\section{Conclusion}
This paper proposes a new field data benchmark for the
validation of tsunami inundation models. Currently, there is a
scarcity of appropriate validation datasets due to a lack of well-documented
historical tsunami impacts. The benchmark proposed here
utilises the uniquely large amount of observational data for model
comparison obtained during, and immediately following, the
Sumatra--Andaman tsunami of 26 December 2004. The proposed benchmark is intended to aid validation of tsunami inundation, which is the most important stage 
of tsunami evolution. However individual tests are presented to 
facilitate model evaluation for the generation and propagation 
phases as well. In an attempt to provide higher visibility and easier
accessibility for tsunami benchmark problems, the data used to
construct the proposed benchmark is documented and freely available at
\url{http://tinyurl.com/patong2004-data}.

 An associated aim of this paper was to further validate the
\textsc{ursga--anuga} tsunami modelling methodology employed by Geoscience 
Australia which is used to simulate tsunami inundation. 
This study shows that the tsunami modelling methodology adopted is credible 
and able to predict detailed inundation extents and dynamics with reasonable accuracy. 
Model predictions matched well a detailed inundation survey
of Patong City, Thailand as well as altimetry data from the \textsc{jason} satellite, 
eye-witness accounts of wave front arrival times and onshore flow speeds.

A simple sensitivity analysis was performed to assess the influence of
small changes in friction, wave height at the 100 m depth contour and
the presence of buildings on the model
predictions. Of these three, the presence of buildings was shown to 
have the greatest influence on the simulated inundation extent. This result 
indicates that the influence of human-made structures should be included, 
where possible, in any future studies. The value of friction and small
perturbations in the waveheight at the \textsc{anuga} boundary have
comparatively little effect on the model results.
