Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Detalhes bibliográficos
Autor(a) principal: Blain,Gabriel C.
Data de Publicação: 2014
Outros Autores: Meschiatti,Monica C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662014000300010
Resumo: Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.
id UFCG-1_8d37278dd19b873fbe02905288b05b7a
oai_identifier_str oai:scielo:S1415-43662014000300010
network_acronym_str UFCG-1
network_name_str Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
repository_id_str
spelling Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall dataWakebyKappaclimate trendSoil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.Departamento de Engenharia Agrícola - UFCG2014-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662014000300010Revista Brasileira de Engenharia Agrícola e Ambiental v.18 n.3 2014reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)instname:Universidade Federal de Campina Grande (UFCG)instacron:UFCG10.1590/S1415-43662014000300010info:eu-repo/semantics/openAccessBlain,Gabriel C.Meschiatti,Monica C.eng2014-02-21T00:00:00Zoai:scielo:S1415-43662014000300010Revistahttp://www.scielo.br/rbeaaPUBhttps://old.scielo.br/oai/scielo-oai.php||agriambi@agriambi.com.br1807-19291415-4366opendoar:2014-02-21T00:00Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)false
dc.title.none.fl_str_mv Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
title Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
spellingShingle Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
Blain,Gabriel C.
Wakeby
Kappa
climate trend
title_short Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
title_full Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
title_fullStr Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
title_full_unstemmed Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
title_sort Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data
author Blain,Gabriel C.
author_facet Blain,Gabriel C.
Meschiatti,Monica C.
author_role author
author2 Meschiatti,Monica C.
author2_role author
dc.contributor.author.fl_str_mv Blain,Gabriel C.
Meschiatti,Monica C.
dc.subject.por.fl_str_mv Wakeby
Kappa
climate trend
topic Wakeby
Kappa
climate trend
description Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.
publishDate 2014
dc.date.none.fl_str_mv 2014-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662014000300010
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662014000300010
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1415-43662014000300010
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
publisher.none.fl_str_mv Departamento de Engenharia Agrícola - UFCG
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental v.18 n.3 2014
reponame:Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
instname:Universidade Federal de Campina Grande (UFCG)
instacron:UFCG
instname_str Universidade Federal de Campina Grande (UFCG)
instacron_str UFCG
institution UFCG
reponame_str Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
collection Revista Brasileira de Engenharia Agrícola e Ambiental (Online)
repository.name.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental (Online) - Universidade Federal de Campina Grande (UFCG)
repository.mail.fl_str_mv ||agriambi@agriambi.com.br
_version_ 1750297682750996480