Functions of probability for fitting monthly rainfall in sites of Mato Grosso do Sul state
DOI:
https://doi.org/10.14393/BJ-v32n2a2016-29394Keywords:
time series adherence, exponential distribution, gamma distribution, normal distribution.Abstract
The identification of the probability distribution function for the representation of the monthly rainfall is relevant in agricultural planning, mainly regard to the establishment of crops. The aim of this work was to verify the probability distribution (exponential, gamma or normal) which best fits to data monthly rainfall of 14 sites in the state of Mato Grosso do Sul. Rainfall data of 14 stations (sites) of the State of Mato Grosso do Sul it were obtained from the National Water Agency (ANA) database, collected in the period 1975 - 2013. At each of the 168 time series of monthly rainfall was applied the Kolmogorov-Smirnov test to assess the fit to probability distributions exponential, gamma and normal. The normal probability distribution presented the best fit to monthly rainfall series of Mato Grosso do Sul and it can be used for the estimation the monthly rainfall, especially in the rainy season months (October to March). The exponential probability distribution can be used for the estimation of monthly rainfall in the driest months of the year (May to September). Thus, we recommend that these distributions be used in future research, aimed to estimate the probable rainfall for the Mato Grosso do Sul State.Downloads
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Published
2016-04-04
How to Cite
TEODORO, P.E., CARGNELUTTI FILHO, A., TORRES, F.E., RIBEIRO, L.P., CARPISTO, D.P., GUEDES CORRÊA, C.C., DA CUNHA, E.R. and BACANI, V.M., 2016. Functions of probability for fitting monthly rainfall in sites of Mato Grosso do Sul state . Bioscience Journal [online], vol. 32, no. 2, pp. 319–327. [Accessed22 November 2024]. DOI 10.14393/BJ-v32n2a2016-29394. Available from: https://seer.ufu.br/index.php/biosciencejournal/article/view/29394.
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Agricultural Sciences
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Copyright (c) 2016 Paulo Eduardo Teodoro, Alberto Cargnelutti Filho, Francisco Eduardo Torres, Larissa Pereira Ribeiro, Denise Prevedel Carpisto, Caio Cézar Guedes Corrêa, Elias Rodrigues da Cunha, Victor Matheus Bacani
This work is licensed under a Creative Commons Attribution 4.0 International License.