EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Extreme rainfall quantile estimation based on SSP scenarios: Focusing on the Hangang river basin

Sunghun Kim1, Heechul Kim2, Gyobeom Kim3, and Jun-Haeng Heo2
Sunghun Kim et al.
  • 1Institute of Engineering Research, Yonsei University, Seoul, Republic of Korea
  • 2School of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea
  • 3Division of Resources and Energy Assessment, Korea Environment Institute, Sejong, Republic of Korea

This study attempts to estimate the extreme rainfall quantile using the climate model data of the Shared Socioeconomic Pathways (SSP) scenarios presented in the sixth Assessment Report (AR6), published by the Intergovernmental Panel on Climate Change (IPCC). Generally, the applied research related to climate change is conducted using numerical simulation data from various climate models. Generally, an ensemble scenario based on various regional climate models is used as a way to reduce the uncertainty from one climate model. In this study, the ensemble rainfall data (based on HadGEM3-RA, WRF, CCLM, GRIMs, and RegCM4) were obtained from the climate information portal (CIP, The observed rainfall data was extracted and the regional quantile delta mapping (RQDM) method was applied for bias correction. Regional frequency analysis (RFA) was performed to estimate the rainfall quantile. In addition, the generalized extreme value (GEV) distribution was applied as an appropriate probability distribution and the L-moments method was used for parameter estimation. As a result, the rainfall quantiles were estimated, and the effects of climate change were analyzed quantitatively in the study area.

How to cite: Kim, S., Kim, H., Kim, G., and Heo, J.-H.: Extreme rainfall quantile estimation based on SSP scenarios: Focusing on the Hangang river basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10822,, 2022.


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