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http://dx.doi.org/10.3741/JKWRA.2010.43.4.337

Analysis of Uncertainty of Rainfall Frequency Analysis Including Extreme Rainfall Events  

Kim, Sang-Ug (National Assembly Research Service)
Lee, Kil-Seong (Department of Civil and Environmental Engineering, Seoul National University)
Park, Young-Jin (Department of Civil Engineering, Seoil College)
Publication Information
Journal of Korea Water Resources Association / v.43, no.4, 2010 , pp. 337-351 More about this Journal
Abstract
There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.
Keywords
uncertainty; extreme rainfall event; bayesian MCMC; MLE;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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