General model for stage–discharge prediction in multi-stage compound channels

ABSTRACT

Simple and accurate estimates of stage–discharge characteristics are extremely important for supporting appropriate management of two- or multi-stage compound channels, which commonly form in natural rivers or are constructed in regulation projects for urban rivers. Although various methods are available for conveyance estimation of two-stage compound channels, very few are concerned with the multi-stage case. After a critical review of predictive methods for two-stage channels, this paper presents a general model for predicting the stage–discharge relationship in multi-stage compound channels. A multi-stage compound section was divided laterally into several homogeneous subsections, which were further classified into seven subdivision elements. In each element, we employed a general expression to analyse momentum balance, which accounts for momentum exchange between adjacent subsections. Then, a linear system with a tridiagonal coefficient matrix was used to estimate discharge in multi-stage compound channels. Experimental data from the literature were used to calibrate model parameters and examine the performance of the proposed model. The comparative results indicate that the proposed model produces satisfactory predictions. Finally, the main model parameters were discussed.

Keywords:

• Apparent shear stress
• compound channel
• discharge prediction
• momentum transfer
• stage–discharge relationship

Acknowledgements

We thank Steven Hunter, M.S, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. The authors are grateful to the anonymous reviewers for providing numerous constructive suggestions.

ORCID

Gang Chen http://orcid.org/0000-0001-7491-257X

Additional information

Funding

Financial support for this work was provided by the Major Science and Technology Program for Water Pollution Control and Treatment [grant number 2013ZX07102-006] and the Key Program of Natural Science Foundation of China [grant number 51439007].