Author(s): Juey Sui; Todd Whitcombe; Mohammad Reza Namaee

Linked Author(s):

Keywords: Gravel-bed channel; Flow resistance; Relative submergence; Field hydraulic data; Flume hydraulic dat

Abstract: This work develops empirical equations for determining flow resistance through fitting a large number of empirically obtained data. Two models for determining flow resistance are developed to predict mean flow velocity by using an extensive database ( N = 3507) from miscellaneous range of gravel-bed and mountainous rivers, representative of a wide hydraulic and geomorphologic condition. The first model is composed of six individual empirical equations which were fitted to the ratio of mean flow depth and hydraulic radius to coarser percentiles, D 90 and D 84 , and median diameter, D 50 . Results indicate that the model which is fitted to the ratio of hydraulic radius to D 84 is more preferable than the other equations. Since it is difficult to measure flow depth accurately in shallow water, another model which uses discharge as input was developed. A statistical analysis concluded results calculated using either the Ferguson equation or the Rickenmann and Recking equations are in good agreement with the measured values but better agreement is achieved by the newly parameterized equations presented here. In order to analyse the accuracy and the applicability of the proposed equations and the other recently developed flow resistance predictors, the equations were used to predict mean flow velocity of extensive database consisting of N = 1447 flume data. The newly developed equation and the Rickenmann and Recking equations which use discharge as input were also tested by using this new data set. One can see from the results that the new equation can predict mean flow velocity even more accurately than the Rickenmann and Recking equations. The results indicate better agreement between empirical data and calculated results for the newly parameterized equations than existing research.

DOI: https://doi.org/10.1080/15715124.2017.1287710

Year: 2017