Exploration of Daily Rainfall Intensity Change in South Korea 1900–2010 Using Bias-Corrected ERA-20C

Dong-Ik Kim, Hyun-Han Kwon, Dawei Han, Yong-Tak Kim

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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
Original languageEnglish
Number of pages17
JournalASCE Journal of Hydrologic Engineering
Early online date27 Apr 2020
Publication statusE-pub ahead of print - 27 Apr 2020

Structured keywords

  • Water and Environmental Engineering


  • Composite distribution
  • ERA-20C
  • Precipitation
  • Markov chain Monte Carlo (MCMC)
  • Quantile delta mapping
  • Uncertainty


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