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

Atkinson, Alexander L., Power, Hannah E., Moura, Theo, Hammond, Tim, Callaghan, David P. and Baldock, Tom E. (2017) Assessment of runup predictions by empirical models on non-truncated beaches on the south-east Australian coast. Coastal Engineering, 119 15-31. doi:10.1016/j.coastaleng.2016.10.001


Author Atkinson, Alexander L.
Power, Hannah E.
Moura, Theo
Hammond, Tim
Callaghan, David P.
Baldock, Tom E.
Title Assessment of runup predictions by empirical models on non-truncated beaches on the south-east Australian coast
Journal name Coastal Engineering   Check publisher's open access policy
ISSN 0378-3839
1872-7379
Publication date 2017-01-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.coastaleng.2016.10.001
Open Access Status Not yet assessed
Volume 119
Start page 15
End page 31
Total pages 17
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Subject 2305 Environmental Engineering
2212 Ocean Engineering
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 (R-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 tan beta root 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.
Formatted 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 0.30 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 √HoLo 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 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.
Keyword Beaches
Model accuracy
Remote sensing
Runup
Swash
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID LP100100375
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
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