Bivariate hydrologic risk analysis based on a coupled entropy-copula method for the Xiangxi River in the Three Gorges Reservoir area, China

Fan, Yurui; Huang, Wei; Huang, Guohe; Huang, Kai; Li, Y. P.; Kong, Xiangming

doi:10.1007/s00704-015-1505-z

Cited by 49 publications

(42 citation statements)

References 64 publications

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“…These parametric distributions are popular for characterizing the probability distributions of extreme hydrological events due to their better performance [6,44]. Second, we constructed copula models to depict the dependence structure of AMF and Pr series by joining their marginal distributions.…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…Additionally, we should also note that there exist some disadvantages to the MEP distribution; for example, the CDF of MEP-related distributions cannot be expressed in a closed form, parameter estimation is computationally expensive, and the mathematical expression of MEP-related distributions is more complex to develop as a computer program [6,66].…”

Section: Marginal Distribution Selectionmentioning

confidence: 99%

“…Its primary advantages are super-linear convergence, simple recurrence formula, and less calculation. Readers interested in the detailed process of determining the Lagrange multipliers λ r through the conjugate gradient method are referred to the papers of [6].…”

Section: Maximum Entropy Principle Distribution (Mep)mentioning

confidence: 99%

“…A report issued by UNISDR (2015) highlights the fact that, between 1995 and 2015, floods affected 2.3 billion people, worldwide, accounting for 56% of the people affected by weather-related disasters [4,5]. Flood risk analysis can provide extremely valuable information by estimating the occurrence of floods for flood control and disaster mitigation, hydraulic structure design, reservoir management, and so on [6,7]. It is widely known that, in rain-dominant watersheds, river floods are commonly triggered by extreme precipitation events [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Guo

Chang

Wang

et al. 2017

Entropy

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Copula functions have been extensively used to describe the joint behaviors of extreme hydrological events and to analyze hydrological risk. Advanced marginal distribution inference, for example, the maximum entropy theory, is particularly beneficial for improving the performance of the copulas. The goal of this paper, therefore, is twofold; first, to develop a coupled maximum entropy-copula method for hydrological risk analysis through deriving the bivariate return periods, risk, reliability and bivariate design events; and second, to reveal the impact of marginal distribution selection uncertainty and sampling uncertainty on bivariate design event identification. Particularly, the uncertainties involved in the second goal have not yet received significant consideration. The designed framework for hydrological risk analysis related to flood and extreme precipitation events is exemplarily applied in two catchments of the Loess plateau, China. Results show that (1) distribution derived by the maximum entropy principle outperforms the conventional distributions for the probabilistic modeling of flood and extreme precipitation events; (2) the bivariate return periods, risk, reliability and bivariate design events are able to be derived using the coupled entropy-copula method; (3) uncertainty analysis highlights the fact that appropriate performance of marginal distribution is closely related to bivariate design event identification. Most importantly, sampling uncertainty causes the confidence regions of bivariate design events with return periods of 30 years to be very large, overlapping with the values of flood and extreme precipitation, which have return periods of 10 and 50 years, respectively. The large confidence regions of bivariate design events greatly challenge its application in practical engineering design.

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Section: Methodological Frameworkmentioning

confidence: 99%

Section: Marginal Distribution Selectionmentioning

confidence: 99%

Section: Maximum Entropy Principle Distribution (Mep)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Guo

Chang

Wang

et al. 2017

Entropy

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“…Thus the first submodel of model (1) would correspond to f + . It can be formulated as follows (assume that 0 i b   and 0 f   ) (Zeng et al, 2016;Fan et al, 2016): …”

Section: Interval Linear Programming (Ilp)mentioning

confidence: 99%

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Huang¹,

Nie²,

Guo³

et al. 2017

J ENVIRON INFORM

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ABSTRACT. Nuclear power accidents are one of the most dangerous disasters posing a lethal threat to human health and have detrimental effects lasting for decades. Therefore, emergency evacuation is important to minimize injuries and prevent lethal consequences resulting from a nuclear power accident. An inexact fuzzy stochastic chance constrained programming (IFSCCP) method is developed to address various uncertainties in evacuation management problems. It integrates the interval-parameter programming (IPP) and fuzzy stochastic chance constrained programming (FSCCP) methods into a general framework, in which the IPP method addresses the uncertainties presented as intervals defined by crisp lower and upper bounds, and the FCCP treat the dual-uncertainties expressed as fuzzy random variables. The measures of possibility and necessity were employed to convert the fuzzy random variables into crisp values to reflect the decision maker's pessimistic and optimistic preferences. The IFSCCP model is applied to support nuclear emergency evacuation management in the Qinshan Nuclear Power Site, which is one of the largest nuclear plants in China. The results provide stable intervals for the objective function and decision variables with different fuzzy and probability confidence levels regarding the local residents' distribution. Nine scenarios are analyzed to reflect the impacts of the imprecision (fuzziness and randomness) associated with the size of the population in a plume emergency planning zone. The results are valuable for supporting local decision makers to generate effective emergency evacuation strategies.

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Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

Fan

Huang

Baetz

et al. 2017

Water Resources Research

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In this study, a copula-based particle filter (CopPF) approach was developed for sequential hydrological data assimilation by considering parameter correlation structures. In CopPF, multivariate copulas are proposed to reflect parameter interdependence before the resampling procedure with new particles then being sampled from the obtained copulas. Such a process can overcome both particle degeneration and sample impoverishment. The applicability of CopPF is illustrated with three case studies using a twoparameter simplified model and two conceptual hydrologic models. The results for the simplified model indicate that model parameters are highly correlated in the data assimilation process, suggesting a demand for full description of their dependence structure. Synthetic experiments on hydrologic data assimilation indicate that CopPF can rejuvenate particle evolution in large spaces and thus achieve good performances with low sample size scenarios. The applicability of CopPF is further illustrated through two real-case studies. It is shown that, compared with traditional particle filter (PF) and particle Markov chain Monte Carlo (PMCMC) approaches, the proposed method can provide more accurate results for both deterministic and probabilistic prediction with a sample size of 100. Furthermore, the sample size would not significantly influence the performance of CopPF. Also, the copula resampling approach dominates parameter evolution in CopPF, with more than 50% of particles sampled by copulas in most sample size scenarios. However, the EnKF can achieve good performances with small ensembles; but related studies have claimed Key Points: A copula-based particle filter approach is developed for hydrological data assimilation Synthetic experiments and real-case studies demonstrate performance of the CopPF method Results suggest that CopPF provides greater opportunities to access new samples which in turn lead to more accurate predictions

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Bivariate hydrologic risk analysis based on a coupled entropy-copula method for the Xiangxi River in the Three Gorges Reservoir area, China

Cited by 49 publications

References 64 publications

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty

Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

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