Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed

Detalhes bibliográficos
Autor(a) principal: Valle Junior,Luiz Claudio Galvão do
Data de Publicação: 2019
Outros Autores: Rodrigues,Dulce Buchala Bicca, Oliveira,Paulo Tarso Sanches de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: RBRH (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100202
Resumo: ABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.
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spelling Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershedHydrologic modelingRainfall-runoff eventsCNUngauged basinsABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.Associação Brasileira de Recursos Hídricos2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100202RBRH v.24 2019reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.241920170199info:eu-repo/semantics/openAccessValle Junior,Luiz Claudio Galvão doRodrigues,Dulce Buchala BiccaOliveira,Paulo Tarso Sanches deeng2019-01-28T00:00:00Zoai:scielo:S2318-03312019000100202Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2019-01-28T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false
dc.title.none.fl_str_mv Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
title Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
spellingShingle Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
Valle Junior,Luiz Claudio Galvão do
Hydrologic modeling
Rainfall-runoff events
CN
Ungauged basins
title_short Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
title_full Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
title_fullStr Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
title_full_unstemmed Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
title_sort Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
author Valle Junior,Luiz Claudio Galvão do
author_facet Valle Junior,Luiz Claudio Galvão do
Rodrigues,Dulce Buchala Bicca
Oliveira,Paulo Tarso Sanches de
author_role author
author2 Rodrigues,Dulce Buchala Bicca
Oliveira,Paulo Tarso Sanches de
author2_role author
author
dc.contributor.author.fl_str_mv Valle Junior,Luiz Claudio Galvão do
Rodrigues,Dulce Buchala Bicca
Oliveira,Paulo Tarso Sanches de
dc.subject.por.fl_str_mv Hydrologic modeling
Rainfall-runoff events
CN
Ungauged basins
topic Hydrologic modeling
Rainfall-runoff events
CN
Ungauged basins
description ABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100202
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100202
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2318-0331.241920170199
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
dc.source.none.fl_str_mv RBRH v.24 2019
reponame:RBRH (Online)
instname:Associação Brasileira de Recursos Hídricos (ABRH)
instacron:ABRH
instname_str Associação Brasileira de Recursos Hídricos (ABRH)
instacron_str ABRH
institution ABRH
reponame_str RBRH (Online)
collection RBRH (Online)
repository.name.fl_str_mv RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)
repository.mail.fl_str_mv ||rbrh@abrh.org.br
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