Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil

Detalhes bibliográficos
Autor(a) principal: Cardoso, Dione Pereira
Data de Publicação: 2022
Outros Autores: Avanzi, Junior Cesar, Ferreira, Daniel Furtado, Acuña Guzman, Salvador Francisco, Silva, Marx Leandro Naves, Pires, Fábio Ribeiro, Curi, Nilton
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/54451
Resumo: Rainfall erosivity (R factor) is one of the six factors of the Universal Soil Loss Equation, being calculated based on the product of rainfall kinetic energy multiplied by its 30-minute maximum intensity. However, the lack of detailed and reliable rainfall data in many parts of the world has driven the use of other methods to estimate rainfall erosivity based on daily, monthly or annual data. These methods still need to be assessed to determine if their estimates are consistent with the standard method for calculating rainfall erosivity. This study aimed to select a consistent method for such replacement in Brazilian conditions without access the rainfall intensity data. The tested methods included: modified Fournier, MF; modified Fournier by Zhang, MF-Z; modified Fournier by Men, MF-M; Rainfall Disaggregation, RD; TRMM Satellite with modified Fournier coefficient, TRMM-F; and TRMM Satellite with monthly rainfall, TRMM-M. The rainfall data were obtained from the USP Meteorological Station, referring to the period from 2009 to 2015. The analyses were performed according to the Additive Main effects and Multiplicative Interaction (AMMI) model and Scott-Knott statistical tests. Considering the 1:1 line, all methods had a good adjustment, presenting similar behavior in relation to the standard method. The methods behaved differently for monthly and annual periods. The MF method proved to be capable of consistently replacing the standard method in all aforementioned situations. Considering the driest period, any method can be used. For annual rainfall erosivity estimation, the RD, MF, TRMM-F and TRMM-M methods can be applied; highlighting that the TRMM-based methods are optimal for locations without on-site rain gauges. Additionally, it was computed that the modified Fournier by Men and the modified Fournier by Zhang underestimated and overestimated the rainfall erosivity, respectively.
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spelling Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern BrazilModified FournierRainfall disaggregationTropical Rainfall Measurement Mission (TRMM)Erosividade da chuvaFournier modificadoSatélite TRMMRainfall erosivity (R factor) is one of the six factors of the Universal Soil Loss Equation, being calculated based on the product of rainfall kinetic energy multiplied by its 30-minute maximum intensity. However, the lack of detailed and reliable rainfall data in many parts of the world has driven the use of other methods to estimate rainfall erosivity based on daily, monthly or annual data. These methods still need to be assessed to determine if their estimates are consistent with the standard method for calculating rainfall erosivity. This study aimed to select a consistent method for such replacement in Brazilian conditions without access the rainfall intensity data. The tested methods included: modified Fournier, MF; modified Fournier by Zhang, MF-Z; modified Fournier by Men, MF-M; Rainfall Disaggregation, RD; TRMM Satellite with modified Fournier coefficient, TRMM-F; and TRMM Satellite with monthly rainfall, TRMM-M. The rainfall data were obtained from the USP Meteorological Station, referring to the period from 2009 to 2015. The analyses were performed according to the Additive Main effects and Multiplicative Interaction (AMMI) model and Scott-Knott statistical tests. Considering the 1:1 line, all methods had a good adjustment, presenting similar behavior in relation to the standard method. The methods behaved differently for monthly and annual periods. The MF method proved to be capable of consistently replacing the standard method in all aforementioned situations. Considering the driest period, any method can be used. For annual rainfall erosivity estimation, the RD, MF, TRMM-F and TRMM-M methods can be applied; highlighting that the TRMM-based methods are optimal for locations without on-site rain gauges. Additionally, it was computed that the modified Fournier by Men and the modified Fournier by Zhang underestimated and overestimated the rainfall erosivity, respectively.Sociedade Brasileira de Ciência do Solo2022-09-02T17:39:29Z2022-09-02T17:39:29Z2022-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCARDOSO, D. P. et al. Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 46, e0210122, 2022. DOI: https://doi.org/10.36783/18069657rbcs20210122.http://repositorio.ufla.br/jspui/handle/1/54451Revista Brasileira de Ciência do Soloreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessCardoso, Dione PereiraAvanzi, Junior CesarFerreira, Daniel FurtadoAcuña Guzman, Salvador FranciscoSilva, Marx Leandro NavesPires, Fábio RibeiroCuri, Niltoneng2023-05-30T19:48:25Zoai:localhost:1/54451Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-30T19:48:25Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
title Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
spellingShingle Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
Cardoso, Dione Pereira
Modified Fournier
Rainfall disaggregation
Tropical Rainfall Measurement Mission (TRMM)
Erosividade da chuva
Fournier modificado
Satélite TRMM
title_short Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
title_full Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
title_fullStr Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
title_full_unstemmed Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
title_sort Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil
author Cardoso, Dione Pereira
author_facet Cardoso, Dione Pereira
Avanzi, Junior Cesar
Ferreira, Daniel Furtado
Acuña Guzman, Salvador Francisco
Silva, Marx Leandro Naves
Pires, Fábio Ribeiro
Curi, Nilton
author_role author
author2 Avanzi, Junior Cesar
Ferreira, Daniel Furtado
Acuña Guzman, Salvador Francisco
Silva, Marx Leandro Naves
Pires, Fábio Ribeiro
Curi, Nilton
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Cardoso, Dione Pereira
Avanzi, Junior Cesar
Ferreira, Daniel Furtado
Acuña Guzman, Salvador Francisco
Silva, Marx Leandro Naves
Pires, Fábio Ribeiro
Curi, Nilton
dc.subject.por.fl_str_mv Modified Fournier
Rainfall disaggregation
Tropical Rainfall Measurement Mission (TRMM)
Erosividade da chuva
Fournier modificado
Satélite TRMM
topic Modified Fournier
Rainfall disaggregation
Tropical Rainfall Measurement Mission (TRMM)
Erosividade da chuva
Fournier modificado
Satélite TRMM
description Rainfall erosivity (R factor) is one of the six factors of the Universal Soil Loss Equation, being calculated based on the product of rainfall kinetic energy multiplied by its 30-minute maximum intensity. However, the lack of detailed and reliable rainfall data in many parts of the world has driven the use of other methods to estimate rainfall erosivity based on daily, monthly or annual data. These methods still need to be assessed to determine if their estimates are consistent with the standard method for calculating rainfall erosivity. This study aimed to select a consistent method for such replacement in Brazilian conditions without access the rainfall intensity data. The tested methods included: modified Fournier, MF; modified Fournier by Zhang, MF-Z; modified Fournier by Men, MF-M; Rainfall Disaggregation, RD; TRMM Satellite with modified Fournier coefficient, TRMM-F; and TRMM Satellite with monthly rainfall, TRMM-M. The rainfall data were obtained from the USP Meteorological Station, referring to the period from 2009 to 2015. The analyses were performed according to the Additive Main effects and Multiplicative Interaction (AMMI) model and Scott-Knott statistical tests. Considering the 1:1 line, all methods had a good adjustment, presenting similar behavior in relation to the standard method. The methods behaved differently for monthly and annual periods. The MF method proved to be capable of consistently replacing the standard method in all aforementioned situations. Considering the driest period, any method can be used. For annual rainfall erosivity estimation, the RD, MF, TRMM-F and TRMM-M methods can be applied; highlighting that the TRMM-based methods are optimal for locations without on-site rain gauges. Additionally, it was computed that the modified Fournier by Men and the modified Fournier by Zhang underestimated and overestimated the rainfall erosivity, respectively.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-02T17:39:29Z
2022-09-02T17:39:29Z
2022-02
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.uri.fl_str_mv CARDOSO, D. P. et al. Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 46, e0210122, 2022. DOI: https://doi.org/10.36783/18069657rbcs20210122.
http://repositorio.ufla.br/jspui/handle/1/54451
identifier_str_mv CARDOSO, D. P. et al. Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 46, e0210122, 2022. DOI: https://doi.org/10.36783/18069657rbcs20210122.
url http://repositorio.ufla.br/jspui/handle/1/54451
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://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 Sociedade Brasileira de Ciência do Solo
publisher.none.fl_str_mv Sociedade Brasileira de Ciência do Solo
dc.source.none.fl_str_mv Revista Brasileira de Ciência do Solo
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
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
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