# Changeset 7303

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Timestamp:
Jul 7, 2009, 1:07:44 AM (14 years ago)
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Jakeman: Made minor grammar and spelling changes to validation paper

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anuga_work/publications/boxing_day_validation_2008
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 r7255 In this paper a new benchmark for tsunami model validation is proposed. The benchmark is based upon the 2004 Indian Ocean tsunami, which provides a uniquely large amount of observational data for model which affords a uniquely large amount of observational data for model comparison. Unlike the small number of existing benchmarks, the proposed test validates all three stages of tsunami evolution - inundation. Important buildings and other structures were incorporated into the underlying computational mesh and shown to have a large influence of inundation extent. Sensitivity analysis also showed that influence on inundation extent. Sensitivity analysis also showed that the model predictions are comparatively insensitive to large changes in friction and small perturbations in wave weight at the 100 m depth subsequent propagation and inundation of the tsunami, the effectiveness of hazard mitigation procedures and the economic impact of such measures and the event itself. Here we focus on modelling of of such measures and of the event itself. Here we focus on modelling of the physical processes. %OLE: I commented this out 23 June 2009 as there was no reference. geodetic and sometimes tsunami data must be used to provide estimates of initial sea floor and ocean surface deformation. The complexity of these models range from empirical to deformation. The complexity of these models ranges from empirical to non-linear three-dimensional mechanical models. The shallow water wave equations, linearised shallow water wave equations, and Inaccuracies in model prediction can result in inappropriate evacuation plans and town zoning which may result in loss of life and evacuation plans and town zoning, which may result in loss of life and large financial losses. Consequently tsunami models must undergo sufficient end-to-end testing to increase scientific and community Complete confidence in a model of a physical system cannot be established.  One can only hope to state under what conditions the established.  One can only hope to state under what conditions and to what extent the model hypothesis holds true. Specifically the utility of a model can be assessed through a process of verification and The sources of data used to validate and verify a model can be separated into three main categories; analytical solutions, scale separated into three main categories: analytical solutions, scale experiments and field measurements. Analytical solutions of the governing equations of a model, if available, provide the best means experiments, typically in the form of wave-tank experiments, provide a much more realistic source of data that better captures the complex dynamics of flows such as those generated by tsunami, whilst allowing dynamics of flows such as those generated by a tsunami, whilst allowing control of the event and much easier and accurate measurement of the tsunami properties. Comparison of numerical predictions with field statements~\cite{bates01}. Currently, the extent of tsunami related field data is limited. The Currently, the extent of tsunami-related field data is limited. The cost of tsunami monitoring programs, bathymetry and topography surveys prohibits the collection of data in many of the regions in which standards, criteria and procedures for evaluating numerical models of tsunami. They propose three analytical solutions to help identify the validity of a model and five scale comparisons (wave-tank benchmarks) validity of a model, and five scale comparisons (wave-tank benchmarks) and two field events to assess model veracity. The first field data benchmark introduced by Synolakis compares model The first field data benchmark introduced in \cite{synolakis07} compares model results against observed data from the Hokkaido-Nansei-Oki tsunami that occurred around Okushiri Island, Japan on the 12th of July 1993. This tsunami provides an example of extreme runup generated from that occurred around Okushiri Island, Japan on the 12 July 1993. This tsunami provides an example of extreme run-up generated from reflections and constructive interference resulting from local topography and bathymetry. The benchmark consists of two tide gauge records and numerous spatially distributed point sites at which modelled maximum runup elevations can be compared. The second benchmark is based upon the Rat Islands Tsunami that occurred off the coast of Alaska on the 17th of November 2003. The Rat island tsunami provides a good test for real-time forecasting models since tsunami records and numerous spatially-distributed point sites at which modelled maximum run-up elevations can be compared. The second benchmark is based upon the Rat Islands tsunami that occurred