Selection of variables for regional frequency analysis of annual maximum precipitation using multivariate techniques

The regional frequency analysis is useful to estimate more accurate precipitation quantiles than the at-site frequency analysis, especially in case of regions with short record length like South Korea. In this study, the regionalization of annual maximum precipitation in South Korea was considered. The identification of homogeneous regions has a significant effect on quantile estimation in the regional frequency analysis. Various variables related to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques such as principal component analysis, factor analysis, and Procrustes analysis were used for this purpose. Finally, 33 variables were selected from the 42 candidate variables using multivariate techniques. A big loss of information due to dimension reduction was not found. Therefore, dimension reduction can promote the efficiency of cluster analysis. The selected variables can be successfully used to form regions for regional frequency analysis of annual maximum precipitation in South Korea.