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10%\title{Inundation Modelling of the December 2004 Indian Ocean Tsunami}
11\title{Benchmarking an Inundation Model using the December 2004 Indian Ocean Tsunami Impact at Patong Beach}
14% FIXME(Ole): The 'ands' appear in the text, they shouldn't
18\institute{$^1$The Australian National University, Canberra, Australia \\
19$^2$Geoscience Australia, Canberra, Australia\\
20Email: \href{}{}}
21\keywords{ANUGA, Finite Volume Method, Natural Hazards, Indian Ocean Tsunami, Inundation, Thailand, Phuket, Patong Bay, Post Tsunami Runup Survey, Bathymetry, Model Verification, Shallow Water Wave Equations}
29%======================Section 1=================
32Tsunami are 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 tsunami hazard mitigation. Tsunami modelling is major component of hazard mitigation, which involves detection, forecasting, and emergency preparedness (Synolakis {\it et al.} 2005). Accurate models can be used to provide information that increases the effectiveness of action undertaken before the event to minimise damage (early warning systems, breakwalls etc.) and protocols put in place to be followed when the flood waters subside.
34Several approaches are currently used to model tsunami propagation and inundation. These methods differ in both the formulation used to describe the evolution of the tsunami and the numerical methods used to solve the governing equations. The shallow water wave equations, linearised shallow water wave equations, and Boussinesq-type equations are commonly accepted descriptions of flow. The nonlinear nature of these equations, the highly variable nature of the phenomena that they describe and the complex reality of the geometry they operate in necessitate the use of numerical models. These models are typically used to predict quantities such as arrival times, wave speeds and heights and inundation extents which are used to develop efficient hazard mitigation plans. Inaccuracies in model prediction can result in inappropriate evacuation plans and town zoning which may result in loss of life and large financial losses. Consequently tsunami models must undergo sufficient testing to increase scientific and community confidence in the model predictions.
36Complete confidence in a model of a physical system cannot be established. A model only be shown not to fail for a specific experiment (FIXME - fiddle with this sentence). However, the utility of a model can be assessed through a process of validation and verification. Validation assesses the accuracy of the numerical method used to solve the governing equations and verification is used to investigate whether the model adequately represents the physical system (\cite{XXX}). %Verification must be used to reduce numerical error before validation is used to assess model structure. In some situations it may be possible to increase the numerical accuracy of a model and produce a worse fit of the observed data.
38The sources of data used to validate and verify a model can be 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 of validating a numerical hydrodynamic model.
39(FIXME: CAN WE GET RID OF THIS: The solutions provide spatially and temporally distributed values of important observables that can be compared against modelled results). However analytical solutions to the governing equations are frequently limited to a small set of idealised examples that do not completely capture the more complex behaviour of 'real' events. Scale experiments, typically in the form of wave-tank experiments provide a much more realistic source of data that better captures the complex dynamics of natural tsunami, whilst allowing control of the event and much easier and accurate measurement of the tsunami properties. However comparison of numerical predictions with field data provides one of the most stringent test of model veracity. The use of field data increases the generality and significance of conclusions made regarding model utility. However the use of field data also significantly increase the uncertainty of the validation experiment that may constrain the ability to make unequivacol statements~\cite{lane94}.
41Currently the extent of tsunami related field data is limited. The cost of tsunami monitoring programs and bathymetry and topography surveys prohibits the collection of data in many of the regions in which tsunamis pose greatest threat. The resulting lack of data has limited the number of field data sets available to validate tsunami models, particularly those modelling tsunami inundation. 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 validity of a model and  five scale comparisons (wave-tank benchmarks) and two field events to assess model veracity.  The two field data benchmarks are very useful but only capture a small subset of possible tsunami behaviours and only one of the benchmarks can be used to validate tsunami inundation. The type and size of a tsunami source, propagation extent, and local bathymetry and topography all affect the energy, waveform and subsequent inundation of a tsunami. Consequently additional field data benchmarks that further capture the variability and sensitivity of the real world system would be useful to allow model developers verify their models and subsequently use their models with greater confidence.
