Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Kong, Xiangming; Zeng, Xin; Chen, Cong; Fan, Yurui; Huang, Guohe; Li, Y. P.; Wang, Chunxiao

doi:10.3390/w11010042

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Bayesian networks are probabilistic models that describe the conditional dependencies of a set of random variables by means of directed acyclic graphs (DAG) [45]. The joint density for Bayesian networks can be expressed as follows: [46][47][48]…”

Section: Establishment Of Bayesian Networkmentioning

confidence: 99%

“…The future flood control standard of the basin is more than 100 years, implying limited observed data. Therefore, instead of expert experience, the copula functions (machine learning method) were used to calculate the occurrence probabilities of network nodes [46]. This modeling work not only takes advantage of expertise but also avoids the limited knowledge of experts.…”

Section: Establishment Of Bayesian Networkmentioning

confidence: 99%

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Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

Luo

Dong

Guan

et al. 2019

Water

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We propose a flood risk management model for the Taihu Basin, China, that considers the spatial and temporal differences of flood risk caused by the different climatic phenomena. In terms of time, the probability distribution of climatic phenomenon occurrence time was used to divide the flood season into plum rain and the typhoon periods. In terms of space, the Taihu Basin was divided into different sub-regions by the Copula functions. Finally, we constructed a flood risk management model using the Copula-based Bayesian network to analyze the flood risk. The results showed the plum rain period occurs from June 24 to July 21 and the typhoon period from July 22 to September 22. Considering the joint distribution of sub-region precipitation and the water level of Taihu Lake, we divided the Taihu Basin into three sub-regions (P-I, P-II, and P-III) for risk analysis in the plum rain period. However, the Taihu Basin was used as a whole for flood risk analysis in the typhoon period. Risk analysis indicated a probability of 2.4%, and 0.8%, respectively, for future adverse drainage during the plum rain period and the typhoon period, the flood risk increases rapidly with the rising water level in the Taihu Lake.

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Section: Establishment Of Bayesian Networkmentioning

confidence: 99%

Section: Establishment Of Bayesian Networkmentioning

confidence: 99%

Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

Luo

Dong

Guan

et al. 2019

Water

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show abstract

“…Examples include sea-level rise and fluvial flood (Moftakhari et al, 2017), drought and heat waves (Sun et al, 2019), and soil moisture and precipitation (AghaKouchak, 2015). Moreover, even one specific hydrological extreme may have multiple attributes, such as the peak and volume for a flood, duration and severity for a drought, and duration and intensity of a storm (Karmakar and Simonovic, 2009;Kong et al, 2019). Traditional univariate approaches, mainly focusing on one variable or one attribute of hydrological extremes (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Also, these kinds of methods are easily implemented since the marginal distributions and dependence models can be estimated in separate processes which also give flexibility in the selection of both marginal and dependence models. A large amount of research has been developed for multivariate hydrologic simulation through copula functions, such as multivariate flood frequency analysis (Sraj et al, 2014;Xu et al, 2016;Fan et al, 2018Fan et al, , 2020; drought assessments (Song and Singh, 2010; Kao and Govindaraju, 2010;Ma et al, 2013); storm or rainfall dependence analysis (Zhang and Singh, 2007;Vandenberghe et al, 2010); streamflow simulation (Lee and Salas, 2011;Kong et al, 2015); and other water and environmental engineering applications (Fan et al, 2017;.…”

Section: Introductionmentioning

confidence: 99%

An uncertainty partition approach for inferring interactive hydrologic risks

Fan

Huang

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Extensive uncertainties exist in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. In this study, an iterative factorial copula (IFC) approach is proposed to quantify parameter uncertainties and further reveal their contributions to predictive uncertainties in risk inferences. Specifically, an iterative factorial analysis (IFA) approach is developed to diminish the effect of the sample size and provide reliable characterization for parameters' contributions to the resulting risk inferences. The proposed approach is applied to multivariate flood risk inference for the Wei River basin to demonstrate the applicability of IFC for tracking the major contributors to resulting uncertainty in a multivariate risk analysis framework. In detail, the multivariate risk model associated with flood peak and volume will be established and further introduced into the proposed iterative factorial analysis framework to reveal the individual and interactive effects of parameter uncertainties on the predictive uncertainties in the resulting risk inferences. The results suggest that uncertainties in risk inferences would mainly be attributed to some parameters of the marginal distributions, while the parameter of the dependence structure (i.e. copula function) would not produce noticeable effects. Moreover, compared with traditional factorial analysis (FA), the proposed IFA approach would produce a more reliable visualization for parameters' impacts on risk inferences, while the traditional FA would remarkably overestimate the contribution of parameters' interaction to the failure probability in AND (i.e. all variables would exceed the corresponding thresholds) and at the same time underestimate the contribution of parameters' interaction to the failure probabilities in OR (i.e. one variable would exceed its corresponding threshold) and Kendall (i.e. the correlated variables would exceed a critical multivariate threshold).

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“…Examples include sea level rise and fluvial flood (Moftakhari et al, 2017), drought and heat waves (Sun et al, 2019), soil moisture and precipitation (AghaKouchak, 2015). Moreover, even one specific hydrological extreme may have multiple attributes, such as the peak and volume for a flood, duration and severity for a drought, and duration and intensity of a storm (Karmakar and Simonovic, 2009;Kong et al, 2019). Traditional univariate approaches, mainly focusing on one variable or one attribute of hydrological extremes (e.g.…”

Section: Introductionmentioning

confidence: 99%

An Uncertainty Partition Approach for Inferring Interactive Hydrologic Risks

Fan¹,

Huang²,

Huang³

et al. 2019

Preprint

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Abstract. Extensive uncertainties exist in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. In this study, a full-subsampling factorial copula (FSFC) approach is proposed to quantify parameter uncertainties and further reveal their contributions to predictive uncertainties in risk inferences. Specifically, a full-subsampling factorial analysis (FSFA) approach is developed to diminish the effect of the sample size and provide reliable characterization for parameters’ contributions to the resulting risk inferences. The proposed approach is applied to multivariate flood risk inference for Wei River basin to demonstrate the applicability of FSFC for tracking the major contributors to resulting uncertainty in a multivariate risk analysis framework. In detail, the multivariate risk model associated with flood peak and volume will be established and further introduced into the proposed full-subsampling factorial analysis framework to reveal the individual and interactive effects of parameter uncertainties on the predictive uncertainties in the resulting risk inferences. The results suggest that uncertainties in risk inferences would mainly be attributed to some parameters of the marginal distributions while the parameter of dependence structure (i.e. copula function) would not produce noticeable effects. Moreover, compared with traditional factorial analysis (FA), the proposed FSFA approach would produce more reliable visualization for parameters' impacts on risk inferences, while the traditional FA would remarkable overestimate contribution of parameters' interaction to the failure probability in AND, and at the same time, underestimate the contribution of parameters' interaction to the failure probabilities in OR and Kendall.

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Development of a Maximum Entropy-Archimedean Copula-Based Bayesian Network Method for Streamflow Frequency Analysis—A Case Study of the Kaidu River Basin, China

Cited by 15 publications

References 38 publications

Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

An uncertainty partition approach for inferring interactive hydrologic risks

An Uncertainty Partition Approach for Inferring Interactive Hydrologic Risks

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