Post on 27-Nov-2023
transcript
ORI GIN AL PA PER
Numerical simulation of erosion and depositionat the Thailand Khao Lak coast during the 2004 IndianOcean tsunami
Linlin Li • Zhenhua Huang • Qiang Qiu
Received: 16 October 2013 / Accepted: 19 June 2014� Springer Science+Business Media Dordrecht 2014
Abstract A case study was conducted for the Thailand Khao Lak coast using a forward
numerical model to understand uncertainties associated with interpreting tsunami deposits
and relating them to their tsunami sources. We examined possible effects of the charac-
teristics of tsunami source, multiple waves, sediment supply and local land usages.
Numerical results showed that tsunami-deposit extent and thickness could be indicative of
the slip value in the source earthquake near the surveyed coastal locations, provided that
the sediment supply is unlimited and all the deposits are well preserved. Deposit thickness
was found to be largely controlled by the local topography and could be easily modified by
backwash flows or subsequent tsunami flows. Between deposit extent and deposit thick-
ness, using deposit extent to interpret the characteristics of a tsunami source is preferable.
The changing of land usages between two tsunami events could be another important factor
that can significantly alter deposit thickness. There is a need to develop inversion models
based on tsunami heights and/or run-up data for studying paleotsunamis.
Keywords Tsunami deposits � Sediment transport � Tsunami source � Numerical
simulations � Tsunami inundation � Coastal erosion
L. Li � Z. Huang (&) � Q. QiuEarth Observatory of Singapore, Nanyang Technological University, Singapore 639798, Singaporee-mail: zhhuang@ntu.edu.sg; zhenhua@hawaii.edu
Z. HuangSchool of Civil and Environmental Engineering, Nanyang Technological University,Singapore 639798, Singapore
Z. HuangDepartment of Ocean and Resources Engineering, School of Ocean and Earth Science and Technology,University of Hawaii at Manoa, Honolulu, HI 96822, USA
123
Nat HazardsDOI 10.1007/s11069-014-1301-6
1 Introduction
Tsunami deposits have been increasingly investigated in the past several decades
(Dawson et al. 1996; Moore et al. 2007; Shi et al. 1995; Gelfenbaum and Jaffe 2003;
Peters et al. 2007; Richmond et al. 2012; Sato et al. 1995). The characteristics of tsunami
deposits (spatial distribution, thickness, grain size, etc.) are believed to be indicative of
the characteristics of tsunami flows and have been used to reconstruct tsunami-flow
patterns by establishing qualitative relationships between tsunami deposits and tsunami
hydrodynamic characteristics (Moore et al. 2007; Morton et al. 2007; Smith et al. 2007;
Spiske et al. 2010; Jaffe and Gelfenbuam 2007; Soulsby et al. 2007). With tsunami-flow
information derived from tsunami deposits, constraints could be further put on the
locations and nature of the corresponding source earthquakes (Martin et al. 2008; Nelson
et al. 2006; Bourgeois 2009). Previous studies have shown promising potential of using
tsunami deposits to reconstruct slip distributions of historic or prehistoric earthquakes,
and to infer the recurrence intervals and magnitudes of great earthquakes (Bourgeois
et al. 2006; Macinnes et al. 2010; Martin et al. 2008; Nanayama et al. 2003; Satake et al.
2005). In some instances, these kinds of inversion processes might be the only approach
to extend the short historical tsunami records in tsunami-prone coastal areas, and are
therefore of great importance in paleo-tsunami research. However, large uncertainties
may exist in the tsunami source derived from tsunami deposits using an inversion pro-
cedure. The uncertainties could be attributed to factors such as the complex mechanism
of rupture process, our lack of knowledge of the sedimentation process during each
tsunami event, the initial sediment setting, and the details of bathymetric and topographic
data at the tsunami attacking time.
Forward numerical models are particularly suitable for tackling issues of high uncer-
tainty and complexity, as they enable numerous and repeatable numerical tests in a highly
controlled environment. The aim of this study is to understand the uncertainties associated
with interpreting tsunami deposits and relating them to their tsunami sources through a
case study at the Thailand Khao Lak coast using a forward numerical model. Several sets
of numerical experiments were conducted to examine the influence of tsunami source,
multiple waves, sediment supply and bottom roughness. A thin layer of tsunami deposits
have been formed in Khao Lak area during the 2004 Indian Ocean tsunami, and the filed
data such as the grain size and thickness have been collected along several transects by
Fujino et al. (2010) and Hori et al. (2007). The availability of the filed data provides us
with an opportunity to compare simulation results with the field measurements and to
further our understanding of the following issues: (1) relationship between the earthquake
source parameters and the resulting tsunami heights, inundation extent, deposit distribution
and deposit thickness; (2) possible effects of multiple waves and backwash flows on the
horizontal variation of tsunami-deposit thickness; (3) effects of local morphology, sedi-
ment supply and local land use on the characteristics of tsunami deposits.
