Table Of ContentTsunami Science
Four Years after the 2004
Indian Ocean Tsunami
Part I: Modelling and Hazard Assessment
Edited by
Phil R. Cummins
Laura S. L. Kong
Kenji Satake
Birkhäuser
Basel · Boston · Berlin
Reprint from Pure and Applied Geophysics
(PAGEOPH), Volume 165 (2008) No. 11/12
Phil R. Cummins Laura S. L. Kong
Geoscience Australia UNESCO Intergovernmental Oceanographic
P.O. Box 378 Commission (IOC)
Canberra, ACT 2601 International Tsunami Information Centre
Australia 737 Bishop Street
Email: [email protected] Suite 2200
Honolulu, HI, 96813
Kenji Satake USA
Earthquake Research Institute Email: [email protected]
University of Tokyo
1-1-1 Yayoi
Bunkyo-ku
Tokyo 113-0032
Japan
Email: [email protected]
Library of Congress Control Number: 2009920437
Bibliographic information published by Die Deutsche Bibliothek:
Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed
bibliographic data is available in the Internet at <http://dnb.ddb.de>
ISBN 978-3-0346-0056-9 Birkhäuser Verlag AG, Basel · Boston · Berlin
This work is subject to copyright. All rights are reserved, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, re-use of illustra-
tions, recitation, broadcasting, reproduction on microfilms or in other ways, and storage in
data banks. For any kind of use, permission of the copyright owner must be obtained.
© 2009 Birkhäuser Verlag AG
Basel · Boston · Berlin
P.O. Box 133, CH-4010 Basel, Switzerland
Part of Springer Science+Business Media
Printed on acid-free paper produced from chlorine-free pulp. TCF ∞
Cover graphic: Based on a picture provided by Dr. Pål Wessel, Department of Geology and
Geophysics, School of Ocean and Earth Science and Technology (SOEST), University of Ha-
waii at Manoa, Honolulu, USA.
Printed in Germany
ISBN 978-3-0346-0056-9 e-ISBN 978-3-0346-0057-6
9 8 7 6 5 4 3 2 1 www.birkhauser.ch
Contents
1983 Introduction to ‘‘Tsunami Science Four Years After the 2004 Indian Ocean
Tsunami,PartI:ModellingandHazardAssessment’’
P.R.Cummins,L.S.L.Kong,K.Satake
1991 AComparisonStudyofTwoNumericalTsunamiForecastingSystems
D.J.M.Greenslade,V.V.Titov
2003 TheEffectoftheGreatBarrierReefonthePropagationofthe2007Solomon
IslandsTsunamiRecordedinNortheasternAustralia
T.Baba,R.Mleczko,D.Burbidge,P.R.Cummins,H.K.Thio
2019 Numerical Modeling and Observations of Tsunami Waves in Alberni Inlet
andBarkleySound,BritishColumbia
I.V.Fine,J.Y.Cherniawsky,A.B.Rabinovich,F.Stephenson
2045 EvaluatingTsunamiHazardintheNorthwesternIndianOcean
M.Heidarzadeh,M.D.Pirooz,N.H.Zaker,C.E.Synolakis
2059 AProbabilisticTsunamiHazardAssessmentforWesternAustralia
D.Burbidge,P.R.Cummins,R.Mleczko,H.K.Thio
2089 TsunamiProbabilityintheCaribbean Region
T.Parsons,E.L.Geist
2117 ScenariosofEarthquake-GeneratedTsunamisfortheItalianCoast
oftheAdriaticSea
M.M.Tiberti,S.Lorito,R.Basili,V.Kastelic,A.Piatanesi,G.Valensise
2143 Stromboli Island (Italy): Scenarios of Tsunamis Generated by Submarine
Landslides
S.Tinti,F.Zaniboni,G.Pagnoni,A.Manucci
2169 NumericalModellingoftheDestructiveMeteotsunamiof15June,2006
ontheCoastoftheBalearicIslands
I.Vilibic´,S.Monserrat,A.Rabinovich,H.Mihanovic´
2197 ValidationandVerificationofTsunamiNumericalModels
C.E.Synolakis,E.N.Bernard,V.V.Titov,U.Kaˆnog˘lu,F.I.Gonza´lez
