Changeset 6917


Ignore:
Timestamp:
Apr 28, 2009, 2:31:17 PM (16 years ago)
Author:
jakeman
Message:

draft for 19-04-2009 meeting

Location:
anuga_work/publications/boxing_day_validation_2008
Files:
1 added
2 edited

Legend:

Unmodified
Added
Removed

  • anuga_work/publications/boxing_day_validation_2008/patong_validation.tex

    r6915 r6917  
    44\usepackage{amsfonts}
    55\usepackage{url}      % for URLs and DOIs
    6 \newcommand{\doi}[1]{\url{http://dx.doComparison of URS model with JASON satellite altimetry. Upper Panel: URS wave heights 120 minutes after the initial earthquake with the JASON satellite track and its observed sea level anomalies overlaid. Note the URS data has not been corrected for the flight path time. Lower Panel: URS wave heights corrected for the time the satellite passed overhead compared to JASON sea level anomaly. The URS model matches the timing and amplitude of the first wave peak and trough but becomes out of phase for later waves, thought to be reflected waves from Aceh Peninsula that are not resolved in the URS model. 
    7 i.org/#1}}
     6\newcommand{\doi}[1]{\url{http://dx.doi.org/#1}}
    87
    98%----------title-------------%
    … …  
    6261We use near field global positioning surveys (\textsc{gps}) in northwestern Sumatra and the Nicobar-Andaman islands and  continuous and campaign \textsc{gps} measurements from Thailand and Malaysia to verify the \textsc{ursga} model used to generate the tsunami ...
    6362
    64 FIXME: David and/or Richard could you complete this please
     63FIXME(David and/or Richard): Could you complete this please?
    6564
    6665\begin{figure}[ht]
    … …  
    127126\end{figure}
    128127
     128\subsubsection{Buildings and Other Structures}
     129FIXME(John): Complete
     130Where did elevation data come from???
     131
    129132\subsubsection{Eyewitness Accounts}
    130133Eyewitness accounts detailed in~\cite{papadopoulos06} report that most people at Patong Beach observed an initial retreat of the shoreline of more than 100m 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 the strong wave between 2 hours and 55 minutes to 3 hours and 5 minutes after the source rupture (09:55am to 10:05am local time).
    … …  
    136139\begin{center}
    137140\includegraphics[width=8.0cm,keepaspectratio=true]{patongescapemap.jpg}
    138 \caption{Tsunami survey mapping the maximum observed inundation at Patong beach courtesy of the Thai Department of Mineral Resources \protect \cite{szczucinski}.}
     141\caption{Tsunami survey mapping the maximum observed inundation at Patong beach courtesy of the Thai Department of Mineral Resources \protect \cite{szczucinski06}.}
    139142\label{fig:patongescapemap}
    140143\end{center}
    … …  
    142145
    143146\subsection{Validation Check-List}
     147\label{sec:checkList}
    144148The data described in this section can be used to construct a benchmark to validate all three stages of the evolution of a tsunami. In particular we propose that a legitimate tsunami model should reproduce the following behaviour:
    145149\begin{itemize}
    … …  
    153157%================Section===========================
    154158\section{Modelling the Event}\label{sec:models}
    155 Numerous models are currently used to model and predict tsunami generation, propagation and run-up\cite{titov97,satake95}. Here we introduce the modelling methodology employed by Geoscience Australia to illustrate the utility of the proposed benchmark. Geoscience Australia's tsunami model can again be decomposed into three parts which simulate generation, propagation and inundation (Sections~\ref{modelGeneration},\ref{sec:modelPropagation} and \ref{sec:modelInundation} respectively).
     159Numerous 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 model can again be decomposed into three parts which simulate generation, propagation and inundation (Sections~\ref{sec:modelGeneration},\ref{sec:modelPropagation} and \ref{sec:modelInundation} respectively).
