 2019). To enhance comprehensive benefit through delivery of water and energy to the multiobjective demand side, joint operation of reservoir systems with capability to coordinate spatiotemporal water release and water head is essential to resolve conflicts regarding the allocation of water and energy resources to competing demands. In practical application of realtime hydropower operation based on forecasts, one of the most critical challenges in informing multiobjective and joint reservoir operation is how best to make efficient and robust decisions under the condition of uncertainty owing to incomplete knowledge and inaccurate forecasts, such that negative consequences of imbalanced demand and supply are manageable and expected returns or costs are acceptable (Roach et al., 2016).Forecast errors are widely recognized as the primary factors affecting the reliability of hydropower operation strategies (Xu, Liu, et al., 2021), especially in the long-term planning horizon, because forecast models have inherent uncertainties attributable to their input, structure, and parameters in addition to errors associated with human-induced impression or fuzziness (Li et al., 2011;Mo et al., 2021). Under the perturbation of error sources,

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Section: Methodsmentioning

confidence: 99%

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

Sun

Huang

et al. 2022

Water Resources Research

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“…In addition to the inherent uncertainties in GCMs, the application of GCMs to different regions needs to be assessed as it has different impacts (Zaremehrjardy et al, 2021). In essence, the forecast uncertainty can be reduced if the above-discussed uncertainties are addressed using supporting techniques (Xu et al, 2021).…”

Section: Uncertainties In Gcmsmentioning

confidence: 99%

An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Panchanathan

Ahrari

Ghag

et al. 2023

Preprint

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In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the studies to address uncertainty, (4) multi-criteria approach for reducing uncertainty in hydrological forecasting and its applications (5) role of remote sensing data sources for hydrological forecasting and uncertainty handling, (6) selection of hydrological models for hydrological forecasting. Especially, a synthesis of the literature showed that approaches such as multi-data usage, multi-model development, multi-objective functions, and pre-/post-processing are widely used in recent studies to improve forecasting capabilities. This study reviews the current state-of-the-art and explores the constraints and advantages of using these approaches to reduce uncertainty. The comparative summary provided in this study offers insights into various methods of uncertainty reduction, highlighting the associated advantages and challenges for readers, scientists, hydrological modelers, and practitioners in improving the forecast task. A set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

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“…The Markov chain is a stochastic model that describes a sequence of possible events wherein the probability of each event depends only on the state attained in previous events (Xu et al, 2021).…”

Section: Risk Factor Identificationmentioning

confidence: 99%

“…At the same time, due to the influence of factors such as model parameter error, the prediction error in each period of runoff prediction has a certain correlation. In order to describe this correlation, Copula function (Xu et al 2021;Li et al 2022;Dodangeh et al 2020), Bayesian network (Lu et al 2020), Monte Carlo Markov chain (Hadfield 2010) and other methods have been applied to runoff simulation in recent years, theoretically, when the forecast period is long enough, inputting the hourly updated hydrological forecast into the operation model can update the operation decision hourly, so as to make full use of the hydrological forecast information and improve the benefits of the reservoir system (Zhao et al 2011). At the same time, combined with the simulation of uncertain factors such as runoff forecast error, the possible risks of operational decision can be better analyzed, providing clear guidance for reservoir operation.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis of Dynamic Water Level Setting of Reservoir in Flood Season Based on Multi-index

Ding

et al. 2022

Preprint

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The long-term operation of the reservoir at the flood limit water level in the flood season is not conducive to the exertion of the comprehensive benefits of the reservoir, especially in the flood free period, the operation of the reservoir at the flood limit water level will cause a certain waste of resources. On the one hand, with the continuous improvement of the forecast accuracy, the effective forecast period of flood forecast will continue to extend, when there is excess water in the flood free period, the water level can be raised instead of abandoning the water to increase the benefit of power generation. If there is a flood, it can be discharged within the effective forecast period of flood forecast to make the water level drop back to the flood limit level, so as to ensure the safety of flood control; On the other hand, affected by the forecast uncertainty and other factors, the higher the operating water level in the flood free period, the greater the risk that the water level can return to the flood limit water level through discharge, and the decision-making based only on the power generation benefit and risk rate is difficult to deal with the possible large-scale flood events in the future. This study proposes a dynamic water level decision-making system in flood free period. The system considers three factors: power generation benefit, discharge in the effective forecast period of flood forecast and risk rate. It can provide decision-makers with a variety of decision-making schemes and screen the optimal scheme, thus improving the operational benefit of hydropower station in flood season and reducing flood control risk. Firstly, historical data and various flood season staging methods are used to divide the flood season into multiple sub-seasons, and then the maximum inflow process in the effective forecast period of flood forecast in each sub-season is selected. According to this inflow process, the upper limit of operating water level in the flood free period of each sub-season is determined based on the reservoir discharge capacity, and the operating water level points and discharge ratio are discretized, Then the multi-order Monte Carlo Markov chain (MCMC) and Monte Carlo method are used to calculate the risk rate. Then, the scheme set is established by the three variables of power generation benefit, discharge ratio and risk rate. Finally, the weighted Topsis method is used to determine the optimal scheme. In the practical application, the influence of each risk factor on the risk rate and the influence of weight on the final decision are also compared and analyzed. This method has been verified in Youjiang reservoir in Yujiang River Basin. The main conclusions are as follows: (1) Combined with the discharge in the forecast period, the reservoir operating water level has a certain lifting space in the flood free period, which can effectively reduce the waste of resources. (2) In the discharge process in the forecast period, runoff forecast error and discharge error are the two factors that have the greatest impact on the risk rate, and the multi-order MCMC method effectively reflects the relationship between the adjacent periods of runoff forecast error, and its simulation process is closer to the reality. (3) Topsis with the combination of AHP and entropy weight method can fully consider the characteristics of power generation benefit, discharge ratio and risk rate, and it is effective to select the optimal solution from the set of non-inferior schemes. This method provides a new idea for determining the operating water level of the reservoir in the flood season.

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Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties

Cited by 21 publications

References 34 publications

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

Scenario‐Based Multiobjective Robust Optimization and Decision‐Making Framework for Optimal Operation of a Cascade Hydropower System Under Multiple Uncertainties

An overview of approaches for reducing uncertainties in hydrological forecasting: progress, and challenges

Risk Analysis of Dynamic Water Level Setting of Reservoir in Flood Season Based on Multi-index

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