BIVARIATE FREQUENCY ANALYSIS OF FLOODS USING COPULAS1

Authors

  • Jenq-Tzong Shiau,

    1. Respectively, Associate Professor, Department of Water Resources and Environmental Engineering, Tamkang University, 151 Ying-chuan Rd., Tarnsui 251, Taiwan, ROC; Doctoral Candidate and Professor, Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, 1 Ta-hsueh Rd., Tainan 701, Taiwan, ROC (E-Mail/Shiau: jtshiau@mail.tku.edu.tw)
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  • Hsin-Yi Wang,

    1. Respectively, Associate Professor, Department of Water Resources and Environmental Engineering, Tamkang University, 151 Ying-chuan Rd., Tarnsui 251, Taiwan, ROC; Doctoral Candidate and Professor, Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, 1 Ta-hsueh Rd., Tainan 701, Taiwan, ROC (E-Mail/Shiau: jtshiau@mail.tku.edu.tw)
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  • Chang-Tai Tsai

    1. Respectively, Associate Professor, Department of Water Resources and Environmental Engineering, Tamkang University, 151 Ying-chuan Rd., Tarnsui 251, Taiwan, ROC; Doctoral Candidate and Professor, Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, 1 Ta-hsueh Rd., Tainan 701, Taiwan, ROC (E-Mail/Shiau: jtshiau@mail.tku.edu.tw)
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  • 1

    Paper No. 05145 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open until June 1, 2007.

Abstract

Abstract: Bivariate flood frequency analysis offers improved understanding of the complex flood process and useful information in preparing flood mitigation measures. However, difficulties arise from limited bivariate distribution functions available to jointly model the correlated flood peak and volume that have different univariate marginal distributions. Copulas are functions that link univariate distribution functions to form bivariate distribution functions, which can overcome such difficulties. The objective of this study was to analyze bivariate frequency of flood peak and volume using copulas. Separate univariate distributions of flood peak and volume are first fitted from observed data. Copulas are then employed to model the dependence between flood peak and volume and join the predetermined univariate marginal distributions to construct the bivariate distribution. The bivariate probabilities and associated return periods are calculated in terms of univariate marginal distributions and copulas. The advantage of using copulas is that they can separate the effect of dependence from the effects of the marginal distributions. In addition, explicit relationships between joint and univariate return periods are made possible when copulas are employed to construct bivariate distribution of floods. The annual floods of Tongtou flow gauge station in the Jhuoshuei River, Taiwan, are used to illustrate bivariate flood frequency analysis.

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