off the coast of Alaska on the 17 November 2003. The Rat Island tsunami provides a good test for real-time forecasting models since the tsunami was recorded at three tsunameters. The test requires matching the propagation model data with the DART recording to constrain the tsunami source model and then using it to reproduce the tide gauge tsunami source model, and then using it to reproduce the tide gauge record at Hilo. In this paper we develop a field data benchmark to be used in conjunction with the other tests proposed by Synolakis et al~\cite{synolakis07} to validate and verify tsunami models. Unlike the aforementioned tests, the proposed benchmark allows evaluation of model structure during all three distinctive stages of the evolution of a tsunami. The benchmark consists of geodetic measurements of the Sumatra--Andaman earthquake which are used to validate the description al~\cite{synolakis07} to validate and verify tsunami models. The benchmark proposed here allows evaluation of model structure during all three distinct stages tsunami evolution. It consists of geodetic measurements of the Sumatra--Andaman earthquake that are used to validate the description of the tsunami source, altimetry data from the JASON satellite to test open ocean propagation, eye-witness accounts to assess near shore propagation and a detailed inundation survey of Patong Bay, Thailand propagation, and a detailed inundation survey of Patong Bay, Thailand to compare model and observed inundation. A description of the data required to construct the benchmark is given in measurements of coseismic displacements and bathymetry from ship-based expeditions, have now been made available.%~\cite{vigny05,amnon05,kawata05,liu05}. In this section we present the data necessary to implement the proposed benchmark corresponding to each of the three stages of the tsunami's evolution. available. %~\cite{vigny05,amnon05,kawata05,liu05}. In this section we present the corresponding data necessary to implement the proposed benchmark for each of the three stages of the tsunami's evolution. \subsection{Generation}\label{sec:gen_data} commonly caused by coseismic displacement of the sea floor, but submarine mass failures, landslides, volcanoes or asteroids can also cause tsunami. In this section we detail the information we used in cause tsunami. In this section we detail the information used in this study to validate models of the sea floor deformation generated by the 2004 Sumatra--Andaman earthquake. earthquakes on record. The mega-thrust earthquake started on the 26 December 2004 at 0h58'53'' UTC (or just before 8 am local time) approximately 70 km offshore North Sumatra approximately 70 km offshore of North Sumatra (\url{http://earthquake.usgs.gov/eqcenter/eqinthenews/2004/usslav}). The rupture propagated 1000-1300 km along the Sumatra-Andaman trench to the north at a rate of 2.5-3 km.s$^{-1}$ and lasted approximately 8-10 minutes~\cite{ammon05}. Estimates of the moment magnitude of this event range from about 9.1 to 9.3~\cite{chlieh07,stein07}. The unusually large surface deformation caused by this earthquakes event range from about 9.1 to 9.3 $M_w$~\cite{chlieh07,stein07}. The unusually large surface deformation caused by this earthquake means that there were a range of different geodetic measurements of the surface deformation available. These include field measurements of uplifted or subsided coral heads, continuous or campaign \textsc{GPS} measurements and remote sensing measurements of uplift or subsidence (see~\cite{chlieh07} and references therein). Here we use the the near field estimates of vertical deformation in northwestern Sumatra and (see~\cite{chlieh07} and references therein). Here we use the the near-field estimates of vertical deformation in northwestern Sumatra and the Nicobar-Andaman islands collated by~\cite{chlieh07} to validate that our crustal deformation model of the 2004 Sumatra--Andaman \subsection{Propagation} Once generated a tsunami will propagate outwards from the source until Once generated, a tsunami will propagate outwards from the source until it encounters the shallow water bordering coastal regions. This period of the tsunami evolution is referred to as the propagation stage. The The nested bathymetry grid was generated from: \begin{itemize} \item A two arc minute grid data set covering the Bay of Bengal, \item a two arc minute grid data set covering the Bay of Bengal, DBDB2, obtained from US Naval Research Labs; \item A 3 second arc grid covering the whole of the Andaman Sea based on Thai Navy charts no 45 and no 362; and \item A one second grid created from the digitised Thai Navy bathymetry chart, no 358. which covers Patong Bay and the \item a 3 second arc grid covering the whole of the Andaman Sea based on Thai Navy charts no. 45 and no. 362; and \item a one second grid created from the digitised Thai Navy bathymetry chart, no. 358, which covers Patong Bay and the immediately adjacent regions. \end{itemize} four grids are shown in Figure~\ref{fig:nested_grids}.  