SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL

Detalhes bibliográficos
Autor(a) principal: Batista,Marcelo L.
Data de Publicação: 2019
Outros Autores: Coelho,Gilberto, Mello,Carlos R. de, Oliveira,Marcelo S. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000100097
Resumo: 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. GEV probability distribution parameters were then estimated. The results enabled satisfactory spatial interpolation by ordinary kriging and the generation of maps of the distribution parameters. The semivariogram model with the best fit to the three GEV distribution parameters was the exponential model.
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spelling SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZILExtreme rainfallGEVordinary krigingABSTRACT 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. GEV probability distribution parameters were then estimated. The results enabled satisfactory spatial interpolation by ordinary kriging and the generation of maps of the distribution parameters. The semivariogram model with the best fit to the three GEV distribution parameters was the exponential model.Associação Brasileira de Engenharia Agrícola2019-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000100097Engenharia Agrícola v.39 n.1 2019reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v39n1p97-109/2019info:eu-repo/semantics/openAccessBatista,Marcelo L.Coelho,GilbertoMello,Carlos R. deOliveira,Marcelo S. deeng2019-02-26T00:00:00Zoai:scielo:S0100-69162019000100097Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2019-02-26T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
title SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
spellingShingle SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
Batista,Marcelo L.
Extreme rainfall
GEV
ordinary kriging
title_short SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
title_full SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
title_fullStr SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
title_full_unstemmed SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
title_sort SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
author Batista,Marcelo L.
author_facet Batista,Marcelo L.
Coelho,Gilberto
Mello,Carlos R. de
Oliveira,Marcelo S. de
author_role author
author2 Coelho,Gilberto
Mello,Carlos R. de
Oliveira,Marcelo S. de
author2_role author
author
author
dc.contributor.author.fl_str_mv Batista,Marcelo L.
Coelho,Gilberto
Mello,Carlos R. de
Oliveira,Marcelo S. de
dc.subject.por.fl_str_mv Extreme rainfall
GEV
ordinary kriging
topic Extreme rainfall
GEV
ordinary kriging
description 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. GEV probability distribution parameters were then estimated. The results enabled satisfactory spatial interpolation by ordinary kriging and the generation of maps of the distribution parameters. The semivariogram model with the best fit to the three GEV distribution parameters was the exponential model.
publishDate 2019
dc.date.none.fl_str_mv 2019-02-01
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=S0100-69162019000100097
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000100097
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1809-4430-eng.agric.v39n1p97-109/2019
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 Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.39 n.1 2019
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
instacron_str SBEA
institution SBEA
reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
repository.mail.fl_str_mv revistasbea@sbea.org.br||sbea@sbea.org.br
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