    156160
    157161\subsection{Generation}\label{sec:modelGeneration}
    … …  
    172176%================Section===========================
    173177\section{Results}\label{sec:results}
    174 
     178This section presents validates the modelling practice of Geoscience Australia against the new proposed benchmark. The criteria in outlined in Section\ref{sec:checkList} are addressed for each three stages of tsunami evolution.
    175179
    176180\subsection{Generation}
    177 
    178 The resulting sea floor displacement ranges from about $-5.0$ to $5.0$ metres and is shown in Figure~\ref{fig:chlieh_slip_model}.
     181The location and magnitude of the sea floor displacement associated with the 2004 Sumatra--Andaman tsunami are shown in Figure~\ref{fig:chlieh_slip_model}. The magnitude of the sea floor displacement ranges from about $-5.0$ to $5.0$ metres. The source model detailed in Section~\ref{sec:modelGeneration} matches the horizontal displacements in the Nicobar-Andaman islands, Thailand and Malaysia reasonably well.
    179182
    180183\begin{figure}[ht]
    … …  
    187190
    188191\subsection{Propagation}
    189 Figure \ref{fig:jasonComparison} provides a comparison of the \textsc{ursga} prediceted surface elevation with the JASON satellite altimetry data. The \textsc{ursga} model replicates the amplitude and timing of the first peak and trough well. However the model does not resolve the double peak of the first wave. The generation model presented in Section~\ref{sec:modelGeneration} simulates a single uplift displacement, howver the observed double peak may have been generated by superposition of the initial waves from the rupture of two fault sections \cite{harig08}.
    190 
    191 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{chileh07} also observe this misfit 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.
     192The source model described in Section~\ref{modelGeneration} was used to provide an profile of the initial ocean surface displacement. This profile was used as an initial condition for \textsc{ursga} which propagated the tsunami throughtout the Bay of Bengal. The rectangular computational domain extended from .. to ..East and .. North and containned ...,000 finite difference points. A nested sequence of grids was used ranging from ... in the coarsest grid and ... in the finest grid. The computational domain is shown in Figure\ref{gif:ursgaDomain}. FIXME(David/Richard): Could you please fill out these details.
     193
     194Figure \ref{fig:jasonComparison} provides a comparison of the \textsc{ursga} predicted surface elevation with the JASON satellite altimetry data. The \textsc{ursga} model replicates the amplitude and timing of the first peak and trough well. However the model does not resolve the double peak of the first wave. The generation model presented in Section~\ref{sec:modelGeneration} simulates a single uplift displacement, however the observed double peak may have been generated by superposition of the initial waves from the rupture of two fault sections \cite{harig08}.
     195
     196Also 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 this misfit 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.
    192197
    193198\begin{figure}[ht]
    194199\begin{center}
    195200\includegraphics[width=12.0cm,keepaspectratio=true]{jasonComparison.jpg}
    196 \caption{Comparison of the \textsc{ursga} prediceted surface elevation with the 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.
     201\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 overhead compared to JASON sea level anomaly.
    197202}
    198203\label{fig:jasonComparison}
    … …  
    201206
    202207\subsection{Inundation}
    203 In this case the interface betwen the \textsc{ursga} and \textsc{anuga} models 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}
    204 \begin{figure}[ht]
    205 \begin{center}
     208After propagating the tsunami in the open ocean using \textsc{ursga} the approximated ocean and surface elevation and tsunami velocities were extracted and used to construct a boundary condition for the \textsc{anuga} model. The interface betwen the \textsc{ursga} and \textsc{anuga} models 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}
     209\begin{figure}[ht]
     210\begin{center}
     211%\includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ursga_model.jpg}
    206212\includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ANUGA_model.jpg}
    207 \caption{Computational domain of the \textsc{anuga} simulation. FIXME: Add lat longs}
     213\includegraphics[width=5.0cm,keepaspectratio=true]{extent_of_ANUGA_model.jpg}
     214\caption{Computational domain of the ursga simulation (left) and the \textsc{anuga} simulation (rights). FIXME: Add lat longs to anuga and make fig for ursga}
    208215\label{fig:computational_domain}
    209216\end{center}
    … …  
    214221The 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 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 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.
