Depth inversion using Argus images and accounting for nonlinearities

Abstract

cBathy is a recently developed tool designed to predict the nearshore bathymetry using Argus images. The algorithm estimates the water depth in a predefined analysis domain. For each analysis location the dominant wave frequencies are detected through spectral analysis while the wavenumber is given by the phase structure of the first EOF of the cross spectral matrix. Subsequently the linear dispersion relationship is inverted and solved yielding a depth estimate. However, past studies have found the latter to be inaccurate in a nonlinear environment. The current work aims at both the application of cBathy in the Dutch coast of Egmond aan Zee and the improvement of the algorithm in the surf zone by testing different nonlinear celerity predictors. The algorithm is well compared with field measurements over a big domain and for the offshore part. A closer examination in the intertidal zone and over a cross shore transect reveals that cBathy predicts celerity systematically higher than linear theory. Errors of O(0.5 m) are found inside the inner surf zone where a composite model for phase speed that incorporates both amplitude and dispersion effects predicts better the bathymetry. Higher errors occur on the difficult to be modeled location of the onset of breaking (O(1 m)) where the solitary model matches better with the ground truth data. In addition, performance of the algorithm substantially degrades during stormy conditions due to camera issues with bathymetric errors being O(>1 m).