Quantis mensais de precipitação no Estado do Paraná utilizando técnicas multivariadas de agrupamento.

Abstract

The rainfall is the only way of entry of water in river basins, therefore the knowledge of their spatial and temporal behavior as well as their frequency is of vital importance for the planning, operation and design of hydraulic works. To this end, probabilistic models are built based on observations made in the place of interest in order to obtain future risks. It often happens that a network of hydrological monitoring data does not show the place of interest is necessary to use procedures that allow the transport of this information. Hydrological regionalization is a statistical procedure that allows, within a homogeneous region, estimate probabilities of occurrence, by data from neighboring stations, in site of interest. The problem is how to define homogeneous region. The solution proposed in this paper was the use of multivariate clustering methods, k-means and hierarquical cluster, validated by quality index. The clusters were then subjected to measures of discordancy and heterogeneity to evaluate homogeneity of groups. The clustering methodology that obtained the best performance was a hybrid approach between k-means and ward, getting errors of at most 10% in the estimation of quantiles regional dimensionless.