Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

  • Aryane Araujo Rodrigues Centro de Desenvolvimento Tecnológico. Programa de Pós-Graduação em Recursos Hídricos. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Tirzah Moreira Siqueira Centro de Engenharias. Programa de Pós-Graduação em Ciências Ambientais. Universidade Federal de Pelotas (UFPel), Rua Benjamin Constant, n° 989, CEP: 96010-020, Pelotas, RS, Brazil.
  • Tamara Leitzke Caldeira Beskow Centro de Engenharias. Programa de Pós-Graduação em Recursos Hídricos. Universidade Federal de Pelotas (UFPel), Rua Benjamin Constant, n° 989, CEP: 96010-020, Pelotas, RS, Brazil.
  • Samuel Beskow Centro de Desenvolvimento Tecnológico. Programa de Pós-Graduação em Recursos Hídricos. Universidade Federal de Pelotas (UFPel), Rua Gomes Carneiro, n° 1, CEP: 96010-610, Pelotas, RS, Brazil.
  • Carlos Rogério de Mello Escola de Engenharia. Departamento de Recursos Hídricos. Universidade Federal de Lavras (UFLA), Campus Universitário, Caixa Postal: 3037, CEP: 37200-900, Lavras, MG, Brazil.
DOI: https://doi.org/10.4136/ambi-agua.2878

Abstract

Heavy rainfall information is essential for environmental studies and water engineering. This study therefore aimed to adjust Intensity-Duration-Frequency (IDF) equations for 247 locations in the Rio Grande do Sul (RS) using stationary rainfall series. Mann-Kendall’s test was applied to identify the temporal trends in the Annual Maximum Daily Rainfall (AMDR) series of 271 rain gauges in RS. The Kappa, Generalized Extreme Value (GEV), Gumbel, two-parameters Log-Normal and three-parameters Log-Normal probabilistic distributions were adjusted to the AMDR series without significant temporal trend. The best distribution fit was given by Anderson-Darling’s test, so the AMDR was discretized up to 5 minutes. IDF equations coefficients were adjusted in RStudio, using Nash-Sutcliffe’s Coefficient and the Root-Mean-Square Error to evaluate them. In conclusion: the most suitable distributions for the AMDR were the multiparametric Kappa and GEV; the IDF equations coefficients adjustment was classified as “excellent”; coefficients a and b varied across the RS and are correlated with the AMDR and geographical positions; and the c and d coefficients were practically constant.

Keywords: goodness of fit test, Kappa probabilistic distribution, trends test.


Published
17/03/2023
Issue
Vol. 18 (2023)
Section
Papers

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