SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
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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Engenharia Agrícola |
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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 |
_version_ |
1752126274084536320 |