    215222
    216 FIXME: Need a commentary on the dynamics of what is being observed and whether it aligns with eye witness observations.
     223FIXME(John): Need a commentary on the dynamics of what is being observed and whether it aligns with eye witness observations.
    217224Both the URS model and the \textsc{anuga} inundation model shows that the event comprises a train of waves some with preceding drawdown effects (ADD details of waveform with a graph from URL and a gauge from \textsc{anuga} and discuss).
    218225
    219 Maximum onshore 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 \textsc{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 receding waters. The precision of the observed inundation map is, most likely, at least an order of magnitude worse than the \textsc{anuga} simulation. The simulated inundation based upon a 10cm threshold is shown in Figure~\ref{fig:inundationcomparison10cm}. An inundation threshold of 10cm was selected for all future simulations to reflect the likely accuracy of the survey and subsequently faciliate a more appropriate comparison between the modelled and observed inundation area.
    220 
    221 \begin{figure}[ht]
    222 \begin{center}
    223 \includegraphics[width=10.0cm,keepaspectratio=true]{Depth_small_transmissive_d0.jpg}
    224 \caption{Simulated inundation versus observed inundation using an inundation threshold of 1cm}
     226Maximum onshore 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 \textsc{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 receding waters. The precision of the observed inundation map is, most likely, at least an order of magnitude worse than the \textsc{anuga} simulation. The simulated inundation based upon a 10cm threshold is shown in Figure~\ref{fig:inundationcomparison10cm}. An inundation threshold of 10cm was selected for all future simulations to reflect the likely accuracy of the survey and subsequently facilitate a more appropriate comparison between the modelled and observed inundation area.
     227
     228\begin{figure}[ht]
     229\begin{center}
     230\includegraphics[width=5.0cm,keepaspectratio=true]{Depth_small_transmissive_d0.jpg}
     231\includegraphics[width=5.0cm,keepaspectratio=true]{sensitivity_reference.jpg}
     232\caption{Simulated inundation versus observed inundation using an inundation threshold of 1cm (left) and 10cm (right). FIXME: NEED Graph for 10cm}
    225233\label{fig:inundationcomparison1cm}
    226234\end{center}
    227235\end{figure}
    228236
    229 \begin{figure}[ht]
    230 \begin{center}
    231 \includegraphics[width=10.0cm,keepaspectratio=true]{Depth_small_transmissive_d0.jpg}
    232 \caption{Simulated inundation versus observed inundation using an inundation threshold of 10cm}
    233 \label{fig:inundationcomparison10cm}
    234 \end{center}
    235 \end{figure}
    236 
    237 Here we introudce the measure
     237Here we introduce the measure
    238238\begin{equation}
    239 \mathcal{A}_{in}=\frac{A_m\cap A_o}{A_o}
     239A_{in}=\frac{A_m\cap A_o}{A_o}
    240240\end{equation}
    241 to quantify the fraction of the obesrved inundation area $A_o$ captured by the model $A_m$. Another useful measure is the fraction of the modelled inundation area that falls outside the observed inundation area given by the formula
     241to quantify the fraction of the observed inundation area $A_o$ captured by the model $A_m$. Another useful measure is the fraction of the modelled inundation area that falls outside the observed inundation area given by the formula
    242242\begin{equation}
    243 \mathcal{A}_{out}=\frac{A_m\setminus (A_m\cap A_o)}{A_o}
     243A_{out}=\frac{A_m\setminus (A_m\cap A_o)}{A_o}
    244244\end{equation}
    245 These values for the two aformentioned simulations are given in Table~\ref{table:inundationAreas}
    246 \begin{center}
    247 % use packages: array
    248 \begin{tabular}{|c|c|c|}\label{table:inundationAreas}
    249  & × & × \\
     245These values for the two aforementioned simulations are given in Table~\ref{table:inundationAreas}
     246
     247Additional causes of the discrepancies between the survey data and the modelled inundated include: unknown distribution of surface roughness, inappropriate 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 missing data in the field survey data itself. The impact of some of these sources of uncertainties are is investigated in Section~\ref{sec:sensitivity}
     248
     249%================Section===========================
     250\section{Sensitivities Analysis}
     251\label{sec:sensitivity}
     252This section shows how model maximum inundation varies with: different values of Manning's friction coefficient; changing waveheight at the ANUGA boundary (where it was coupled with the URSGA model); and finally the presence and absence of buildings in the elevation dataset.
