Abstract

Estimates of extreme sea levels have mainly been derived using atmospheric and tidal forcing only, but surface gravity waves are a key process that can cause a substantial elevation of mean sea level at the coast. In this paper a coupled wave-circulation model was used to simulate storm surge and wave setup across a broad range of coastal topographies and storm types around Australia. The main aim was a practical assessment of the benefits and limitations of using a coupled wave-surge model to determine wave effects for extreme sea level studies on the continental scale. The simulations included: tropical cyclones Yasi (2011) and George (2007) on the northeast and northwest coasts of Australia; Cyclone Alby (1978) that underwent extratropical transition in southwest Australia; and a large extratropical winter storm in the Southern Ocean (2016). Predicted wave setup was highly variable both spatially and temporally, with values up to 0.45 m, 35% of maximum storm surge height, and 9% of incident significant wave heights. In general, where storm surge heights were smaller, the relative importance of wave setup increased in the presence of large waves. The model best resolved wave setup in shallow gently sloping coastal areas, whilst unrealistically low values were predicted for steep coastlines. Predictions improved when wave setup was included for most sites and events, however, these wave effects were often secondary to other assumptions implicit in modeling extreme events. This was especially true for tropical cyclones where reliable wind forcing was not available. The results showed similar contributions from wave setup to other studies but highlighted the high temporal and spatial variability of wave setup across a wider variety of storms and coastal morphologies. The results also revealed that the maximum wave setup did not always coincide with the maximum storm surge. Overall, the coupled wave-surge model presented a useful tool to predict extreme water levels including wave setup when accurate depth and atmospheric data are available, however, significant challenges still exist to model this process at the continental scale.

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