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Spatial characteristics of future precipitation distribution in Korea – Bias corrected by a Quantile Mapping Method

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  131620986,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: @2013
Authors:   M. S. Kang, Inhong Song, J. H. Park, S. M. Kim, M. W. Jang
Keywords:   Spatial distribution Quantile mapping method RCP scenario Future precipitation

Abstract. The global warming is an ongoing process and subsequently meteorological anomalies tend to occur more frequently. It is important to understand spatial characteristics of future precipitation at a local level for the vulnerability evaluation. The objective of this study was to investigate spatial characteristics of future precipitation at a local scale on the basis of the RCP based climate predictions in Korea. The future precipitation data that the Korean Meteorological Administration predicted provided was used in this study. However, the future precipitation data are known to be biased that annual precipitation amounts as well as the occurrence of extreme rainfall events tend to be underestimated. To correct this bias, the Quantile Mapping method was applied to the predictions using the distribution of the historical rainfall data. The Generalized Extreme Value (GEV) distribution, which is commonly used for rainfall analyses, was chosen as the probability distribution of the precipitation data. The precipitation data of spatial resolution of 12.5 km were averaged over a local county level. The future precipitation was divided into three different time spans, which are 2011 to 2040, 2041 to 2070, and 2071 to 2100. Overall the amounts of future annual precipitation were increased by 7.0%, 10.9%, and 21.7% for the respective time periods. The occurrence of extreme rainfall events was also much improved as compared to the tendency of the historical data. It was concluded that the quantile mapping approach can be a useful tool for the bias correction of the future precipitations in amount as well as frequency.

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