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http://dx.doi.org/10.3741/JKWRA.2009.42.6.465

Orographic Precipitation Analysis with Regional Frequency Analysis and Multiple Linear Regression  

Yun, Hye-Seon (Dept. of Civil Eng., Yonsei Univ., Saman Corporation)
Um, Myoung-Jin (Dept. of Civil Eng., Yonsei Univ.)
Cho, Won-Cheol (School of Civil and Environmental Eng., Yonsei Univ.)
Heo, Jun-Haeng (School of Civil and Environmental Eng., Yonsei Univ.)
Publication Information
Journal of Korea Water Resources Association / v.42, no.6, 2009 , pp. 465-480 More about this Journal
Abstract
In this study, single and multiple linear regression model were used to derive the relationship between precipitation and altitude, latitude and longitude in Jejudo. The single linear regression analysis was focused on whether orographic effect was existed in Jejudo by annual average precipitation, and the multiple linear regression analysis on whether orographic effect was applied to each duration and return period of quantile from regional frequency analysis by index flood method. As results of the regression analysis, it shows the relationship between altitude and precipitation strongly form a linear relationship as the length of duration and return period increase. The multiple linear regression precipitation estimates(which used altitude, latitude, and longitude information) were found to be more reasonable than estimates obtained using altitude only or altitude-latitude and altitude-longitude. Especially, as results of spatial distribution analysis by kriging method using GIS, it also provides realistic estimates for precipitation that the precipitation was occurred the southeast region as real climate of Jejudo. However, the accuracy of regression model was decrease which derived a short duration of precipitation or estimated high region precipitation even had long duration. Consequently the other factor caused orographic effect would be needed to estimate precipitation to improve accuracy.
Keywords
orographic effect; annual average precipitation; quantile; index flood method; multiple linear regression;
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Times Cited By KSCI : 2  (Citation Analysis)
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