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Evaluation of flow resistance equations for high gradient rivers using geometric standard deviation of bed material

Version 4 2024-03-12, 20:00
Version 3 2023-10-29, 17:15
journal contribution
posted on 2024-03-12, 20:00 authored by Anshul Yadav, Sumit Sen, Luca MaoLuca Mao, Wolfgang Schwanghart
<p>A dataset of 2184 field measurements reported in the literature was used to evaluate the predictive capability of eight conventional flow resistance equations to predict the mean flow velocity in gravel-bed rivers. The results reveal considerable disagreement with the observed flow velocities for relative submergence less than 4 and for the non-uniformity of the bed material greater than 7.5 for all the equations. However, the predictions made using the Smart and Jäggi, 1983, Ferguson, 2007, and Rickenmann and Recking (2011) equations were closer to the observed values. Furthermore, bedload sediment transport also reduces the predictive capability of the equations considered in this study except for the Recking et al. (2008) equation, which was developed considering active bedload transport. The performance of flow resistance equations improves when corrected by considering the geometric standard deviation of the bed material. Here we present an empirical approach using the whole dataset and its subsets for accounting for the additional energy losses occurring due to the wake vortices, spill losses, and free surface instabilities occurring due to the protrusions from the bed. The results obtained using the validation dataset shows the importance and usefulness of this approach to account for the additional energy losses, especially for the Strickler, 1923, Keulegan, 1938 equations.</p>

History

School affiliated with

  • Department of Geography (Research Outputs)

Publication Title

Journal of Hydrology

Volume

605

Pages/Article Number

127292

Publisher

Elsevier

ISSN

0022-1694

Date Submitted

2022-01-31

Date Accepted

2021-11-24

Date of First Publication

2021-12-11

Date of Final Publication

2022-02-28

Date Document First Uploaded

2022-01-25

ePrints ID

47884

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