The coarsest bathymetry was obtained by interpolating the DBDB2 grid to a 27 second arc grid. A subsection of this region was then replaced by 9 second data which was generated by sub-sampling the 3 second of arc grid from NOAA. A subset of the 9 second grid was replaced by the 3 second arc grid. A subsection of this region was then replaced by nine second data which was generated by sub-sampling the three second of arc grid from NOAA. A subset of the nine second grid was replaced by the three second data. Finally, the one second grid was used to approximate the bathymetry in Patong Bay and the immediately adjacent regions. Any deleted. The sub-sampling of larger grids was performed by using {\bf resample} The sub-sampling of larger grids was performed by using {\bf resample}, a Generic Mapping Tools (\textsc{GMT}) program (\cite{wessel98}). The gridding of data was performed using {\bf Intrepid} a commercial gridding of data was performed using {\bf Intrepid}, a commercial geophysical processing package developed by Intrepid Geophysics. The gridding scheme employed the nearest neighbour algorithm followed by \subsection{Inundation} Inundation refers to the final stages of the evolution a tsunami and Inundation refers to the final stages of the evolution of a tsunami and covers the propagation of the tsunami in shallow coastal water and the subsequent run-up on to land. This process is typically the most subsequent run-up onto land. This process is typically the most difficult of the three stages to model due to thin layers of water flowing rapidly over dry land.  Aside from requiring robust solvers benchmark the authors have obtained a high resolution bathymetry and topography data set and a high quality inundation survey map from the CCOP in Thailand (\cite{szczucinski06}) which can be used to validate model inundation. CCOP in Thailand (\cite{szczucinski06}) to validate model inundation. The datasets necessary for reproducing the results of the inundation stage are available on Sourceforge under the ANUGA of the inundation stage are available on Sourceforge under the \textsc{anuga} project (\url{http://sourceforge.net/projects/anuga}). At the time of writing the direct link is \url{http://tinyurl.com/patong2004-data}. Bay. This elevation data was again created from the digitised Thai Navy bathymetry chart, no 358. A visualisation of the elevation data set used in Patong bay is shown in set used in Patong Bay is shown in Figure~\ref{fig:patong_bathymetry}. The continuous topography is an interpolation of known elevation measured at the coloured dots. \subsubsection{Buildings and Other Structures} Human made build and structures can significantly effect tsunami Human-made build and structures can significantly effect tsunami inundation. The location and size and number of floors of the buildings in Patong Bay were extracted from a GIS data set provided by \subsubsection{Inundation Survey} Tsunami run-up is often the cause of the largest financial and human losses yet run-up data that can be used to validate model runup predictions is scarce. Of the two field benchmarks proposed by Synolakis only the Okushiri benchmark facilitates comparison between losses, yet run-up data that can be used to validate model run-up predictions is scarce. Of the two field benchmarks proposed in~\cite{synolakis07}, only the Okushiri benchmark facilitates comparison between modelled and observed run-up. One of the major strengths of the benchmark proposed here is that modelled runup can be compared to an inundation survey which maps the maximum run-up along an entire coast line rather than at a series of discrete sites. The survey map is benchmark proposed here is that modelled run-up can be compared to an inundation survey which maps the maximum run-up along an entire coastline rather than at a series of discrete sites. The survey map is shown in Figure~\ref{fig:patongescapemap} and plots the maximum run-up of the 2004 tsunami in Patong bay. Refer to Szczucinski et of the 2004 tsunami in Patong Bay. Refer to Szczucinski et al~\cite{szczucinski06} for further details. Eyewitness accounts detailed in~\cite{papadopoulos06} report that most people at Patong Beach observed an initial retreat of the shoreline of more than 100 m followed a few minutes later by a the shoreline of more than 100 m followed a few minutes later, by a strong wave (crest). Another less powerful wave arrived another five or ten minutes later. Eyewitness statements place the arrival time of which include footage of the tsunami in Patong Bay on the day of the Indian Ocean Tsunami. Both videos show an already inundated group of buildings, they then show what is to be assumed as the second and third waves approaching and further flooding the buildings and street.  The first video is in the very north filmed from what is group of buildings. They also show what is to be assumed as the second and third waves approaching and further flooding of the buildings and street.  