The knowledge of the precipitation behavior is indispensable, since any change in its
quantity and spatial and temporal distributions have an important impact on nature and
consequently on the various human activities. However, in precipitation studies, the lack
of rainfall monitoring, generating the lack of information over time and spatially in the
river basins is a problem for the understanding of this variable. In order to overcome this
problem, the rainfall regionalization method was proposed. The main idea was to divide
the Tocantins Araguaia - RHTA hydrographic region into homogeneous regions, defined
by the Fuzzy C-means method. The Euclidean distance was adopted as a measure of
similarity and the fuzzification parameter, ranging from 1.2 to 2.0, and the explanatory
variables of rainfall (altitude, latitude and longitude) were used as input data. Three
homogeneous regions were obtained, which were validated by the PBM index and the
heterogeneity test. The frequencies of observed rainfall events were generated for the 83
rain gauge stations, distributed in their respective regions, and calibrated by the Normal,
Log-Normal, Gama, Gumbel, Exponential, Logarithmic and Weibull probability
functions. With the application of the chi-square test, we defined the best probability
function in the occurrence of rainfall in each homogeneous region. The validation of the
probabilities functions was performed in 9 target stations, using the chi-square test. In this
stage, it was observed that for annual average precipitation, data adherence occurred to
all the rain gauge stations, since they presented results of the chi-square test of less than
5.99 (for Log-normal distribution functions). It was also observed that for monthly
average precipitation, data were adhered to all the rainfall stations with the Gama and
Weibull functions. For the simulation of rain depth, Linear, Potential, Exponential and
Logarithm models were tested through the multiple regression method, using as
independent variables, altitude, latitude and longitude. As performance criterion of the
models, the R², R²_a, E%, ε%, NASH and RMSE were used. In the simulation of annual
averages, the Linear model presented the best performance indices. In the estimation ofviii
monthly averages, all multiple regression models did not perform well, with errors above
50%, which motivated the estimation of monthly rainfall for rainy and dry periods. In this
new approach the regression models presented excellent performance criteria with errors
below 10%. The performance indexes allowed us to conclude that the regional models
developed for the homogeneous regions of rainfall, defined by the Fuzzy C-Means
method, are a good option in the estimation of annual and monthly average rainfall and
are important for a better understanding of the rainfall regime in RHTA, and can serve as
a tool for better planning of water resources in the region.
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