Comparative evaluations of multivariate methods in spatial clustering of precipitation using GPCC V7 gridded data set: application to the Northern Territory of Australia

Abstract

This study intends to evaluate whether or not there is the meaningful preference between performances of four multivariate methods of principle component analysis (PCA), factor analysis (FA), independent component analysis (ICA) and nonlinear principle component analysis based on autoassociative neural network (NLPCA-ANN) in spatial clustering of the seasonal and annual precipitation in the Northern Territory of Australia. Monthly precipitation’s gridded data of Global Precipitation Climate Centre (GPCC) version 7 from January 1901 until December 2013 was used in this research, and two homogeneity tests of H index and Cronbach’s alpha (α) test were employed for examining the accuracy of the derived clusters. The results showed that NLPCA-ICA, at the annual scale, and ICA, at the seasonal scale, present marginally more accurate clustering compared to others. Therefore, solely relying on the multivariate methods for spatial precipitation clustering in areas with complex climate and topographic conditions is not reasonable and it is quite necessary to take advantages of other advance statistical methods to refine the outcome of multivariate methods as well.