43In this paper we develop a field data benchmark to be used in conjunction with the other tests proposed by Synolakis et al. to validate and verify tsunami inundation. The benchmark is constructed from data collected around Patong Bay, Thailand immediately following the 2004 Indian Ocean tsunami. This area was chosen because the authors were able to obtain high resolution bathymetry and topography data in this area and an inundation map generated from a survey performed in the aftermath of the tsunami. A description of this data is give in Section~\ref{sec:data}.
45An associated aim of this paper is to illustrate the use of this new benchmark to validate an operational tsunami model called ANUGA (see Secion~\ref{sec:veri_procedure}). The specific intention is to test the ability of ANUGA to reproduce the inundation survey of maximum runup. ANUGA is a hydrodynamic modelling tool used to simulate the tsunami propagation and run rain-induced floods.
49\section{Indian Ocean tsunami of 24th December 2004}
50The devastation caused by the 2004 Indian Ocean tsunami has heightened community, scientific and governmental interest in tsunami and in doing so has provided a unique opportunity for further validation of tsunami models. Data sets from seismometers, tide gauges, GPS stations, a few satellite overpasses, subsequent coastal field surveys of run-up and flooding and measurements from ship-based expeditions, have now been made available (Vigny {\it et al.} 2005, Amnon {\it et al.} 2005, Kawata {\it et al.} 2005, and Liu {\it et al.} 2005)\nocite{vigny05,amnon05,kawata05,liu05}. A number of studies have utilised this data to calibrate models of the tsunami source\cite{grilli07} , match tide gauge recordings\cite{}, maximum wave heights~\cite{asavanant08} and runup locations~\cite{ioualalen07}. We propose to use this event as an additional field-data benchmark for verification of tsunami models. This event captures certain tsunami behaviours that are not present in the benchmarks proposed by Synolakis et.\ al~\cite{synolakis07}. FIXME: What kind of behaviours???
52Synolakis detail two field data benchmarks. The first test 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 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 maximum runup elevations were observed. The second benchmark is based upon the Rat Islands Tsunami that occurred off the coast of Alaska on the 17th of November 2003. Rat island tsunami provides a good test for real-time forecasting models since 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 using a propagation model to to reproduce the tide gauge record at Hilo.
54%The tsunamis used by the two standard benchmarks and the 2004 tsunami are quite different. They all arise from coseismic displacement resulting from an earthquake, however they all occur in very different geographical regions. The Hokkaido-Nansei-Oki tsunami was generated by an earthquake with a magnitude of 7.8 and only travelled a small distance before inundating Okishiri Island. The event provides an example of extreme runup generated from reflections and constructive interference resulting from local topography and bathymetry. In comparison the Rat islands tsunami was generated by an earthquake of the same magnitude but had to travel a much greater distance. The event provides a number of tide gauge recordings that capture the change in wave form as the tsunami evolved.
56The 2004 Indian Ocean tsunami was a much larger event than the previous two described. It was generated by a disturbance, resulting from a M$_w$=9.3 mega-thrust earthquake, that propagated 1200-1300 km. Consequently the energy of the resulting wave was much larger than the waves generated from the more localised and smaller magnitude aforementioned events. WAS THE WAVELENGTH< VELOCITY (and thus average ocean depth) DIFFERENT FROM THESE TWO EVENTS??? If so state something like. This larger wavelength and energy and simply the different geology of the area produced different a wave signal and different pattern of inundation. Here we focus on the large inundation experienced at Patong Bay on the west coast of Thailand.
59(FIXME (OLE): Remove? Hydrodynamic simulations require very little data in comparison to models of many other environmental systems). Tsunami models typically only require baythymetry and topography data to approximate the local geography, parameterisation of the tsunami source from which appropriate initial conditions can be generated, and a locally distributed quantity such as Manning's friction coefficient to approximate friction. Here we discuss the bathymetric and topographical data sets and source condition that are necessary to implement the proposed benchmark. Friction is discussed in Section~\ref{sec:inundation}
61An unusually large amount of data for the 2004 tsunami, necessary for tsunami verification, is available at Patong Bay and surrounding regions. 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.
63\subsection{Bathymetric and topographic data}
65FIXME(OLE): Need Intro to this section aka: we obtained data sets at different resolutions from various sources and merged them to build a model appropriate for inundation modelling. The resolution required was generally relatively coarse in the deeper water and progressively finer towards the bay itself with the finest data in the intertidal zone and around the built environment.
