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Hierarchical Bayesian Model Based Nonstationary Frequency Analysis for Extreme Sea Level
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 Title & Authors
Hierarchical Bayesian Model Based Nonstationary Frequency Analysis for Extreme Sea Level
Kim, Yong-Tak; Uranchimeg, Sumiya; Kwon, Hyun-Han; Hwang, Kyu Nam;
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Urban development and population increases are continuously progressed in the coastal areas in Korea, thus it is expected that vulnerability towards coastal disasters by sea level rise (SLR) would be accelerated. This study investigated trend of the sea level data using Mann-Kendall (MK) test, and the results showed that the increasing trends of annual average sea level at 17 locations were statistically significant. For annual maximum extremes, seven locations exhibited statistically significant trends. In this study, non-stationary frequency analysis for the annual extreme data together with average sea level data as a covariate was performed. Non-stationary frequency analysis results showed that sea level at the coastal areas of Korean Peninsula would be increased from a minimum of 60.33 mm to a maximum of 214.90 mm by 2100.
nonstationary frequency analysis;coastal disaster;annual maximum tide;annual average tide;sea level rise;
 Cited by
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