Seagrass blade dynamics were explored through numerical and laboratory experiments in order to improve parameterization of wave attenuation by submerged aquatic vegetation in the presence of a background current. In the numerical model, a single blade was modeled as a series of rigid plates attached by torsion springs. For the laboratory model, strips of low‐density polyethylene were placed in a recirculating wave flume. A new form of the Keulegan—Carpenter number based on the horizontal excursion of the blade tip was found to be an excellent predictor of drag coefficient. An algebraic model for predicting wave attenuation was developed based on the following observations. During the portion of the wave period when the fluid velocities are highest, the blade motion is almost completely arrested and the vast majority of the turbulence production occurs during this time. Turbulence production when the blade is pronated is accurately predicted by the maximum fluid velocity over the wave period. The relative contribution to the total turbulence production over the wave period is determined by the relative strength of the waves and the current. Therefore, using a simple algebraic fit, the total depth‐integrated, time‐averaged turbulence production can be accurately predicted by two flow parameters: the maximum fluid velocity over the wave period, and a non‐dimensional number that compares the wave and current velocities. By fitting the algebraic model to data from a particular site, it can be used to efficiently estimate wave attenuation and drag coefficient in seagrass exposed to waves with a background current.

Laboratory experiments were used to evaluate and improve modelling of combined wave-current flow through submerged aquatic canopies. Horizontal in-canopy particle image velocimetry (PIV) and wavemaker-measurement synchronization allowed direct volume averaging and ensemble averaging by wave phase, which were used to fully resolve the volume-averaged unsteady momentum budget. Parameterizations for the drag, Reynolds stress, vertical advection, wake production and shear production were tested against the laboratory measurements. The drag was found to have small errors due to unsteadiness and the finite aspect ratio of the canopy elements. The Smagorinsky model for the Reynolds stress showed much better agreement with the measurements than the quadratic friction parameterization used in the literature. A proposed parameterization for the vertical advection based on linear wave theory was also found to be effective and is much more computationally efficient than solving the pressure Poisson equation. A simple 1D 0-equation Reynolds-averaged Navier-Stokes (RANS) model was developed to use these parameterizations. The basic framework of the model is an extrapolation from previous 2-and 3-box models to N boxes. While the resolution of the model is flexible, the filter length for the Smagorinsky parameterization has to be chosen appropriately. With the proper filter length, the N-box model demonstrated good agreement with the measurements at both low and high resolution. Scaling analysis was used to establish a region of parameter space where the N-box model is expected to be effective. The following conditions define this region: the wave-induced velocity is of similar or greater magnitude than the background current, the drag to shear length ratio is small enough to produce canopy behaviour, the wave orbital excursion is not much larger than the drag length, the Froude number is small and the canopy is under shallow submergence, yet far from emergent. Under these assumptions, the dominant balance is between pressure and unsteadiness, the drag is secondary, and the other terms are small. The simple Reynolds stress parameterization in the N-box model is appropriate under these conditions because the Reynolds stress is unlikely to be the dominant source of error. This finding is important because the Reynolds stress is typically one of the dominant drivers of computational cost and model complexity. Based on these findings, the N-box model is expected to be a practical tool for a wide range of combined wave-current canopy flows because of its simplicity and computational efficiency.

Historically, submerged vegetative canopies have either been reported as or modeled after unispecific examples—communities comprised of only a single vegetative species or element type. Field surveys of a shallow Florida Bay seagrass meadow highlighted a more diverse benthic landscape. Although dominated by Thalassia testudinum, the communities were distinctly multispecific, composed of a mixture of both plant and algal species. Strap‐like seagrass elements defined the upper portion of these canopies (the upperstory) while broad‐bodied algal species were found concentrated close to the bed (the understory). To predict the hydrodynamic implications of this dual‐story canopy structure, we derived a new canopy flow attenuation model, formulated to account for vertical canopy heterogeneities like those seen at our field site. The model was validated through a series of laboratory experiments: multispecific canopy mimics were installed in a current‐wave flume and exposed to a range of unidirectional and oscillatory flows. Mean and fluctuating velocity was measured above and within each canopy to determine vegetation‐induced flow attenuation. Velocities near the bed were markedly reduced through the addition of understory elements, results that were consistent with model predictions. These findings suggest that accurate prediction of flow‐regulated processes like sediment transport and propagule dissemination depends on a thorough accounting of community composition. These properties are also expected to change in response to seasonal variability and episodic environmental stresses.

The interaction of surface waves and currents with kelp forests was examined under controlled conditions using a dynamically matched 1/25-scale physical model in a laboratory flume. In experiments with kelp mimics, waves increased the time-averaged drag by a factor of 2 and altered the shape of current profiles. Relative motion between model kelp and water under waves increased wake generation of turbulence, resulting in turbulent kinetic energies 2-5 times larger, and eddy viscosities 20-50% larger, than for experiments without waves. Because mixing lengths were reduced to wake-scales in the model kelp forest, eddy viscosities were 25-50% smaller than when kelp was absent. In the model kelp-forest surface canopy where solid obstacles were most densely spaced, wave orbital velocities were reduced by , 10% from linear wave theory predictions. This decrease in wave orbital velocities is thought to result primarily from inertial forces exerted on water by model kelp. Stokes drift was reduced by , 20% as a result of the change in wave orbital velocities. Although hydrodynamics within kelp forests are more complex than in the laboratory experiments and a wide range of flow conditions can occur, laboratory results suggest that (1) kelp forest drag is increased by waves; (2) wave properties can be altered by drag and inertial forces; and (3) wake production of turbulence caused by waves may be the main source of turbulence in dense kelp stands. Interactions between kelp and waves must therefore be taken into account when developing models for drag and mixing in these systems.