(untitled) (Research, Society and Development, 2021)
Objective: Use multivariate analysis techniques, in particular principal component analysis, to find the best description of drought rates, and then use cluster analysis to determine the homogeneous regions of the São Francisco River. Method: The indices to characterize the drought were calculated: Average annual maximum daily precipitation (annual max), annual average accumulated precipitation (amount), annual average of days without rain (<1 mm) (noprec), annual average of consecutive days without rain (<1 mm) (consecdry), annual average of consecutive days with rain (>=1 mm) (consecwet) and the annual average of Rainy Days with Precipitation exceeding the 90% percentile (prec90). These indices were orthogonalized using the principal component method and later grouped using the K-means method. Results: The variables amount and prec90 are the most important, and together in the first component they are responsible for 40.56%, and the variables noprec and consecwet were important to explain 31.04% in the second component, and together they explain 71.60% the total variability of the data. Through the variability of 86.40% in the first three main components retained, the technique of K-means clusters allowed the division of four homogeneous areas in the São Francisco basin. Conclusions: Four regions were observed, which are composed of the regions of the Lower and Sub-Middle São Francisco, Alto São Francisco and the Middle São Francisco, dividing into two parts that there is no perfect correspondence with the established otto-regions.