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http://dx.doi.org/10.15681/KSWE.2017.33.3.256

An Hourly Extreme Data Estimation Method Developed Using Nonstationary Bayesian Beta Distribution  

Kim, Yong-Tak (Chonbuk National University-Department of Civil Engineering)
Kim, Jin-Young (Chonbuk National University-Department of Civil Engineering)
Lee, Jae Chul (Chungnam State University-Department of Civil Engineering and Informatics)
Kwon, Hyun-Han (Chonbuk National University-Department of Civil Engineering)
Publication Information
Journal of Korean Society on Water Environment / v.33, no.3, 2017 , pp. 256-272 More about this Journal
Abstract
Extreme rainfall has become more frequent over the Korean peninsula in recent years, causing serious damages. In a changing climate, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to overestimate (or underestimate) the design rainfalls. A main objective of this study is to develop a stochastic disaggregation method of seasonal rainfall to hourly extreme rainfall, and offer a way to derive the nonstationary IDF curves. In this study, we propose a novel approach based on a Four-Parameter Beta (4P-beta) distribution to estimate the nonstationary IDF curves conditioned on the observed (or simulated) seasonal rainfall, which becomes the time-varying upper bound of the 4P beta distribution. Moreover, this study employed a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters. The proposed model showed a comparable design rainfall to that of GEV distribution under the stationary assumption. As a nonstationary rainfall frequency model, the proposed model can effectively translate the seasonal variation into the sub-daily extreme rainfall.
Keywords
Bayesian; Four Parameter Beta; GEV; IDF; Nonstatioanry;
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Times Cited By KSCI : 17  (Citation Analysis)
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