This paper is organized as follows: Sect. 2 briefly describes the capabilities of the
numerical model used in this study. In Sect. 3, we describe the impact of the 2004 Indian
Ocean tsunami on the studied area, which is followed by a description of model setup,
including the bathymetric and topographic data process, source model selection and initial
sediment setting. The model results are presented in Sect. 4. A discussion of the difficulties
in deriving tsunami source parameters from the characteristics of tsunami deposits is given
in Sect. 5, where issues in using inversion models and surveyed tsunami heights to infer the
source parameters are also discussed. Finally, Sect. 6 summarizes the main findings from
this study.
Nat Hazards
123
2 Methodology
In this study, a two-way coupled model COMCOT-SED (Li et al. 2012) was used to
understand the sediment transportation and deposition process in Thailand Khao Lak coast
during the 2004 Indian Ocean tsunami. COMCOT-SED was based on two open source
codes: COMCOT (Liu et al. 1995; Wang 2009) and XBeach (Roelvink et al. 2008). This
model employs a nested grid system in which linear/nonlinear shallow-water equations are
solved on each grid using a finite difference method. The sediment transport module in
XBeach is incorporated into the innermost grid of COMCOT to model erosion and
deposition processes. Compared with other models (Apotsos et al. 2011a; Kihara and
Matsuyama 2010; Goto and Imamura 2007), which have been used in the past to simulate
tsunami-induced sediment movement, COMCOT-SED has the following advantages: (1)
The two-way coupling algorithm enables COMCOT-SED to simulate the entire lifespan of
a tsunami event seamlessly, from its generation, propagation, inundation on coastal
regions, to its resulting morphological changes; (2) The parallelized COMCOT-SED code
ensures high computational efficiency; (3) COMCOT-SED is also capable of handling
multiple sand layers with mixed grain sizes, which makes it possible to track the sediment
movement quantitatively. This model has been used to study the morphological change in
Lhoknga, west Banda Aceh, during the 2004 Indian Ocean tsunami (Li et al. 2012).
For completeness, the sediment transport model used in COMCOT-SED is outlined
below. For further details of COMCOT-SED the reader is referred to Li et al. (2012). The
sediment motion is modeled by a depth-averaged advection–diffusion equation, with a
source term formulated using the concept of equilibrium sediment concentration (Ga-
lappatti and Vreugdenhil 1985):
ohC
otþ ohCu
oxþ ohCv
oyþ o
oxDhh
oC
ox
� �þ o
oyDhh
oC
oy
� �¼ hCeq � hC
Ts
; ð1Þ
where h is the total water depth; u, v are the depth-averaged velocities in the x- and y-
directions, respectively; C is the depth-averaged concentration of suspended sediment; Dh
is the sediment diffusion coefficient; Ts is the adaptation time of sediment concentration,
given by the following approximation
Ts ¼ max fTs
h
ws
; 0:2
� �s: ð2Þ
The adaptation time Ts depends on the local water depth h, the sediment fall velocity ws
and a sediment transport depth factor fTs(default value is 0.1). As Ts approaches zero, the
sediment concentration responses to the change of flow instantaneously. Equation (1)
implies that both the entrainment and deposition of sediment are determined by the mis-
match between the actual sediment concentration C and the equilibrium concentration Ceq.
In this study, Ceq is calculated using the formula proposed by Van Rijn (1993). The
formulas for Ceq and ws can be found in the user manual of XBeach (Roelvink et al. 2008).
It is worth noting that the parameter Dh in Eq. (1) is related not only to turbulent flow
motion, but also to other factors such as numerical diffusion caused by coarse grids,
discretization of differential equations, and numerical dispersion introduced by depth-
averaging. The default value of Dh = 1.0 was adopted for all simulations presented in the
present study. A detailed discussion of the effects of key model parameters on the
numerical results can be found in Li and Huang (2013).
Nat Hazards
123
The bottom elevation zb changes with time and is updated using the following
equations,
1� pð Þ ozb
otþ oSx
oxþ oSy
oy¼ 0; ð3Þ
Sx ¼ohCu
oxþ o
oxDhh
oC
ox
� �; ð4Þ
Sy ¼ohCv
oyþ o
oyDhh
oC
oy
� �; ð5Þ
with p being the porosity of bed material, and Sx and Sy being the sediment transport rates
in x- and y- directions, respectively.
3 Study area and model setup
3.1 The study area
Khao Lak coast is located in the coastal lowlands of south-western Thailand, which faces
the Andaman Sea. During the 2004 Indian Ocean tsunami, Khao Lak coast was reported as
one of the most severely damaged areas in south-western Thailand in terms of strong
erosion and coastal deformation. According to the post-tsunami field surveys carried out
along four transects (S1–S4 in Fig. 1) by Fujino et al. (2010) and Hori et al. (2007), the
tsunami heights in this area generally exceeded 5 m with a maximum up to 12 m, the
inundation extended about 2 km inland, and the tsunami deposits almost covered the whole
inundated area with a deposit thickness typically \10 cm.
