Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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An Hourly Extreme Data Estimation Method Developed Using Nonstationary Bayesian Beta Distribution

비정상성 Bayesian Beta 분포를 이용한 시 단위 극치자료 추정기법 개발

  • 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)
  • Received : 2017.01.25
  • Accepted : 2017.04.12
  • Published : 2017.05.30
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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.

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References

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