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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. 

Coastal wetlands act as natural buffers against wave energy and storm surges. In the course of energy dissipation, vegetation stems are exposed to wave action, which may lead to stem breakage. An integral component of wave attenuation modeling involves quantifying the extent of damaged vegetation, which relies on determining the maximum drag force (FDmax) and maximum moment of drag (MDmax) experienced by vegetation stems. Existing closed-form theoretical equations for MDmax and FDmax are only valid for linear and weakly nonlinear deep water waves. To address this limitation, this study first establishes an extensive synthetic dataset encompassing 256,450 wave and vegetation scenarios. Their corresponding wave crests, wave troughs, MDmax, and FDmax, which compose the dataset, are numerically computed through an efficient algorithm capable of fast computing fully nonlinear surface gravity waves in arbitrary depth. Seven dominant wave and vegetation related dimensionless parameters that impact MDmax and FDmax are discerned and incorporated as input feature parameters into an innovative sparse regression algorithm to reveal the underlying nonlinear relationships between MDmax, FDmax and the input features. Sparse regression is a subfield of machine learning that primarily focuses on identifying a subset of relevant feature functions from a feature function library. Leveraging this synthetic dataset and the power of sparse regression, concise yet accurate closed-form equations for MDmax and FDmax are developed. The discovered equations exhibit good accuracy compared with the ground truth in the synthetic dataset, with a maximum relative error below 6.6% and a mean relative error below 1.4%. Practical applications of these equations involve assessment of the extent of damaged vegetation under wave impact and estimation of MDmax and FDmax on cylindrical structures. 

Abstract This study investigates the sensitivity of the Calcasieu Lake estuarine region to channel deepening in southwest Louisiana in the USA. We test the hypothesis that the depth increase in a navigational channel in an estuarine region results in the amplification of the inland penetration of storm surge, thereby increasing the flood vulnerability of the region. We run numerical experiments using the Delft3D modeling suite (validated with observational data) with different historic channel depth scenarios. Model results show that channel deepening facilitates increased water movement into the lake–estuary system during a storm surge event. The inland peak water level increases by 37% in the presence of the deepest channel. Moreover, the peak volumetric flow rate increases by 291.6% along the navigational channel. Furthermore, the tidal prism and the volume of surge prism passing through the channel inlet increase by 487% and 153.3%, respectively. In our study, the presence of the deepest channel results in extra 56.72 km²of flooded area (approximately 12% increase) which is an indication that channel deepening over the years has rendered the region more vulnerable to hurricane-induced flooding. The study also analyzes the impact of channel deepening on storm surge in estuaries under different future sea-level rise (SLR) scenarios. Simulations suggest that even the most conservative scenario of SLR will cause an approximately 51% increase in flooded area in the presence of the deepest ship channel, thereby suggesting that rising sea level will cause increased surge penetration and increased flood risk.