Comparison of different quantile delta mapping schemes in frequency analysis of precipitation extremes over mainland Southeast Asia under climate change

• Main Authors: Qin, Xiaosheng, Dai, Chao
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Civil engineering; Frequency Analysis; Cordex
• Online Access: https://hdl.handle.net/10356/161779
• Institution: Nanyang Technological University

This study investigated the frequency analysis of precipitation indices of maximum daily precipitation amount (Rx1day), maximum 5-day cumulative precipitation amount (Rx5day), and maximum length of consecutive dry days (CDD) using various bias-correction schemes over mainland Southeast Asia (MSEA). The bias-correction schemes were based on quantile delta mapping (QDM) with different strategies in selecting the data series, including monthly correction (MC), annual correction (AC), peak correction using annual raw data (PCRAW), and annual correction using extreme-index data series (ACEX). Two regional climate model (RCM) outputs from CORDEX-SEA and APHRODITE rainfall datasets were used as modelled and observed data for methodology verification. The study first compared four frequency analysis methods based on generalized extreme value (GEV), Gumbel (GB), Log-Pearson Type III (LP3), and Lognormal (LOGN) distributions, at 9 selected sites over MSEA, and found that LP3 was the best choice for frequency analysis for the study region. Then, the study compared the four bias-correction schemes at both individual sites and all grids over the entire MSEA and indicated that the PCRAW was the best performer in terms of bias-correction for frequency information. Finally, the study gave an ensembled projection of future extreme indices with 200-yr return based on seven CORDEX-SEA RCMs and suggested a general increasing trend of all indices over MSEA. The study explored the effect of uncertainty originated from adopting various bias-correction schemes in mapping future frequency of precipitation indices and is valuable in revealing the spatiotemporal distribution of precipitation extremes over large areas under climate change which is important for flood/drought risk assessment and adaptation planning.