Tsunami hazard models have been available for a while. They generally
work by converting the energy released by a subduction earthquake into
a vertical displacement of the ocean surface. The resulting wave is
then propagated across a sometimes vast stretch ocean using a
relatively coarse model based on bathymetries with a typical
resolution of 2 arc minutes (check this with David). Near a coastline the maximal wave height at a fixed contour line (e.g.\ 50m) is then reported as the 
hazard to communities ashore. 
Models such as Method of Splitting Tsunamies (MOST) \cite{VT:MOST} and ``URS model'' \cite{xxx} follow this paradigm.

To capture the \emph{impact} of a hydrological disaster such as tsunamies on a community one must model the the details of how waves are reflected and otherwise shaped by the local bathymetries as well as the dynamics of the runup process onto the topography in question.
It is well known that local bathymetric and topographic effects are critical in determining the severity of a hydrological disaster (\cite{yyy}). To model the
details of tsunami inundation of a community one must therefore capture what is known as non-linear effects and use a much higher resolution for the elevation data. The model ANUGA (\cite{ON:ANUGA}) is suitable for this type of modelling.
However, using a non-linear model capable of resolving local bathymetric effects and runup using detailed elevation data will require much more computational resources than the typical hazard model making it infeasible to use it for the entire, end-to-end, modelling. 

We have adopted a hybrid approach whereby we use the output from the the 
hazard model MOST as input to ANUGA at the seaward boundary of its study area. In other words, the output of MOST serves as boundary condition for the ANUGA model. In this way, we restrict the computationally intensive part only to 
regions where we are interested in the detailed inundation process.  

Furthermore, to avoid unnecessary computations ANUGA works with an unstructured triangular mesh rather than the rectangular grids used by e.g.\ MOST. The advantage of an unstructured mesh is that different regions can have different resolutions allowing computational resources to be directed where they are needed the most. For example, one might use very high resolution near a community or in an estuary whereas a coarser resolution may be sufficient in deeper water where the bathymetric effects are less pronounced. Figure \ref{fig:xxx} shows a mesh of
variable resolution.


   



 