Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature

Detalhes bibliográficos
Autor(a) principal: Souza,Amaury de
Data de Publicação: 2022
Outros Autores: Abreu,Marcel Carvalho, de Oliveira-Júnior,José Francisco, Aristone,Flavio, Fernandes,Widinei Alves, Aviv-Sharon,Elinor, Graf,Renata
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Archives of Biology and Technology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704
Resumo: Abstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul.
id TECPAR-1_d2a83d8daf4a5f108b9638e85ca43a39
oai_identifier_str oai:scielo:S1516-89132022000100704
network_acronym_str TECPAR-1
network_name_str Brazilian Archives of Biology and Technology
repository_id_str
spelling Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperaturecluster analysisclimatic zonesclimate regionalizationMato Grosso do SulAbstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul.Instituto de Tecnologia do Paraná - Tecpar2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704Brazilian Archives of Biology and Technology v.65 2022reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2022210331info:eu-repo/semantics/openAccessSouza,Amaury deAbreu,Marcel Carvalhode Oliveira-Júnior,José FranciscoAristone,FlavioFernandes,Widinei AlvesAviv-Sharon,ElinorGraf,Renataeng2022-05-12T00:00:00Zoai:scielo:S1516-89132022000100704Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2022-05-12T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false
dc.title.none.fl_str_mv Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
title Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
spellingShingle Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
Souza,Amaury de
cluster analysis
climatic zones
climate regionalization
Mato Grosso do Sul
title_short Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
title_full Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
title_fullStr Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
title_full_unstemmed Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
title_sort Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
author Souza,Amaury de
author_facet Souza,Amaury de
Abreu,Marcel Carvalho
de Oliveira-Júnior,José Francisco
Aristone,Flavio
Fernandes,Widinei Alves
Aviv-Sharon,Elinor
Graf,Renata
author_role author
author2 Abreu,Marcel Carvalho
de Oliveira-Júnior,José Francisco
Aristone,Flavio
Fernandes,Widinei Alves
Aviv-Sharon,Elinor
Graf,Renata
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Souza,Amaury de
Abreu,Marcel Carvalho
de Oliveira-Júnior,José Francisco
Aristone,Flavio
Fernandes,Widinei Alves
Aviv-Sharon,Elinor
Graf,Renata
dc.subject.por.fl_str_mv cluster analysis
climatic zones
climate regionalization
Mato Grosso do Sul
topic cluster analysis
climatic zones
climate regionalization
Mato Grosso do Sul
description Abstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
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=S1516-89132022000100704
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-4324-2022210331
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 Instituto de Tecnologia do Paraná - Tecpar
publisher.none.fl_str_mv Instituto de Tecnologia do Paraná - Tecpar
dc.source.none.fl_str_mv Brazilian Archives of Biology and Technology v.65 2022
reponame:Brazilian Archives of Biology and Technology
instname:Instituto de Tecnologia do Paraná (Tecpar)
instacron:TECPAR
instname_str Instituto de Tecnologia do Paraná (Tecpar)
instacron_str TECPAR
institution TECPAR
reponame_str Brazilian Archives of Biology and Technology
collection Brazilian Archives of Biology and Technology
repository.name.fl_str_mv Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)
repository.mail.fl_str_mv babt@tecpar.br||babt@tecpar.br
_version_ 1750318281710895104