Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Detalhes bibliográficos
Autor(a) principal: Teodoro, Paulo Eduardo
Data de Publicação: 2016
Outros Autores: da Cunha, Elias Rodrigues, Corrêa, Caio Cézar Guedes, Ribeiro, Larissa Pereira, Torres, Francisco Eduardo, Oliveira-Junior, José Francisco de, Gois, Givanildo, Bacani, Victor Matheus
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Bioscience journal (Online)
Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/29387
Resumo: Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.
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spelling Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul correlationmultiple linear regressionpath analysis.Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.EDUFU2016-01-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://seer.ufu.br/index.php/biosciencejournal/article/view/2938710.14393/BJ-v32n1a2016-29387Bioscience Journal ; Vol. 32 No. 1 (2016): Jan./Feb.; 41-47Bioscience Journal ; v. 32 n. 1 (2016): Jan./Feb.; 41-471981-3163reponame:Bioscience journal (Online)instname:Universidade Federal de Uberlândia (UFU)instacron:UFUenghttps://seer.ufu.br/index.php/biosciencejournal/article/view/29387/17749Copyright (c) 2016 Paulo Eduardo Teodoro, Elias Rodrigues da Cunha, Caio Cézar Guedes Corrêa, Larissa Pereira Ribeiro, Francisco Eduardo Torres, José Francisco de Oliveira-Junior, Givanildo Gois, Victor Matheus Bacanihttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessTeodoro, Paulo Eduardoda Cunha, Elias RodriguesCorrêa, Caio Cézar GuedesRibeiro, Larissa PereiraTorres, Francisco EduardoOliveira-Junior, José Francisco deGois, GivanildoBacani, Victor Matheus2022-02-23T11:16:57Zoai:ojs.www.seer.ufu.br:article/29387Revistahttps://seer.ufu.br/index.php/biosciencejournalPUBhttps://seer.ufu.br/index.php/biosciencejournal/oaibiosciencej@ufu.br||1981-31631516-3725opendoar:2022-02-23T11:16:57Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)false
dc.title.none.fl_str_mv Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
title Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
spellingShingle Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
Teodoro, Paulo Eduardo
correlation
multiple linear regression
path analysis.
title_short Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
title_full Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
title_fullStr Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
title_full_unstemmed Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
title_sort Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul
author Teodoro, Paulo Eduardo
author_facet Teodoro, Paulo Eduardo
da Cunha, Elias Rodrigues
Corrêa, Caio Cézar Guedes
Ribeiro, Larissa Pereira
Torres, Francisco Eduardo
Oliveira-Junior, José Francisco de
Gois, Givanildo
Bacani, Victor Matheus
author_role author
author2 da Cunha, Elias Rodrigues
Corrêa, Caio Cézar Guedes
Ribeiro, Larissa Pereira
Torres, Francisco Eduardo
Oliveira-Junior, José Francisco de
Gois, Givanildo
Bacani, Victor Matheus
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Teodoro, Paulo Eduardo
da Cunha, Elias Rodrigues
Corrêa, Caio Cézar Guedes
Ribeiro, Larissa Pereira
Torres, Francisco Eduardo
Oliveira-Junior, José Francisco de
Gois, Givanildo
Bacani, Victor Matheus
dc.subject.por.fl_str_mv correlation
multiple linear regression
path analysis.
topic correlation
multiple linear regression
path analysis.
description Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-20
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/29387
10.14393/BJ-v32n1a2016-29387
url https://seer.ufu.br/index.php/biosciencejournal/article/view/29387
identifier_str_mv 10.14393/BJ-v32n1a2016-29387
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://seer.ufu.br/index.php/biosciencejournal/article/view/29387/17749
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv 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 EDUFU
publisher.none.fl_str_mv EDUFU
dc.source.none.fl_str_mv Bioscience Journal ; Vol. 32 No. 1 (2016): Jan./Feb.; 41-47
Bioscience Journal ; v. 32 n. 1 (2016): Jan./Feb.; 41-47
1981-3163
reponame:Bioscience journal (Online)
instname:Universidade Federal de Uberlândia (UFU)
instacron:UFU
instname_str Universidade Federal de Uberlândia (UFU)
instacron_str UFU
institution UFU
reponame_str Bioscience journal (Online)
collection Bioscience journal (Online)
repository.name.fl_str_mv Bioscience journal (Online) - Universidade Federal de Uberlândia (UFU)
repository.mail.fl_str_mv biosciencej@ufu.br||
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