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Coastal canopies formed by aquatic vegetation can significantly modify nearshore wave processes, which has motivated numerous laboratory and field studies investigating how wave attenuation occurs for a wide range of canopies as a function of wave conditions and canopy properties. To predict wave dissipation by canopies, numerous formulations have been derived to describe canopy drag through use of empirical drag coefficients. In many cases, drag coefficients are used as a tuning parameter to account for complex processes that govern how wave-driven flows interact with individual plants within canopies. This approach, however, can greatly limit the predictive capabilities of both empirical and numerical models. In this study, we extend a non-hydrostatic (phase-resolving) depth-integrated wave model (XBeach non-hydrostatic) with an efficient and robust subgrid canopy flow model to account for the important properties of submerged canopy flows that govern canopy drag forces and wave dissipation. A combination of wave heights, velocities and direct canopy force measurements, as well as data obtained from literature, were used to validate the model, showing the model has good skill in capturing the governing flow dynamics for both rigid and flexible vegetation canopies. By incorporating the canopy flow dynamics, the extended model allows for application of well-established drag coefficients for individual canopy elements to accurately predict wave attenuation over submerged vegetation canopies, rather than relying on empirical formulations or calibration. With the current extension, the model remains computationally efficient, allowing for use on coastal (∼km) scales.
•A canopy flow subgrid model was implemented in the open-source XBeach model.•The model was verified using observed wave heights, velocities and canopy forces.•It can accurately predict wave evolution over rigid and flexible submerged canopies.•The model is robust and computationally efficient for use in large scale applications.•Not accounting for canopy flow can lead to substantial errors in wave predictions.