Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil

The objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitu...

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Detalles Bibliográficos
Autores: Carvalho, Luiz G. de, Alves, Marcelo de Carvalho, Oliveira, Marcelo S. de, Vianello, Rubens L., Sediyama, Gilberto C., Carvalho, Luis Marcelo Tavares de
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2010
País:Brasil
Institución:Universidade Federal de Lavras (UFLA)
Repositorio:Repositório Institucional da UFLA
Idioma:inglés
OAI Identifier:oai:repositorio.ufla.br:1/869
Acceso en línea:http://link.springer.com/article/10.1007%2Fs00704-010-0273-z
https://repositorio.ufla.br/handle/1/869
Access Level:acceso abierto
Palabra clave:Remote Sensing,
Nulti sensing
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spelling 2013-08-06T15:18:16Z2013-08-06T15:18:16Z2010CARVALHO, L. G. et al. Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil. Theoretical and Applied Climatology, Wien, v. 102, n. 3/4, p. 471-428, Nov. 2010.http://link.springer.com/article/10.1007%2Fs00704-010-0273-zhttps://repositorio.ufla.br/handle/1/869http://link.springer.com/article/10.1007%2Fs00704-010-0273-zThe objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitude. The climatic element data referred to 39 INMET climatic stations located in the state of Minas Gerais and in nearby areas and the covariables altitude, latitude, and longitude to the SRTM digital elevation model. Spatial dependence of data was observed through spherical cross semivariograms and cross covariance models. Box–Cox and log transformation were applied to the positive variables. In these situations, kriged predictions were back-transformed and returned to the same scale as the original data. Trend was removed using global polynomial interpolation. Universal simple cokriging best characterized the climate variables without tendentiousness and with high accuracy and precision when compared to simple cokriging. Considering the satisfactory implementation of universal simple cokriging for the monitoring of climatic elements, this methodology presents enormous potential for the characterization of climate change impact in Minas Gerais state.SpringerTheor Appl Climatolreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLARemote Sensing,Nulti sensingMultivariate geostatistical application for climate characterization of Minas Gerais State, Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessengCarvalho, Luiz G. deAlves, Marcelo de CarvalhoOliveira, Marcelo S. deVianello, Rubens L.Sediyama, Gilberto C.Carvalho, Luis Marcelo Tavares deLICENSElicense.txtlicense.txttext/plain; charset=utf-8953https://repositorio.ufla.br/bitstreams/2e7ab4b2-cc15-4bb9-be70-eba9d89aa820/download760884c1e72224de569e74f79eb87ce3MD52falseAnonymousREAD1/8692025-07-17 11:06:25.151metadata.onlyoai:repositorio.ufla.br:1/869https://repositorio.ufla.brRepositório InstitucionalPUBhttps://repositorio.ufla.br/server/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2025-07-17T14:06:25Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)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
dc.title.pt_BR.fl_str_mv Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
title Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
spellingShingle Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
Carvalho, Luiz G. de
Remote Sensing,
Nulti sensing
title_short Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
title_full Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
title_fullStr Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
title_full_unstemmed Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
title_sort Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil
dc.creator.none.fl_str_mv Carvalho, Luiz G. de
Alves, Marcelo de Carvalho
Oliveira, Marcelo S. de
Vianello, Rubens L.
Sediyama, Gilberto C.
Carvalho, Luis Marcelo Tavares de
author Carvalho, Luiz G. de
author_facet Carvalho, Luiz G. de
Alves, Marcelo de Carvalho
Oliveira, Marcelo S. de
Vianello, Rubens L.
Sediyama, Gilberto C.
Carvalho, Luis Marcelo Tavares de
author_role author
author2 Alves, Marcelo de Carvalho
Oliveira, Marcelo S. de
Vianello, Rubens L.
Sediyama, Gilberto C.
Carvalho, Luis Marcelo Tavares de
author2_role author
author
author
author
author
dc.subject.por.fl_str_mv Remote Sensing,
Nulti sensing
topic Remote Sensing,
Nulti sensing
description The objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitude. The climatic element data referred to 39 INMET climatic stations located in the state of Minas Gerais and in nearby areas and the covariables altitude, latitude, and longitude to the SRTM digital elevation model. Spatial dependence of data was observed through spherical cross semivariograms and cross covariance models. Box–Cox and log transformation were applied to the positive variables. In these situations, kriged predictions were back-transformed and returned to the same scale as the original data. Trend was removed using global polynomial interpolation. Universal simple cokriging best characterized the climate variables without tendentiousness and with high accuracy and precision when compared to simple cokriging. Considering the satisfactory implementation of universal simple cokriging for the monitoring of climatic elements, this methodology presents enormous potential for the characterization of climate change impact in Minas Gerais state.
publishDate 2010
dc.date.issued.fl_str_mv 2010
dc.date.accessioned.fl_str_mv 2013-08-06T15:18:16Z
dc.date.available.fl_str_mv 2013-08-06T15:18:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv CARVALHO, L. G. et al. Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil. Theoretical and Applied Climatology, Wien, v. 102, n. 3/4, p. 471-428, Nov. 2010.
dc.identifier.uri.fl_str_mv https://repositorio.ufla.br/handle/1/869
http://link.springer.com/article/10.1007%2Fs00704-010-0273-z
dc.identifier.other.none.fl_str_mv http://link.springer.com/article/10.1007%2Fs00704-010-0273-z
identifier_str_mv CARVALHO, L. G. et al. Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil. Theoretical and Applied Climatology, Wien, v. 102, n. 3/4, p. 471-428, Nov. 2010.
url http://link.springer.com/article/10.1007%2Fs00704-010-0273-z
https://repositorio.ufla.br/handle/1/869
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.pt_BR.fl_str_mv Theor Appl Climatol
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
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repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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