Abstract
A rainfall event, simplified by a rectangular pulse, is defined by three components: the rainfall duration, the total rainfall depth, and mean rainfall intensity. However, as the mean rainfall intensity can be calculated by the total rainfall depth divided by the rainfall duration, any two components can fully define the rainfall event (i.e., one component must be redundant). The frequency analysis of a rainfall event also considers just two components selected rather arbitrarily out of these three components. However, this study argues that the two components should be selected properly or the result of frequency analysis can be significantly biased. This study fully discusses this selection problem with the annual maximum rainfall events from Seoul, Korea. In fact, this issue is closely related with the multicollinearity in the multivariate regression analysis, which indicates that as interdependency among variables grows the variance of the regression coefficient also increases to result in the low quality of resulting estimate. The findings of this study are summarized as follows: (1) The results of frequency analysis are totally different according to the selected two variables out of three. (2) Among three results, the result considering the total rainfall depth and the mean rainfall intensity is found to be the most reasonable. (3) This result is fully supported by the multicollinearity issue among the correlated variables. The rainfall duration should be excluded in the frequency analysis of a rainfall event as its variance inflation factor is very high.
Highlights
The bivariate frequency analysis (BFA) is used for the quantification of the probabilistic characteristic of two correlated variables
The BFA was conducted with the optimal copula model that was selected for each bivariate data of the annual maximum rainfall events
A rainfall event simplified by a rectangular pulse is defined by three components: the rainfall duration, thesimplified total rainfall and mean rainfall intensity.by
Summary
The bivariate frequency analysis (BFA) is used for the quantification of the probabilistic characteristic of two correlated variables. In the case of the rainfall event, two variables among several characteristics, such as the mean rainfall intensity, rainfall duration, total rainfall depth, and maximum rainfall intensity, are selected to perform the analysis [4,5]. Iran and conducted drought derived bivariate distribution of rainfall variables using copula and tested the applicability on the data frequency analysis. This study considered various result, the hours of was effective to separate independent rainfall event. IETD was separating considered the in rainfall event, a threshold value was applied to distinguish the rain/no rain condition. In the rectangular pulse model, a rainfall event is quantified by three components; therainfall mean rainfall intensity be calculated bymean the total rainfall depthHere, divided therainfall rainfall the duration, the totalcan rainfall depth and rainfall intensity.
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