     253
     254%========================Friction==========================%
     255\subsection{Friction}
     256The first study investigated the impact of surface roughness on the predicted run-up. According to Schoellte~\cite{schoettle2007} appropriate values of Manning's coefficient range from 0.007 to 0.030 for tsunami propagation over a sandy sea floor.  Consequently we simulated the maximum onshore inundation using the a Manning's coefficient of 0.0003 and 0.03. The resulting run-up is shown in Figures
     257\ref{fig:sensitivity_friction} and  the maximum flow speeds\ref{fig:sensitivity_friction_speed}. These figurers show that the on-shoer inundation extent decreases with increasing 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 comparison to the motion of the wave.
     258
     259%========================Wave-Height==========================%
     260\subsection{Input Wave Height}\label{sec:waveheightSA}
     261The 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 10cm seem to have little effect on the final on-shore run-up. Obviously larger perturbations will have greater impact, however unlike the uncertainty in the friction, the range of uncertainty in the propagation wave-height is hard to estimate.
     262
     263
     264
     265%========================Buildings==========================%
     266\subsection{Buildings and Other Structures}
     267The presence of buildings has the greatest influence on the maximum on-shore inundation extent. Figure \ref{fig:sensitivity_nobuildings} and shows the maximum run-up 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. Maximal flow speeds for these two model parameterisations are shown in Figure~\ref{fig:sensitivity_nobuildings_speed}.
     268
     269\begin{table}
     270\begin{center}
     271\label{table:inundationAreas}
     272\caption{$A_{in}$ and $A_{out}$ of the reference simulation and all sensitivity studies}
     273\begin{tabular}{|c|c|c|}
     274\hline
     275 & $A_{in}$ & $A_{out}$ \\
    250276\hline\hline
    251 Ã— & × & \\
    252 Ã— & × & \\
     277Reference & × & \\
     278Min. Friction & × & \\
     279Max. Friction & × & \\
     280Min. Wave-Height× & × & \\
     281Max. Wave-Height× & × & \\
     282No Buildings × & × & \\
    253283\hline
    254284\end{tabular}
    255285\end{center}
    256 
    257 Additional causes of the discrepancies between the survey data and the modelled inundated include: unknown distribution of surface roughness, inappropriate paramterisation 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 missing data in the field survey data itself. The impact of some of these sources of uncertainties are is invetigated in Section~\ref{sec:sensitivity}
    258 
    259 %================Section===========================
    260 \section{Sensitivities of inundation model}
    261 \label{sec:sensitivity}
    262 This section shows how model results vary as a result of changing the waveheight at the ANUGA boundary where it was coupled with the URSGA model
    263 (Figures \ref{fig:sensitivity_boundary} and
    264 \ref{fig:sensitivity_boundary_speed}), how model
    265 results vary with different values of Manning's friction coefficient
    266 (Figures \ref{fig:sensitivity_friction} and
    267 \ref{fig:sensitivity_friction_speed}),
    268 and finally
    269 the effect of removing buildings from the elevation dataset.
    270 (Figures \ref{fig:sensitivity_nobuildings} and
    271 \ref{fig:sensitivity_nobuildings_speed}).