The first video is in the very north, filmed from what is believed to be the roof of the Novotel Hotel marked north'' in Figure \ref{fig:gauge_locations}. The second video is in the very south \ref{fig:gauge_locations}. The second video is in the very south, filmed from the second story of a building next door to the Comfort Resort near the corner of Ruam Chai St and Thaweewong Road.  This location is marked south'' in Figure \ref{fig:gauge_locations} and location is marked south'' in Figure \ref{fig:gauge_locations}. Figure~\ref{fig:video_flow} shows stills from this video. Both videos were used to estimate flow speeds and inundation depths over time. should reproduce the following behaviour: \begin{itemize} \item Reproduce the vertical deformation observed in north-western Sumatra and along the Nicobar--Andaman islands, see Section~\ref{sec:gen_data}. \item Reproduce the \textsc{jason} satellite altimetry sea surface anomalies, see Section~\ref{sec:data_jason}. \item Reproduce the inundation survey map in Patong bay \item reproduce the vertical deformation observed in north-western Sumatra and along the Nicobar--Andaman islands (see Section~\ref{sec:gen_data}). \item reproduce the \textsc{jason} satellite altimetry sea surface anomalies (see Section~\ref{sec:data_jason}). \item reproduce the inundation survey map in Patong bay (Figure~\ref{fig:patongescapemap}). \item Simulate a leading depression followed by two distinct crests \item simulate a leading depression followed by two distinct crests of decreasing magnitude. \item Predict the water depths and flow speeds, at the locations of \item predict the water depths and flow speeds, at the locations of the eye-witness videos, that fall within the bounds obtained from the videos. Numerous models are currently used to model and predict tsunami generation, propagation and run-up~\cite{titov97a,satake95}. Here we introduce the modelling methodology employed by Geoscience Australia to illustrate the utility of the proposed benchmark. Geoscience Australia's tsunami modelling methodology comprises the three parts; generation, propagation and inundation (Sections~\ref{sec:modelGeneration},\ref{sec:modelPropagation} and \ref{sec:modelInundation} respectively). introduce the three part modelling methodology employed by Geoscience Australia to illustrate the utility of the proposed benchmark. \subsection{Generation}\label{sec:modelGeneration} There are various approaches to modelling the expected crustal deformation from an earthquake at depth. Most approaches model the earthquake as a dislocation in a linear, elastic medium. Here we use earthquake as a dislocation in a linear elastic medium. Here we use the method of Wang et al~\cite{wang03}. One of the main advantages of their method is that it allows the dislocation to be located in a subfault. This step is possible because of the linearity of the governing equations. For this study, we have made minor modifications to \textsc{edcmp} in order for it to output in a file format compatible with the propagation code in the following section but it is otherwise the similar to the original code. to \textsc{edcmp} in order for it to provide output in a file format compatible with the propagation code in the following section. Otherwise it is similar to the original code. In order to calculate the crustal deformation using these codes we thus need to have a model describing the variation in elastic need a model that describes the variation in elastic properties with depth and a slip model of the earthquake to describe the dislocation. The elastic parameters used for this study are the same as those in Table 2 of Burbidge~\cite{burbidge08}. For the slip model, there are many possible models for the 2004 Andaman--Sumatran earthquake to choose from earthquake to select from ~\cite{chlieh07,asavanant08,arcas06,grilli07,ioualalen07}. Some are determined from various geological surveys of the site, others solve determined from various geological surveys of the site. Others solve an inverse problem which calibrates the source based upon the tsunami wave signal, the seismic signal and/or the runup. The source wave signal, the seismic signal and/or the run-up. The source parameters used here to simulate the 2004 Indian Ocean tsunami were taken from the slip model G-M9.15 from Chlieh taken from the slip model G-M9.15 of Chlieh et al~\cite{chlieh07}. This model was created by inversion of wide range of geodetic and seismic data. The slip model consists of 686 discussion of this model and its derivation. Note that the geodetic data used in the validation was also included by~\cite{chlieh07} in the inversion used to find G-M9.15, thus the validation is not completely independent. However, a successful validation would still the inversion used to find G-M9.15. Thus the validation is not completely independent. However, a reasonable validation would still show that the crustal deformation and elastic properties model used here is at least as valid as the one used by Chlieh We use the \textsc{ursga} model described below to simulate the propagation of the 2004 tsunami in the deep ocean ocean, based on a discrete representation of the initial deformation of the sea floor, discrete representation of the initial deformation of the sea floor, as