Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil

Detalhes bibliográficos
Autor(a) principal: Xavier Júnior,Sílvio Fernando Alves
Data de Publicação: 2020
Outros Autores: Jale,Jader da Silva, Stosic,Tatijana, Santos,Carlos Antonio Costa dos, Singh,Vijay P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Meteorologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862020000200187
Resumo: Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.
id SBMET-1_12291dfd1ca46604bd4b0cfe4df4a905
oai_identifier_str oai:scielo:S0102-77862020000200187
network_acronym_str SBMET-1
network_name_str Revista Brasileira de Meteorologia (Online)
repository_id_str
spelling Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, BrazilprecipitationtrendsParaíbageostatisticsAbstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.Sociedade Brasileira de Meteorologia2020-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862020000200187Revista Brasileira de Meteorologia v.35 n.2 2020reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-7786351013info:eu-repo/semantics/openAccessXavier Júnior,Sílvio Fernando AlvesJale,Jader da SilvaStosic,TatijanaSantos,Carlos Antonio Costa dosSingh,Vijay P.eng2020-08-10T00:00:00Zoai:scielo:S0102-77862020000200187Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2020-08-10T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false
dc.title.none.fl_str_mv Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
title Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
spellingShingle Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
Xavier Júnior,Sílvio Fernando Alves
precipitation
trends
Paraíba
geostatistics
title_short Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
title_full Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
title_fullStr Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
title_full_unstemmed Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
title_sort Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil
author Xavier Júnior,Sílvio Fernando Alves
author_facet Xavier Júnior,Sílvio Fernando Alves
Jale,Jader da Silva
Stosic,Tatijana
Santos,Carlos Antonio Costa dos
Singh,Vijay P.
author_role author
author2 Jale,Jader da Silva
Stosic,Tatijana
Santos,Carlos Antonio Costa dos
Singh,Vijay P.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Xavier Júnior,Sílvio Fernando Alves
Jale,Jader da Silva
Stosic,Tatijana
Santos,Carlos Antonio Costa dos
Singh,Vijay P.
dc.subject.por.fl_str_mv precipitation
trends
Paraíba
geostatistics
topic precipitation
trends
Paraíba
geostatistics
description Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.
publishDate 2020
dc.date.none.fl_str_mv 2020-06-01
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=S0102-77862020000200187
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862020000200187
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0102-7786351013
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 Sociedade Brasileira de Meteorologia
publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
dc.source.none.fl_str_mv Revista Brasileira de Meteorologia v.35 n.2 2020
reponame:Revista Brasileira de Meteorologia (Online)
instname:Sociedade Brasileira de Meteorologia (SBMET)
instacron:SBMET
instname_str Sociedade Brasileira de Meteorologia (SBMET)
instacron_str SBMET
institution SBMET
reponame_str Revista Brasileira de Meteorologia (Online)
collection Revista Brasileira de Meteorologia (Online)
repository.name.fl_str_mv Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)
repository.mail.fl_str_mv ||rbmet@rbmet.org.br
_version_ 1752122086265978880

Registros relacionados