https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46460 Wed 23 Nov 2022 14:25:59 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41962 Wed 22 Mar 2023 18:11:16 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:37303 Wed 20 Jan 2021 17:17:43 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34865 Wed 14 Jun 2023 17:22:01 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47135 Wed 14 Dec 2022 15:13:41 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33489 Wed 04 Sep 2019 09:39:45 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40519 Wed 04 Oct 2023 10:51:37 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34300 2%) and maximum runup (R_max) were highly variable between models, with predictions shown to vary by a factor of 1.5 for the same incident wave conditions. No single model provided the best predictions on all beaches in the dataset. Overall, model root mean square errors are of the order of 25% of the R_2% value. Models for R_2% derived from field data were shown to be more accurate for predicting runup in the field than those developed from laboratory data, which overestimate the field data significantly. The most accurate existing models for predicting R_2% were those developed by Holman [12] and Vousdoukas et al. [40], with mean RMSE errors of 0.30 m or 25%. A new model-of-models for R_2% was developed from a best fit to the predictions from six existing field and one large scale laboratory R_2% data-derived models. It uses the Hunt [17] scaling parameter tanβ√H₀L₀ and incorporates a setup parameterisation. This model is shown to be as accurate as the Holman and Vousdoukas et al. models across all tidal stages. It also yielded the smallest maximum error across the dataset. The most accurate predictions for R_max were given by Hunt [17] but this tended to under predict the observed maximum runup obtained for 15-min records. Mase's [22] model has larger errors but yielded more conservative estimates. Greater observed values of R_max are expected with increased record length, leading to greater differences in predicted values. Given the large variation in predictions across all models, however, it is clear that predictions by uncalibrated runup models on a given beach may be prone to significant error and this should be considered when using such models for coastal management purposes. It should be noted that in extreme events, which are lacking in the dataset, runup may be truncated by beach scarps, cliffs, and dunes, or may overtop, and as a result, the probability density functions will have different tail shapes. The uncertainty already present in current models is likely to increase in such conditions.]]> Tue 26 Feb 2019 14:15:34 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:37845 Tue 18 May 2021 15:48:03 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33791 W from the Puysegur and New Hebrides trenches as well as representations of historical events from Chile in 1960 and Japan in 2011. TsuDAT wave trains include those modelled from the Tonga, Chile, New Hebrides and Puysegur trenches and have ARI ranging from 200 to 36500. Using the hydrodynamic model ANUGA, results show that the events modelled have the potential to cause terrestrial inundation, high current speeds, hazardous depths and rapid changes in water level. Maximum current speeds vary in both range and magnitude according to location. Tsunami wave trains sourced from lower magnitude events and those with lower ARI result in lower maximum current speeds. As T2 source event increases in magnitude and TsuDAT ARI increases, maximum current speeds become higher. For T2 wave trains of ≤8.5M_W, although current speeds may be a hazard, terrestrial inundation is minimal. As the T2 wave trains increase in source magnitude, inundation becomes apparent at Manly and in the low-lying embayments on the south side of the harbour. For the most impactful wave trains, TsuDAT wave trains which include ARI from 5000-36500, inundation is widespread and in some locations occurs above 10m elevation.]]> Tue 15 Jan 2019 12:58:00 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54259 Tue 13 Feb 2024 13:33:07 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19325 Tue 11 Oct 2022 09:02:33 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:28005 pd) is introduced that combines the cross-reef distance from the reef crest (X_d) and the temporally specific along-reef distance from the first point of wave breaking on the reef rim (X_p, where X_pd= X_d+ X_p). X_pd is shown to accurately describe the changes in wave height and sediment entrainment if deep water significant wave height, wave direction, and depth over the reef flat are known. The results in this study shows that wave conditions, sediment entrainment, and longer term trends in sediment characteristics can be predicted in back-reef environments from a few simple geomorphic inputs.]]> Tue 11 Oct 2022 09:02:30 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:36547 80% (> 99%) of seismic-derived depths were within ± 2% (± 5%) of multibeam DBMs. At 2σ, 94% of differences are in the range - 3.48 to 1.69% (- 2.73% to 2.44%) for the 50-m (5-m) multibeam DBM. Increasing morphological complexity and slope angle were the most important factors affecting DBM comparisons, with seismic-derived depths typically underestimated in canyon thalwegs. Despite these differences, the higher data density, multichannel stacking and migration of the 3D seismic data resulted in seismic-derived DBMs with high resolution and improved feature relief and clarity when compared to multibeam DBMs for the conditions in this study (depths of 120 - 1900m), particularly for morphological features such as individual rills and gullies. This method has the potential to expand the spatial coverage of high-resolution DBMs, for example, in Australia, by over 150,000km2.]]> Tue 11 Oct 2022 09:01:10 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32930 Tue 11 Oct 2022 08:56:03 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45908
This paper investigates the rates of SLR in two coastal lakes in NSW - Lake Macquarie and Lake Illawarra, the latter of which had entrance training completed in 2008. Tide gauge data is used to assess trends in exceedance probability values of low, median and high water levels represented by 95%, 50% and 5% exceedance probability respectively within coastal lakes and ocean conditions at Patonga for proxy. In addition, the relationship between coastal lake water levels and ENSO are investigated. Within Lake Macquarie both median and high water levels have shown significant increases, however, high water levels have shown the greatest increases most noticeable in the entrance channel at Swansea. This indicates increases in water level range and increased exposure to ocean tides and conditions. ENSO, represented by the Southern Oscillation Index (SOI) was shown to be responsible for up to 6% of water level variability within Lake Macquarie, highlighting the need to incorporate large-scale oscillations when assessing potential inundation hazards in these systems.

