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Probabilistic Risk Assessment of Flood Disaster in South Korea Under The Impact Of Climate Change
Author(s) -
Park Jihoon,
Kang Moon Seong,
Song Inhong,
Song JungHun,
Jun Sang Min
Publication year - 2016
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.2049
Subject(s) - flood myth , climate change , environmental science , flood risk assessment , weighting , return period , 100 year flood , proxy (statistics) , flooding (psychology) , geography , climatology , physical geography , statistics , mathematics , medicine , psychology , ecology , archaeology , radiology , geology , psychotherapist , biology
Abstract The objective of this study was to develop a probabilistic method for flood‐risk assessment under climate change. The Intergovernmental Panel on Climate Change (IPCC) released the Representative Concentration Pathways (RCP) in preparation for climate change; the RCP4.5 and RCP8.5 scenarios were selected to examine future flood damage. The study area was South Korea. The spatial and temporal resolutions were on a city and county basis, and four time periods (1990s: 1976–2005, 2025s: 2011–2040, 2055s: 2041–2070 and 2085s: 2071–2100). Proxy variables were selected to assess flood risk. Indicators were standardized using the Z ‐score method and principal component analysis was conducted to calculate the weighting of indicators. Probability distribution of each indicator was fitted to analyse the flood risk. The generalized extreme value (GEV) distribution was selected as the appropriate distribution. Convolution was used to merge all probability density functions. The results show flood risk increasing during the 2025s period and continuing to rise up to the 2055s period. Especially, the increase from the 1990s to the 2025s is so large that countermeasures need to be developed. It also predicted that southern areas are at greater risk of flooding. These are expected to be used as appropriate data to establish an adaptation policy for climate change. Copyright © 2016 John Wiley & Sons, Ltd.