described in Section~\ref{sec:modelGeneration}. For the models shown here, we assume that the uplift is instantaneous and creates a wave of \subsubsection{URSGA} \textsc{ursga} is a hydrodynamic code that models the propagation of the tsunami in deep water using a finite difference method on a staggered grid to solve the depth integrated linear or nonlinear shallow water equations in the tsunami in deep water using a finite difference method on a staggered grid. It solves the depth integrated linear or nonlinear shallow water equations in spherical co-ordinates with friction and Coriolis terms. The code is based on Satake~\cite{satake95} with significant modifications made by \subsubsection{ANUGA} \textsc{Anuga} is an Open Source hydrodynamic inundation tool that solves the conserved form of the depth integrated nonlinear shallow solves the conserved form of the depth-integrated nonlinear shallow water wave equations. The scheme used by \textsc{anuga}, first presented by Zoppou and Roberts~\cite{zoppou99}, is a high-resolution %================Section=========================== \section{Results}\label{sec:results} This section presents a validation of the modelling practice of Geoscience Australia against the new proposed benchmarks. The criteria outlined in Section~\ref{sec:checkList} are addressed for each three stages of tsunami evolution. This section presents a validation of the modelling practice of Geoscience Australia against the new proposed benchmarks. The criteria outlined in Section~\ref{sec:checkList} are addressed for each of the three stages of tsunami evolution. \subsection{Generation}\label{modelGeneration} the areas that were observed to uplift (arrows pointing up) or subside (arrows point down) during and immediately after the earthquake. Most of this data comes uplifted or subsided coral heads. The length of vector increases with the magnitude of the displacement, the length of this data comes from uplifted or subsided coral heads. The length of vector increases with the magnitude of the displacement; the length corresponding to 1 m of observed motion is shown in the top right corner of the figure. As can be seen, the source model detailed in points) is only 0.06 m, well below the typical error of the observations of between 0.25 and 1.0 m. However, the occasional point has quite a large error (over 1 m), for example a couple has quite a large error (over 1 m); for example a couple uplifted/subsided points appear to be on a wrong side of the predicted pivot line~\ref{fig:surface_deformation}. The excellence of the fit is not surprising, since the original slip model was chosen by~\cite{chlieh07} to fit this (and the seismic data) well. However, this does demonstrate that \textsc{edgrn} and our modified version of by~\cite{chlieh07} to fit this (and the seismic data) well. This does demonstrate, however, that \textsc{edgrn} and our modified version of \textsc{edstat} can reproduce the correct pattern of vertical deformation very well when the slip distribution is well constrained based on the slip model, G-M9.15. The black arrows which point up show areas observed to uplift during and immediately after the earthquake, those point down are locations which subsided. The length of increases with the magnitude of the deformation. The arrow earthquake; those pointing down are locations which subsided. The length of the arrow increases with the magnitude of the deformation. The arrow length corresponding to 1 m of deformation is shown in the top right hand corner of the figure. The crosses marks show the location of hand corner of the figure. The cross marks show the location of the pivot line (the region between the uplift and subsided region where the uplift is zero) derived from remote sensing. All the observational data come from the dataset collated observational data are from the dataset collated by~\cite{chlieh07}.} \label{fig:surface_deformation} 1335$\times$1996 finite difference points. Inside this grid, a nested sequence of grids was used. The grid resolution of the nested grids went from 27 arc seconds in the coarsest grid, down to 9 arc seconds in the second grid, 3 arc seconds in the third grid and finally 1 arc went from 27 arc seconds in the coarsest grid, down to nine arc seconds in the second grid, three arc seconds in the third grid and finally one arc second in the finest grid near Patong. The computational domain is shown in Figure~\ref{fig:computational_domain}. Figure \ref{fig:jasonComparison} provides a comparison of the \textsc{ursga} predicted sea surface elevation with the JASON \textsc{ursga}-predicted sea surface elevation with the JASON satellite altimetry data. The \textsc{ursga} model replicates the amplitude and timing of the the wave observed at 2.5 degrees South, amplitude and timing of the the wave observed at $2.5^0$ South, but underestimates the amplitude of the wave further to the south at 4 degrees South. In the model, the southern most of these two waves appears only as a small bump in the cross section of the model shown in Figure~\ref{fig:jasonComparison} instead of being