Assessment of runup predictions by empirical models on non-truncated beaches on the south-east Australian coast

Abstract

This paper assesses the accuracy of 11 existing runup models against field data collected under moderate wave conditions from 11 non-truncated beaches in New South Wales and Queensland, Australia. Beach types spanned the full range of intermediate beach types from low tide terrace to longshore bar and trough. Model predictions for both the 2% runup exceedance (R2%) 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 0.30m 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 tanβHoLo 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.