|Year of Publication||2015||Division||Climate Research Division|
|Title||Assessing changes in observed and future projected precipitation extremes in South Korea|
|Coauthor||Y.-S. Lee, J.-S. Park, M.-K. Kim, C.-H. Cho|
|ISBN(ISSN)||0899-8418||Name of Journal||Internation Journal of Climatology|
|Category (International/Domestic)||SCI||Vol. No.||35|
|Research Project Title||±âÈÄº¯È ¿¹Ãø±â¼ú Áö¿ø ¹× È°¿ë¿¬±¸ (2015³â)||Publication Date||2015-05-12|
|Keywords||climatic change; four-parameter kappa distribution; generalized extreme value distribution; historical data; L-moment estimation; regional climate model; regional frequency analysis; return level; ret|
Attempts to assess the changes between the observed (or historical) and future projected daily rainfall extremes for 59 stations throughout Korea have been made with descriptive statistics and extreme value analysis. For the comparison, three different periods and four different data sets are considered: observation and historical data from 1976 to 2005 (period 0), simulation from 2021 to 2050 (period 1) and from 2066 to 2095 (period 2). The historical and projected rainfalls are obtained from RCP 4.5 and RCP 8.5 scenarios, which are based on a regional climate model HadGEM3-RA. For the comparison of extreme values, the 20- and 50-year return levels and the return period estimates are obtained by using the best one between two extreme value distributions, the method of L-moments and the regional frequency analysis. From the descriptive statistics, we find that the numbers of heavy rainfall events will increase in the future. The total precipitation is projected to remain unchanged or slightly increased, compared to the observation. From the extreme value analysis, we realize that a 1-in-20 year and a 1-in-50 year annualmaximum daily precipitation will likely become a 1-in-10 year and a 1-in-16 year event, respectively, when compared to the observation (a 1-in-5 year and a 1-in-7 year event, compared to the historical data), by the end of the 21st century. But this finding is based on only one simulation model, which confines the confidence of the result and suggests an ensemble approach based on multiple models to get more reliable result.