Premium
Predicting Bed Shear Stresses in Vegetated Channels
Author(s) -
Etminan Vahid,
Ghisalberti Marco,
Lowe Ryan J.
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr022811
Subject(s) - shear stress , turbulence , turbulence kinetic energy , geology , sediment transport , geotechnical engineering , dissipation , mechanics , shear (geology) , sediment , boundary layer , soil science , environmental science , geomorphology , petrology , physics , thermodynamics
Shear stresses on vegetated beds play an important role in driving a wide range of processes at the sediment‐water interface, including sediment transport. Existing methods for the estimation of bed shear stress are not applicable to vegetated beds due to the significant alteration of the near‐bed velocity profile and turbulence intensities by the vegetation. In addition, bed shear stress distributions are highly spatially variable in the presence of vegetation. In this study, computational fluid dynamics simulations were used to investigate the spatial variability of bed shear stresses in the presence of emergent vegetation (modeled as arrays of circular cylinders) and the variation of bed stress with characteristics of both the bulk flow and the array. A recently proposed model that assumes a linear variation of stress in the viscous layer immediately above the bed is shown to be a reliable tool for estimating the spatially averaged bed shear stress over a wide range of flow conditions and vegetation densities. However, application of this model is found to be restrictive due to the lack of a reliable predictive tool for the thickness of the viscous layer. Based on a balance between turbulent kinetic energy production in the vegetation stem wakes and the viscous dissipation of turbulent kinetic energy at the bed, an enhanced formulation is proposed to predict the thickness of the viscous layer, which significantly improves the accuracy of model predictions. This improved model enhances the predictive capability for important benthic processes (such as sediment transport) in vegetated aquatic systems.