68The two minute arc grid data set, DBDB2, was obtained from US Naval Research Labs and used to approximate the bathymetry in the Bay of Bengal. This grid was further interpolated to a 27 second arc grid. In the Andaman Sea the DBDB2 data was replaced with a 3 second grid obtained from NOAA (REF?). Finally, a 1 second grid was used to approximate the bathymetry in Patong Bay and the immediately adjacent regions (FROM WHERE?). This elevation data was created from the digitised Thai Navy bathymetry chart, no 358. 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.
70The sub-sampling of larger grids was performed by using {\bf resample} a GMT program (\cite{XXX}). The 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 and application of minimum curvature akima spline smoothing.
76\caption{Is there a new picture with river included???}
81Details of the lineage of this dataset is outlined in Appendix~\ref{XXXXX} and the final dataset
82is available at XXXX.
85\subsection{Tsunami source}\label{sec:source}
86The Indian Ocean tsunami of 2004 was generated by severe coseismic displacement of the sea floor as a result of one of the largest earthquakes on record. The M$_w$=9.2-9.3 mega-thrust earthquake occurred on the 26 December 2004 at 0h58'53'' UTC approximately 70 km offshore North Sumatra. The disturbance propagated 1200-1300 km along the Sumatra-Andaman trench time at a rate of 2.5-3 km.s$^{-1}$ and lasted approximately 8-10 minutes (Amnon {\it et al.} 2005)\nocite{amnon05}.
88Many parameterisations of the 2004 tsunami source are available. Some are determined from various geological surveys of the site, others solve an inverse problem which calibrates the source based upon the tsunami wave signal and or runup. The source parameters used to simulate the 2004 Indian Ocean Tsunami were taken from Chlieh (2007). This model was created by carefull inversion of the seismic data and fits both coseismic, tsunami and GPS data in the Andaman Sea well. DOES ANYONE HAVE A COPY THEY COULD SEND ME PLEASE? The resulting sea floor displacement ranges from about - 5.0 to 5.0 metres and is shown in Figure~\ref{fig:chlieh_slip_model}.
93\caption{Location and magnitude of the sea floor displacement associated with the December 24 2004 tsunami. Source parameters taken from Chlieh {\it et al.} (2007)}
98\subsection{Inundation survey data}
99The bathymetry data and source parameterisation can be inserted into the tsunami model and run. From the simulation runup and ocean surface elevation can be obtained. We propose that a `correct' tsunami model should reproduce the inundation map shown in Figure~\ref{fig:patongescapemap}. Furthermore the model should simulate a leading depression followed by 3??? crests. Is there any eye witness accounts of how many waves arrived a patong???
104\caption{Map of maximum inundation at Patong bay.}
110\section{Verification Procedure}\label{sec:veri_procedure}
115ANUGA is an inundation tool that solves the depth integrated shallow water wave equations. The scheme used by ANUGA, first presented by Zoppou and Roberts (1999)\nocite{zoppou99}, is a high-resolution Godunov-type method that uses the rotational invariance property of the shallow water equations to transform the two-dimensional problem into local one-dimensional problems. These local Riemann problems are then solved using the semi-discrete central-upwind scheme of Kurganov {\it et al.} (2001) \nocite{kurganov01} for solving one-dimensional conservation equations. The numerical scheme is presented in detail in (Zoppou and Roberts 1999, Zoppou and Roberts 2000, and Roberts and Zoppou 2000, Nielsen {\it et al.} 2005) \nocite{zoppou99,zoppou00,roberts00,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 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. ANUGA has been validated against a number of analytical solutions and the wave tank simulation of the 1993 Okushiri Island tsunami (Roberts {\it et al.} 2006 and Nielsen {\it et al.} 2005) \nocite{roberts06,nielsen05}.
118URSGA is a hydrodynamic code that models the propagation of the tsunami in deep water using the finite difference method to solve the non-linear shallow water equations in spherical co-ordinates with friction and Coriolis terms. The code is based on Satake (1995) with significant modifications made by the URS corporation (Thio et al. 2007) and Geoscience Australia (Burbidge et al. 2007). The tsunami is propagated via a stagered grid system starting with coarser grids and ending with the finest one.
