This section deals with impact modelling which covers damage
modelling and economic impact analysis.

Damage modelling refers to damage
to infrastructure as a result
of the inundation described in the previous sections. The infrastructure
refers to residential structures only and is sourced from the 
the National Building Exposure Database (NBED). The NBED has been
created by Geoscience Australia so that consistent risk assessments for a range
of natural hazards can be 
conducted\footnote{http://www.ga.gov.au/urban/projects/ramp/NBED.jsp}. 
It contains information
about residential buildings, people, infrastructure, 
structure value and building contents.
From this database, we find that there
are 325 residential structures and a population of approximately 770
in Onslow\footnote{Population is determined by census data and an ABS 
housing survey}. 

Impact on indigeneous communities are important considerations when determining
tsunami impact, especially as a number of communities exist in coastal regions.
These communities are typically not included in national residential databases 
and would be therefore overlooked in damage model estimates.
There is one indigeneous community located in this study area as seen
in Figure
\ref{fig:points}. The population of the Bindibindi community is 140 
and is situated below the 1.5m AHD contour as seen in Figure \ref{fig:points}
which indicates it is inundated prior to the tsunami wave arriving.
At 0m AHD, over 3m of water will inundate parts of the community (Figure 
\ref{fig:gaugeBindiBindiCommunity}) indicating 100\% damage of contents.

To develop building damage and casuality estimates, we briefly describe 
residential collapse probability models and casualty models and their 
application to inundation modelling.


There is a paucity of data on the tsunami vulnerability of buildings. 
With reference to the limited data found in the international literature, 
along with reported observations made of building performance during the 
recent Indian Ocean tsunami, vulnerability models have been proposed for 
framed residential construction. The models predict the collapse 
probability for an exposed population and incorporate the following 
parameters known to influence building damage \cite{papathoma:vulnerability}

\begin{itemize}
\item	Inundation Depth at Building	
\item	Building Row From Coast	
\item	Building Material (residential framed construction)	
\item	Inundation Depth at House Above Floor Level
\end{itemize}	

The collapse vulnerability models used are presented in \ref{table:collapse}. 
In applying the model all structures in the inundation zone were 
spatially located and the local water depth and building row 
number from the exposed edge of the suburb were determined for each.

Casualty models were developed by making reference to the 
storm surge models used for the Cairns Cyclone Scenario and 
through consultation with Dr David Cooper of NSW Health, \cite{cooper:2005}. 
The injury probabilities for exposed populations were selected 
based on the nocturnal nature of the event, the collapse outcome 
for the structure, the water depth with respect to 
sleeping height (1.0 m) and the limited warning noise for people 
in the first three city blocks (6 house rows) that could potentially 
awaken them. The three injury categories corresponded with the 
categories presented in HAZUS-MH \cite{NIBS:2003} for earthquake 
related injury. The casualty model used is presented in 
\ref{table:casualty} 
and the injury categories are presented in \ref{table:injury}. 
Input data comprised resident population data at CD level derived 
from the ABS 2001 census.

The damage to the residential structures in the Onslow community
is summarised in Table \ref{table:damageoutput}. The percentage
of repair cost to structural value shown is based on the total structural value
of \$60,187,955. Likewise, the percentage of contents loss shown is
based on the total contents value of \$85,410,060 for
the Onslow region. The injuries sustained in each scenario is summarised
in Table \ref{table:injuries}. Around 21\%
of the population are affected in the 1.5m AHD scenario with around 10\%
affected in the 0m AHD scenario.


\begin{table}[h]
\label{table:damageoutput}
\caption{Residential damage sustained for 1.5m, 0m and -1.5m AHD 
scenarios.}
\begin{center}
\begin{tabular}{|l|l|l|l|l|l|l|}\hline
& Houses  & Houses  & Structural & Repair Cost \% & Contents & Contents Loss \% \\ 
& Inundation & Collapsed & Repair Cost 
& of Total Value & Losses & of Total Value \\ \hline
1.5m AHD & 90 & 14 & \$10,951,887 & 18.2 \% & \$24,020,309 & 28.12 \%\\ \hline
0m AHD & 54 & 1 & \$5,317,783 &  8.8 \% & \$11,592,602 & 13.6 \% \\ \hline
-1.5m AHD & 0 & 0 & 0& 0& 0&  0\\ \hline
\end{tabular}
\end{center}
\end{table}

\begin{table}[h]
\label{table:injuries}
\caption{Injuries sustained for 1.5m, 0m and -1.5m AHD scenarios.}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}\hline
 & Minor & Moderate & Serious & Fatal \\ \hline
1.5m AHD & 59 & 17 & 8 & 83 \\ \hline
0m AHD & 43 & 11 & 6 & 20 \\ \hline
-1.5m AHD & 0 & 0 & 0 & \\ \hline
\end{tabular}
\end{center}
\end{table}

discussion on Mary's outputs