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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 | Hazards include earthquakes, landslides, tsunami, severe winds and cyclones. |
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6 | |
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7 | By modelling the likely impacts on urban communities as accurately as possible and |
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8 | building these estimates into land use planning and emergency |
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9 | management, communities will be better prepared to respond to |
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10 | natural disasters when they occur. |
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11 | |
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12 | |
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13 | %GA bases its risk modelling on the process of understanding the hazard and a community's |
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14 | %vulnerability in order to determine the impact of a particular hazard event. |
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15 | %The resultant risk relies on an assessment of the likelihood of the event. |
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16 | %An overall risk assessment for a particular hazard would then rely on scaling |
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17 | %each event's impact by its likelihood. |
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18 | |
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19 | To develop a tsunami risk assessment, |
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20 | the tsunami hazard itself must first be understood. These events are generally modelled by converting |
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21 | the energy released by a subduction earthquake into a vertical displacement of the ocean surface. |
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22 | %Tsunami hazard models have been available for some time. |
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23 | The resulting wave is |
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24 | then propagated across a sometimes vast stretch of ocean towards the |
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25 | area of interest. |
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26 | %using a relatively coarse model |
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27 | %based on bathymetries with a typical resolution of two arc minutes. |
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28 | The hazard itself is then reported as a maximum wave height at a fixed contour line near the coastline, |
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29 | (e.g. 50m). This is how the preliminary tsunami hazard assessment was reported by GA |
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30 | to FESA in September 2005 \cite{BC:FESA}. The assessment used the Method of Splitting Tsunamis (MOST) |
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31 | \cite{VT:MOST} model. |
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32 | %The maximal wave height at a fixed contour line near the coastline |
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33 | %(e.g. 50m) is then reported as the hazard to communities ashore. |
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34 | %Models such as Method of Splitting Tsunamis (MOST) \cite{VT:MOST} and the |
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35 | %URS Corporation's |
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36 | %Probabilistic Tsunami Hazard Analysis |
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37 | %\cite{somerville:urs} follow this paradigm. |
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38 | |
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39 | While MOST is suitable for generating and propagating the tsunami wave from its source, it is not adequate to |
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40 | model the wave's impact on communities ashore. |
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41 | To capture the \emph{impact} of a tsunami to a coastal community, |
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42 | the model must be capable of capturing more detail about the wave, |
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43 | particularly how it is affected by the local bathymetry, as well as the |
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44 | local topography as the wave moves onshore. |
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45 | %the details of how waves are reflected and otherwise |
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46 | %shaped by the local bathymetries as well as the dynamics of the |
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47 | %runup process onto the topography in question. |
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48 | It is well known that local bathymetric and topographic effects are |
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49 | critical in determining the severity of a hydrological disaster |
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50 | \cite{matsuyama:1999}. To model the impact of the tsunami wave on the |
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51 | coastal community, we use ANUGA \cite{ON:modsim}. In order to capture the |
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52 | details of the wave and its interactions, a much finer resolution is |
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53 | required than that of the hazard model. As a result, ANUGA simulations concentrate |
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54 | on specific coastal communities. MOST by contrast uses a |
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55 | coarser resolution and covers often vast areas. To develop the impact |
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56 | from an earthquake event from a distant source, we adopt a hybrid approach of |
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57 | modelling the event itself with MOST and modelling the impact with ANUGA. |
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58 | In this way, the output from MOST serves as an input to ANUGA. |
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59 | In modelling terms, the MOST output is a boundary condition for ANUGA. |
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60 | |
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61 | \bigskip %FIXME (Ole): Should this be a subsection even? |
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62 | The risk of the scenario tsunami event cannot be determined until the |
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63 | likelihood of the event is known. GA is currently building a |
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64 | complete probabilistic hazard map which is due for completion |
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65 | in late 2006. We therefore report on the impact of a single |
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66 | tsunami event only. When the hazard map is completed, the impact |
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67 | will be assessed for a range of events which will ultimately |
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68 | determine a tsunami risk assessment for the NW shelf. |
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69 | %To model the |
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70 | %details of tsunami inundation of a community one must therefore capture %what is |
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71 | %known as non-linear effects and use a much higher resolution for the |
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72 | %elevation data. |
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73 | %Linear models typically use data resolutions of the order |
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74 | %of hundreds of metres, which is sufficient to model the tsunami waves |
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75 | %in deeper water where the wavelength is longer. |
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76 | %Non-linear models however require much finer resolution in order to %capture |
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77 | %the complexity associated with the water flow from offshore |
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78 | %to onshore. By contrast, the data |
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79 | %resolution required is typically of the order of tens of metres. |
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80 | %The model ANUGA \cite{ON:modsim} is suitable for this type of non-linear |
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81 | %modelling. |
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82 | %Using a non-linear model capable of resolving local bathymetric effects |
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83 | %and runup using detailed elevation data will require more computational |
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84 | %resources than the typical hazard model making it infeasible to use it |
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85 | %for the entire, end-to-end, modelling. |
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86 | |
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87 | %We have adopted a hybrid approach whereby the output from the |
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88 | %hazard model MOST is used as input to ANUGA at the seaward boundary of its %study area. |
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89 | %In other words, the output of MOST serves as boundary condition for the |
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90 | %ANUGA model. In this way, we restrict the computationally intensive part %only to |
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91 | %regions where we are interested in the detailed inundation process. |
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92 | |
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93 | %Furthermore, to avoid unnecessary computations ANUGA works with an |
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94 | %unstructured triangular mesh rather than the rectangular grids |
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95 | %used by e.g.\ MOST. The advantage of an unstructured mesh |
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96 | %is that different regions can have different resolutions allowing |
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97 | %computational resources to be directed where they are most needed. |
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98 | %For example, one might use very high resolution near a community |
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99 | %or in an estuary, whereas a coarser resolution may be sufficient |
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100 | %in deeper water where the bathymetric effects are less pronounced. |
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101 | %Figure \ref{fig:refinedmesh} shows a mesh of variable resolution. |
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102 | |
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103 | %\begin{figure}[hbt] |
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104 | % |
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105 | % \centerline{ \includegraphics[width=100mm, height=75mm] |
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106 | % {../report_figures/refined_mesh.jpg}} |
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107 | % |
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108 | % \caption{Unstructured mesh with variable resolution.} |
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109 | % \label{fig:refinedmesh} |
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110 | %\end{figure} |
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115 | |
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