|Title||Improving resilience to storm surge hazards|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Authors||Gravois, U, Baldock, T, Callaghan, D, Davies, G, Jiang, W, D, M, Nichol, S|
|Publisher||Bushfire and Natural Hazards CRC|
Winds, waves and tides associated with storms are capable of causing severe damage to coastal property and infrastructure. Locations that are prone to erosion and inundation first require an accurate assessment of risk before deciding the most cost effective mitigation option. This research aims to produce probabilistic assessments of the coastal erosion and inundation risks associated with storms, particularly for coincident or clustered events, thereby helping to strengthen the resilience of coastal communities.
Coastal erosion and inundation hazard is modelled in this study by simulations of realistic storm condition forcing (waves and tides) through a morphodynamic model to calculate return periods for maximum extent of shoreline retreat. This approach of estimating erosion return periods is superior to the assumption that the most energetic storm causes maximum erosion. The methodology is demonstrated at Old Bar, NSW, which us currently an erosion hotspot. The model will also be applied for the metropolitan Adelaide beaches. These sites were selected to test the methodology for a span of geographic conditions in terms of storm climate and deep-water wave exposure, working towards developing this method into a transportable framework applicable to other coastal areas.
Desktop and field assessments of each site were conducted to document geomorphic and sediment characteristics to inform shoreline modelling. Having established the historical framework at each location, multivariate statistical analysis of wave (buoy or hindcast models) and tides for peak storm events has allowed for the synthesis of realistic future conditions. This complex sequencing of cycling between accretion and erosion incorporating cross-shore and alongshore sediment transport has been estimated using a probabilistic shoreline translation model. Here, model outputs for Old Bar are illustrated, which indicate a complex response over decadal time frames. Further work will then assess risk to infrastructure based on the most probable envelope of shoreline position. This information can then be used to inform coastal management strategies.