Lake Illawarra exhibited a response to entrance training through a significant increase in high water levels within the lake. Since entrance training, high water levels have increased at rate of up to 9.3 mm per year which is over three times the global SLR estimate. The minimal association between water level variation within Lake Illawarra and the SOI, together with increased water level ranges due to rapid annual increases in high water levels indicated a dynamic response to entrance training. This dynamic nature highlights the necessity of regular monitoring of SLR within the lake and robust inundation hazard modelling.]]> Tue 08 Nov 2022 09:25:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45907 Tue 08 Nov 2022 09:18:06 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38407 60 kilometre long, >10 metre thick valley-wide deposit of finely laminated mud. This previously unrecognised sediment trap persisted from 8,518 to 5,067 cal yr BP preventing sediment delivery to the marine environment. Its identification requires that mid-Holocene climate reconstructions for southeastern Australia based on fluctuations in the delivery of fine-grained sediment to the ocean offshore the lower Murray River's mouth must be re-evaluated.]]> Tue 07 Sep 2021 12:17:32 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39957 th percentile of the maximum wave amplitudes (over time) of the relevant T2 scenario within each coastal zone (P95). Threshold values for P95 have previously been derived through analysis of observed impacts for recent events. Given that historical records are available for only a short time period and no observations exist for which a Land Threat would have been issued for Australia, it has been difficult to determine the appropriate threshold for a Land Threat. Several recent tsunami hazard assessment studies have used inundation models nested within T2 scenarios. These modelling results are used to evaluate the threshold values for JATWC tsunami warnings and provide guidance on a possible further warning tier - Major Land Threat. The optimum Land Threat threshold for P95 is found to be 48.5cm, however, it is not recommended that any changes are made from the existing operational threshold of 55cm. The optimum threshold for P95 a Major Land Threat is found to be 150.5 cm.]]> Tue 04 Oct 2022 15:13:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33490 Tue 03 Sep 2019 17:54:21 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34010 Thu 31 Jan 2019 10:14:09 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45388 Thu 27 Oct 2022 15:22:27 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:39858 Thu 21 Jul 2022 09:34:26 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35909 2 m/s), wave amplification and rapid changes in water level. Significant land inundation only occurs for scenarios modelled with the largest waves (9.0 M_W source). The degree of exposure to the open ocean and the geomorphology of locations within the Harbour determine the relative level of these impacts. Narrow, shallow channels, even those sheltered from the open ocean, create a bottleneck effect and experience the highest relative current speeds as well as elevated water levels. The largest maximum water levels (>8 m) occur in exposed, funnel-shaped bays and wave amplification is greatest at locations exposed to the open ocean: >7 times deep water wave heights for 9.0 M_W source waves. Upstream attenuation rates of runup and maximum water level show a linear correlation with wave height parameters at the 100 m depth contour and may provide some predictive capabilities for potential tsunami impacts at analogous locations. In the event of a tsunami in Sydney Harbour, impacts may threaten marine traffic and infrastructure.]]> Thu 16 Jan 2020 13:25:09 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50908 20 cm were found across tidal regimes and continents, demonstrating that the peaks approach is preferable over the constituent approach where such differences are non-negligible. An open source Tide Peaks Toolbox for use in MATLAB is presented for computing peak-based datums from any high frequency water level observation record.]]> Thu 10 Aug 2023 13:45:31 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:18293 Sat 24 Mar 2018 08:04:26 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19139 s=1m) oblique wave forcing. In-situ wave and current data, and bathymetric data were also collected. Beach morphology was dominated by a large mid-beach rip channel with lesser headland rip channels. Mean flow rates were 0.6ms⁻¹ in the mid-beach channel and 0.4ms⁻¹ in the headland channels, with the majority of cross-shore water volume flux (~60%) through the central channel. A weak alongshore current O (0.1ms⁻¹) was forced by the oblique offshore wave angle. Rip current velocities, flow variability, and rate of surfzone exits by Lagrangian drifters increased as water level decreased. Transient currents on a planar bar along the northern half of the beach, with mean speeds velocity standard deviation up to 0.2ms⁻¹, were not tidally modulated. Lagrangian time series were used to differentiate four current regimes (rip cell, rip head, planar bar and offshore low energy zone) based on mean velocity, velocity variability and degree of tidal modulation. An increase in surfzone exit rates by drifters was observed from south (upwave) to north (downwave), with exit rates per drifter deployment of 22% at the south headland rip, 65% at the mid-beach open rip, and 80% at the north headland rip. The high rate of drifter exits contrasts previous observations on open coast beaches. Observed flow behaviours are attributed to wave shadowing at the upwave (protected) end of the beach, and longshore currents forced by oblique waves deflected offshore at the downwave headland. These field observations are in good agreement with recent numerical modelling. A relationship between bathymetric variability and current intensity was determined, with cross-shore average mean velocity correlating with a parameterisation of bathymetric alongshore non-uniformity. This study demonstrates that flow behaviour and exchange rates can vary along the length of an embayed beach due to geological control. This research has implications for transport of organisms, nutrients and pollutants, is relevant to beach safety practitioners, and can be used in calibration of numerical models.]]