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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
03/12/2020 |
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Data da última atualização: |
03/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALTHOFF, D.; DIAS, S. H. B.; FILGUEIRAS, R.; RODRIGUES, L. N. |
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Afiliação: |
LINEU NEIVA RODRIGUES, CPAC. |
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Título: |
ETo-Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000-2018). |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Water Resources Research, v. 56, 2020. |
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Páginas: |
16 p. |
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DOI: |
https://doi.org/ 10.1029/2020WR027562 |
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Idioma: |
Português |
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Conteúdo: |
The reference evapotranspiration (ETo) has long been used as a climate parameter for many studies in climatology and hydrology. However, many regions suffer from shortage of both meteorological monitoring stations and historical information on ETo. Thus, the objective of this study was to develop a daily gridded reference evapotranspiration data set for Brazil that matches the period and grid cells of the Global Precipitation Measurement (GPM) data. ETo was calculated using data from 849 weather stations over the period from 1 June 2000 to 31 December 2018. The features used to model ETo were the GPM daily data set, WorldClim averages monthly, and two engineered features. Among the machine learning algorithms assessed, the Cubist presented the best performance‐computation cost trade‐off in a subset of the entire data and, therefore, was selected to model ETo daily. The developed data set presented root mean square error of 0.65 mm day−1, or 16% lower than previous ETo data set developed for Brazil using interpolation techniques. The GPM and engineered features showed higher importance for the models trained during the wet season, while the WorldClim maximum temperature averages monthly were more important during the dry and cold season. The new gridded reference evapotranspiration data set for Brazil (ETo‐Brazil) was made freely available to the community. |
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Palavras-Chave: |
Estação meteorológica. |
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Thesagro: |
Evapotranspiração; Modelo Matemático. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02062naa a2200217 a 4500
001 2127560
005 2020-12-03
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/ 10.1029/2020WR027562$2DOI
100 1 $aALTHOFF, D.
245 $aETo-Brazil$bA Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000-2018).$h[electronic resource]
260 $c2020
300 $a16 p.
520 $aThe reference evapotranspiration (ETo) has long been used as a climate parameter for many studies in climatology and hydrology. However, many regions suffer from shortage of both meteorological monitoring stations and historical information on ETo. Thus, the objective of this study was to develop a daily gridded reference evapotranspiration data set for Brazil that matches the period and grid cells of the Global Precipitation Measurement (GPM) data. ETo was calculated using data from 849 weather stations over the period from 1 June 2000 to 31 December 2018. The features used to model ETo were the GPM daily data set, WorldClim averages monthly, and two engineered features. Among the machine learning algorithms assessed, the Cubist presented the best performance‐computation cost trade‐off in a subset of the entire data and, therefore, was selected to model ETo daily. The developed data set presented root mean square error of 0.65 mm day−1, or 16% lower than previous ETo data set developed for Brazil using interpolation techniques. The GPM and engineered features showed higher importance for the models trained during the wet season, while the WorldClim maximum temperature averages monthly were more important during the dry and cold season. The new gridded reference evapotranspiration data set for Brazil (ETo‐Brazil) was made freely available to the community.
650 $aEvapotranspiração
650 $aModelo Matemático
653 $aEstação meteorológica
700 1 $aDIAS, S. H. B.
700 1 $aFILGUEIRAS, R.
700 1 $aRODRIGUES, L. N.
773 $tWater Resources Research$gv. 56, 2020.
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