Local bathymetry and onshore features can have a substantial effect on the spatial variability of impact from an incoming tsunami. In a warning context, being able to provide localised tsunami forecasts at strategic locations would therefore mitigate the damage posed. Despite the recent advancements in computing powers and the development of highly efficient tsunami codes, capturing this local variability can oftentimes be unfeasible in a warning setting. Traditional high resolution simulations which can capture these localised effects are often too costly to run ‘on-the-fly’. Alternative approaches which capture the localised response to an incoming tsunami, which are based upon utilising the maximum wave heights from a computationally cheap regional forecast, are developed here. These alternative approaches are envisaged to aid in a warning centre’s ability at providing extremely rapid localised forecasts. The approaches focus upon two different methods: transfer functions and machine learning techniques. The transfer functions are based upon recent extensions to the established Green’s Law. The extended versions introduce site specific amplification parameters, with the aim of capturing the neglected localised effects. An automative approach which optimises for these site specific parameters is outlined and the performance of these transfer functions is explored. A machine learning model is also trained and utilised to predict the localised tsunami hazard. Its performance is compared to the extended Green’s Law approach for several sites along the French coast. These developed methods showcase promising techniques that a tsunami warning centre could utilise to provide high resolution warnings.

Operational ocean and wave models are used to produce forecasts for navigation but also required for a wide range of services, most of them relying on particle tracking methods. Improving the forecast capabilities and accuracy of a model is then a constant necessity in order to deliver more reliable services. The Marine Institute in Ireland is running several coastal operational models, one of them focuses on the area of Galway Bay in the west coast. At the moment it consists of a stand-alone ocean application, it is the purpose of the work presented here to set-up a coupled application with a wave model. Coupled models are a recent development in ocean modelling, developer teams have included ocean and wave coupling by combining existing models each dedicated to a specific physics. Two theoretical formulations are mostly used for the implementation, both giving the same equations of evolution and interaction terms known as the vortex-force formalism. One approach is using a Lagrangian framework defining an exact averaged operator following the fluid particles, the other approach is Eulerian making use of a multi-scale expansion. In both cases the larger current components are found to be forced by gravity and infra-gravity waves. The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) modelling system is a widely used code, the vortex-force formalism has been implemented in 2012 coupling the Regional Ocean Modeling System (ROMS) with the Simulation Wave Nearshore wave model (SWAN). The implementation has been validated with academic cases and used in several real case studies in the last decade. The work presented here is making use of COAWST, a coupled model is set-up for Galway Bay running a 1-year hind-cast application for 2017 and preliminary results are shown here. The performance of the coupled model is compared with each stand-alone model, using in-situ data as a reference. In the last releases of COAWST the wave model WAVEWATCHIII has been added and can be used in the coupled system. This new feature is tested and the results are compared against SWAN, both wave codes are solving the same equations but different technical choices have been made resulting in different capabilities.