# Changeset 7377

Ignore:
Timestamp:
Aug 16, 2009, 8:28:05 PM (13 years ago)
Message:

Jane's (and some of mine) comments and suggestions.
See log file and FIXME's

File:
1 edited

### Legend:

Unmodified
 r7303 %----------title-------------% \title{Benchmarking Tsunami Models using the December 2004 Indian Ocean Tsunami and its Impact at Patong Beach} Ocean Tsunami and its Impact at Patong Bay} %-------authors----------- %------Abstract-------------- \begin{abstract} In this paper a new benchmark for tsunami model validation is proposed. The benchmark is based upon the 2004 Indian Ocean tsunami, which affords a uniquely large amount of observational data for model comparison. Unlike the small number of existing benchmarks, the This paper proposes a new benchmark for tsunami model validation. The benchmark is based upon the 2004 Indian Ocean tsunami, which affords a uniquely large amount of observational data for events of this kind. Unlike the small number of existing benchmarks, the proposed test validates all three stages of tsunami evolution - generation, propagation and inundation. Specifically we use geodetic generation (FIXME (Jane): really?), propagation and inundation. Specifically we use geodetic measurements of the Sumatra--Andaman earthquake to validate the tsunami source, altimetry data from the \textsc{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 city, Thailand to compare model and observed inundation. Furthermore we utilise this benchmark to further validate the hydrodynamic modelling tool \section{Introduction} Tsunami are a potential hazard to coastal communities all over the Tsunami is a potential hazard to coastal communities all over the world. A number of recent large events have increased community and scientific awareness of the need for effective detection, forecasting, and emergency preparedness. Probabilistic, geological, hydrodynamic, and economic models are required to predict the location and and emergency preparedness. Probabilistic, geophysical and hydrodynamic models are required to predict the location and likelihood of an event, the initial sea floor deformation and subsequent propagation and inundation of the tsunami, the effectiveness of hazard mitigation procedures and the economic impact 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. %For discussion on economic and decision based %models refer to~\cite{} and the references therein. subsequent propagation and inundation of the tsunami. Engineering, economic and social vulnerability models can then be used to estimate the impact of the event as well as the effectiveness of hazard mitigation procedures. In this paper, we focus on modelling of the physical processes only. Various approaches are currently used to assess the potential impact data also significantly increases the uncertainty of the validation experiment that may constrain the ability to make unequivocal statements~\cite{bates01}. statements~\cite{bates01}. FIXME (Jane): Because? FIXME (Phil): references to all of the paragraph above, please Currently, the extent of tsunami-related field data is limited. The tsunamis pose greatest threat. The resulting lack of data has limited the number of field data sets available to validate tsunami models. Synolakis et al~\cite{synolakis07} have developed a set of models. Synolakis et al~\cite{synolakis07} have developed a set of standards, criteria and procedures for evaluating numerical models of tsunami. They propose three analytical solutions to help identify the 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 propagation model output with the DART recording to constrain the tsunami source model, and then using it to reproduce the tide gauge record at Hilo. record at Hilo, Hawaii. FIXME (Jane): Are the tsunameters and the DART recordings the same thing? In this paper we develop a field data benchmark to be used in 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 city, Thailand to compare model and observed inundation. A description of the data required to construct the benchmark is given in An associated aim of this paper is to illustrate the use of this new benchmark to validate an operational tsunami inundation model called benchmark to validate a dedicated inundation model called \textsc{anuga} used by Geoscience Australia. A description of \textsc{anuga} is given in Section~\ref{sec:models} and the validation results are given in Section~\ref{sec:results}. The numerical models used to model tsunami are extremely computationally intensive. Full resolution models of the entire The numerical models used to simulate tsunami impact are computationally intensive and high resolution models of the entire evolution process will often take a number of days to run. Consequently the uncertainty in model predictions is difficult to quantify. However model uncertainty should not be ignored. Section ~\ref{sec:sensitivity} provides a simple