121\subsection{Tsunami Source and Propagation}
122The utility of the URSGA model decreases with water depth unless an intricate sequence of nested grids is employed. In comparison ANUGA is designed to produce robust and accurate predictions of on-shore inundation in mind, but is less suitable for earth quake source modelling and large study areas. Consequently, the Geoscience Australia tsunami modelling methodology is based on a hybrid approach using models like URSGA for tsunami generation and propagation up to a 100m depth contour where the wave is picked up by ANUGA and propagated on shore. Specifically we use the URSGA model to simulate the propagation of the 2004 Indian Ocean tsunami in the deep ocean ocean, based on a discrete representation of the initial deformation of the sea floor, described in Section~\ref{sec:source}. The resulting tsunami was propagated over the entire Bay of Bengal and the wave signal measured along the -100m contour offshore of Phuket, Thailand. The wave signal is then used as a time varying boundary condition for the ANUGA inundation simulation.
124???The URS code is also capable of calculating inundation. CAN WE PRODUCE AN INUNDATION MAP OVER THE SAME AREA TO COMPARE WITH ANUGA???
126\subsection{Tsunami Inundation}\label{sec:inundation}
127In this case the open ocean boundary of the ANUGA study area was chosen to roughly follow the 100m depth contour along the west coast of Phuket Island. The computational domain is shown in Figure \ref{fig:computational_domain}
131\caption{Computational domain of the ANUGA simulation. CAN WE CREATE A PICTURE LIKE THIS FOR OUR NEW SCENARIO???}
136The domain was discretised into approximately ...,000 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 $...\times 10^5$ m$^2$ near the Western ocean boundary to $...$ m$^2$ in the small regions surrounding the inundation region in Patong Bay. Due to a lack of available data, friction was set constant througout the computational domain. A Manning's coefficient of 0.01 was chosen based upon previous numerical experiments conducted by the authors.
140Maximum on-shore inundation elevation was simulated throughout the entire Patong Bay region. Figure~\ref{fig:inundationcomparison1cm} shows very good agreement between the measured and simulated inundation. The discrepencies between the two inundation regions may be a result of the large measurement error in the field survey data. The ANUGA simulation determines a region to be inundated if at some point in time it was covered by at least 1cm of water. This precision in field measurements is impossible to obtain. The inundation boundary is determined by observing water marks and other signs left by the receeding waters. The precision of the observed inundation map is, most likely, at least an order of magnitude worse than the ANUGA simulation. The simulated inundation based upon a 10cm threshold is shown in Figure~\ref{fig:inundationcomparison10cm}.
145\caption{Simulated inundation versus observed inundation}
155This paper proposes an additional field data benchmark for the verification of tsunami inundation models. Currently the number of appropriate tests are limited due to a lack of tsunami data. The new benchmark involves the comparison of model predictions of on-shore inundation in Patong Bay, Phuket Thailand caused by the mega tsunami of December 26th 2004. Specifically a field survey mapping observed inundation is used as a spatially distributed test of model performance. Although two other field data benchmarks exists, the proposed benchmark provides a novel investigation of the dynamics of extreme tsunami events not before tested. The benchmark could be further improved with the inclusion of local tide gauge data, against which wave signal could be compared, however, to the authors knowledge no such data exists.
157This paper also illustrates the effectiveness of the proposed new benchmark. The benchmark is used to test the veracity of the hydrodynamic ANUGA designed spcefically to model on-shore inundation. Very good agreement is obtained between the observed and simulated runup. The URSGA tsunami package was also tested. Much worse results were obtained??
162This 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 the post 2004 tsunami survey data and the elevation data for Patong beach.
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240\section*{Appendix A. Figures and Tables}
242\subsection (datasets and gridding)
244Gridded data sets used:
246DBDB2 2 minute of arc grid from the US Naval Research Labs.
247This grid was also interpolated to 27 sec of arc and used in a nested grid scheme.
249Indian Ocean 27 sec of arc grid created by:
250Interpolating the DBDB2 2 minute of arc grid.
