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

Sumiya Uranchimeg,Byungsik Kim,Hyun-Han Kwon,Tae-Woong Kim

doi:10.2166/nh.2020.003

Sumiya Uranchimeg, Byungsik Kim + Show 2 more

Open Access

https://doi.org/10.2166/nh.2020.003

Copy DOI

Abstract

Abstract 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.

Highlights

It has been reported that precipitation changes associated with a climate change can differ spatially and temporally for a region due to the geographic distribution of rain, which is largely driven by climate and topography (Schmidli et al ; Pall et al ; Giorgi et al ; Trenberth ; So et al ; Donat et al )
This study proposed a Bayesian multiple non-crossing quantile regression (BMN-QR) model to assess nonstationarity by exploring distributional changes in the annual daily maximum rainfalls (ADMRs) over the period of 1973–2015 in South Korea
The distributional changes were categorized into a number of classes in which nonstationarity was largely defined by changes in location and scale parameters of the probability distribution

Summary

Introduction

It has been reported that precipitation changes associated with a climate change can differ spatially and temporally for a region due to the geographic distribution of rain, which is largely driven by climate and topography (Schmidli et al ; Pall et al ; Giorgi et al ; Trenberth ; So et al ; Donat et al ). Changes in rainfall distribution affect current operations and future design criteria for water resource systems; it might be necessary to explore rainfall trends. In this context, statistical modeling of extreme rainfall trends (or nonstationarity) has been extensively conducted. Various approaches such as the Mann–Kendall (MK) test (Mann ; Kendall ; Gilbert ), Kwiatkowski– Phillips–Schmidt–Shin (KPSS) test (Kwiatkowski et al ), and Augmented Dickey–Fuller (ADF) test (Dickey & Fuller ) have been extensively used to confirm stationarity These methods have certain limitations as they are both sensitive to outliers and serial correlations (i.e., persistence) within time series that can lead to inflated estimates of temporal trends. A traditional approach for the trend analysis is not appropriate for extreme rainfall events (Muhlbauer et al ; Timofeev & Sterin ) as noticeable distribution changes in the extremes and the hydro-meteorological variables are often undetectable in traditional approaches (Shiau & Huang )

Objectives

Results

Discussion

Conclusion