a distinct peak $4^0$ South. In the model, the southern most of these two waves appears only as a small bump in the cross section of the model (shown in Figure~\ref{fig:jasonComparison}) instead of being a distinct peak as can be seen in the satellite data. Also note that the \textsc{ursga} model prediction of the ocean surface elevation becomes out of phase with the JASON data at 3 to 7 degrees latitude. Chlieh et al~\cite{chlieh07} also observe these misfits and elevation becomes out of phase with the JASON data at $3^0$ to $7^0$ North latitude. Chlieh et al~\cite{chlieh07} also observed these misfits and suggest it is caused by a reflected wave from the Aceh Peninsula that is not resolved in the model due to insufficient resolution of the \begin{center} \includegraphics[width=12.0cm,keepaspectratio=true]{jasonComparison.jpg} \caption{Comparison of the \textsc{ursga} predicted surface elevation \caption{Comparison of the \textsc{ursga}-predicted surface elevation with the JASON satellite altimetry data. The \textsc{ursga} wave heights have been corrected for the time the satellite passed \subsection{Inundation} After propagating the tsunami in the open ocean using \textsc{ursga} After propagating the tsunami in the open ocean using \textsc{ursga}, the approximated ocean and surface elevation and horisontal flow velocities were extracted and used to construct a boundary condition The boundary condition at each side of the domain towards the south and the north where no data was available was chosen as a transmissive boundary condition effectively replicating the time dependent wave boundary condition, effectively replicating the time dependent wave height present just inside the computational domain. Momentum was set to zero. Other choices include applying the mean tide value as a Dirichlet type boundary condition but experiments as well as the Dirichlet type boundary condition. But experiments as well as the result of the verification reported here showed that this approach tends to under estimate the tsunami impact due to the tempering of the wave near the side boundaries whereas the transmissive boundary tends to underestimate the tsunami impact due to the tempering of the wave near the side boundaries, whereas the transmissive boundary condition robustly preserves the wave. the inundation boundary of the survey is likely to vary significantly and somewhat unpredictably. Consequently, an inundation threshold of 10 cm was selected for inundation An inundation threshold of 10 cm therefore was selected for inundation extents reported in this paper to reflect the more likely accuracy of the survey and subsequently facilitate a more the more likely accuracy of the survey, and subsequently facilitate a more appropriate comparison between the modelled and observed inundation area. An animation of this simulation is available on the ANUGA website at \url{https://datamining.anu.edu.au/anuga} or directly from \url{http://tinyurl.com/patong2004}. An animation of this simulation is available on the \textsc{anuga} website at \url{https://datamining.anu.edu.au/anuga} or directly from \url{http://tinyurl.com/patong2004}. %\url{https://datamining.anu.edu.au/anuga/attachment/wiki/AnugaPublications/patong_2004_indian_ocean_tsunami_ANUGA_animation.mov}. \end{figure} To quantify the agreement between observed and simulated inundation we To quantify the agreement between the observed and simulated inundation we introduce the measure \rho_{in}=\frac{A(I_m\cap I_o)}{A(I_o)} representing the ratio $\rho_{in}$ of observed representing the ratio $\rho_{in}$ of the observed inundation region $I_o$ captured by the model $I_m$. Another useful measure is the fraction of the modelled inundation area that falls These values for the two aforementioned simulations are given in Table~\ref{table:inundationAreas}. High value of both $\rho_{in}$ and $\rho_{out}$ indicates Table~\ref{table:inundationAreas}. High value of both $\rho_{in}$ and $\rho_{out}$ indicate that the model overestimates the impact whereas low values of both quantities would indicate an underestimation. A high value of $\rho_{in}$ combined with a low value of $\rho_{out}$ indicates a good model prediction of the survey. Discrepancies between the survey data and the modelled inundated Discrepancies between the survey data and the modelled inundation include: unknown distribution of surface roughness, inappropriate parameterisation of the source model, effect of humans structures on \subsection{Eye-witness accounts} Figure \ref{fig:gauge_locations} shows four locations where time series have been extracted from the model. The two offshore timeseries series have been extracted from the model. The two offshore time series are shown in Figure \ref{fig:offshore_timeseries} and the two onshore timeseries are shown in Figure \ref{fig:onshore_timeseries}. The \includegraphics[width=10.0cm,keepaspectratio=true]{gauge_bay_depth.jpg} \includegraphics[width=10.0cm,keepaspectratio=true]{gauge_bay_speed.jpg} \caption{Timeseries obtained from the two offshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \caption{Time