AbstractRecent earthquake-triggered tsunamis, such as the 2004 Indian Ocean tsunami and the 2011 Tohoku Japan tsunami, have caused tremendous economic losses, which emphasised the necessity of tsunami risk assessment. The development of financial risk transfer instruments for tsunamis requires catastrophe modelling which typically consists of hazard, vulnerability, exposure, and financial modules; among these elements, the hazard modelling is a major source of uncertainty. The current tsunami risk assessment, which is based on one or a few selected earthquake rupture scenarios, is not capable of considering all situations for tsunami hazard. The 2011 Tohoku tsunami revealed the great uncertainty in tsunami hazard assessment by exceeding the hazard level predicted by scenario-based tsunami hazard maps for the Tohoku region. To deal with the uncertainty in earthquake source characterisation, an innovative stochastic tsunami risk assessment framework is developed to take into a wide range of possible tsunami scenarios. This method allows the generation of a large number of stochastic slip distributions by using new scaling relationships for the tsunamigenic earthquakes.
This thesis aims to characterise and quantify major sources of uncertainty in the tsunami catastrophe model. Based on the stochastic tsunami risk assessment framework, the influences of various aspects on tsunami loss estimation are investigated, including resolution of elevation data, selection of tsunami intensity measure, building location (coastal topography, distance from the sea, and land elevation), and earthquake recurrence model. The results showed the importance of these aspects quantitatively, which facilitate the decision-making of different stakeholders for tsunami risk management. A multi-hazard earthquake-tsunami insurance rate-making method is proposed and applied to consider the missing link between earthquake and tsunami catastrophe models. Using the new multi-hazard tool, the pure premium rates for tsunami insurance are differentiated for fair pricing by considering structural and location attributes.
|Date of Award||1 Oct 2019|
|Supervisor||Katsuichiro Goda (Supervisor), Nicholas A Alexander (Supervisor) & Dawei Han (Supervisor)|