Classical and Bayesian approach to the prediction of maximum rainfall in the municipality of São João da Boa Vista-SP
DOI:
https://doi.org/10.5902/2179460X69926Keywords:
Extreme rainfall, Generalized Extreme Value distribution, Return levels, Informative priorAbstract
The knowledge of the occurrence and the intensity of maximum rainfall is of fundamental importance to human activities planning, since they might cause social, environmental, economic and human life losses. The present study aims to fit the Generalized Extreme Value (GEV) distribution to the annual maximum precipitation series of the city of São João da Boa Vista–SP. To achieve this, maximum likelihood and Bayesian inference methods were employed to estimate the parameters and, consequently, the annual maximum precipitation. Information about maximum rainfall from Lavras, Machado, Silvianopolis (Minas Gerais state) and Jaboticabal (São Paulo state) were used in informative prior distribution elicitation. The use of prior information improved the precision and accuracy of the maximum rainfall estimates. Then, the GEV distribution, using an informative prior distribution based on data from Machado-MG, presented better accuracy and lower prediction error. This methodology was applied to predict the maximum rainfall for return periods of 2, 5, 10, 20, 50, and 100 years in São João da Boa Vista - SP. Based on the results obtained, for a return period of 2 years, the expected maximum rainfall is equal to or greater than 72.87 mm.
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