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Abstract.A new remote sensing technique based on video image processing has been developed for the estimation of nearshore bathymetry. The shoreward propagation of waves is measured using pixel intensity time series collected at a cross-shore array of locations using remotely operated video cameras. The incident band is identified, and the cross-spectral matrix is calculated for this band.

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[1] Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

To better understand how individual processes combine to cause flooding and erosion events, we investigate the relative contribution of tides, waves, and nontidal residuals to extreme total water levels (TWLs) at the shoreline of U.S. West Coast sandy beaches. Extreme TWLs, defined as the observed annual maximum event and the simulated 100 year return level event, peak in Washington, and are on average larger in Washington and Oregon than in California. The relative contribution of wave‐induced and still water levels (SWL) to the 100 year TWL event is similar to that of the annual maximum event; however, the contribution of storm surge to the SWL doubles across events. Understanding the regional variability of TWLs will lead to a better understanding of how sea level rise, changes in storminess, and possible changes in the frequency of major El Niños may impact future coastal flooding and erosion along the U.S. West Coast and elsewhere.

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