ABSTRACT

Martins, K.; Bonneton, P.; Bayle, P.M.; Blenkinsopp, C.E.; Mouragues, A., and Michallet, H., 2020. Surf zone wave measurements from lidar scanners: Analysis of non-hydrostatic processes. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1189-1194. Coconut Creek (Florida), ISSN 0749-0208.

Lidar scanners provide the ability to directly measure the free surface of breaking waves, however it is generally necessary to mount the scanners above the water surface on a nearshore structure such as a jetty. Pressure sensors on the other hand are easy to deploy and remain the simplest alternative to collect field measurements of surf zone waves, the free surface being generally reconstructed from the linear transfer function. Recent studies have highlighted the limitations of this traditional approach to describe geometric properties of nonlinear waves. In the surf zone, this issue remains largely overlooked, principally due to the absence of direct measurements of the free surface elevation. The present contribution addresses this gap by using data collected by collocated sub-surface pressure sensors and lidar during the DynaRev set of experiments, which were performed at the prototype scale. During these experiments, a 1:15 sandy beach was exposed to irregular waves for 20 hours, and reached a quasi-equilibrium state at the end of the test phase, exhibiting a bar-terrace profile. In the inner surf zone, errors between 10% and 40% are obtained on second and third-order wave parameters with the classic transfer function based on linear wave theory. Only a recently developed non-linear weakly dispersive reconstruction method is found to be capable of describing wave-by-wave parameters and thus the root-mean square wave height Hrms in the surf zone. This has important implications for the estimation of wave height distributions based on pressure data, which are illustrated here, and calls for a reanalysis of old datasets and reconsideration of hypotheses based on pressure transducer datasets.

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