Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
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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RBRH (Online) |
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|
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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 |
_version_ |
1754734701861404672 |