2229 An Efficient and Robust Tsunami Model on Unstructured Grids. Part I:
InundationBenchmarks
Y.J.Zhang,A.M.Baptista
2249 RunupCharacteristicsofSymmetricalSolitaryTsunamiWaves
of‘‘Unknown’’Shapes
I.Didenkulova,E.Pelinovsky,T.Soomere
2265 ATsunamiDetectionandWarning-focusedSeaLevelStationMetadataWeb
Service
J.J.Marra,U.S.Kari,S.A.Weinstein
2275 Long-Term Tsunami Data Archive Supports Tsunami Forecast, Warning,
Research,andMitigation
P.K.Dunbar,K.J.Stroker,V.R.Brocko,J.D.Varner,S.J.McLean,
L.A.Taylor,B.W.Eakins,K.S.Carignan,R.R.Warnken
Pureappl.geophys.165(2008)1983–1989 (cid:2)Birkha¨userVerlag,Basel,2008
0033–4553/08/111983–7 Pure and Applied Geophysics
DOI10.1007/s00024-008-0423-8
Introductionto‘‘TsunamiScienceFourYearsAfterthe2004IndianOcean
Tsunami, Part I: Modelling and Hazard Assessment’’
PHIL R. CUMMINS,1 LAURA S. L. KONG,2 and KENJI SATAKE3
Abstract—Inthisintroductionwebrieflysummarizethe14contributionstoPartIofthisspecialissueon
TsunamiScienceFourYearsafterthe2004IndianOceanTsunami.Thesepapersarerepresentativeofthenew
tsunamisciencebeingconductedsincetheoccurrenceofthattragicevent.Mostofthesewerepresentedatthe
session:TsunamiGenerationandHazard,oftheInternationalUnionofGeodesyandGeophysicsXXIVGeneral
AssemblyheldatPerugia,Italy,inJulyof2007.Thatsessionincludedoveronehundredpresentationsonawide
rangeoftopicsintsunamiresearch.ThepapersgroupedintoPartI,andintroducedhere,covertopicsdirectly
related to tsunami mitigation such as numerical modelling, hazard assessment and databases. Part II of this
specialissue,ObservationsandDataAnalysis,willbepublishedinasubsequentvolumeofPureandApplied
Geophysics.
Keywords: Tsunami,seiche,harborresonance,numericalmodeling,hazardassessment,inundation,
tsunamimitigation,tsunamiwarningsystem,runup,tsunamidatabase,rissaga.
1. Introduction
Four years after the 2004 Indian Ocean Tsunami, the most lethal tsunami disaster in
human history, tsunami science continued to move forward rapidly. The research and
disaster management community that supports tsunami mitigation has expanded greatly.
Observationplatforms,especiallyintheIndianOcean,havefarsurpassedtheirpre-2004
capacity for detecting and measuring tsunamis and the earthquakes that most frequently
cause them. A remarkable crosssection of this research was presented in the session
Tsunami Generation and Hazard, at the International Union of Geodesy and Geophysics
(IUGG) XXIV General Assembly in Perugia, Italy, held in July of 2007. Over one
hundred presentations were made at this session, spanning topics ranging from
paleotsunami research, to nonlinear shallow-water theory, to tsunami hazard and risk
1 GeoscienceAustralia,GPO,Box378,Canberra,ACT2601,Australia.
E-mail:[email protected]
2 UNESCOIOCInternationalTsunamiInformationCentre,737BishopSt.Ste.2200,Honolulu,
Hawaii96813,USA.E-mail:[email protected]
3 EarthquakeResearchInstitute,UniversityofTokyo,1-1-1Yayoi,Bunkyo-ku,Tokyo113-0032,Japan.
E-mail:[email protected]
1984 P.R.Cumminsetal. Pureappl.geophys.,
assessment. The IUGG’s Tsunami Commission arranged for a selection of this work,
alongwithotherpapersonsimilartopics,tobepublishedindetailinthe28papersofthis
2-part special issue of Pure and Applied Geophysics.
