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Evaluation of flow resistance in gravel-bed rivers through a large field data set
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Evaluation of flow resistance in gravel-bed rivers through a large field data set
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Evaluation of flow resistance in gravel-bed rivers through a large field data set
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Journal Article
Evaluation of flow resistance in gravel-bed rivers through a large field data set
2011
Overview
A data set of 2890 field measurements was used to test the ability of several conventional flow resistance equations to predict mean flow velocity in gravel bed rivers when used with no calibration. The tests were performed using both flow depth and discharge as input since discharge may be a more reliable measure of flow conditions in shallow flows. Generally better predictions are obtained when using flow discharge as input. The results indicate that the Manning‐Strickler and the Keulegan equations show considerable disagreement with observed flow velocities for flow depths smaller than 10 times the characteristic grain diameter. Most equations show some systematic deviation for small relative flow depth. The use of new definitions for dimensionless variables in terms of nondimensional hydraulic geometry equations allows the development of a new flow resistance equation. The best overall performance is obtained by the Ferguson approach, which combines two power law flow resistance equations that are different for deep and shallow flows. To use this approach with flow discharge as input, a logarithmic matching equation in terms of the new dimensionless variables is proposed. For the domains of intermediate and large‐scale roughness, the field data indicate a considerable increase in flow resistance as compared with the domain of small‐scale roughness. The Ferguson approach is used to discuss the importance of flow resistance partitioning for bed load transport calculations at flow conditions with intermediate‐ and large‐scale roughness in natural gravel, cobble, and boulder bed streams. Key Points Some conventional flow resistance equations should not be used in shallow flows Between‐site and at‐a‐site variation of flow resistance show similarities Flow resistance partitioning can improve bedload transport predictions
