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Emergency managers have an increasing need for tools to enhance preparedness to extreme coastal storms and support disaster risk reduction measures. With the emergence of Early Warning Systems (EWSs) for coastal storm hazards, a fundamental challenge is the accurate prediction of sandy beach erosion at lead times of days to weeks corresponding to an approaching storm event. This work presents a data-driven modelling approach to predict storm-driven beach erosion (shoreline change) using a large dataset of 276 individual storm events at Narrabeen-Collaroy Beach, SE Australia. Correlation analysis between individual storm characteristics and shoreline response at three locations along the embayment with varying exposure to the prevailing waves indicates that cumulative storm wave energy is the dominant driver of storm erosion at this site. This is followed by the pre-storm beach width, storm wave direction and to a minimal extent, storm wave period and water levels. A multi-linear regression model of storm erosion is developed and found to accurately predict shoreline change due to individual storm events (RMSE = 3.7 m–6.4 m). This work highlights the value of high-frequency shoreline data for storm erosion forecasting and provides a framework for real-time forecasting applications. • Large dataset of shoreline change associated with 276 individual storm events at Narrabeen-Collaroy Beach (SE Australia) is used to develop a multi-linear regression model of storm erosion • Cumulative storm wave energy found to be the dominant driver of storm erosion, followed by the pre-storm beach width and storm wave direction • Water levels during storms only a minor contributor to storm erosion at this wave-dominated site • Data-driven erosion model can be used for real-time applications based on global wave forecast systems.
Ibaceta et al. (Wed,) studied this question.