source: anuga_work/production/onslow_2006/report/modelling_methodology.tex @ 4147

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(1) updates to Dampier script based on Perth script (2) minor updates to Onslow report

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2Geoscience Australia aims to define the economic and social threat posed to urban communities
3by a range of rapid onset natural hazards. Through the integration of natural hazard research, defining national exposure and
4estimating socio-economic vulnerabilities, predictions of the likely impacts of events can be made.
5By modelling the likely impacts on urban communities as accurately as possible and
6building these estimates into land use planning and emergency
7management, communities will be better prepared to respond to
8natural disasters when they occur.
11%GA bases its risk modelling on the process of understanding the hazard and a community's
12%vulnerability in order to determine the impact of a particular hazard event.
13%The resultant risk relies on an assessment of the likelihood of the event.
14%An overall risk assessment for a particular hazard would then rely on scaling
15%each event's impact by its likelihood.
17To develop a tsunami risk assessment,
18the tsunami hazard itself must first be understood. These events are generally modelled by converting
19the energy released by a subduction earthquake into a vertical displacement of the ocean surface.
20%Tsunami hazard models have been available for some time.
21The resulting wave is
22then propagated across a sometimes vast stretch of ocean towards the
23area of interest.
24%using a relatively coarse model
25%based on bathymetries with a typical resolution of two arc minutes.
26The hazard itself is then reported as a maximum wave height at a fixed contour line near the coastline,
27(e.g. 50m). This is how the preliminary tsunami hazard assessment was reported by GA
28to FESA in September 2005 \cite{BC:FESA} for a suite of Mw 9 earthquakes
29evenly spaced along the Sunda Arc subduction zone.
30The assessment used the Method of Splitting Tsunamis (MOST)
31\cite{VT:MOST} model. This preliminary hazard map had no probability attached to the event which is
32required to conduct a tsunami risk assessment. Using
33the Probabilistic Tsunami Hazard Analysis \cite{somerville:urs} paradigm
34used by the URS corporation, a detailed probabilistic hazard map has now been completed
35for the WA coastline \cite{prob:fesa}.
37While MOST and URS are suitable for generating and propagating the tsunami wave from its source,
38they are not adequate to
39model the wave's impact on communities ashore. 
40To capture the \emph{impact} of a tsunami to a coastal community,
41the model must be capable of capturing more detail about the wave,
42particularly how it is affected by the local bathymetry, as well as the
43local topography as the wave moves onshore.
44%the details of how waves are reflected and otherwise
45%shaped by the local bathymetries as well as the dynamics of the
46%runup process onto the topography in question.
47It is well known that local bathymetric and topographic effects are
48critical in determining the severity of a hydrological disaster
49\cite{matsuyama:1999}. To model the impact of the tsunami wave on the
50coastal community, we use ANUGA \cite{ON:modsim}. In order to capture the
51details of the wave and its interactions, a much finer resolution is
52required than that of the hazard model. As a result, ANUGA simulations concentrate
53on specific coastal communities. MOST and URS by contrast use a
54coarser resolution and cover often vast areas. To develop the impact
55from an earthquake event from a distant source, we adopt a hybrid approach of
56modelling the event itself with MOST or URS and modelling the impact with ANUGA.
57In this way, the output from MOST or URS serve as an input to ANUGA.
58In modelling terms, the MOST or URS output is a boundary condition for ANUGA.
59Further details
60regarding the inundation modelling requirements for this study can be found in
61Appendix \ref{sec:anugasetup}.
63The risk of a given tsunami scenario can only be determined when the likelihood
64of the event is known. The probabilistic hazard map for WA \cite{prob:fesa}
65calculates which events pose the most threat to an identified region. Figure
66\ref{fig:probonslow} describes the probability of each event generated along the Java trench
67impacting Onslow. For example, an event generated at point A end would have a
68smaller chance of impacting Onslow than an event generated at point B.
72%\centerline{ \includegraphics[width=140mm, height=100mm]{../report_figures/}}
74  \caption{}
75  \label{fig:probonslow}
78To prepare a tsunami risk assessment, a number of events will be chosen
79for a range of probabilities (or return periods). As Figure \ref{fig:probonslow}
80shows, for a given probability, a number of events are possible. The resulting
81impact to Onslow would then vary depending on the source of the event.  The
82tsunami scenarios selected for the tsunami risk assessment
83are discussed in Section \ref{sec:tsunamiscenario}.
85% used for the 2005 report when looking at one event
86%\bigskip %FIXME (Ole): Should this be a subsection even?
87%The risk of a given tsunami scenario cannot be determined until the
88%likelihood of the tsunami is known. GA is currently building a
89%complete probabilistic hazard map which is due for completion
90%in late 2006. We therefore report on the impact of a single
91%tsunami event only. When the hazard map is completed, the impact
92%will be assessed for a range of events which will ultimately
93%determine a tsunami risk assessment for the NW shelf.
95%FESA is interested in the ``most frequent worst case scenario''. Whilst
96%we currently cannot determine exactly what that event may be, the
97%preliminary hazard assessment suggested that the maximum
98%magnitude of earthquakes off Java was considered to be at
99%least 8.5 and could potentially be as high as 9. Therefore,
100%the Mw 9 event
101%provides a plausible worst case scenario and is used as the tsunami
102%source in this report.
103%Figure \ref{fig:mw9} shows the maximum wave height of a tsunami initiated
104%by a Mw 9 event off
105%the coast of Java. This event provides the source and
106%boundary condition to the
107%inundation model presented in Section \ref{sec:anuga}.
111%  \centerline{ \includegraphics[width=140mm, height=100mm]
114%  \caption{Maximum wave height (in cms) for a Mw 9 event off the
115%coast of Java}
116%  \label{fig:mw9}
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