sensitivity analysis that can run. Consequently, the uncertainty in model predictions is difficult to quantify as it would require a very large number of runs. However, model uncertainty should not be ignored. Section ~\ref{sec:sensitivity} provides a simple analysis that can be used to investigate the sensitivity of model predictions to model parameters. seismometers, tide gauges, \textsc{gps} surveys, satellite overpasses, subsequent coastal field surveys of run-up and flooding, and measurements of coseismic displacements and bathymetry from ship-based measurements of coseismic displacements as well as bathymetry from ship-based expeditions, have now been made available. %~\cite{vigny05,amnon05,kawata05,liu05}. available. %~\cite{vigny05,amnon05,kawata05,liu05}. FIXME (Ole): Refs? 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. by the 2004 Sumatra--Andaman earthquake. The 2004 Sumatra--Andaman tsunami was generated by a severe coseismic displacement of the sea floor as a result of one of the largest The 2004 Sumatra--Andaman tsunami was generated by a coseismic displacement of the sea floor resulting from one of the largest earthquakes on record. The mega-thrust earthquake started on the 26 December 2004 at 0h58'53'' UTC (or just before 8 am local time) \subsection{Propagation} \label{sec:propagation data} Once generated, a tsunami will propagate outwards from the source until it encounters the shallow water bordering coastal regions. This period \subsubsection{Bathymetry Data} A number of raw data sets were obtained, analysed and checked for quality and subsequently gridded for easier visualisation and input into the tsunami models. The resulting grid data is relatively coarse in the deeper water and becomes progressively finer as the distance to Patong Bay decreases. The nested bathymetry grid was generated from: The bathymetry data used in this study was derived from the following sources: \begin{itemize} \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 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. immediately adjacent regions. (FIXME (Ole): How was the grid created from these digitised points?) \end{itemize} The final bathymetry data set consists of four nested grids obtained via interpolation and resampling of the aforementioned data sets. The four grids are shown in Figure~\ref{fig:nested_grids}.  The coarsest FIXME (Jane): Refs for all these. %A number of raw data sets were obtained, analysed and checked for %quality and subsequently gridded for easier visualisation and input %into the tsunami models. These sets were combined via interpolation and resampling to produce four nested grids which are relatively coarse in the deeper water and progressively finer as the distance to Patong Beach decreases as 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 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 NOAA (FIXME (Jane): This was not mentioned in the dots above). 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 points that deviated from the general trend near the boundary were deleted. deleted as a quality check. The sub-sampling of larger grids was performed by using {\bf resample}, \begin{center} \includegraphics[width=0.75\textwidth,keepaspectratio=true]{nested_grids} \caption{Nested grids of the elevation data.} \caption{Nested bathymetry grids.} \label{fig:nested_grids} \end{center} \subsubsection{JASON Satellite Altimetry}\label{sec:data_jason} During the 26 December 2004 event, the Jason satellite tracked from During the 26 December 2004 event, the \textsc{jason} satellite tracked from north to south and over the equator at 02:55 UTC nearly two hours after the earthquake \cite{gower05}. The satellite recorded the sea level anomaly compared to the average sea level from its previous five passes over the same region in the 20-30 days prior.  This data was passes over the same region in the 20-30 days prior. This data was used to validate the propagation stage in Section \ref{sec:resultsPropagation}. \subsection{Inundation} \label{sec:inundation data} FIXME (Ole): Technically propagation covers everything up to the coastline and inundation everything on-shore. This means that ANUGA covers the final part of the propagation and the inundation part. Should we adopt this distiction throughout the paper? 