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This study presents a novel method for obtaining the probability of wave breaking (Pb) of deep water, dominant wind-sea waves (i.e., waves made of the energy within ±30% of the peak wave frequency) derived from Gaussian wave field theory. For a given input wave spectrum, we demonstrate how it is possible to derive a joint probability density function between wave phase speed (c) and horizontal orbital velocity at the wave crest (u) from which a model for Pb can be obtained. A nonlinear kinematic wave breaking criterion consistent with the Gaussian framework is further proposed. Our model would allow, therefore, the application of the classical wave breaking criterion (i.e., wave breaking occurs if u/c > 1) in spectral wave models which, to the authors' knowledge, has not been done to date. Our results show that the proposed theoretical model has errors in the same order of magnitude as six other historical models when assessed using three field data sets. With optimization of the proposed model's single free parameter, it can become the best performing model for specific data sets. Although our results are promising, additional, more complete wave breaking data sets collected in the field are needed to comprehensively assess the present model, especially in regards to the dependence on phenomena such as direct wind forcing, long wave modulation, and wave directionality. [ABSTRACT FROM AUTHOR]

Bore-bore capture occurs when a faster moving bore captures a slower moving bore or the shoreline while both are propagating shoreward in the surf or swash zones and is quantified here for the first time using wave tracking methods applied to data collected at seven sandy Australian beaches. The results showed that, for the locations where bore-bore capture occurred, there was a high probability (≈40%) of bore-bore capture occurring in the surf or swash zones. Of these ≈45% consisted of a bore capturing the instantaneous shoreline (i.e., they occurred in the swash zone). The landward-most 10% of the nearshore region (i.e., the time-varying surf-swash extent) was found to be the most likely location for a capture event. Infragravity wave energy (Eig) at the shoreline, number of bore-bore capture events, and beach morphodynamic state are shown to be correlated: more dissipative beaches had higher levels of Eig at the shoreline which directly correlated with higher rates of bore-bore capture. Approximately 20% of the capture events led to extreme horizontal shoreline maxima; however, >97% of extreme shoreline maxima were directly driven by bore-bore capture events. There was a clear relation between the probability of bore-bore capture driving an extreme shoreline maxima and beach morphodynamic state: The more dissipative the beach, the lower the probability of a bore-bore capture event causing an extreme shoreline maxima event. Such correlation has direct importance for the future development of predictive runup models, which do not currently account for this phenomenon. [ABSTRACT FROM AUTHOR]

This paper presents a novel quantification of the fraction of broken waves (Qb) in natural surf zones using data from seven microtidal, wave‐dominated, sandy Australian beaches. Qb is a critical, but rarely quantified, parameter for parametric surf zone energy dissipation models, which are commonly used as coastal management tools. Here, Qb is quantified using a combination of remote sensing and in situ data. These data and machine learning techniques enable quantification of Qb for a substantial data set (>330,000 waves). The results show that Qb 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 Qb 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 Qb at the surf‐swash boundary. Qb also correlates well with the Australian beach morphodynamic model: For more dissipative beaches Qb increases rapidly in the outer surf zone, whereas for more reflective beaches Qb increases slowly throughout the surf zone. Finally, when comparing data to existing models, three commonly used theoretical formulations for Qb are observed to be poor predictors with errors of the order of 40%. Existing theoretical Qb 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 Summary: An 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. Key Points: A neural network was developed based on pressure transducer data and used to quantify the fraction of broken waves in natural beachesThe fraction of broken waves correlates with tides and infragravity waves and varies according to the beach morphodynamic stateModels for the fraction of broken waves improve by 30% when Rayleigh wave height distributions are replaced by Weibull distributions [ABSTRACT FROM AUTHOR]