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S. Mishra, R. Sahu, T. Eldho and M. Jain, “An Improved IaS Relation Incorporating Antecedent Moisture in SCS-CN Methodology,” Water Resources Management, Vol. 20, No. 5, 2006, pp. 643-660. doi:10.1007/s11269-005-9000-4

has been cited by the following article:

• TITLE: Comparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation for Klang Watershed in Malaysia

  AUTHORS: Reza Kabiri, Andrew Chan, Ramani Bai

  KEYWORDS: SCS Curve Number; Green-Ampt; Loss Method; GIS; HEC-Geo-HMS; HEC-HMS; Runoff; Flood Modeling

  JOURNAL NAME: Open Journal of Modern Hydrology, Vol.3 No.3, July 5, 2013

  ABSTRACT: The main aim in this research is comparison the parameters of some storm events in the watershed using two loss models in Unit hydrograph method by HEC-HMS. SCS Curve Number and Green-Ampt methods by developing loss model as a major component in runoff and flood modeling. The study is conducted in the Kuala Lumpurwatershed with674 km2 area located in Klang basin inMalaysia. The catchment delineation is generated for the Klang watershed to get sub-watershed parameters by using HEC-GeoHMS extension in ARCGIS. Then all the necessary parameters are assigned to the models applied in this study to run the runoff and flood model. The results showed that there was no significant difference between the SCS-CN and Green-Ampt loss method applied in the Klang watershed. Estimated direct runoff and Peak discharge (r = 0.98) indicates a statistically positive correlations between the results of the study. And also it has been attempted to use objective functions in HEC-HMS (percent error peaks and percent error volume) to classify the methods. The selection of best method is on the base of considering least difference between the results of simulation to observed events in hydrographs so that it can address which model is suit for runoff-flood simulation in Klang watershed. Results showed that SCS CN and Green-Ampt methods, in three events by fitting with percent error in peak and percent error in volume had no significant difference.