PROBABLE RAINFALL OF DIVINÓPOLIS CITY, MINAS GERAIS STATE, BRAZIL

Resumo

The aim of this paper was to analyze the behavior of non-parametric statistical distributions on the prediction of probable monthly and total annual rainfall as well as to determine the monthly and annual probable rainfall with different levels of probability for Divinópolis, Minas Gerais State, Brazil. The analysis consisted in adjusting the theoretical probability distribution to a data series of 66 years of monthly and annual rainfall. The data were obtained from the Hidroweb service, controlled by the National Water Agency (ANA, in Portuguese). The frequency distributions of Gumbel for Maximus, Fréchet and Gamma were adjusted to the observed series, where the adherence of these models to the data were tested by the Kolmogorov-Smirnov and Chi-Squared test, both with 5% of probability. The model that best represented, in most cases, the frequency distributions of the series of total monthly precipitation was Gumbel for Maximus, while the Fréchet model had the worst result, not fitting to the data of the historical series for both tests performed in the study. The probable monthly maximum precipitation for Divinópolis is 527 mm, associated with a probability of 5% and for January, while the lowest one is 0.0042 mm, with probability of 95% in July.

Downloads

Não há dados estatísticos.

Biografia do Autor

Felipe Rodolfo Vieira

Engenheiro Ambiental e Sanitarista

Michael Silveira Thebaldi, Universidade Federal de Lavras
Doutor em Recursos Hídricos em Sistemas Agrícolas. Professor Adjunto da UFLA
Bruno Gonçalves Silveira

Engenheiro Civil

Virgílio Henrique Barros Nogueira

Engenheiro Agrícola, mestrando em Recursos Hídricos em Sistemas Agrícolas

Publicado
2020-02-05
Como Citar
Vieira, F. R., Thebaldi, M. S., Silveira, B. G., & Barros Nogueira, V. H. (2020). PROBABLE RAINFALL OF DIVINÓPOLIS CITY, MINAS GERAIS STATE, BRAZIL. REVISTA ENGENHARIA NA AGRICULTURA - REVENG, 28, 89-99. https://doi.org/10.13083/reveng.v28i.6385
Seção
Recursos Hídricos e Ambientais