> Sat 24 Mar 2018 07:55:55 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21137 0.05. Hz) to long-wave (f < 0.05. Hz) frequency bands is found to be significantly different between the three beach types. Swash energy at short-wave frequencies is dominant on reflective and intermediate beaches and swash at long-wave frequencies is dominant on dissipative beaches; consistent with previously reported spectral signatures for the surf zone on these beach types. The available swash spectra were classified using an automated algorithm (CLARA) into five different classes. The ordered classes represent an evolution in the spectrum shape, described by a frequency downshifting of the energy peak from the short-wave into the long-wave frequency band and an increase in the long-wave swash energy level compared to a relatively minor variation in the short-wave swash energy level. A universally common feature of spectra from all beach-states was an ƒ⁻⁴ energy roll-off in the short-wave frequency band. In contrast to the broadly uniform appearance of the short-wave frequency band, the appearance of the long wave frequency band was highly variable across the beach-states. We incorporate the results presented here and previously published observations into the morphodynamic beach-state model, and propose an ordered sequence of swash spectra under increasing and decreasing incident wave energy level. This extension of the beach-state model to include the swash zone leads to the following propositions for morphodynamic controls on the nature of the swash spectrum. (1) The short-wave part of the swash spectrum is relatively constant in form across all beach-states (ƒ⁻⁴ energy roll-off) and the energy density per unit frequency is controlled by the beach face gradient alone. (2) The spectral bandwidth of the energy roll-off varies directly with offshore wave energy level and inversely with beach face gradient (or beach-state), in a manner consistent with the non-linear wave breaking criterion. (3) The infragravity part of the swash spectrum is highly variable in form across all beach-states and the energy level is related to the offshore wave energy level and surf zone morphology.]]> Sat 24 Mar 2018 07:53:53 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22805 Sat 24 Mar 2018 07:15:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22806 Sat 24 Mar 2018 07:15:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24804 Sat 24 Mar 2018 07:15:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24817 Sat 24 Mar 2018 07:15:13 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24613 Sat 24 Mar 2018 07:11:55 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24072 RIP1= 0.66, t¯_RIP2 = 2.68 min) in both systems, but durations and distances to safety in the RBB system often exceeded swimming abilities of weaker bathers. Although Swim Onshore was more successful (t¯_RIP1= 0.22, t¯_RIP2= 1.65 min) than Swim Parallel, promotion of such a strategy is strongly discouraged in conventional safety advice. Results suggest that contemporary rip current escape strategies may be inappropriate in non-TBR rip current systems and that alternative strategies should be considered, including Swim Onshore and a greater focus on preventative strategies, particularly in relation to bathers with limited swimming ability.]]> Sat 24 Mar 2018 07:09:39 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33334 Mon 30 Sep 2019 13:21:49 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32738 Mon 23 Sep 2019 12:59:59 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46550 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.]]> Mon 23 Jan 2023 11:48:08 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41139 Mon 23 Jan 2023 11:40:30 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46112 Mon 23 Jan 2023 09:12:02 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42688 Mon 15 May 2023 13:23:28 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:55289 Mon 13 May 2024 09:10:04 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54190 Mon 12 Feb 2024 14:22:02 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33491 Mon 05 Nov 2018 14:34:18 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41305 Mon 01 Aug 2022 12:23:44 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54953 Fri 22 Mar 2024 15:29:20 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42243  0.85) at the outer reef flat and small γs values (γs < 0.1) at the inner reef flat. This indicates that there is an increase in wave energy dissipation in shallow water, most likely due to increased breaker and bed frictional dissipation. The measured wave energy dissipation across the entire reef flat could, on average, be modelled accurately; however, this required location specific calibration of the free parameters, the wave friction factor (fw) and γ, and further suggests that there is no value for either parameter that is universally applicable to coral reef flats. Despite model calibration inaccuracies were still observed, primarily at the outer reef flat. These inaccuracies reflected the observed cross-reef variation of γ on the reef flat and potentially the limitations of random wave breaker dissipation models in complex surf zones. Our results have implications for the use of wave energy dissipation models in predicting breaker dissipation and subsequent benthic community change on coral reef flats, and suggest that careful consideration of the free parameters in such models (such as fw and γ) is required.]]> Fri 19 Aug 2022 11:56:44 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46568 Fri 17 Feb 2023 16:26:43 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:36265 Fri 10 Mar 2023 14:51:22 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53609 Fri 08 Dec 2023 16:08:17 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:53589 Fri 08 Dec 2023 15:49:09 AEDT ]]> 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 ]]>