The Fraction of Broken Waves in Natural Surf Zones

Abstract

This paper presents a novel quantification of the fraction of broken waves (Q b ) in natural surf zones using data from seven microtidal, wave-dominated, sandy Australian beaches. Q b is a critical, but rarely quantified, parameter for parametric surf zone energy dissipation models, which are commonly used as coastal management tools. Here, Q b is quantified using a combination of remote sensing and in situ data. These data and machine learning techniques enable quantification of Q b for a substantial data set (>330,000 waves). The results show that Q b is a highly variable parameter with a high degree of interbeach and intrabeach variability. Such variability could be correlated to environmental parameters: tidal variations correlated with changes in Q b of up to 70% for a given local water depth (h) on a low tide terrace beach, and increased infragravity relative to sea-swell energy correlated to lower values of Q b at the surf-swash boundary. Q b also correlates well with the Australian beach morphodynamic model: For more dissipative beaches Q b increases rapidly in the outer surf zone, whereas for more reflective beaches Q b increases slowly throughout the surf zone. Finally, when comparing data to existing models, three commonly used theoretical formulations for Q b are observed to be poor predictors with errors of the order of 40%. Existing theoretical Q b models are shown to improve (revised errors of the order of 10%) if the Rayleigh probability distribution that describes the wave height is in these models is replaced by the Weibull distribution. Plain Language SummaryAn observer looking at a beach from a headland or sand dune can easily distinguish between the waves that are breaking and those that are not. Several wave models that are used as coastal management tools require the percentage of waves that are broken to describe how the wave energy changes as the waves travel across the surf zone. Despite its importance, this "fraction of broken waves" has rarely been quantified for natural beaches. In this paper, we use a combination of video cameras to image the waves and sensors that directly record the waves in the water to quantify the number of broken waves as a percentage of the total number of waves for several beaches. This allows us to accurately distinguish between broken and unbroken waves, obtaining a large field data set of what the fraction of broken waves is and how it varies between beaches. We also compared our data with three widely used models and showed that these models are poor predictors with errors of approximately 40%. We could reduce these errors to approximately 10% using our field data to better describe the probability of a given wave being broken.

Non‐hydrostatic, Non‐linear Processes in the Surf Zone

“…The largest 𝜇 values, which coincide with the highest ⟨𝜁 2 ⟩, correspond to the boundary between the outer surf zone and the shoaling region, where the largest waves first break. The scatter observed in Figure 3 can be explained by the slight intertidal variations in wave conditions observed during the 2 days considered here (Martins, Blenkinsopp, Power, et al, 2017) as well as by the natural variation of Q b in natural surf zones (Stringari & Power, 2019).…”

Simulating storm waves in the nearshore area using spectral model: Current issues and a pragmatic solution

“…The accuracy of the total wave energy dissipation rate estimates strongly depends on the fraction of breaking waves Q b , which remains a poorly understood quantity due to complications associated with its measurements and its natural variability (e.g., see Thornton and Guza, 1983;Stringari and Power, 2019;Martins et al, 2020).…”

Deep neural networks for active wave breaking classification

“…Due to a lack of observed data, the constants involved in these models have been derived from limited datasets that may not adequately represent the natural environment. For example, a recent study has shown that popular surf zone parametric wave breaking models incorrectly represented the fraction of broken waves in their formulations with errors > 50%, despite these models being able to adequately represent surf zone energy dissipation possibly due to parameter tuning 10 . This paper aims to start addressing the data unavailability issue by providing a reliable and reproducible method to detect and track waves that are actively breaking in video imagery data.…”