251In the region where the 9 sec grid sits the data was cut out and replaced by the 9 sec data.
252Any points that deviated from the general trend near the boundary were deleted.
253The data was then re-gridded.
255Andaman Sea 9 sec of arc grid created by:
256Sub-sampling the 3 sec of arc grid from NOAA.
257In the region where the 3 sec grid sits the data was cut out and replaced by the 3 sec data.
258Any points that deviated from the general trend near the boundary were deleted.
259The data was then re-gridded.
261Thailand off-shore 3 sec of arc grid created by:
262cropping a much larger 3 sec of arc grid covering the whole of the Andaman Sea which itself was based on Thai charts 45 and 362.
263This grid was obtained from NOAA.
264In the region where the 1 sec grid sits the data was cut out and replaced by the 1 sec data.
265Any points that deviated from the general trend near the boundary were deleted.
266The data was then re-gridded.
268Patong Bay 1 second of arc grid created from:
269elevation data contained in a GIS of Patong Bay supplied by Niran Chaimanee, Geo-environment Sector Manager, CCOP T/S, Bangkok.
270Digitised Thai Navy bathymetry chart no 358.
272The sub-sampling of larger grids was performed by using {\bf resample}  a GMT program.
273The gridding of data was performed using {\bf Intrepid} a commercial geophysical processing package developed by Intrepid Geophysics.
274The gridding scheme was nearest neighbour followed by minimum curvature akima spline smoothing.
276\subsection (earthquake source model)
278The earthquake source model of Chlieh was adopted to generate the tsunami simulation. This model was created by carefull inversion of the seismic
279data and fits both coseismic, tsunami and GPS data in the Andaman Sea well.
281\subsection (tsunami propagation)
283To to generate and propagate the tsunami the URS code was used. This program solves the shallow water equations using the finite difference method.
284It can also be used in a nexted grid scheme and does on-shore inundation.
291Main source of uncertainty arises from inaccuracies in initial condition (source), inaccurate bathymetry data, to a lesser extent friction
293single experiment can refute model but cannot validate it. Need as many tests as possible to be confident in rpediction. Question arises. How mnay should we do. With finite experiments more weight should be given to a particular experiment if the range of the inout function and the material properties are both broad so that the universal character of the model is tested.
296sufficient verification/falsification of model
297Confidently utilise a model
299Predictive valdiation of only one aspect of model evaluation. Need to assess model explanation.
301Conservation of mass
304spatial and temporal discretisation errors, round off errors due to limited numerical precision
306analytical benchmarking:
307ensuring equations are solved accurately
308single wave on a beach
309Solitary wave on composite beach
310subaerial landslide on simple beach
312Analytical solutions only represent idealised and simplfied events that do not fully capture the complexity of 'real' flows. Provide temporally and spatially distributed data that field data can rearely match.
314scale comparisions (laboratory benchmarking):
315Scale differences are not belived to be important. scale experiments generally do not have same bootom firction characteristics as real scenario but has not proven to be a problem. The long wavelngth of tsunami tends to mean that the friction is less important in comparison to the motion of the wave
316Single wave on a simple beacj
317Solitary wave on composite beach
318Conical island
319Monai Valley
322includes comparisons with validation data sets generated by other models of higher dimensionality and resolution.
324Often flow geometries are simplified
327Field benchmarking:
328Most important verification process
329Hydrodynamic inversion to predict the source is an ill posed problem
33012 July 1993 Hokkaido-Nansei-Oki tsunami around Okushiri Island Japan exreme runup height of 31.7m was found at the tip of a narrow gulley with the small cove at Monai
33117 November 2003 Rat Islands Tsunami
333Construction of more than one model can reveal biases in a single model. Two types of comparisons 1 between those that are comceptually simailar and those that re different. In former case interested in how choice of numerical solver and discretisation effects results and the later can help determine the level of physical processs representation necessary to represent an observed data set.
335Movinf to field data increases the gnereality and siginificance of svientifice evidence obatined. However we also significantly incerase the uncertainty of the validation experioment that may constrain the ability to make unequivacol statments. E.g. in bathymetry source condition friction.
337Calibratino of the model is often used to compensate for uncertainty in the model inputs. Calibartion results in a further loss of experimental control as a unique solution may not exist.
