https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:36314 Wed 01 Apr 2020 14:33:03 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33488 Tue 11 Oct 2022 09:02:22 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39866 Q_b) is a key parameter for parametric surf zone models. It is via this variable that these models control the energy dissipation in the surf zone. Historically, Q_b has been obtained using probability distribution functions (PDFs) of the wave height (p(H)). This paper describes an alternative, data-driven approach to obtaining the fraction of broken waves that is a significant improvement over the more traditional approaches. This new model is based on an ensemble of regression trees in which Q_b is learnt directly from an extensive field dataset. The ensemble uses three input parameters that are often available to coastal engineers: offshore significant wave height (𝐻_𝑚0∞), offshore peak wave period (𝑇_𝑚01∞), and time-averaged relative water depths relative to the mean sea level (h/𝐻_𝑚0∞), and predicts Q_b at an averaged given relative water depth. The results indicate that the model can predict the depth-dependent variability of Q_b with a high degree of accuracy (averaged r² ≥ 0.95, averaged root mean square error ≤ 0.05, averaged mean absolute error ≤ 0.04) in virtually no computational time. When compared to three widely used Q_b models that are derived from PDFs of the wave heights, the model developed here showed significant improvement with reductions in the errors (average error reduction of 25%) and significant improvement for r²-scores (average increase ≥ 30%). Although complex, the method developed here could be advantageous over the more traditional approach because of its high degree of precision and accuracy and because it does not depend on prior knowledge of p(H). In summary, the present model could be used as a replacement for the formulation of Q_b in parametric wave models, which should result in better overall predictions, and thus, in better coastal management tools.]]> Thu 21 Jul 2022 09:34:35 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:36264 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.]]> Thu 01 Jun 2023 18:46:58 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34017 b) is a fundamental variable in parametric wave height transformation models. It plays a key role in quantifying how much energy dissipation occurs due to wave breaking. Several authors have used different parameterisations to account for Q_b; however, to the authors' knowledge, very few studies have experimentally obtained a value for the fraction of broken waves across the surf zone using field data. This paper addresses this issue by describing a methodology to quantify Q_b using a combination measured pressure transducer data and remotely sensed data collected at the northern end of Seven Mile Beach, Gerroa, NSW. Pixel intensity timeseries were extracted from a timestack at the exact locations where the pressure transducers were deployed. These timeseries are compared to individual waves identified in the pressure record and the waves are classified as broken if a strong pixel peak matches a wave crest. When compared to visually identified waves, the broken wave classification algorithm was found to be correct 94.25% of the time. Results indicate that Q_b is inversely proportional to water depth but highly variable at similar mean water depths. The variability in Q_b showed a degree of correlation with the variation in the ratio between short (seaswell) and long (infragravity) wave energy in the inner surf zone. Probability density functions for all waves and broken waves are calculated and results indicate that wave heights in the surf zone (broken and unbroken) are not Rayleigh distributed. In fact, wave height distributions were statistically different to the Rayleigh distribution for all cases analysed, whereas they are fully described by a normal distribution in 87.65% of the cases for broken waves and in 80.25% of the cases for all waves.]]> Fri 01 Feb 2019 10:37:09 AEDT ]]>