https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Prediction of wave runup on beaches using gene-expression programming and empirical relationships https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34865 Wed 14 Jun 2023 17:22:01 AEST ]]> High-resolution monitoring of wave transformation in the surf zone using a LiDAR scanner array https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33489 Wed 04 Sep 2019 09:39:45 AEST ]]> Assessment of runup predictions by empirical models on non-truncated beaches on the south-east Australian coast https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34300 2%) and maximum runup (Rmax) 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 R2% value. Models for R2% 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 R2% 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 R2% was developed from a best fit to the predictions from six existing field and one large scale laboratory R2% 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 Rmax 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 Rmax 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 ]]> Flow convergence at the tip and edges of a viscous swash front - experimental and analytical modeling https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19325 Tue 11 Oct 2022 09:02:33 AEDT ]]> An improved method for computing tidal datums 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 ]]> A novel method for tracking individual waves in the surf zone https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24613 Sat 24 Mar 2018 07:11:55 AEDT ]]> A novel machine learning algorithm for tracking remotely sensed waves in the surf zone https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:36265 Fri 10 Mar 2023 14:51:22 AEDT ]]>