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| Xavier,Ana Carolina Freitas; Blain,Gabriel Constantino; Morais,Marcos Vinicius Bueno de; Sobierajski,Graciela da Rocha. |
| ABSTRACT The selection of an appropriate nonstationary Generalized Extreme Value (GEV) distribution is frequently based on methods, such as Akaike information criterion (AIC), second-order Akaike information criterion (AICc), Bayesian information criterion (BIC) and likelihood ratio test (LRT). Since these methods compare all GEV-models considered within a selection process, the hypothesis that the number of candidate GEV-models considered in such process affects its own outcomehas been proposed. Thus, this study evaluated the performance of these four selection criteria as function of sample sizes, GEV-shape parameters and different numbers candidate GEV-models. Synthetic series generated from Monte Carlo experiments and annual maximum daily rainfall... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Monte Carlo; GEV; MIROC5; Downscaling. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052019000400606 |
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| Batista,Marcelo L.; Coelho,Gilberto; Mello,Carlos R. de; Oliveira,Marcelo S. de. |
| ABSTRACT Extreme rainfall can lead to heavy damage and losses, such as landslides, floods and agricultural productivity as well as the loss of human and animal lives. To mitigate these losses, water resources management policies are needed, among other goals, to study and predict the frequency of such events in a given region to minimize their harmful effects. The present study investigated the Generalized Extreme Value (GEV) probability distribution applied to the annual maximum daily precipitation data from rainfall stations in the southeastern Brazil. A total of 1,921 rainfall stations were considered, among which the stations with at least 15 years of uninterrupted observations were selected. Subsequently, the stationarity and adherence were tested.... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Extreme rainfall; GEV; Ordinary kriging. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000100097 |
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