Journal
MATHEMATICS
Volume 9, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/math9080812
Keywords
rainfall-runoff model; curve number; inferential statistics; 3D runoff difference model; model calibration
Categories
Funding
- Institute of Postgraduate Studies & Research (IPSR) of Universiti Tunku Abdul Rahman (UTAR) [IPSR/RMC/UTARRF/2019-C2/L07]
- Brunsfield Engineering Sdn. Bhd., Malaysia [Brunsfield 8013/0002]
- FRGS from the Centre for Environmental Sustainability and Water Security of Universiti Teknologi Malaysia [RJ130000.7809.4F208]
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This article reassesses the effectiveness of the SCS Curve Number (CN0.2) runoff model and performs model calibration using inferential statistics. Results show that the uncalibrated SCS model underestimates runoff amounts for rainfall depths less than 70 mm, while overpredicting in larger storm events. The study highlights the importance of validating the SCS model with rainfall-runoff datasets before its application for runoff prediction.
Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN0.2) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km(2), it underpredicted runoff amount the most (2.4 million liters) at CN0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.
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