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 covers the propagation of the tsunami in coastal waters and the subsequent run-up onto land. This process is typically the most difficult of the three stages to model due to thin layers of water data which is often not available. In the case of model validation high quality field measurements are also required. For the proposed benchmark the authors have obtained a high resolution bathymetry and benchmark a high resolution bathymetry (FIXME (Ole): Bathymetry ?) and topography data set and a high quality inundation survey map from the 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 \textsc{anuga} project (\url{http://sourceforge.net/projects/anuga}). At the time of writing the direct link is \url{http://tinyurl.com/patong2004-data}. % %\url{http://sourceforge.net/project/showfiles.php?group_id=172848&package_id=319323&release_id=677531}. In this section we also present eye-witness accounts which can be used to qualitatively validate tsunami inundation. Coordinating Committee Co-ordinating Committee for Geoscience Programmes in East and Southeast Asia (CCOP) (\cite{szczucinski06}) was obtained to validate model inundation. See also acknowledgements at the end of this paper. In this section we also present eye-witness accounts which can be used to qualitatively validate tsunami inundation. \subsubsection{Topography Data} A one second grid was used to approximate the topography in Patong Bay. This elevation data was again created from the digitised Thai Navy bathymetry chart, no 358. A visualisation of the elevation data Navy bathymetry chart, no 358. FIXME (Ole): I don't think so. The Navy chart is only offshore. A visualisation of the elevation data 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. Figure~\ref{fig:patong_bathymetry}. The continuous topography (FIXME(Jane): What is meant by this?) is an interpolation of known elevation measured at the coloured dots. FIXME ?? \begin{figure}[ht] \begin{center} \includegraphics[width=8.0cm,keepaspectratio=true]{patong_bay_data.jpg} \caption{Visualisation of the elevation data set used in Patong Bay showing data points, contours, rivers and roads draped over the final model.} \caption{3D visualisation of the elevation data set used in Patong Bay showing data points, contours, rivers and roads draped over the final model.} \label{fig:patong_bathymetry} \end{center} \end{figure} FIXME (Jane): legend? Were the contours derived from the final dataset? This is not the entire mode, only the bay and the beach. \subsubsection{Buildings and Other Structures} 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 the CCOP in Thailand (see acknowledgements at the end of this paper). Human-made buildings and structures can significantly affect tsunami inundation. The footprint and number of floors of the buildings in Patong Bay were extracted from a GIS data set which was also provided by the CCOP (see Section \ref{sec:inundation data} for details). The heights of these buildings were estimated assuming that each floor has a height of 3 m. buildings were estimated assuming that each floor has a height of 3 m and they were added to the topographic dataset. \subsubsection{Inundation Survey} Tsunami run-up is often the cause of the largest financial and human Tsunami run-up is the cause of the largest financial and human 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}, 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 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 Indian Ocean tsunami in Patong city. Refer to Szczucinski et al~\cite{szczucinski06} for further details. \begin{figure}[ht] \begin{center} \includegraphics[width=8.0cm,keepaspectratio=true]{patongescapemap.jpg} \caption{Tsunami survey mapping the maximum observed inundation at Patong beach courtesy of the CCOP \protect \cite{szczucinski06}.} \label{fig:patongescapemap} \end{center} \end{figure} \subsubsection{Eyewitness Accounts}\label{sec:eyewitness data} minutes after the source rupture (09:55am to 10:05am local time). Two videos were sourced from the internet\footnote{The footage is Two videos were sourced\footnote{The footage is widely available and can for example be obtained from \url{http://www.archive.org/download/patong_bavarian/patong_bavaria.wmv} %http://wizbangblog.com/content/2005/01/01/wizbang-tsunami.php which include footage of the tsunami in Patong Bay on the day of the Indian Ocean Tsunami. Both videos show an already inundated of the 2004 Indian Ocean Tsunami. Both videos show an already inundated 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 were found to be in the range of 5 to 7 metres per second (+/- 2 m/s) in the north and 0.5 to 2 metres per second (+/- 1 m/s) in the south. FIXME (Jane): How were these error bounds derived? Water depths could also be estimated from the videos by the level at which water rose up the speeds in the range of 2 to 5 m/s. \begin{figure}[ht] \begin{center} \includegraphics[width=8.0cm,keepaspectratio=true]{patongescapemap.jpg} \caption{Tsunami survey mapping the maximum observed inundation at Patong beach courtesy of the Thai Department of Mineral Resources \protect \cite{szczucinski06}.} \label{fig:patongescapemap} \end{center} \end{figure} \subsection{Validation Check-List} \item reproduce the vertical deformation observed in north-western Sumatra and along the Nicobar--Andaman islands (see Section~\ref{sec:gen_data}). 