The Faculty of Engineering

West Indian Journal of Engineering

Volume 44 Number 2 Jan 2022

Quantifying Wave Runup in Data-Sparse Locations for Planning

by Deborah Villarroel-Lamb, and Andrew H. Williams

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Abstract: The determination of wave runup is important to coastal management, including engineering designs and hazard assessments. In data-sparse regions such as the Caribbean, where critical coastal parameters are lacking for adequate decision-making, optimal use must be made of limited datasets to access continuous wave runup data. A video camera system was established at Mayaro Beach in Trinidad and collected video data for a short duration. The waterline variations were rectified and then digitised by sampling pixel intensities along a cross-shore transect. A wave runup time series of 15-minute duration was generated to represent the selected hour of video, from which statistical wave runup estimates including the maximum runup, Rmax, and the runup exceeded by 2% of swash events, Ru2%, were determined. Numerous expressions exist to estimate runup elevations, with the Stockdon et al. (2006) Ru2% predictor being a good performer. The predictive skill of this formulation was assessed, by comparing the measured and predicted magnitudes of the Ru2% using a calibrated/validated model for wave parameters. For the video data analysed, it was found that the coefficient of determination (R2) and the root mean square error (RMSE) were 0.414 and 0.673m for the Stockdon et al. (2006) predictor, but improved to 0.587 and 0.055m using a modified predictor, respectively. Disparities between predicted and observed values were attributed primarily to site-specific conditions and the lack of concurrent in-situ wave data and beach slope characteristics; these were accounted for using the modified predictor and thus enabled an improved wave runup description at the data-sparse site.

Keywords: Video Camera; Image Processing; Wave Runup; Remote Monitoring; Coastal Risk; Beach Data.

https://doi.org/10.47412/GDWM2126