A modified SCS-CN method incorporating storm duration and antecedent soil moisture estimation for runoff prediction

A - Papers appearing in refereed journals

Shi, W., Huang, M., Gongadze, K. and Wu, L. 2017. A modified SCS-CN method incorporating storm duration and antecedent soil moisture estimation for runoff prediction. Water Resources Management. 31, pp. 1713-1727.

AuthorsShi, W., Huang, M., Gongadze, K. and Wu, L.
Abstract

In one of the widely used methods to estimate surface runoff - Soil Conservation Service Curve Number (SCS-CN), the antecedent moisture condition (AMC) is categorized into three AMC levels causing irrational abrupt jumps in estimated runoff. A few improved SCS-CN methods have been developed to overcome several in-built inconsistencies in the soil moisture accounting (SMA) procedure that lies behind the SCS-CN method. However, these methods still inherit the structural inconsistency in the SMA procedure. In this study, a modified SCS-CN method was proposed based on the revised SMA procedure incorporating storm duration and a physical formulation for estimating antecedent soil moisture (V 0 ). The proposed formulation for V 0 estimation has shown a high degree of applicability in simulating the temporal pattern of soil moisture in the experimental plot. The modified method was calibrated and validated using a dataset of 189 storm-runoff events from two experimental watersheds in the Chinese Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 88% in both calibration and validation cases, performed better than the original SCS-CN and the Singh et al. (2015) method, a modified SCS-CN method based on SMA. The proposed method was then applied to a third watershed using the tabulated CN value and the parameters of the minimum infiltration rate (f c ) and coefficient (β) derived for the first two watersheds. The root mean square error between the measured and predicted runoff values was improved from 6 mm to 1 mm. Moreover, the parameter sensitivity analysis indicated that the potential maximum retention (S) parameter is the most sensitive, followed by f c . It can be concluded that the modified SCS-CN method, may predict surface runoff more accurately in the Chinese Loess Plateau.

Year of Publication2017
JournalWater Resources Management
Journal citation31, pp. 1713-1727
Digital Object Identifier (DOI)doi:10.1007/s11269-017-1610-0
Open accessPublished as non-open access
FunderNatural Environment Research Council
NSFC
Funder project or codeModelling and managing critical zone relationships between soil, water and ecosystem processes across the Loess Plateau
41571130082
Output statusPublished
Publication dates
Print02 Mar 2017
Publication process dates
Accepted22 Feb 2017
PublisherSpringer
Copyright licenseCC BY
ISSN0920-4741

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