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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/46UCCU8
Repositóriosid.inpe.br/mtc-m21d/2022/05.25.15.55   (acesso restrito)
Última Atualização2022:05.25.15.55.15 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/05.25.15.55.15
Última Atualização dos Metadados2023:01.03.16.46.06 (UTC) administrator
DOI10.3390/w14091473
ISSN2073-4441
Chave de CitaçãoSilvaSSPCGCFH:2022:PeAsDi
TítuloPerformance Assessment of Different Precipitation Databases (Gridded Analyses and Reanalyses) for the New Brazilian Agricultural Frontier: SEALBA
Ano2022
MêsMay
Data de Acesso18 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho3510 KiB
2. Contextualização
Autor1 Silva, Ewerton Hallan de Lima
2 Silva, Fabrício Daniel dos
3 Silva Júnior, Rosiberto Salustiano da
4 Pinto, David Duarte Cavalcante
5 Costa, Rafaela Lisboa
6 Gomes, Heliofábio Barros
7 Cabral Júnior, Jório Bezerra
8 Freitas, Ismael Guidson Farias de
9 Herdies, Dirceu Luís
Identificador de Curriculo1
2
3
4
5
6
7
8
9 8JMKD3MGP5W/3C9JGTU
ORCID1
2 0000-0002-3185-6413
3
4 0000-0001-7032-6354
5
6
7 0000-0002-4207-2155
8
9 0000-0002-2872-8453
Grupo1
2
3
4
5
6
7
8
9 DIMNT-CGCT-INPE-MCTI-GOV-BR
Afiliação1 Universidade Federal de Alagoas (UFAL)
2 Universidade Federal de Alagoas (UFAL)
3 Universidade Federal de Alagoas (UFAL)
4 Universidade Federal de Alagoas (UFAL)
5 Universidade Federal de Alagoas (UFAL)
6 Universidade Federal de Alagoas (UFAL)
7 Universidade Federal de Alagoas (UFAL)
8 Universidade Federal de Campina Grande (UFCG)
9 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 ewerton.hallan@ifal.edu.br
2 fabricio.santos@icat.ufal.br
3 rosiberto@icat.ufal.br
4 david.duarte@icat.ufal.br
5 rafaela.costa@icat.ufal.br
6 heliofabio@icat.ufal.br
7 jorio.cabral@igdema.ufal.br
8 ismael.freitas@icat.ufal.br
9 dirceu.herdies@inpe.br
RevistaWater (Switzerland)
Volume14
Número9
Páginase1473
Nota SecundáriaB3_ENGENHARIAS_I
Histórico (UTC)2022-05-25 15:57:35 :: simone -> administrator :: 2022
2023-01-03 16:46:06 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chaveagribusiness
climate
grain
precipitation
reanalysis
SEALBA
ResumoSince the early 2000s, Brazil has been one of the worlds leading grain producers, with agribusiness accounting for around 28% of the Brazilian GDP in 2021. Substantial investments in research, coupled with the expansion of arable areas, owed to the advent of new agriculture frontiers, led the country to become the worlds greatest producer of soybean. One of the newest agricultural frontiers to be emerging in Brazil is the one known as SEALBA, an acronym that refers to the three Brazilian states whose areas it is comprised ofSergipe, Alagoas, and Bahiaall located in the Northeast region of the country. It is an extensive area with a favorable climate for the production of grains, including soybeans, with a rainy season that takes place in autumn/winter, unlike the Brazilian regions that are currently the main producers of these kinds of crops, in which the rainfall regime has the wet period concentrated in spring/summer. Considering that precipitation is the main determinant climatic factor for crops, the scarcity of weather stations in the SEALBA region poses an obstacle to an accurate evaluation of the actual feasibility of the region to a given crop. Therefore, the aim of this work was to carry out an assessment of the performance of four different precipitation databases of alternative sources to observations: two from gridded analyses, MERGE and CHIRPS, and the other two from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them to observational records from stations along the region. The analysis was based on a comparison with data from seven weather stations located in SEALBA, in the period 20012020, through three dexterity indices: the mean absolute error (MAE), the root mean squared errors (RMSE), and the coefficient of Pearsons correlation (r), showing that the gridded analyzes performed better than the reanalyses, with MERGE showing the highest correlations and the lowest errors (global average r between stations of 0.96, followed by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm and ERA5 with 71.4 mm). Since all databases provide up-to-date data, our findings indicate that, for any research that needs a complete daily precipitation dataset for the SEALBA region, preference should be given to use the data in the following order of priority: MERGE, CHIRPS, ERA5Land, and ERA5.
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5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.03.22.23 1
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
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