Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Mariane Furtado
Data de Publicação: 2018
Outros Autores: Blanco, Claudio José Cavalcante, Santos, Vanessa Conceição dos, Oliveira, Luciana Leal dos Santos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/37742
Resumo: The determination of homogeneous regions with precipitation and probability models when considering the El Niño- Southern Oscillation (ENSO) phenomenon is important for the planning of water resources and for the study of how climate change affects precipitation regimes. Thus, six homogeneous regions with annual mean precipitation were determined through a cluster analysis using Ward's agglomeration method and applied to a historical series of 31 years (1960-1990) at 413 satellite monitoring points in the state of Pará, where the selected years occurred during an El Niño or a La Niña event. When adjusting the probability models, the chi-square test was applied to 413 monitoring points spread over the six homogeneous regions during years with a La Niña or an El Niño, as well as the complete set of years. The normal model (i.e., the normal function) had the best fit, with chi-square values below 3.84 (tabulated chi-square values). The model was validated using 12 rainfall stations of the National Water Agency (ANA), which were distributed across the six homogeneous regions. In this case, the chi-square test for the 12 stations also had values lower than 3.84. A good fit between the observed and the regionalized data demonstrated the potential of the methodology developed and used for estimating annual average precipitation probabilities. 
id UEM-6_211c049b93dfd168609c0f3600b9b45e
oai_identifier_str oai:periodicos.uem.br/ojs:article/37742
network_acronym_str UEM-6
network_name_str Acta scientiarum. Technology (Online)
repository_id_str
spelling Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazonclimate changecluster analysisaverage annual rainfallAmazon.RegionalizaçãoThe determination of homogeneous regions with precipitation and probability models when considering the El Niño- Southern Oscillation (ENSO) phenomenon is important for the planning of water resources and for the study of how climate change affects precipitation regimes. Thus, six homogeneous regions with annual mean precipitation were determined through a cluster analysis using Ward's agglomeration method and applied to a historical series of 31 years (1960-1990) at 413 satellite monitoring points in the state of Pará, where the selected years occurred during an El Niño or a La Niña event. When adjusting the probability models, the chi-square test was applied to 413 monitoring points spread over the six homogeneous regions during years with a La Niña or an El Niño, as well as the complete set of years. The normal model (i.e., the normal function) had the best fit, with chi-square values below 3.84 (tabulated chi-square values). The model was validated using 12 rainfall stations of the National Water Agency (ANA), which were distributed across the six homogeneous regions. In this case, the chi-square test for the 12 stations also had values lower than 3.84. A good fit between the observed and the regionalized data demonstrated the potential of the methodology developed and used for estimating annual average precipitation probabilities. Universidade Estadual De Maringá2018-07-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionanálise estatísticaapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/3774210.4025/actascitechnol.v40i1.37742Acta Scientiarum. Technology; Vol 40 (2018): Publicação Contínua; e37742Acta Scientiarum. Technology; v. 40 (2018): Publicação Contínua; e377421806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/37742/pdfCopyright (c) 2018 Acta Scientiarum. Technologyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessGonçalves, Mariane FurtadoBlanco, Claudio José CavalcanteSantos, Vanessa Conceição dosOliveira, Luciana Leal dos Santos2019-07-17T11:53:49Zoai:periodicos.uem.br/ojs:article/37742Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2019-07-17T11:53:49Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
title Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
spellingShingle Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
Gonçalves, Mariane Furtado
climate change
cluster analysis
average annual rainfall
Amazon.
Regionalização
title_short Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
title_full Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
title_fullStr Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
title_full_unstemmed Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
title_sort Homogenous regions and rainfall probability models considering El Niño and La Niña in the State of Pará in the Amazon
author Gonçalves, Mariane Furtado
author_facet Gonçalves, Mariane Furtado
Blanco, Claudio José Cavalcante
Santos, Vanessa Conceição dos
Oliveira, Luciana Leal dos Santos
author_role author
author2 Blanco, Claudio José Cavalcante
Santos, Vanessa Conceição dos
Oliveira, Luciana Leal dos Santos
author2_role author
author
author
dc.contributor.author.fl_str_mv Gonçalves, Mariane Furtado
Blanco, Claudio José Cavalcante
Santos, Vanessa Conceição dos
Oliveira, Luciana Leal dos Santos
dc.subject.por.fl_str_mv climate change
cluster analysis
average annual rainfall
Amazon.
Regionalização
topic climate change
cluster analysis
average annual rainfall
Amazon.
Regionalização
description The determination of homogeneous regions with precipitation and probability models when considering the El Niño- Southern Oscillation (ENSO) phenomenon is important for the planning of water resources and for the study of how climate change affects precipitation regimes. Thus, six homogeneous regions with annual mean precipitation were determined through a cluster analysis using Ward's agglomeration method and applied to a historical series of 31 years (1960-1990) at 413 satellite monitoring points in the state of Pará, where the selected years occurred during an El Niño or a La Niña event. When adjusting the probability models, the chi-square test was applied to 413 monitoring points spread over the six homogeneous regions during years with a La Niña or an El Niño, as well as the complete set of years. The normal model (i.e., the normal function) had the best fit, with chi-square values below 3.84 (tabulated chi-square values). The model was validated using 12 rainfall stations of the National Water Agency (ANA), which were distributed across the six homogeneous regions. In this case, the chi-square test for the 12 stations also had values lower than 3.84. A good fit between the observed and the regionalized data demonstrated the potential of the methodology developed and used for estimating annual average precipitation probabilities. 
publishDate 2018
dc.date.none.fl_str_mv 2018-07-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
análise estatística
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/37742
10.4025/actascitechnol.v40i1.37742
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/37742
identifier_str_mv 10.4025/actascitechnol.v40i1.37742
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/37742/pdf
dc.rights.driver.fl_str_mv Copyright (c) 2018 Acta Scientiarum. Technology
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 Acta Scientiarum. Technology
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 40 (2018): Publicação Contínua; e37742
Acta Scientiarum. Technology; v. 40 (2018): Publicação Contínua; e37742
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
_version_ 1799315336829337600