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http://dx.doi.org/10.3741/JKWRA.2012.45.8.827

Bivariate Frequency Analysis of Rainfall using Copula Model  

Joo, Kyung-Won (School of Civil and Environmental Engineering, Yonsei University)
Shin, Ju-Young (Engineering Institute, Yonsei University)
Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Journal of Korea Water Resources Association / v.45, no.8, 2012 , pp. 827-837 More about this Journal
Abstract
The estimation of the rainfall quantile is of great importance in designing hydrologic structures. Conventionally, the rainfall quantile is estimated by univariate frequency analysis with an appropriate probability distribution. There is a limitation in which duration of rainfall is restrictive. To overcome this limitation, bivariate frequency analysis by using 3 copula models is performed in this study. Annual maximum rainfall events in 5 stations are used for frequency analysis and rainfall depth and duration are used as random variables. Gumbel (GUM), generalized logistic (GLO) distributions are applied for rainfall depth and generalized extreme value (GEV), GUM, GLO distributions are applied for rainfall duration. Copula models used in this study are Frank, Joe, and Gumbel-Hougaard models. Maximum pseudo-likelihood estimation method is used to estimate the parameter of copula, and the method of probability weighted moments is used to estimate the parameters of marginal distributions. Rainfall quantile from this procedure is compared with various marginal distributions and copula models. As a result, in change of marginal distribution, distribution of duration does not significantly affect on rainfall quantile. There are slight differences depending on the distribution of rainfall depth. In the case which the marginal distribution of rainfall depth is GUM, there is more significantly increasing along the return period than GLO. Comparing with rainfall quantiles from each copula model, Joe and Gumbel-Hougaard models show similar trend while Frank model shows rapidly increasing trend with increment of return period.
Keywords
copula; bivariate frequency analysis; rainfall quantile;
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Times Cited By KSCI : 8  (Citation Analysis)
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