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Event Based Modeling of a Watershed Using HEC-HMS

Authors: H. K. Nandalal; U. R. Ratmayake;

Event Based Modeling of a Watershed Using HEC-HMS

Abstract

Modeling a watershed can be very much useful to quantify water resources for effective system management. Rainfall runoff models are very much useful in forecasting floods, which will enable taking mitigation measures. Kalu-Ganga River basin in Sri Lanka is subjected to frequent floods. This paper presents a rainfall-runoff model developed for this basin using HEC-HMS lumped conceptual hydrologic model. Two different models, one having four sub basins and the other having ten sub basins were formulated. They were calibrated and verified using four historical flood events. Streamflow data at three gauging stations along the river were used in the calibration and verification. The resulting hydrographs at these three gauging stations were compared with the observed hydrographs based on flood peaks, time to peak and the Nash Sutcliffe coefficient The results show the suitability of the HECHMS software in the modeling of the Kalu-Ganga River basin. Further the results of the two models indicate that there is no impact of the number of sub basins considered in the modeling of the basin on the prediction of floods due to rainfalls.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Average
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