| Title |
An Hourly Extreme Data Estimation Method Developed Using Nonstationary Bayesian Beta Distribution |
| Authors |
김용탁 ( Yong-tak Kim ) ; 김진영 ( Jin-young Kim ) ; 이재철 ( Jae Chul Lee ) ; 권현한 ( Hyun-han Kwon ) |
| DOI |
https://doi.org/10.15681/KSWE.2017.33.3.256 |
| Keywords |
Bayesian; Four Parameter Beta; GEV; IDF; Nonstatioanry |
| 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. |