Comparison of the Three Different Abstraction Estimation Methods of Rainfall in HEC-HMS Model in Runoff Simulation (Case study: Kermanshah Gharasoo watershed)

Document Type : Research Article

Authors

1 M. Sc. Graduate of Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University, Kerman, Iran

2 Department of Water Engineering, University of Tabriz, Tabriz, Iran

3 M. Sc. Hydraulic Structures, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz

Abstract

Rainfall-runoff modeling is one of the key items that considered in hydrology to achieve flood characteristics. In this study, HEC-HMS model performance was evaluated using the SCS-CN, Green-Ampt and Initial and Constant infiltration methods in predicting runoff volume, peak flow and time to peak, in simulation of rainfall- runoff hydrograph at Gharasoo watershed, located in Kermanshah province. Eight rainfall–runoff events were simulated by HEC-HMS model and compared with the corresponding observations events. Results shown a well accuracy in predicting runoff volume (R2=0.84, E=0.81 and CRM=0.06) was achieved using the SCS-CN method (after calibration). However, Peak flow was better estimated using the Initial and Constant method (R2=0.96, E=0.95 and CRM=0.01). Furthermore, shape of the calibrated hydrographs were very similar to the observations hydrographs for both SCS-CN and Initial and Constant methods. However, adopting the Green-Ampt method, showed low reliability in total runoff volume and peak flow estimating. Model accuracy in estimates of modeled the time to floods peak, were evaluated by comparing observed and simulated values through the selected approaches, so that the results of time to floods peak showed the highest reliability in SCS-CN method (6.36%).

Keywords

Main Subjects

References

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Iranian journal of Ecohydrology
Volume 5, Issue 2
July 2018
Pages 433-447

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History

  • Receive Date: 22 May 2017
  • Revise Date: 29 January 2018
  • Accept Date: 01 February 2018
  • First Publish Date: 22 June 2018
  • Publish Date: 22 June 2018

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