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http://dx.doi.org/10.3741/JKWRA.2018.51.3.263

Statistical significance test of polynomial regression equation for Huff's quartile method of design rainfall  

Park, Jinhee (Agricultural Policy Department, Gumi City Hall)
Lee, Jaejoon (Department of Civil Engineering, Kumoh National Institute of Technology)
Lee, Sungho (Department of Civil Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of Korea Water Resources Association / v.51, no.3, 2018 , pp. 263-272 More about this Journal
Abstract
For the design of hydraulic structures, the design flood discharge corresponding to a specific frequency is generally used by using the design storm calculated according to the rainfall-runoff relationship. In the past, empirical equations such as rational equations were used to calculate the peak flow rate. However, as the duration of rainfall is prolonged, the outflow patterns are different from the actual events, so the accuracy of the temporal distribution of the probability rainfall becomes important. In the present work, Huff's quartile method is used for the temporal distribution of rainfall, and the third quartile is generally used. The regression equation for Huff's quadratic curve applies a sixth order polynomial equation because of its high accuracy throughout the duration of rainfall. However, in statistical modeling, the regression equation needs to be concise in accordance with the principle of simplicity, and it is necessary to determine the regression coefficient based on the statistical significance level. Therefore, in this study, the statistical significance test for regression equation for temporal distribution of the Huff's quartile method, which is used as the temporal distribution method of design rainfall, is conducted for 69 rainfall observation stations under the jurisdiction of the Korea Meteorological Administration. It is statistically significant that the regression equation of the Huff's quartile method can be considered only up to the 4th order polynomial equation, as the regression coefficient is significant in most of the 69 rainfall observation stations.
Keywords
Design rainfall; Temporal distribution; Regression equation; Statistical significance test;
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