Performance of multiparameter distributions in estimating rainfall extremes and deriving IDF equations in Paraná

Desempenho das distribuições multiparamétricas na estimativa de extremos de chuva e derivação das equações IDF no Paraná

ABSTRACT

Frequency analysis of rainfall involves selecting a probabilistic model to represent sample data and perform design estimates. Various probability distributions are available for this purpose, but there is no general consensus on the most appropriate one. In Brazil, distributions such as Log-Normal, Gumbel, and Generalized Extreme Value (GEV) are commonly used, while less conventional models like Kappa (KAP) and Wakeby (WAK) require further investigation. This study provides a comparative evaluation of the Gumbel, GEV, KAP, and WAK distributions for modeling extreme precipitation events in the state of Paraná, selected for their ability to represent a broad range of rainfall data types. Additionally, an alternative methodology was applied to derive Intensity-Duration-Frequency (IDF) equations. The results demonstrate that the GEV distribution exhibited performance comparable to the WAK and KAP distributions, while outperforming the Gumbel distribution. Furthermore, the shape parameter of the GEV distribution exerted a significant effect on the accuracy of IDF relationship estimations.

Keywords:
Wakeby; GEV; IDF equations