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A calibrated, watershed-specific SCS-CN method: Application to Wangjiaqiao watershed in the three Gorges Area, China

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dc.citedby21
dc.contributor.authorLing L.en_US
dc.contributor.authorYusop Z.en_US
dc.contributor.authorYap W.-S.en_US
dc.contributor.authorTan W.L.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorLing J.L.en_US
dc.contributor.authorid56203785300en_US
dc.contributor.authorid6507841909en_US
dc.contributor.authorid14827620500en_US
dc.contributor.authorid55804400500en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57214935382en_US
dc.date.accessioned2023-05-29T08:14:05Z
dc.date.available2023-05-29T08:14:05Z
dc.date.issued2020
dc.descriptionAbstracting; Curve fitting; Forecasting; Rain; Regression analysis; Runoff; Scandium; Soil conservation; Watersheds; Bootstrap; Curve numbers; Inferential statistics; Initial abstraction ratio; Linear regression models; Rainfall-runoff modeling; Residual sum of squares; Soil conservation service curve numbers; Infiltration; bootstrapping; calibration; confidence interval; error analysis; prediction; rainfall-runoff modeling; regression analysis; watershed; Chinaen_US
dc.description.abstractThe Soil Conservation Service curve number (SCS-CN) method is one of the most popular methods used to compute runoff amount due to its few input parameters. However, recent studies challenged the inconsistent runoff results obtained by the method which set the initial abstraction ratio ? as 0.20. This paper developed a watershed-specific SCS-CN calibration method using non-parametric inferential statistics with rainfall-runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for SCS-CN model calibration. Subsequently, the runoff depth and curve number were calculated. The proposed method outperformed the runoff prediction accuracy of the asymptotic curve number fitting method, linear regression model and the conventional SCS-CN model with the highest Nash-Sutcliffe index value of 0.825, the lowest residual sum of squares value of 133.04 and the lowest prediction error. It reduced the residual sum of squares by 66% and the model prediction errors by 96% when compared to the conventional SCS-CN model. The estimated curve number was 72.28, with the confidence interval ranging from 62.06 to 78.00 at a 0.01 confidence interval level for the Wangjiaqiao watershed in China. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo60
dc.identifier.doi10.3390/w12010060
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85079537325
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079537325&doi=10.3390%2fw12010060&partnerID=40&md5=ac8418e0fa3767e99adf59e0cac7460e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25772
dc.identifier.volume12
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleWater (Switzerland)
dc.titleA calibrated, watershed-specific SCS-CN method: Application to Wangjiaqiao watershed in the three Gorges Area, Chinaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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