Inthisintroductorypaper,webrieflydiscussthepapersinPartIof‘‘TsunamiScience
Four Years After the 2004 Indian Ocean Tsunami’’. In Section 2 we discuss new
advancesinthefundamentalnumericaltechniquesusedtomodeltsunamisandSection 3
reviewsseveralcontributionsthatapplysuchtechniquestoimproveourunderstandingof
how near-coast processes affect tsunami propagation. Sections 4 and 5 discuss the
application of tsunami modeling to tsunami hazard assessment, in a deterministic and a
probabilistic sense, respectively. Section 6 describes two database efforts that are
supporting researchers and warning system operators to access the most current and
accurate information on tsunami events.
2. Advances in Analytical and Numerical Modeling of Tsunamis
Theincreasedfocusontsunamisciencesincethe2004Sumatra-AndamanEarthquake
and subsequent Indian Ocean Tsunami has seen a rapid expansion in the tsunami
modeling community. Prior to 2004, this community consisted of a handful of research
groups that utilized a few well-tested codes for numerical modeling of tsunamis. Since
2004,manymoreresearch groups have takenaninterest intsunamimodeling, andthese
groupshaveinsomecaseseitherdevelopednewcodesfortsunamimodeling,oradapted
hydrodynamic codes that had originally been developed for other purposes. Tsunami
forecastingvianumericalmodelingisalsoseeingincreaseduseasanoperationalpartof
tsunami warning systems.
In response to this recent proliferation of tsunami modeling codes, SYNOLAKIS et al.
(2008)pointedouttheimportanceofvalidationandverificationofcodesusedintsunami
hazard assessmentorforecastingsystems.Theyreviewedseveralmethodsbywhichthis
can be accomplished. These included analytical and laboratory validation benchmarks,
as well as examples of field observations against which numerical models can be
verified. A summary of the theoretical development for formulae used as analytical
benchmarks was given, and details of the laboratory experiments and field observations
were reviewed. In addition to the use of these benchmarks for inundation modeling in
tsunami hazard studies, operational requirements for forecasting tsunamis in real-time
were discussed.
ZHANGandBATISTA(2008)presentedanexampleoftheadaptationofamulti-purpose
barocliniccirculationmodel(SELFE)totsunamiinundationandpropagation.Themodel
hasseveralinterestingfeaturesthatmakeitausefuladditiontothesuiteoftoolsalready
available: Release as open source, facilitating further development by a community of
users; use of an unstructured grid, allowing for variable resolution to accommodate
complexgeometryonlywherenecessary;animplicittime-steppingalgorithmthatavoids
the stringent Courant–Friedrichs–Lewy stability condition, and a simple wetting/drying
Vol.165,2008 TsunamiScienceFourYearsAfterthe2004IndianOceanTsunami 1985
algorithm that implements inundation. Along the lines suggested by SYNOLAKIS et al.
(2008),theauthorspresentedvalidationresultsagainstscenariosfromthe3rdInternational
WorkshopinLong-WaveRunupModels(http://www.cee.cornell.edu/longwave).
GREENSLADEandTITOV(2008),ontheotherhand,useatriedandtestedtsunamicode
(MOST – see TITOV and GOZAREZ, 1997) to compare two tsunami forecasting systems
used by the U.S. National Oceanic and Atmospheric Administration (NOAA), and the
Australian Bureau of Meteorology. The tsunamis caused by the 2006 Tonga and 2007
Sumatra earthquakes were used as test cases. Both systems use results of tsunami
computationsmadepriortoearthquakesandstoredinadatabase.Whilethespecifications
of numerical simulation of tsunami propagation are very similar, the scenario sources
distributed around the Pacific Basins are slightly different. The Australian system
assumes different faults depending on the earthquake magnitude, while the U.S. system
scales the slip amount by assimilation of tsunameter (a.k.a. DART) data. Because
different sources are used to approximate the Tonga and Sumatra earthquakes, the
forecasted waveforms at offshore tsunameter locations are slightly different. However,
the differences in waveforms computed at coastal tide gauges are considerably smaller,
indicating insensitivity of coastal tsunamis to the details of the tsunami source.
