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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-616
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
08 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Including Effects of Watershed Heterogeneity in the Curve Number Method Using Variable Initial Abstraction
Vijay P. Santikari and Lawrence C. Murdoch Department of Environmental Engineering and Earth Sciences, Clemson University, Clemson, SC 29634, USA
Abstract. The curve number (CN) method was developed more than half a century ago and is still used in many watershed/water quality models to estimate direct runoff from a rainfall event. Despite its popularity, the method is plagued by a conceptual problem where CN is assumed to be constant for a given set of watershed conditions, but many field observations show that CN decreases with event rainfall (P). Recent studies indicate that heterogeneity within the watershed is the cause of this behavior, but the governing mechanism remains poorly understood. This study shows that heterogeneity in initial abstraction, Ia, can be used to explain how CN varies with P. By conventional definition, Ia is equal to the cumulative rainfall before the onset of runoff, and is assumed to be constant for a given set of watershed conditions. Our analysis shows that the total storage in Ia (IaT) is constant, but the effective Ia varies with P, and is equal to the filled portion of IaT, which we call IaF. CN calculated using IaF varies with P similar to published field observations. This motivated modifications to the CN method, called Variable Ia Models (VIMs), which replace Ia with IaF. VIMs were evaluated against Conventional Models CM0.2 (λ = 0.2) and CMλ (calibrated λ) in their ability to predict runoff data generated using a distributed parameter CN model. The performance of CM0.2 was the poorest whereas those of the VIMs were the best in predicting overall runoff and watershed heterogeneity. VIMs also predicted the runoff from smaller events better than the CMs, and eliminated the false prediction of zero-runoffs, which is a common shortcoming of the CMs. We conclude that including variable Ia accounts for heterogeneity and improves the performance of the CN method while retaining its simplicity.

Citation: Santikari, V. P. and Murdoch, L. C.: Including Effects of Watershed Heterogeneity in the Curve Number Method Using Variable Initial Abstraction, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-616, in review, 2017.
Vijay P. Santikari and Lawrence C. Murdoch
Vijay P. Santikari and Lawrence C. Murdoch
Vijay P. Santikari and Lawrence C. Murdoch

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Short summary
While working on a land use change study, we uncovered a conceptual problem in the Curve Number (CN) method where CN varies with rainfall (P) although it is expected to remain constant. In this paper, we present a theoretical proof that this behavior is due to watershed heterogeneity. Turning it around, we also show that treating CN method's parameters as functions of P can improve runoff predictions. The findings would benefit hydrologists and watershed models that continue to use the method.
While working on a land use change study, we uncovered a conceptual problem in the Curve Number...
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