    272 
    273 The observations are as expected; The inundation extent increases with
    274 boundary waveheight and decreases with friction. It also increases if
    275 buildings are removed from the model. From the maximal inundation figures it
    276 appears that the presence or absence of buildings is the most important parameter followed by the right choice of friction whereas a small pertubation in
    277 the waveheight at the ANUGA boundary has comparatively little effect on the model results.
     286\end{table}
    278287
    279288FIXME(Ole): It would be nice if we could be a little more quantitative - e.g. along the lines of the MISG study that John and Jane participated in. Thoughts anyone?
    280289
     290%================Section===========================
     291
     292\section{Conclusion}
     293This 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. FIXME(John): Complete...
     294
     295From the sensitivity studies it appears that the presence or absence of buildings is the most important parameter followed by the right choice of friction whereas a small perturbation in the waveheight at the ANUGA boundary has comparatively little effect on the model results.
     296
     297%================Acknowledgement===================
     298\section*{Acknowledgements}
     299This 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.
     300
     301\section{Appendix}
    281302\begin{figure}[ht]
    282303\begin{center}
    … …  
    284305\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_minus10}
    285306\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_plus10}
    286 \caption{Model results with waveight at ANUGA boundary artifically modified
    287 to asses sensitivities. The first image is the reference inundation extent as reported in Section \protect \ref{sec:results} while the second and third show the inundation results if the wave at the ANUGA boundary is reduced or increased by 10cm respectively. As expected the inundation severity varies in proportion to the boundary waveheight, but the model results are not overly sensitive to
    288 this parameter.}
     307\caption{Model results with wav height at ANUGA boundary artificially modified
     308to asses sensitivities. The first image is the reference inundation extent as reported in Section \protect \ref{sec:results} while the second and third show the inundation results if the wave at the ANUGA boundary is reduced or increased by 10cm respectively. The inundation severity varies in proportion to the boundary waveheight, but the model results are only slightly sensitive to this parameter for the range of values tested.}
    289309\label{fig:sensitivity_boundary}
    290310\end{center}
    … …  
    297317\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_minus10_speed}
    298318\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_plus10_speed}
    299 \caption{Same models as in Figure \protect \ref{sensitivity_boundary} but
    300 showing the maximal flow speeds.}
     319\caption{The maximal flow speeds for the same model parameterisations found in  Figure \protect \ref{fig:sensitivity_boundary}.}
    301320\label{fig:sensitivity_boundary_speed}
    302321\end{center}
    303322\end{figure}
    304 
    305 
    306 
    307 \begin{figure}[ht]
    308 \begin{center}
    309 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_reference}
    310 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f0003}
    311 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f03}
    312 \caption{Model results for different values of Manning's friction coefficient.
    313 The first image is the reference inundation extent as reported in Section \protect \ref{sec:results} where the friction value $0.01$ was used across the
    314 entire domain while the second and third show the inundation results for friction values of 0.0003 and 0.03 respectively. As expected, the inundation extent increases for the lower friction value while the higher slows the flow and decreases the inundation extent. Ideally, friction should vary across the entire domain depending on terrain and vegetation, but this has not been done in this study.}
    315 \label{fig:sensitivity_friction}
    316 \end{center}
    317 \end{figure}
    318 
    319 
    320 
    321 
    322 \begin{figure}[ht]
    323 \begin{center}
    324 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_reference_speed}
    325 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f0003_speed}
    326 \includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f03_speed}
    327 \caption{Same models as in Figure \protect \ref{sensitivity_friction} but
    328 showing the maximal flow speeds.}
    329 \label{fig:sensitivity_friction_speed}
    330 \end{center}
    331 \end{figure}
    332 
    333323
    334324\begin{figure}[ht]
    … …  
    350340\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_reference_speed}
    351341\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_nobuildings_speed}
    352 \caption{Same models as in Figure \protect \ref{sensitivity_nobuildings} but
    353 showing the maximal flow speeds.}
     342\caption{The maximal flow speeds for the same model parameterisations found in Figure \protect \ref{fig:sensitivity_nobuildings}.}
    354343\label{fig:sensitivity_nobuildings_speed}
    355344\end{center}
    356345\end{figure}
    357346
    358 
    359 
    360 
    361 
    362 
    363 
    364 
    365 
    366 
    367 
    368 %================Section===========================
    369 
    370 \section{Conclusion}
    371 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.