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}). anomalies (see Section~\ref{sec:data_jason}), \item reproduce the inundation survey map in Patong city (Figure~\ref{fig:patongescapemap}), \item simulate a leading depression followed by two distinct crests of decreasing magnitude. of decreasing magnitude at the beach, and \item predict the water depths and flow speeds, at the locations of the eye-witness videos, that fall within the bounds obtained from Ideally, the model should also be compared to measured timeseries of waveheights and velocities but the authors are not aware of the availability of such data. availability of such data near Patong Bay. There are various approaches to modelling the expected crustal deformation from an earthquake at depth. Most approaches model the deformation from an earthquake. Most approaches model the 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 using a combination of Hankel's transform and Wang et al's implementation of the Thomson-Haskell propagator algorithm~\cite{wang03}. Once the Green's functions are calculated we use a slightly modified version of \textsc{edcmp} to calculate the sea algorithm~\cite{wang03}. Once the Green's functions are calculated a slightly modified version of \textsc{edcmp}\footnote{For this study, we have made minor modifications 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.} is used to calculate the sea floor deformation for a specific subfault. This second code discretises the subfault into a set of unit sources and sums the deformation caused by a two dimensional dislocation along the 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 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 need a model that describes the variation in elastic governing equations. In order to calculate the crustal deformation using these codes 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 the dislocation is required. The elastic parameters used for this study are the same as those in Table 2 of Burbidge et al~\cite{burbidge08}. For the slip model, there are many possible models for the 2004 Andaman--Sumatran earthquake to select from 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 run-up. The source wave signal, the seismic signal and/or even the run-up. The source parameters used here to simulate the 2004 Indian Ocean tsunami were taken from the slip model G-M9.15 of Chlieh \subsection{Propagation}\label{sec:modelPropagation} We use the \textsc{ursga} model described below to simulate the propagation of the 2004 tsunami in the deep ocean ocean, based on a The \textsc{ursga} model described below was used to simulate the propagation of the 2004 Indian Ocean tsunami across the open ocean, based on a 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 here, the uplift is assumed to be instantaneous and creates a wave of the same size and amplitude as the co-seismic sea floor deformation. spherical co-ordinates with friction and Coriolis terms. The code is based on Satake~\cite{satake95} with significant modifications made by the \textsc{urs} corporation~\cite{thio08} and Geoscience Australia~\cite{burbidge08}. The tsunami is propagated via the nested grid system. Coarse grids are used in the open ocean and the finest resolution grid is employed in the region of most interest. \textsc{Ursga} is not publicly available. the \textsc{urs} corporation, Thio et al~\cite{thio08} and Geoscience Australia, Burbidge et al~\cite{burbidge08}. The tsunami was propagated via the nested grid system described in Section \ref{sec:propagation data} where the coarse grids were used in the open ocean and the finest resolution grid was employed in the region closest to Patong bay. \textsc{Ursga} is not publicly available. \subsection{Inundation}\label{sec:modelInundation} Geoscience Australia tsunami modelling methodology is based on a hybrid approach using models like \textsc{ursga} for tsunami propagation up to a 100 m depth contour. propagation up to an offshore depth contour, typically 100 m. %Specifically we use the \textsc{ursga} model to simulate the %propagation of the 2004 Indian Ocean tsunami in the deep ocean, based %on a discrete representation of the initial deformation of the sea %floor, described in Section~\ref{sec:modelGeneration}. The wave signal is then used as a time varying boundary condition for The wave signal and the velocity field is then used as a time varying boundary condition for the \textsc{anuga} inundation simulation. % A description of \textsc{anuga} is the following section. \subsubsection{ANUGA} \textsc{Anuga} is an Open Source hydrodynamic inundation tool that \textsc{Anuga} is a Free and Open Source hydrodynamic inundation tool that solves the conserved form of the depth-integrated nonlinear shallow water wave equations. The scheme used by \textsc{anuga}, first water wave equations using a Finite-Volume scheme on an unstructured triangular mesh. The scheme, first presented by Zoppou and Roberts~\cite{zoppou99}, is a high-resolution Godunov-type