Regional Frequency Analysis applied to extreme rainfall events: Evaluating its conceptual assumptions and constructing null distributions

• Published in: Anais da Academia Brasileira de Ciências Vol. 93; no. 1; p. e20190406
• Main Authors: Blain, Gabriel C, Sobierajski, Graciela DA R, Xavier, Ana Carolina F, DE Carvalho, João Paulo
• Format: Journal Article
• Language: English; Portuguese
• Published: Brazil Academia Brasileira de Ciências 2021
• Subjects: Brazil; Extreme value; fitting process; generalized logistic; homogeneous groups; Monte Carlo Method; MULTIDISCIPLINARY SCIENCES; Probability; tropical-subtropical region; Weather; Brazil; tropical-subtropical region; Extreme value; generalized logistic; fitting process; homogeneous groups
• ISSN: 0001-3765; 1678-2690; 1678-2690
• DOI: 10.1590/0001-3765202120190406

Besides increasing the amount of data that can be used in a fitting process, the Regional Frequency Analysis (RFA) also assesses the quality of weather station networks. This technique assumes that it is possible to form homogeneous groups of meteorological series presenting independent and identically distributed data. Based on the hypothesis that such homogeneous groups can be formed under tropical-subtropical conditions, this study applied the RFA to assess the probability of one-day annual maximum rainfall in the State of São Paulo, Brazil. Critical limits used in previous studies to declare a region/group as 'acceptable homogeneous' (H≤1.00) or to select a distribution (|Z|≤1.64) were evaluated through Monte Carlo simulations. While the limit H≤1 is appropriate, the limit |Z|≤1.64 may lead to unacceptably high rates of rejecting a true null hypothesis. This statement is particularly true for the general logistic distribution. A computational algorithm allowing the selection of critical limits corresponding to pre-specified probabilities of rejecting a true null hypothesis is provided. Considering the new critical limits, data from one of the largest weather station networks of the State have been pooled into four homogeneous groups. Both generalized logistic and extreme value distributions are recommended for the probabilistic assessment of such groups.