    372 
    373 %================Acknowledgement===================
    374 \section*{Acknowledgements}
    375 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 the post 2004 tsunami survey data and the elevation data for Patong beach.
    376 
    377 %===============Appendices========================
    378 
    379 \section*{Appendix A. Figures and Tables}
    380 \label{sec:appendix}
    381 \subsection*{Datasets and gridding}
    382 
    383 This section outlines the origins and processes by which the elevation data was created. In general high resolution data sets were embedded into coarser data sets to match the modelled areas of interest.
     347\begin{figure}[ht]
     348\begin{center}
     349\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_reference}
     350\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f0003}
     351\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f03}
     352\caption{Model results for different values of Manning's friction coefficient.
     353The first image is the reference inundation extent as reported in Section \protect \ref{sec:results} where the friction value $0.01$ was used across the
     354entire domain while the second and third show the inundation results for friction values of 0.0003 and 0.03 respectively. The inundation extent increases for the lower friction value while the higher slows the flow and decreases the inundation extent. Ideally, friction should vary across the entire domain depending on terrain and vegetation, but this is beyond the scope of this study.}
     355\label{fig:sensitivity_friction}
     356\end{center}
     357\end{figure}
     358
     359\begin{figure}[ht]
     360\begin{center}
     361\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_reference_speed}
     362\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f0003_speed}
     363\includegraphics[width=3.5cm,keepaspectratio=true]{sensitivity_f03_speed}
     364\caption{The maximal flow speeds for the same model parameterisations found in Figure \protect \ref{fig:sensitivity_friction}.}
     365\label{fig:sensitivity_friction_speed}
     366\end{center}
     367\end{figure}
    384368
    385369%====================Bibliography==================
    … …  
    387371\bibliography{tsunami07}
    388372\end{document}
    389 
    390 
    391 
    392373===================
    393374NOTES TO BE REMOVED
    … …  
    414395
    415396The two field data benchmarks are very useful but only capture a small subset of possible tsunami behaviours and do not assess all three stages of tsunami evolution (generation,propagation and inundation) together. 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, such as the one proposed here, 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.
    416 
    417 To investigate the impact of these uncertainties a number of sensitivity studies were performed. The first study investigated the impact of surface roughness on the predicted run-up. According to Schoellte~\cite{schoettle2007} appropariate values of Manning's coefficient range from 0.007 to 0.030 for tsunami propagation over a sandy sea floor.  Consequently we simulated the maximum onshore inundation using the a manning's coefficient of 0.0003 and 0.03. The resulting runup is shown in Figures~\ref{fig:inundation0.0003} and \ref{inundation0.03}, respectively.

  • anuga_work/publications/boxing_day_validation_2008/tsunami07.bib

    r6736 r6917  
    10231023}
    10241024
    1025 
     1025@article { harig08,
     1026  author =      "Harig, S. and Chaeroni and Pranowo, W. and Behrens, J.",
     1027  title =       "Tsunami simulations on several scales",
     1028  journal =     "Ocean dynamics",
     1029  volume =      "58",
     1030  month =       "November",
     1031  pages =       "429-440",
     1032  year =        2008,}
     1033
     1034@article { Gower05,
     1035  author =      "Gower, J.",
     1036  title =       "Jason 1 detects the 26 december 2004 tsunami",
     1037  journal =     "EOS",
     1038  volume =      "86",
     1039  number =      "4",
     1040  month =       "25 January",
     1041  pages =       "37-38",
     1042  year =        2005,}
Note: See TracChangeset for help on using the changeset viewer.