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Abstract Currents transport sediment, larvae, pollutants, and people across and along the surfzone, creating a dynamic interface between the coastal ocean and shore. Previous field studies of nearshore flows primarily have relied on relatively low spatial resolution deployments of in situ sensors, but the development of remote sensing techniques using optical imagery and naturally occurring foam as a flow tracer has allowed for high spatial resolution observations (on the order of a few meters) across the surfzone. Here, algorithms optical current meter (OCM) and particle image velocimetry (PIV) are extended from previous surfzone applications and used to estimate both cross-shore and alongshore 2-, 10-, and 60-min mean surface currents in the nearshore using imagery from both oblique and nadir viewing angles. Results are compared with in situ current meters throughout the surfzone for a wide range of incident wave heights, directions, and directional spreads. Differences between remotely sensed flows and in situ current meters are smallest for nadir viewing angles, where georectification is simplified. Comparisons of 10-min mean flow estimates from a nadir viewing angle with in situ estimates of alongshore and cross-shore currents had correlationsr²= 0.94 and 0.51 with root-mean-square differences (RMSDs) = 0.07 and 0.16 m s⁻¹for PIV andr²= 0.88 and 0.44 with RMSDs = 0.08 and 0.22 m s⁻¹for OCM. Differences between remotely sensed and in situ cross-shore current estimates are at least partially owing to the difference between onshore-directed mass flux on the surface and offshore-directed undertow in the mid–water column. 

Abstract The cross‐shore transformation of breaking‐wave roller momentum and energy on observed barred surfzone bathymetry is investigated with a two‐phase Reynolds Averaged Navier Stokes model driven with measured incident waves. Modeled wave spectra, wave heights, and wave‐driven increases in the mean water level (setup) agree well with field observations along transects extending from 5‐m water depth to the shoreline. Consistent with prior results the roller forcing contributes 50%–60% to the setup, whereas the advective terms contribute ∼20%, with the contribution of bottom stress largest (up to 20%) for shallow sandbar crest depths. The model simulations suggest that an energy‐flux balance between wave dissipation, roller energy, and roller dissipation is accurate. However, as little as 70% of the modeled wave energy ultimately dissipated by breaking was first transferred from the wave to the roller. Furthermore, of the energy transferred to the roller, 15%–25% is dissipated by turbulence in the water column below the roller, with the majority of energy dissipated in the aerated region or near the roller‐surface interface. The contributions of turbulence to the momentum balance are sensitive to the parameterized turbulent anisotropy, which observations suggest increases with increasing turbulence intensity. Here, modeled turbulent kinetic energy dissipation decreases with increasing depth of the sandbar crest, possibly reflecting a change from plunging (on the steeper offshore slope of the bar) to spilling breakers (over the flatter bar crest and trough). Thus, using a variable roller front slope in the roller‐wave energy flux balance may account for these variations in breaker type. 

Wave-orbital velocities are estimated with particle image velocimetry (PIV) applied to rapid sequences of images of the surfzone surface obtained with a low-cost camera mounted on an amphibious tripod. Time series and spectra of the remotely sensed cross-shore wave-orbital velocities are converted to the depth of colocated acoustic Doppler velocimeters (ADVs), using linear finite depth theory. These converted velocities are similar to the velocities measured in situ (mean nRMSE for time series =16% and for spectra =10%). Small discrepancies between depth-attenuated surface and in situ currents may be owing to errors in the surface velocity measurements, uncertainties in the water depth, the vertical elevation of the ADVs, and the neglect of nonlinear effects when using linear finite depth theory. These results show the potential to obtain spatially dense estimates of wave velocities 

Waves running up and down the beach (‘swash’) at the landward edge of the ocean can cause changes to the beach topology, can erode dunes, and can result in inland flooding. Despite the importance of swash, field observations are difficult to obtain in the thin, bubbly, and potentially sediment laden fluid layers. Here, swash excursions along an Atlantic Ocean beach are estimated with a new framework, V-BeachNet, that uses a fully convolutional network to distinguish between sand and the moving edge of the wave in rapid sequences of images. V-BeachNet is trained with 16 randomly selected and manually segmented images of the swash zone, and is used to estimate swash excursions along 200 m of the shoreline by automatically segmenting four 1-h sequences of images that span a range of incident wave conditions. Data from a scanning lidar system are used to validate the swash estimates along a cross-shore transect within the camera field of view. V-BeachNet estimates of swash spectra, significant wave heights, and wave-driven setup (increases in the mean water level) agree with those estimated from the lidar data. 

This archive contains field data used to investigate swash velocities by Britt Raubenheimer, Steve Elgar, and Alexandra Muscalus. The quality controlled cross-shore velocity time series are in the .zip folder "u", and the quality-controlled alongshore velocity time series are in the .zip folder "v". Details about the files and data are provided in README.txt. For further questions, please contact Britt Raubenheimer at braubenheimer@whoi.edu, Steve Elgar at elgar@whoi.edu, or Alexandra Muscalus at alexandra.muscalus@whoi.edu Support was provided by the National Science Foundation, the US Coastal Research Program, and the WHOI Build the Base Program. 

This archive contains 2-min mean remotely sensed surface currents generated using optical imagery and PIV (Dooley et al., 2024) from field experiments (2013, 2018, 2021, 2022) occurring at the U.S. Army Corps of Engineers Field Research Facility, in Duck, NC. The README.txt file contains information regarding variables found in the MATLAB (PVLAB_PIV_SurfaceFlows.mat) file, including estimate locations, units, and timestamps. Estimates were generated at times with appropriate conditions for remote sensing (e.g., sufficient foam tracer) that were within 1-day of measured bathymetry at the field site. Additional data for the field site (obtained by the USACE Field Research Facility or by NOAA) including the measured bathymetry and wave conditions can be found at: https://chlthredds.erdc.dren.mil/thredds/catalog/frf/catalog.html Remote sensing estimates are not perfect and errors in filtering out bad data are possible. Please reach out to Ciara Dooley at cdooley@whoi.edu, Steve Elgar at selgar@mac.com, and Britt Raubenheimer at braubenheimer@whoi.edu if you have any questions. Additionally, the optical imagery (large dataset, many TBs) used to generate flow estimates can be accessed by contacting the authors. 

Model results from a double barred system in the surfzone