method that uses the rotational invariance property of et al~\cite{kurganov01} for solving one-dimensional conservation equations. The numerical scheme is presented in detail in Roberts and Zoppou~\cite{zoppou99,roberts00} and Roberts and Zoppou~\cite{zoppou00,roberts00} and Nielsen et al~\cite{nielsen05}. An important capability of the software is that it can model the process of wetting and drying as water enters and leaves an area. This means that it is suitable for finite-volume scheme is that discontinuities in all conserved quantities are allowed at every edge in the mesh. This means that the tool is well suited to adequately resolving hydraulic jumps, transcritical flows and the process of wetting and drying. This means that \textsc{Anuga} is suitable for simulating water flow onto a beach or dry land and around structures such as buildings. It is also capable of adequately resolving hydraulic jumps due to the ability of the finite-volume method to handle discontinuities. The numerical scheme can also handle transitions between sub-critical and super-critical flow regimes seamlessly. \textsc{Anuga} has been validated against a number of analytical solutions and the wave tank simulation of the 1993 Okushiri such as buildings. \textsc{Anuga} has been validated against %a number of analytical solutions and  FIXME: These have not been published the wave tank simulation of the 1993 Okushiri Island tsunami~\cite{nielsen05,roberts06}. FIXME (Ole): Add reference to Tom Baldock's Dam Break valiadation of ANUGA. %================Section=========================== (arrows point down) during and immediately after the earthquake. Most of this data comes from uplifted or subsided coral heads. The length of vector increases with the magnitude of the displacement; the length the 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 uplifted/subsided points appear to be on a wrong side of the predicted has quite a large error (over 1 m); for example a couple of uplifted/subsided points appear to be on a wrong (FIXME (Jane): This is incorrect) 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. This does demonstrate, however, that \textsc{edgrn} and our modified version of \textsc{edstat} can reproduce the correct pattern of vertical \textsc{edstat} (FIXME(Jane): This has never been mentioned before) can reproduce the correct pattern of vertical deformation very well when the slip distribution is well constrained and when reasonable values for the elastic properties are used. 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 where the uplift is zero) derived from remote sensing (FIXME(Jane): How was that possible?). All the observational data are from the dataset collated by~\cite{chlieh07}.} shown in Figure~\ref{fig:computational_domain}. \begin{figure}[ht] \begin{center} %\includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ursga_model.jpg} %\includegraphics[width=5.0cm,keepaspectratio=true]{ursgaDomain.jpg} \includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ANUGA_model.jpg} \caption{Computational domain of the \textsc{ursga} simulation (inset: white and black squares and main: black square) and the \textsc{anuga} simulation (main and inset: red polygon).} \label{fig:computational_domain} \end{center} \end{figure} 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 \textsc{jason} satellite altimetry data. The \textsc{ursga} model replicates the amplitude and timing of the the wave observed at $2.5^0$ South, 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 $3^0$ to $7^0$ North elevation becomes out of phase with the \textsc{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 computational mesh and bathymetry data. This is also a limitation of the model presented here, but probably could be improved by nesting the model presented here which could be improved by nesting grids near Aceh. \includegraphics[width=12.0cm,keepaspectratio=true]{jasonComparison.jpg} \caption{Comparison of the \textsc{ursga}-predicted surface elevation with the JASON satellite altimetry data. The \textsc{ursga} wave with the \textsc{jason} satellite altimetry data. The \textsc{ursga} wave heights have been corrected for the time the satellite passed overhead compared to JASON sea level anomaly.} overhead compared to \textsc{jason} sea level anomaly.} \label{fig:jasonComparison} \end{center} \end{figure} FIXME (Jane): This graph does not look nice. The legend URS Model should be URSGA model. \subsection{Inundation} 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 velocities were extracted and used to construct a boundary condition for the \textsc{anuga} model. The interface between the \textsc{ursga} and \textsc{anuga} models was chosen to roughly follow the 100 m depth and \textsc{anuga} models was chosen to roughly follow the 100~m depth contour along the west coast of Phuket Island. The computational domain is shown in Figure~\ref{fig:computational_domain}. \begin{figure}[ht] \begin{center} %\includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ursga_model.jpg} %\includegraphics[width=5.0cm,keepaspectratio=true]{ursgaDomain.jpg} \includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ANUGA_model.jpg} \caption{Computational domain of the URSGA simulation (inset: white and black squares and main: black square) and the \textsc{anuga} simulation (main and inset: red polygon).