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2 | Geoscience Australia aims to define the economic and social threat posed to urban communities |
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3 | by a range of rapid onset natural hazards. Through the integration of natural hazard research, defining national exposure and |
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4 | estimating socio-economic vulnerabilities, predictions of the likely impacts of events can be made. |
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5 | By modelling the likely impacts on urban communities as accurately as possible and |
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6 | building these estimates into land use planning and emergency |
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7 | management, communities will be better prepared to respond to |
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8 | natural disasters when they occur. |
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9 | |
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10 | |
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11 | %GA bases its risk modelling on the process of understanding the hazard and a community's |
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12 | %vulnerability in order to determine the impact of a particular hazard event. |
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13 | %The resultant risk relies on an assessment of the likelihood of the event. |
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14 | %An overall risk assessment for a particular hazard would then rely on scaling |
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15 | %each event's impact by its likelihood. |
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16 | |
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17 | To develop a tsunami risk assessment, |
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18 | the tsunami hazard itself must first be understood. These events are generally modelled by converting |
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19 | the energy released by a subduction earthquake into a vertical displacement of the ocean surface. |
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20 | %Tsunami hazard models have been available for some time. |
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21 | The resulting wave is |
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22 | then propagated across a sometimes vast stretch of ocean towards the |
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23 | area of interest. |
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24 | %using a relatively coarse model |
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25 | %based on bathymetries with a typical resolution of two arc minutes. |
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26 | The hazard itself is then reported as a maximum wave height at a fixed contour line near the coastline, |
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27 | (e.g. 50m). This is how the preliminary tsunami hazard assessment was reported by GA |
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28 | to FESA in September 2005 \cite{BC:FESA} for a suite of Mw 9 earthquakes |
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29 | evenly spaced along the Sunda Arc subduction zone. |
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30 | The assessment used the Method of Splitting Tsunamis (MOST) |
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31 | \cite{VT:MOST} model. This preliminary hazard map had no probability attached to the event which is |
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32 | required to conduct a tsunami risk assessment. Using |
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33 | the Probabilistic Tsunami Hazard Analysis \cite{somerville:urs} paradigm |
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34 | used by the URS corporation, a detailed probabilistic hazard map has now been completed |
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35 | for the WA coastline \cite{prob:fesa}. |
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36 | |
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37 | While MOST and URS are suitable for generating and propagating the tsunami wave from its source, |
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38 | they are not adequate to |
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39 | model the wave's impact on communities ashore. |
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40 | To capture the \emph{impact} of a tsunami to a coastal community, |
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41 | the model must be capable of capturing more detail about the wave, |
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42 | particularly how it is affected by the local bathymetry, as well as the |
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43 | local topography as the wave moves onshore. |
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44 | %the details of how waves are reflected and otherwise |
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45 | %shaped by the local bathymetries as well as the dynamics of the |
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46 | %runup process onto the topography in question. |
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47 | It is well known that local bathymetric and topographic effects are |
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48 | critical in determining the severity of a hydrological disaster |
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49 | \cite{matsuyama:1999}. To model the impact of the tsunami wave on the |
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50 | coastal community, we use ANUGA \cite{ON:modsim}. In order to capture the |
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51 | details of the wave and its interactions, a much finer resolution is |
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52 | required than that of the hazard model. As a result, ANUGA simulations concentrate |
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53 | on specific coastal communities. MOST and URS by contrast use a |
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54 | coarser resolution and cover often vast areas. To develop the impact |
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55 | from an earthquake event from a distant source, we adopt a hybrid approach of |
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56 | modelling the event itself with MOST or URS and modelling the impact with ANUGA. |
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57 | In this way, the output from MOST or URS serve as an input to ANUGA. |
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58 | In modelling terms, the MOST or URS output is a boundary condition for ANUGA. |
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59 | Further details |
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60 | regarding the inundation modelling requirements for this study can be found in |
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61 | Appendix \ref{sec:anugasetup}. |
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62 | |
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63 | The risk of a given tsunami scenario can only be determined when the likelihood |
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64 | of the event is known. The probabilistic hazard map for WA \cite{prob:fesa} |
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65 | calculates which events pose the most threat to an identified region. Figure |
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66 | \ref{fig:probonslow} describes the probability of each event generated along the Java trench |
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67 | impacting Onslow. For example, an event generated at point A end would have a |
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68 | smaller chance of impacting Onslow than an event generated at point B. |
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69 | |
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70 | \begin{figure}[h] |
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71 | |
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72 | %\centerline{ \includegraphics[width=140mm, height=100mm]{../report_figures/}} |
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73 | |
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74 | \caption{} |
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75 | \label{fig:probonslow} |
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76 | \end{figure} |
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77 | |
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78 | To prepare a tsunami risk assessment, a number of events will be chosen |
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79 | for a range of probabilities (or return periods). As Figure \ref{fig:probonslow} |
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80 | shows, for a given probability, a number of events are possible. The resulting |
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81 | impact to Onslow would then vary depending on the source of the event. The |
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82 | tsunami scenarios selected for the tsunami risk assessment |
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83 | are discussed in Section \ref{sec:tsunamiscenario}. |
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84 | |
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85 | % used for the 2005 report when looking at one event |
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86 | %\bigskip %FIXME (Ole): Should this be a subsection even? |
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87 | %The risk of a given tsunami scenario cannot be determined until the |
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88 | %likelihood of the tsunami is known. GA is currently building a |
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89 | %complete probabilistic hazard map which is due for completion |
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90 | %in late 2006. We therefore report on the impact of a single |
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91 | %tsunami event only. When the hazard map is completed, the impact |
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92 | %will be assessed for a range of events which will ultimately |
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93 | %determine a tsunami risk assessment for the NW shelf. |
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94 | |
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95 | %FESA is interested in the ``most frequent worst case scenario''. Whilst |
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96 | %we currently cannot determine exactly what that event may be, the |
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97 | %preliminary hazard assessment suggested that the maximum |
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98 | %magnitude of earthquakes off Java was considered to be at |
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99 | %least 8.5 and could potentially be as high as 9. Therefore, |
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100 | %the Mw 9 event |
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101 | %provides a plausible worst case scenario and is used as the tsunami |
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102 | %source in this report. |
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103 | %Figure \ref{fig:mw9} shows the maximum wave height of a tsunami initiated |
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104 | %by a Mw 9 event off |
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105 | %the coast of Java. This event provides the source and |
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106 | %boundary condition to the |
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107 | %inundation model presented in Section \ref{sec:anuga}. |
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108 | |
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109 | %\begin{figure}[h] |
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110 | |
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111 | % \centerline{ \includegraphics[width=140mm, height=100mm] |
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112 | %{../report_figures/mw9.jpg}} |
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113 | |
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114 | % \caption{Maximum wave height (in cms) for a Mw 9 event off the |
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115 | %coast of Java} |
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116 | % \label{fig:mw9} |
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117 | %\end{figure} |
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118 | |
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119 | |
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120 | |
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121 | |
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