Multivariate statistical analysis to support the minimum streamflow regionalization

Estatísticas multivariadas como suporte à regionalização de vazões mínimas¹

ABSTRACT

This study aimed to develop a methodology based on multivariate statistical analysis of principal components and cluster analysis, in order to identify the most representative variables in studies of minimum streamflow regionalization, and to optimize the identification of the hydrologically homogeneous regions for the Doce river basin. Ten variables were used, referring to the river basin climatic and morphometric characteristics. These variables were individualized for each of the 61 gauging stations. Three dependent variables that are indicative of minimum streamflow (Q_7,10, Q₉₀ and Q₉₅). And seven independent variables that concern to climatic and morphometric characteristics of the basin (total annual rainfall – P_a; total semiannual rainfall of the dry and of the rainy season – P_ss and P_sc; watershed drainage area – A_d; length of the main river – L_p; total length of the rivers – L_t; and average watershed slope – S_L). The results of the principal component analysis pointed out that the variable S_L was the least representative for the study, and so it was discarded. The most representative independent variables were A_d and P_sc. The best divisions of hydrologically homogeneous regions for the three studied flow characteristics were obtained using the Mahalanobis similarity matrix and the complete linkage clustering method. The cluster analysis enabled the identification of four hydrologically homogeneous regions in the Doce river basin.

principal component analysis; cluster analysis; homogeneous regions