} \label{fig:computational_domain} \end{center} \end{figure} The domain was discretised into 386,338 triangles. The resolution of the grid was increased in certain regions to efficiently increase the accuracy of the simulation. The grid resolution ranged between a maximum triangle area of $1\times 10^5$ m$^2$ near the Western ocean the grid was increased in regions inside the bay and on-shore to efficiently increase the simulation accuracy for the impact area. The grid resolution ranged between a maximum triangle area of $1\times 10^5$ m$^2$ near the western ocean boundary to $20$ m$^2$ in the small regions surrounding the inundation region in Patong Bay. Due to a lack of available data, friction was set to a constant throughout the computational domain. For the reference simulation a Manning's coefficient of 0.01 was chosen to reference simulation, a Manning's coefficient of 0.01 was chosen to represent a small resistance to the water flow. See Section \ref{sec:friction sensitivity} for details on model sensitivity to 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 and the north where no information about the incident wave or its velocity field is available was chosen as a transmissive boundary condition, effectively replicating the time dependent wave height present just inside the computational domain. Momentum was set height present just inside the computational domain. The velocity field on these boundaries 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 boundary condition. But experiments as well as the result of the verification reported here showed that this approach tends to underestimate the tsunami impact due to the tempering of the specified by the Thai Navy tide charts (\url{http://www.navy.mi.th/hydro/}) at the time the tsunami arrived at Patong Bay. Although the tsunami propagated for approximately 3 at Patong Bay. Although the tsunami propagated for approximately three hours before it reach Patong Bay, the period of time during which the wave propagated through the \textsc{anuga} domain is much reasonable. Maximum onshore inundation elevation was computed from the model Maximum onshore inundation depth was computed from the model throughout the entire Patong Bay region. Figure~\ref{fig:inundationcomparison1cm} (left) shows very good The datasets necessary for reproducing the results 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}. %%\url{http://sourceforge.net/project/showfiles.php?group_id=172848&package_id=319323&release_id=677531}. The scripts required are part of the \textsc{anuga} distribution also available from Sourceforge \url{http://sourceforge.net/projects/anuga} under the validation section. 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}. parameterisation of the source model, effect of humans structures on flow, as well as uncertainties in the elevation data, effects of erosion and deposition by the tsunami event, measurement errors, and erosion and deposition by the tsunami event, measurement errors in the GPS survey recordings, and missing data in the field survey data itself. The impact of some of these sources of uncertainties are is investigated in \includegraphics[width=10.0cm,keepaspectratio=true]{gauge_bay_depth.jpg} \includegraphics[width=10.0cm,keepaspectratio=true]{gauge_bay_speed.jpg} \caption{Time series obtained from the two offshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \caption{Time series obtained from the two offshore gauge locations, 7C and 10C, 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{Time series obtained from the two onshore locations shown in Figure \protect \ref{fig:gauge_locations}.} \caption{Time series obtained from the two onshore locations, North and South, shown in Figure \protect \ref{fig:gauge_locations}.