Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach

• Main Authors: Sumiya Uranchimeg, Hyun-Han Kwon, Byungsik Kim, Tae-Woong Kim
• Format: Article
• Language: English
• Published: IWA Publishing 2020-08-01
• Series: Hydrology Research
• Subjects: bayesian quantile regression; design rainfall; distribution; extreme rainfall; nonstationarity; uncertainty
• Online Access: http://hr.iwaponline.com/content/51/4/699

This study aims to explore possible distributional changes in annual daily maximum rainfalls (ADMRs) over South Korea using a Bayesian multiple non-crossing quantile regression model. The distributional changes in the ADMRs are grouped into nine categories, focusing on changes in the location and scale parameters of the probability distribution. We identified seven categories for a distributional change in the selected stations. Most of the stations (28 of 50) are classified as Category III, which is characterized by an upward trend with an increase in variance in the distribution. Moreover, stations with a downward trend with a decrease in the variance pattern (Category VII) are mainly distributed on the southern Korean coast. On the other hand, Category I stations are mostly located in eastern Korea and primarily show a statistically significant upward trend with a decrease in variance. Moreover, this study explored changes in design rainfall estimates for different categories in terms of distributional changes. For Categories I, II, III, and VI, a noticeable increase in design rainfall was observed, while Categories IV, V, and VII showed no evidence of association with risk of increased extreme rainfall.