Imprecise probabilistic estimation of design floods with epistemic uncertainties

Qi, Wei; Zhang, Chi; Fu, Guangtao; Zhou, Huicheng

doi:10.1002/2015wr017663

Cited by 30 publications

(20 citation statements)

References 85 publications

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“…Qi et al (2016) employed a subsampling ANOVA approach (Bosshard et al, 2013) to quantify individual and interactive impacts of the uncertainties in data, probability distribution functions, and probability distribution parameters on the total cost for flood control in terms of flood peak flows. Even though the subsampling ANOVA approach is able to reduce the effect of the biased estimator on quantification of variance contribution resulting from the traditional ANOVA approach, it should be noticed that merely subsampling one uncertainty parameter/factor (referred to as single-subsampling ANOVA), as used in the studies by Bosshard et al (2013) and Qi et al (2016a), will lead to (i) an underestimation of the individual contribution for the factor to be sampled and (ii) overestimation of contributions from those non-sampled factors. Moreover, few studies have been reported to characterize the individual and interactive effects of parameter uncertainties in marginal and dependence models on the multivariate risk inferences.…”

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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Section: Introductionmentioning

confidence: 99%

An uncertainty partition approach for inferring interactive hydrologic risks

Fan

Huang

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Qi et al (2016) employed a subsampling ANOVA approach (Bosshard et al, 2013), to quantify individual and interactive impacts of the uncertainties in data, probability distribution functions, and probability distribution parameters on the total cost for flood control in terms of flood peak flows. Even though the subsampling ANOVA approach is able to reduce the effect of the biased estimator on quantification of variance contribution resulting from traditional ANOVA approach, it would be noticed that merely subsampling one uncertainty parameter/factor (referred as single-subsampling ANOVA), as used in the studies by Bosshard et al (2013) and Qi et al (2016a), will lead to underestimation of individual contribution for the factor to be sampled and overestimation of contributions for those non-sampled factors. Moreover, few studies have been reported to characterize the individual and interactive effects of parameter https://doi.org/10.5194/hess-2019-434 Preprint.…”

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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“…Uncertainty analysis allows the identification of uncertain parameters (Tung & Yen, 2005), a quantitative assessment of model reliability (Merz & Thieken, 2005;Montanari & Koutsoyiannis, 2012;Tung & Yen, 2005), and it provides a means of analyzing the robustness of flood risk management decisions. Furthermore, an analysis of the contribution of individual sources indicates where potential improvements in the method could have the greatest impact (Cullen & Frey, 1999;Hall & Solomatine, 2008;Sikorska et al, 2012) and therefore how uncertainty could be reduced (Qi et al, 2016), which is especially important for ungauged catchments (Sikorska et al, 2012). Although, uncertainty cannot be eliminated, its assessment at least enables its management (Koutsoyiannis, 2014).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

Brunner

Sikorska

Furrer

et al. 2018

Water Resources Research

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Design hydrographs described by peak discharge, hydrograph volume, and hydrograph shape are essential for engineering tasks involving storage. Such design hydrographs are inherently uncertain as are classical flood estimates focusing on peak discharge only. Various sources of uncertainty contribute to the total uncertainty of synthetic design hydrographs for gauged and ungauged catchments. These comprise model uncertainties, sampling uncertainty, and uncertainty due to the choice of a regionalization method. A quantification of the uncertainties associated with flood estimates is essential for reliable decision making and allows for the identification of important uncertainty sources. We therefore propose an uncertainty assessment framework for the quantification of the uncertainty associated with synthetic design hydrographs. The framework is based on bootstrap simulations and consists of three levels of complexity. On the first level, we assess the uncertainty due to individual uncertainty sources. On the second level, we quantify the total uncertainty of design hydrographs for gauged catchments and the total uncertainty of regionalizing them to ungauged catchments but independently from the construction uncertainty. On the third level, we assess the coupled uncertainty of synthetic design hydrographs in ungauged catchments, jointly considering construction and regionalization uncertainty. We find that the most important sources of uncertainty in design hydrograph construction are the record length and the choice of the flood sampling strategy. The total uncertainty of design hydrographs in ungauged catchments depends on the catchment properties and is not negligible in our case.

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Imprecise probabilistic estimation of design floods with epistemic uncertainties

Cited by 30 publications

References 85 publications

An uncertainty partition approach for inferring interactive hydrologic risks

An uncertainty partition approach for inferring interactive hydrologic risks

An Uncertainty Partition Approach for Inferring Interactive Hydrologic Risks

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

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