339verfication need to assess point data, spatially distributed data and bulk (lumped) data.
341Synolakis et. al~\cite{synolakis07} detail two field events that have been previoulsy used to validate tsunami models, the Hokkaido-Nansei-Oki tsunami that occured around Okushiri Island, Japan on 2nd of July 1993 and the Rat Islands Tsunami that inundated the occured off the coast of Alaska on the 17th of November 2003.
344inundation map only useful if mesh and topography resolution fine enough hard to measure what the model predicts how deep does inundation need to be for it to be visible during a field study
347Okushiri provides an example of extreme runup genereated from reflections and constructive interference resulting from local topography and bathymetry. Numerous point sites at which runup elevations were observed are available.  The highest runup of 31.7 m in a valley north of Monai needs to be approximated with the numerical model. In addition, two tide gage records at Iwanai and Esashi need to be estimated.
351Rat Island tsuanmi provides a good test for real-time forecasting models since tsnumai was recorded at three tsunameters. The test requires matching the propagation model data with the recordings to constrain the tsunami source model. The inundation model is to reproduce the tide gauge record at Hilo.
353Patong Bay benchmark provides spatially distributed field data for comparison.
355single experiment can refute model but cannot validate it. Need as many tests as possible to be confident in prediction. Question arises. How mnay should we do.
359Notes:  * Model source developed independently of inundation data.
360        * Patong region was chosen because high resolution inundation map and bathymetry and topography data was available there
362Geoscience Australia, in an open collaboration with the Mathematical Sciences Institute, The Australian National University, is developing a software application, ANUGA, to model the hydrodynamics of tsunamis, floods and storm surges. The open source software implements a finite volume central-upwind Godunov method to solve the non-linear depth-averaged shallow water wave equations. This paper investigates the veracity of ANUGA  when used to model tsunami inundation.  A particular aim was to make use of the comparatively large amount of observed data corresponding to the Indian ocean tsunmai event of December 2004, to provide a conditional assessment of the computational model's performance. Specifically a comparison is made between an inundation map, constructed from observed data, against modelled maximum inundation. This comparison shows that there is very good agreement between the simulated and observed values. The sensitivity of model results to the resolution of bathymetry data used in the model was also investigated. It was found that the performance of the model could be drastically improved by using finer bathymetric data which better captures local topographic features. The effects of two different source models was also explored.
364different even types submarine mass failure generate larger events because of proximity more directional wave generation
366even if data is available it is hard to access
369title={Modeling the 26 December 2004 Indian Ocean tsunami: Case study of impact in Thailand},
370author=-{Ioualalen, M. and Asavanant, J. and  Kaewbanjak, N. and Grilli, S.~T. and Kirby, J.~T. and Watts, P.},
372journal ={ J. Geophys. Res.},
378title={The 2004 Indian Ocean tsunami: Tsunami source model from satellite altimetry},
379author={Hirata, K. and Satake, K. and Tanioka, Y. and  Kuragano, T. and Hasegawa, Y. and   Hayashi, Y. and Hamada, N.},
380journal={Earth, Planets and Space}
388ALTauthor = {Asavanant, J. and  Ioualalen, M. and Kaewbanjak, N. and Grilli, S.~T. and Watts, P. and Kirby, J.~T. and Shi, F.},
389ALTeditor = {},
390title = {Modeling, Simulation and Optimization of Complex Processes},
391chapter = {Numerical Simulation of the December 26, 2004: Indian Ocean Tsunami },
392publisher = {   Springer Berlin Heidelberg},
393year = {2008},
394pages = {59--68},
398author = {St\'{e}phan T. Grilli and Mansour Ioualalen and Jack Asavanant and Fengyan Shi and James T. Kirby and Philip Watts},
399title = {Source Constraints and Model Simulation of the December 26, 2004, Indian Ocean Tsunami},
400publisher = {ASCE},
401year = {2007},
402journal = {Journal of Waterway, Port, Coastal, and Ocean Engineering},
403volume = {133},
404number = {6},
405pages = {414-428},
406url = {},
407doi = {10.1061/(ASCE)0733-950X(2007)133:6(414)}
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