Regional-Scale Forecasting for Coastal Storm Hazard Early Warning Systems

Abstract

Sandy beach and dune systems often provide coastal communities with the first line of defence from the impacts of extreme storm events. During a storm’s approach, communities have a crucial opportunity to take preemptive actions to minimise the social, economic, and environmental impacts of the storm. Predicting coastal storm hazards, especially at the regional scale, however, is challenging due to the complexities of the hydrodynamic and morphodynamic processes occurring on erodible coastlines in high wave energy conditions. This thesis examines the nature and severity of coastal storm hazards and investigates approaches to effectively forecast these hazards for operational Early Warning Systems (EWSs). First, a conceptual framework for classifying coastal storm hazards is introduced. The Storm Hazard Matrix presents an integrated approach to categorising coastal flooding and beach erosion hazards. The Flooding Hazard Scale is based on the Storm Impact Scale first proposed by Sallenger (2000). The new Erosion Hazard Scale is based on several different morphological changes in beaches due to storms, including changes in beach width and dune erosion. The framework is demonstrated on two contrasting extreme storm events and successfully distinguishes between the severity of localised coastal flooding and/or beach erosion hazards. The enhanced insight provided by using the framework has the potential to be especially valuable for EWS applications. Next, a simple classification approach to forecast coastal storm erosion hazards based on the Erosion Hazard Scale is developed based on dune impact exposure (Larson et al., 2004), cumulative storm wave energy (Dolan and Davis, 1992; Harley et al., 2009) and the Dune Stability Factor (Armaroli et al., 2012). Two coastal change datasets are used to investigate the performance of the approach in terms of temporal variability and spatial variability. In each dataset, all events or locations of significant erosion impacts are correctly identified. The approach tended to be conservative with few false alarms (and no misses), demonstrating its predictive capability with limited input data and computational resources. Finally, machine learning techniques are investigated to leverage the increasing availability of coastal topographic and hydrodynamic data. A gradient boosted random forest ensemble model is trained using an extreme coastal storm erosion hazard dataset. The model is demonstrated by hindcasting regional-scale coastal storm erosion hazards over a period of 5 years. Additionally, an investigation of the training data requirements and interpretation of the model feature importance characteristics are also performed. The findings and insights discussed in this thesis represent the state-of-the-art approaches to forecasting coastal storm hazards at the regional scale and can serve to inform the implementation of future EWSs