series obtained from the two offshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \end{center} \label{fig:offshore_timeseries} \includegraphics[width=10.0cm,keepaspectratio=true]{gauges_hotels_depths.jpg} \includegraphics[width=10.0cm,keepaspectratio=true]{gauges_hotels_speed.jpg} \caption{Timeseries obtained from the two onshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \caption{Time series obtained from the two onshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \end{center} \label{fig:onshore_timeseries} and the presence and absence of buildings in the elevation dataset on model maximum inundation. The reference model is the one reported in Figure~\ref{fig:inundationcomparison1cm} (right) with friction = 0.01, Figure~\ref{fig:inundationcomparison1cm} (right) with a friction coefficient of 0.01, buildings included and the boundary condition produced by the URSGA model. for tsunami propagation over a sandy sea floor and the reference model uses a value of 0.01.  To investigate sensitivity to this parameter, we simulated the maximum onshore inundation using the a Manning's we simulated the maximum onshore inundation using a Manning's coefficient of 0.0003 and 0.03. The resulting inundation maps are shown in Figure~\ref{fig:sensitivity_friction} and the maximum flow friction and that small perturbations in the friction cause bounded changes in the output. This is consistent with the conclusions of Synolakis~\cite{synolakis05} who states that the long wavelength of tsunami tends to mean that the friction is less important in Synolakis~\cite{synolakis05} et al, who state that the long wavelength of tsunami tends to mean that friction is less important in comparison to the motion of the wave. %========================Wave-Height==========================% \subsection{Input Wave Height}\label{sec:waveheightSA} The effect of the wave-height used as input to the inundation model The effect of the wave height used as input to the inundation model \textsc{anuga} was also investigated. Figure~\ref{fig:sensitivity_boundary} indicates that the inundation severity is directly proportional to the boundary waveheight but small perturbations in the input wave-height of 10 cm appear to have little perturbations in the input wave height of 10 cm appear to have little effect on the final on-shore run-up. Obviously larger perturbations will have greater impact. However, this value is generally well shows the maximum run-up and associated flow speeds in the presence and absence of buildings. It is apparent that the inundation is much more severe when the presence of man made structures and buildings are ignored. of human made structures and buildings are ignored. \begin{table} This paper proposes an additional field data benchmark for the verification 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 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 26th December 2004. Unlike the small Sumatra--Andaman tsunami of 26 December 2004. Unlike the small number of existing benchmarks, the proposed test validates all three stages of tsunami evolution - generation, propagation and inundation. In an attempt to provide higher visibility and easier accessibility for tsunami benchmark problems the data used to 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}. A simple sensitivity analysis was performed to assess the influence of small changes in friction, wave-height at the 100 m depth contour and small changes in friction, wave height at the 100 m depth contour and the presence of buildings and other structures on the model predictions. The presence of buildings has the greatest influence on the simulated inundation extent. The value of friction and small perturbations in the waveheight at the ANUGA boundary have perturbations in the waveheight at the \textsc{anuga} boundary have comparatively little effect on the model results. \includegraphics[width=6cm,keepaspectratio=true]{sensitivity_minus10cm_depth} \includegraphics[width=6cm,keepaspectratio=true]{sensitivity_plus10cm_depth} \caption{Model results with wave height at ANUGA boundary artificially modified to asses sensitivities. The reference inundation extent is shown in Figure \caption{Model results with wave height at \textsc{anuga} boundary artificially modified to assess sensitivities. The reference inundation extent is shown in Figure \protect \ref{fig:reference_model} (left).  The left and right images show the inundation results if the wave at the ANUGA boundary show the inundation results if the wave at the \textsc{anuga} boundary is reduced or increased by 10cm respectively. The inundation severity varies in proportion to the boundary waveheight, but the \includegraphics[width=6cm,keepaspectratio=true]{sensitivity_f0_03_depth} \caption{Model results for different values of Manning's friction coefficient shown to asses sensitivities. The reference inundation extent for a coefficient shown to assess sensitivities. The reference inundation extent for a friction value of 0.01 is shown in Figure \protect \ref{fig:reference_model} (left).  The left and right images