Bayesian Approach for Estimating the Distribution of Annual Maximum Floods with a Mixture Model
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 6
Abstract
Annual flood peaks frequently stem from distinct flood-producing mechanisms. However, most inference procedures rely on a single distributional model, which can lead to ill-posed inferences of its upper-tail behavior. For addressing this problem, this paper explores a Bayesian mixture model, which combines the Gamma and generalized Pareto distributions under the concept of penalized complexity prior distribution (PCPD) for the tail index, for modeling annual flood peaks. The proposed approach was applied in two catchments in the western US, in which empirical evidence of mixed population exists and historical and paleoflood information is available for validation purposes. The results suggested that despite the increased complexity of the mixture model, describing flood events with distinct distributions was beneficial for the goodness of fit and for extrapolating to large return periods. In addition, the PCPD proved effective in constraining the tail index inference because narrower credible intervals compared with well-established models were obtained for most flood quantiles. Overall, the proposed approach seems feasible for reconciling distinct flood-generating mechanisms and reliability in statistical estimation in flood frequency analysis.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge the support to this research from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG). The authors also wish to acknowledge the anonymous reviewers and editors for the valuable comments and suggestions, which greatly helped improve the paper.
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Journal of Hydrologic Engineering
Volume 26 • Issue 6 • June 2021
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© 2021 American Society of Civil Engineers.
History
Received: Aug 19, 2020
Accepted: Jan 29, 2021
Published online: Mar 24, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 24, 2021
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