Probabilistic estimation of storm erosion using analytical, semi-empirical, and process based storm erosion models

Callaghan, David P., Ranasinghe, Roshanka and Roelvink, Dano (2013) Probabilistic estimation of storm erosion using analytical, semi-empirical, and process based storm erosion models. Coastal Engineering, 82 64-75. doi:10.1016/j.coastaleng.2013.08.007


Author Callaghan, David P.
Ranasinghe, Roshanka
Roelvink, Dano
Title Probabilistic estimation of storm erosion using analytical, semi-empirical, and process based storm erosion models
Journal name Coastal Engineering   Check publisher's open access policy
ISSN 0378-3839
1872-7379
Publication date 2013-12-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.coastaleng.2013.08.007
Open Access Status Not yet assessed
Volume 82
Start page 64
End page 75
Total pages 12
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract Probabilistic estimates for coastal storm erosion volumes are increasingly being sought by contemporary risk based coastal zone management frameworks. Such estimates can be obtained via probabilistic models that incorporate a structural function element which calculates storm erosion (i.e. storm erosion model). Intuitively, the more sophisticated the storm erosion model embedded in the probabilistic model, the more accurate and robust the probabilistic storm erosion volumes should be, albeit at significant additional computational cost. This study assesses the relative performance of three storm erosion models with varying levels of complexity when embedded within Callaghan et al.'s (2008a) probabilistic framework for estimating storm erosion. The storm models tested are: the analytical Kriebel and Dean (1993) model, the more complex semi-empirical SBEACH model and the highly complex and process-based XBEACH model.
Formatted abstract
Highlights
• Probabilistic predictions of storm erosion using Kriebel and Dean & s/xʙᴇᴀᴄʜ.
• Multiple event calibration is needed for xʙᴇᴀᴄʜ when in a probabilistic model.
• xʙᴇᴀᴄʜ and sʙᴇᴀᴄʜ both provide probabilistic estimates of storm erosion volumes.
• xʙᴇᴀᴄʜ predicts storm erosion volumes with the physically more plausible behaviour.

Probabilistic estimates for coastal storm erosion volumes are increasingly being sought by contemporary risk based coastal zone management frameworks. Such estimates can be obtained via probabilistic models that incorporate a structural function element which calculates storm erosion (i.e. storm erosion model). Intuitively, the more sophisticated the storm erosion model embedded in the probabilistic model, the more accurate and robust the probabilistic storm erosion volumes should be, albeit at significant additional computational cost. This study assesses the relative performance of three storm erosion models with varying levels of complexity when embedded within Callaghan et al.'s (2008a) probabilistic framework for estimating storm erosion. The storm models tested are: the analytical Kriebel and Dean (1993) model, the more complex semi-empirical sʙᴇᴀᴄʜ model and the highly complex and process-based xʙᴇᴀᴄʜ model.

The probabilistic model is applied at data rich Narrabeen beach, Australia. Kriebel and Dean (1993) and sʙᴇᴀᴄʜ are used ‘on-line’ in the probabilistic simulations, while xʙᴇᴀᴄʜ is used with an innovative off-line tabulation approach to facilitate reasonable computational times. sʙᴇᴀᴄʜ is calibrated for a mid-range erosion event while xʙᴇᴀᴄʜ is validated for the same single erosion event as well as for all measured storm erosion volumes during the 30 year study period. The Kriebel and Dean (1993) model is used with recommended parameter settings and therefore does not require calibration.

When both sʙᴇᴀᴄʜ and xʙᴇᴀᴄʜ are calibrated against the single erosion event, sʙᴇᴀᴄʜ provides the most accurate and robust probabilistic estimates of storm erosion. However, when xʙᴇᴀᴄʜ is calibrated using the entire erosion volume data series, the results improve significantly raising the accuracy and robustness of the probabilistic estimates of storm erosion volumes obtained with xʙᴇᴀᴄʜ to be on par with those obtained with sʙᴇᴀᴄʜ. However, only xʙᴇᴀᴄʜ predicts storm erosion volumes with the physically more plausible behaviour of a downward concave tail shape when plotted as cross-shore beach-erosion volume on a vertical linear axis against return period on a horizontal logarithmic axis.

The simulation time (on a standard single processor) when using the simple Kriebel and Dean (1993) model is about 1 day, whereas for sʙᴇᴀᴄʜ (on-line) and xʙᴇᴀᴄʜ (tabulation), the simulation time is about 1000 h. However, the physically more plausible and the more accurate and robust results that can be obtained with sʙᴇᴀᴄʜ or xʙᴇᴀᴄʜ justifies the additional computational cost.
Keyword Beach erosion
Probability
BCoastal zone management
Probabilistic modelling
XBEACH
SBEACH
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 1205871
291206 - NEMO
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
Official 2014 Collection
 
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Created: Mon, 16 Sep 2013, 19:52:04 EST by Julie Hunter on behalf of School of Civil Engineering