Author(s): Sunghun Kim; Sung-Hack Lee; Taewon Lee; Jun-Haeng Heo

Linked Author(s): Sunghun Kim

Keywords: Climate Chantge; Shared Socioeconomic Pathways; Extreme Rainfall

Abstract: Many studies demonstrated that atmospheric greenhouse gas concentrations are increased by anthropogenic. Moreover, these changing condition affects major factors (such as temperature, precipitation, etc.) causing the current and future uncertainty of climate change. The significant impact of climate change on precipitation is directly related to not only the extreme flood but also the uncertainty of extreme rainfall quantile. For this reason, lots of studies have been conducting using simulated data from climate models to reduce or to quantify the uncertainty caused by climate change. In this study, 10 suitable global climate models (GCMs) were selected among the entire GCMs data provided by CMIP6 (Coupled Model Intercomparison Project Phase 6). And then, the extreme rainfall quantile was estimated by using the 10 GCMs data based on shared socioeconomic pathways (SSPs). The rainfall data was extracted from 60 observation sites. Furthermore, the simple quantile mapping (SQM) method was used for bias correction between the observed and simulated data. The projection data were divided into 4 periods, which are the historical (HIST, 1983–2014), near-future (S1, 2015–2044), mid-future (S2, 2041–2070), and far-future (S3, 2071–2100) periods. Finally, the rainfall quantiles from 10 GCMs were identified and compared with each other. In addition, the effects of climate change and the ranges of uncertainty were assessed quantitatively in this study.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022160

Year: 2022