Regional Taiwan rainfall frequency analysis using principal component analysis, self-organizing maps and L-moments

The objective of this paper is to propose an approach, which consists of principal component analysis (PCA), self-organizing maps (SOM) and the L-moment method, for improving estimation of desired rainfall quantiles of ungauged sites. Firstly, PCA is applied to obtain the principal components. Then SOM is applied to group the rain gauges into specific clusters and the number of clusters can be objectively decided by visual inspection. Moreover, the L-moment based discordancy and heterogeneity are used to test whether clusters may be acceptable as being homogeneous. After the gauges are grouped into specific clusters, the homogeneous regions are then delineated. Finally, goodness-of-fit measure is used to select the regional probability distributions and the design rainfall quantiles with various return periods for each region can be estimated. The proposed approach is applied to analyze and quantify regional rainfalls in Taiwan. The proposed approach is a robust and efficient way for regional rainfall frequency analysis. Moreover, one can easily assign an ungauged site to a previously defined cluster according to a map of homogeneous regions. Therefore, the proposed approach is expected to be useful for providing the design rainfall quantiles with various return periods at ungauged sites.