Exploration of Daily Rainfall Intensity Change in South Korea 1900–2010 Using Bias-Corrected ERA-20C
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 7
Abstract
Rainfall frequency analysis has been routinely adopted for the estimation of design rainfall for a specific return period. Annual maximum rainfall data are generally used for frequency analysis in practice, but the parameters of the probability distribution are estimated from the limited data that are often available back to the 1970s in many regions, including South Korea. As an alternative, this study aims to utilize century-long the ECMWF twentieth century reanalysis (ERA-20C) daily precipitation data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). To reduce the systematic errors in the reanalysis data, a quantile delta mapping method using a composite gamma-Pareto distribution (QDM-GP) is introduced that can better represent temporal trends and extreme events compared with stationary quantile mapping (SQM). In addition, the degree of uncertainty reduction in the estimation of design rainfall is evaluated using bias-corrected ERA-20C within a Bayesian modeling framework. Finally, the bias-corrected data are applied to explore the spatiotemporal change in design rainfall in South Korea in the 20th century. To investigate changes in design rainfall under the nonstationary assumption, this study estimates the design rainfall using data from three different periods (1900–1936, 1937–1973, and 1974–2010). It is found that QDM can substantially reduce the bias in annual maximum rainfall (AMR). The uncertainty ranges of the design rainfall using the bias-corrected ERA-20C reanalysis data are generally within the design rainfalls in the observed, suggesting that the use of bias-corrected reanalysis data can reduce uncertainties in design rainfall by increasing the sample size. Furthermore, this study explores the role of bias-corrected rainfall for uncertainty reduction in design rainfall via three different experiments in the context of prior information within a Bayesian framework. In the experimental study, it is concluded that the uncertainty reduction in design rainfall can be mainly attributed to the use of a prior distribution for the shape parameter, informed by long-term reanalysis data. Moreover, a significant spatiotemporal change in design rainfall is observed over all of South Korea. The significant change in design rainfall is mainly attributed to the recent increase in rainfall intensity, leading to a potential increase in flood risk in most areas.
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Data Availability Statement
The rainfall data and the ERA-20C reanalysis data used in the study are available from the KMA website (https://data.kma.go.kr) and the ECMWF ERA-20C website (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c).
Acknowledgments
The first author is funded by the Government of South Korea for performing his doctoral studies at the University of Bristol. The authors are grateful for the relevant data provided by KMA and ECMWF. This study was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Advanced Water Management Research Program, funded by the Korea Ministry of the Environment (Grant No. 83073). The work was supported by Korea Hydro & Nuclear Power Co. (No. H18S023000).
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Journal of Hydrologic Engineering
Volume 25 • Issue 7 • July 2020
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©2020 American Society of Civil Engineers.
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Received: Dec 17, 2018
Accepted: Dec 30, 2019
Published online: Apr 27, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 27, 2020
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