Despitesubstantialrecentprogressinnumericaltsunamimodeling,therapidforecast
ofrunupvaluesremainsadifficultproblem,duetoitsnonlinearityandsensitivitytoinput
data such as bathymetry and initial waveform. The paper by DIDENKULOVA et al. (2008)
demonstrated that analytical results still have an important role to play in estimating
runup. They discussed results from nonlinear shallow-water wave theory for runup of
solitary tsunami waves, and showed that appropriate definitions of significant wave
height and length (based on 2/3 of maximum wave height) result in formulae for
computing runup characteristics that are relatively independent of incident wave shape
(for symmetric incident waves). Such formulae may be used in rapidly estimating
potential inundation once the tsunami height, wavelength and period in the open ocean
are known.
3. Modeling of Near-Coast Effects on Tsunami Propagation
Oneofthemostchallengingaspectsoftsunamimodelingisaccuraterepresentationof
propagation effects near the coast, including reflection and refraction by shallow
bathymetry,andresonancesinsemi-enclosedbaysandinlets.Severalpapersinthisissue
address these topics.
BABA et al. (2008) examined the effects of Great Barrier Reef, the world’s largest
coral reef located offshore Australia, on the tsunami generated by the April 1, 2007
Solomon Islands earthquake. They carried out tsunami numerical simulation from a
sourcemodelbasedontheirseismicwaveforminversion.Thesimulatedwaveformsshow
goodagreementwithtidegaugedatainnortheastAustralia.Inordertoexaminetheeffect
of the coral reef, they also made tsunami simulations with artificial bathymetry data
1986 P.R.Cumminsetal. Pureappl.geophys.,
withoutthe coralreef;the reefwasreplacedwithdeep ocean inonedatasetandshallow
ocean in the other. Simulations with these models indicate that the tsunami energy was
reduced by direct reflection outside the reef and by refraction when the tsunami passed
through the reef. As a result, the tsunami was delayed by 10–15 minutes, the amplitude
became about a half or less, and the period became longer.
Semi-enclosed bays and inlets present another challenge to tsunami modeling in the
form of potential resonant enhancement of tsunamis. Alberni inlet is a long (*40 km)
andnarrow(1–2 km)fjordinVancouverIsland,Canada.Thetsunamicausedbythe1964
Alaskan earthquake had a height of about 8 m above mean sea level at Port Alberni,
locatedattheheadoftheinletandabout65 kmfromthePacificOcean.FINEetal.(2008)
examined resonance characteristics of the Alberni inlet by using numerical calculations.
Their results indicated that strong amplification occurred at a period of 112 min with an
amplification factor of more than 10.
VILIBIC´ et al. (2008) presented a comprehensive analysis of a destructive meteots-
unami (rissaga) that occurred in the Balearic Islands in 2006. This event was not
associated with an earthquake source and excited destructive harbor oscillations of
several meters amplitude, resulting in an economic loss estimated at tens of millions of
euros. VILIBIC´ et al. used a numerical model, verified using a series of measurements
made during smaller rissaga events in 1997, and microbarograph measurement to show
how the 2006 rissaga resulted from ocean-atmosphere resonance excited by a travelling
atmospheric disturbance, which in turn induced the hazardous harbor oscillations
observed.Thepotentialfordevelopingarissagawarningsystembasedonthismodelwas
also discussed.
4. Scenario Modeling for Tsunami Hazard Assessment
In addition to rapid tsunami forecasts that may form part of a tsunami warning,
emergency managers and planners in coastal communities need information about how
largetsunamisaffectingtheircommunitiesmightbe.Suchquestionscanbeansweredby
scenario modeling, which forms the basis of a deterministic hazard assessment. Such
assessments are normally designed to encompass the worst credible, as well as the most
likely, scenarios.
TIBERTI et al. (2008) numerically modeled potential tsunamis in the Adriatic Sea.
Maximumcredibleearthquakeswereassumedalongthesixsourcezones.Theyclassified
the computed maximum tsunami heights into three levels: marine, land and severe land
for 0.05 m, 0.5 m and 1.0 m, respectively. The results indicate that the largest tsunamis
are expected on the Apulia and Gargano coasts of southern Italy. They found that
focusing of energy due to bathymetric features enhances the tsunami heights.
At Stromboli volcano, southern Italy, on December 30, 2002, a moderate-sized
landslide(withavolumeof0.02–0.03 km3)generatedatsunami,withamaximumrunup
heightofabout10 m.Becauseitoccurredinthewinter,therewasnolossofhumanlife,
