Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends

The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for t...

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Published inAnais da Academia Brasileira de Ciências Vol. 94; no. suppl 3; p. e20211241
Main Authors Medeiros, Elias S DE, Lima, Renato R DE, Olinda, Ricardo A DE, Dantas, Leydson G, Santos, Carlos A C Dos
Format Journal Article
LanguageEnglish
Published Brazil Academia Brasileira de Ciências 01.01.2022
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Abstract The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.
AbstractList Abstract The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989–2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.
The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.
Author Lima, Renato R DE
Medeiros, Elias S DE
Santos, Carlos A C Dos
Dantas, Leydson G
Olinda, Ricardo A DE
AuthorAffiliation Universidade Federal de Campina Grande
Universidade Federal de Lavras
Universidade Estadual da Paraíba
Universidade Federal da Grande Dourados
AuthorAffiliation_xml – name: Universidade Federal da Grande Dourados
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  fullname: Medeiros, Elias S DE
  organization: Universidade Federal da Grande Dourados, Faculdade de Ciências Exatas e Tecnologia, Rodovia Dourados/Itahum, Km 12, Cidade Universitária, Caixa Postal 364, 79804-970 Dourados, MS, Brazil
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  surname: Dantas
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  organization: Universidade Federal de Campina Grande, Centro de Tecnologia e Recursos Naturais, Rua Aprígio Veloso, 882, Universitário, 58428-830 Campina Grande, PB, Brazil
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  givenname: Carlos A C Dos
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  surname: Santos
  fullname: Santos, Carlos A C Dos
  organization: Universidade Federal de Campina Grande, Centro de Tecnologia e Recursos Naturais, Rua Aprígio Veloso, 882, Universitário, 58428-830 Campina Grande, PB, Brazil
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Issue suppl 3
Keywords geostatistics
Brazil
rainfall
GAMLSS regression
Language English
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Snippet The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality...
Abstract The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the...
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SubjectTerms Brazil
GAMLSS regression
geostatistics
Humans
MULTIDISCIPLINARY SCIENCES
Probability
Rain
rainfall
Spatial Analysis
Title Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends
URI https://www.ncbi.nlm.nih.gov/pubmed/36477235
https://search.proquest.com/docview/2753316332
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000701104&lng=en&tlng=en
https://doaj.org/article/5d552028c3f84616bc9a90aff1d4aadd
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