} \end{center} \label{fig:onshore_timeseries} The estimated max depths and flow rates given in Section The estimated depths and flow rates given in Section \ref{sec:eyewitness data} are shown together with the modelled depths and flow rates obtained from the model in Table \ref{tab:depth and \label{tab:depth and flow comparisons} \end{table} FIXME (Jane): We should perhaps look at average data in area surrounding these points %can be estimated with landmarks found in model maximum inundation. The reference model is the one reported in Figure~\ref{fig:inundationcomparison1cm} (right) with a friction coefficient of 0.01, buildings included and the boundary condition produced by the URSGA model. buildings included and the boundary condition produced by the \textsc{ursga} model. %========================Friction==========================% \subsection{Friction} \label{sec:friction sensitivity} The first study investigated the impact of surface roughness on the The first sensitivity study investigated the impact of surface roughness on the predicted run-up. According to Schoettle~\cite{schoettle2007} appropriate values of Manning's coefficient range from 0.007 to 0.03 severity is directly proportional to the boundary waveheight but small 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 effect on the final inundated area. Obviously larger perturbations will have greater impact. However, wave heights in the open ocean are generally well predicted by the generation and propagation models such as \textsc{ursga}. See e.g.\ \cite{thomas2009}. \textsc{ursga} as demonstrated in Section \ref{sec:resultsPropagation} and also in \cite{thomas2009}. %========================Buildings==========================% \subsection{Buildings and Other Structures} The presence of buildings has the greatest influence on the maximum on-shore inundation extent. Figure~\ref{fig:sensitivity_nobuildings} 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 human made structures and buildings are ignored. The presence or absence of physical buildings in the elevation model was also investigated. Figure~\ref{fig:sensitivity_nobuildings} shows the inundated area and the associated maximum flow speeds in the presence and absence of buildings. It is apparent that densely built-up areas act as dissipators greatly reducing the inundated area. However, flow speeds tend to increase in passages between buildings. \begin{table} This study also shows that the tsunami impact modelling methodology adopted is sane and able to predict inundation extents with reasonable adopted is credible and able to predict inundation extents with reasonable accuracy.  An associated aim of this paper was to further validate the hydrodynamic modelling tool \textsc{anuga} which is used to simulate the tsunami inundation and run rain-induced floods. Model predictions matched well geodetic measurements of the Sumatra--Andaman earthquake, the tsunami inundation. Model predictions matched well the geodetic measurements of the Sumatra--Andaman earthquake, altimetry data from the \textsc{jason}, eye-witness accounts of wave front arrival times and flow speeds and a detailed inundation survey 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 predictions. Of these three, the presence of buildings was shown to have the greatest influence on the simulated inundation extent. The value of friction and small perturbations in the waveheight at the \textsc{anuga} boundary have This project was undertaken at Geoscience Australia and the Department of Mathematics, The Australian National University. The authors would like to thank Niran Chaimanee from the CCOP, Thailand for providing like to thank Niran Chaimanee from the CCOP for providing the post 2004 tsunami survey data, building footprints, aerial photography and the elevation data for Patong beach, Prapasri Asawakun photography and the elevation data for Patong city, Prapasri Asawakun from the Suranaree University of Technology and Parida Kuneepong for supporting this work; and Drew Whitehouse from the Australian National University for preparing the animation of the inundation model. University for preparing the animation of the simulated impact. \clearpage \caption{Results from reference model as reported in Section \protect \ref{sec:results}, i.e.\ including buildings and a friction value of 0.01. The seaward boundary condition is as provided by the URSGA model. The left image shows the maximum provided by the \textsc{ursga} model. The left image shows the maximum modelled depth while the right hand image shows the maximum modelled flow velocities.} \protect \ref{fig:reference_model} (left).  The left and right images show the inundation results if the wave at the \textsc{anuga} boundary is reduced or increased by 10cm respectively. The inundation is reduced or increased by 10 cm respectively. The inundation severity varies in proportion to the boundary waveheight, but the model results are only slightly sensitive to this parameter for the \end{center} \end{figure} FIXME (Jane): How and why was the +/- 10 cm chosen? \includegraphics[width=6cm,keepaspectratio=true]{sensitivity_nobuildings_depth} \includegraphics[width=6cm,keepaspectratio=true]{sensitivity_nobuildings_speed} \caption{This figure shows the effect of having buildings as part of \caption{Model results show the effect of buildings in the elevation data set. The left hand image shows the inundation depth results for The left hand image shows the maximum inundation depth results for a model entirely without buildings.  As expected, the absence of buildings will increase the inundation extent beyond what was