Coastal vulnerability assessment based on video wave run-up observations at a mesotidal, steep-sloped beach

Coastal imagery obtained from a coastal video monitoring station installed at Faro Beach, S. Portugal, was combined with topographic data from 40 surveys to generate a total of 456 timestack images. The timestack images were processed in an open-access, freely available graphical user interface (GUI) software, developed to extract and process time series of the cross-shore position of the swash extrema. The generated dataset of 2% wave run-up exceedence values R 2 was used to form empirical formulas, using as input typical hydrodynamic and coastal morphological parameters, generating a best-fit case RMS error of 0.39 m. The R 2 prediction capacity was improved when the shore-normal wind speed component and/or the tidal elevation η tide were included in the parameterizations, further reducing the RMS errors to 0.364 m. Introducing the tidal level appeared to allow a more accurate representation of the increased wave energy dissipation during low tides, while the negative trend between R 2 and the shore-normal wind speed component is probably related to the wind effect on wave breaking. The ratio of the infragravity-to-incident frequency energy contributions to the total swash spectra was in general lower than the ones reported in the literature E infra/E inci > 0.8, since low-frequency contributions at the steep, reflective Faro Beach become more significant mainly during storm conditions. An additional parameterization for the total run-up elevation was derived considering only 222 measurements for which η total,2 exceeded 2 m above MSL and the best-fit case resulted in RMS error of 0.41 m. The equation was applied to predict overwash along Faro Beach for four extreme storm scenarios and the predicted overwash beach sections, corresponded to a percentage of the total length ranging from 36% to 75%.info:eu-repo/semantics/publishedVersio

Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South Portugal

This study discusses site-specific system optimization efforts related to the capability of a coastal video station to monitor intertidal topography. The system consists of two video cameras connected to a PC, and is operating at the meso-tidal, reflective Faro Beach (Algarve coast, S. Portugal). Measurements from the period February 4, 2009 to May 30, 2010 are discussed in this study. Shoreline detection was based on the processing of variance images, considering pixel intensity thresholds for feature extraction, provided by a specially trained artificial neural network (ANN). The obtained shoreline data return rate was 83%, with an average horizontal cross-shore root mean square error (RMSE) of 1.06 m. Several empirical parameterizations and ANN models were tested to estimate the elevations of shoreline contours, using wave and tidal data. Using a manually validated shoreline set, the lowest RMSE (0.18 m) for the vertical elevation was obtained using an ANN while empirical parameterizations based on the tidal elevation and wave run-up height resulted in an RMSE of 0.26 m. These errors were reduced to 0.22 m after applying 3-D data filtering and interpolation of the topographic information generated for each tidal cycle. Average beach-face slope tan(β) RMSE were around 0.02. Tests for a 5-month period of fully automated operation applying the ANN model resulted in an optimal, average, vertical elevation RMSE of 0.22 m, obtained using a one tidal cycle time window and a time-varying beach-face slope. The findings indicate that the use of an ANN in such systems has considerable potential, especially for sites where long-term field data allow efficient training