3.2 Bathymetric and topographic data
The arrangement of the computational grids is shown in Fig. 2. Five nested grids (Grid 01
through Grid 05) were used, with the grid resolution varying from 1944 m in the source
region to 27 m in the Khao Lak area (see Table 1). In the deep-ocean area, a 30 arc-second
grid (ca. 925 m) derived from the GEBCO digital bathymetry/topography data set was
used to prepare the computational grids Grid 01 through Grid 04. For the innermost Grid
05, the ASTER topographic data was combined with GEBCO bathymetric data to produce
a uniform bathymetric and topographic data set with a spatial resolution of 27 m through
interpolation. Data gaps between the bathymetry and topography were interpolated and
filled up with nautical charts. The details of the five nested grids used in this study are
summarized in Table 1.
3.3 Selected source models
The characteristics of the great Sumatra–Andaman earthquake of 2004 have been only
partially understood due to its exceptionally complex nature. Data collected from different
sources (seismic waves, far- and near-field GPS data, remote sensing measurements of
uplift or subsidence using optical or synthetic aperture radar (SAR) images, tide gauges,
and satellite altimetry measurements) have been used to invert for fault geometry,
coseismic slip distribution, and rupture process of this event (Ammon et al. 2005; Banerjee
Nat Hazards
123
et al. 2007; Chlieh et al. 2007; Fujii and Satake 2007; Meltzner et al. 2006; Piatanesi and
Lorito 2007). In this study, we selected four source models for the 2004 Sumatra–Andaman
earthquake event (Table 2). All the selected source models have been either inversed or
further constrained by tsunami data (e.g. Satellite altimetry data or tide-gauge data).
Referring to Table 2, the fault model M1 was proposed by Piatanesi and Lorito (2007)
by inverting the slip distribution from 14 tsunami waveforms recorded in the Indian Ocean
in consideration of the non-linearity of tsunami propagation. The fault geometry in this
model is the same as that in Banerjee et al. (2007), who subdivided the fault plane into 16
sub-faults. The fault model M2 was proposed by Chlieh et al. (2007), who took the fault
geometry from Ammon et al. (2005) and subdivided the fault into three main segments
(these segments were further discretized into 661 smaller cells). M2 took into account the
near-field GPS data from north-western Sumatra and along the Nicobar–Andaman islands,
far-field GPS data from Thailand and Malaysia, and both the in situ and remotely sensed
observations of the vertical motion of coral reefs. The simulation results predicted by this
Fig. 1 The study area in Phang-nga province, southwestern Thailand. Surveyed locations are marked byblack dots. The locations along transects S1, S3 and S4 were surveyed by Fujino et al. (2010), the locationsalong transect S2 were surveyed by Hori et al. (2007). The runup-limit line (marked by red dots) wasdigitized from Srisutam and Wagner (2010)
Nat Hazards
123
fault model were proved to fit relatively well with the altimetric measurements made by the
JASON and TOPEX satellites. The fault geometry of the fault model M3 was inferred from
the tectonic setting with a total rupture area of 1,155 km by 150 km and the entire rupture
area was subdivided into six fault segments dipping eastward (Koshimura et al. 2009); for
model constraints, they mainly used two kinds of data to determine the fault dislocations:
JASON-1 altimetry data for the southern three sub-faults and the vertical displacement
field revealed by satellite radar imagery (Tobita et al. 2006) and field measurement (Ra-
jendran et al. 2007) for the entire rupture area (Koshimura et al. 2009). The fault model M4
has five fault segments, which are divided along 1,200 km of the Andaman-Sunda trench
based on the geometry that is identified from the bathymetry of the subduction zone. The
slip distributions of the rupture and aftershocks were provided by initial seismic inversion
models (Tanioka et al. 2006), and the slip distribution was then iteratively refined by
further constraining the source and simulating tsunami to match salient features of tide
gauge and satellite altimetry data (Grilli et al. 2007).
Fig. 2 Nested grids for COMCOT-SED simulations
Table 1 Information on the five grids for COMCOT-SED simulations
Grid 01 Grid 02 Grid 03 Grid 04 Grid 05
Lati. (�) 88E–101E 96E–99.5E 97E–99E 97.96E–98.6E 98.11E–98.27E
Longi. (degree) 0–17N 7.0N–10N 7.5N–9.54N 8.2N–9.14N 8.64N–8.72N
Grid size (m) 1944 648 216 108 27
Parent grid (None) Grid 01 Grid 02 Grid 03 Grid 04
Grid size ratio (None) 3 3 2 4
Time step (in s) 0.01 0.01 0.01 0.01 0.01
Coordinate system Spherical Spherical Spherical Spherical Cartesian
SWE Linear Linear Linear Linear Nonlinear
Nat Hazards
123
3.4 Initial sand distribution and composition
After the 2004 Indian Ocean tsunami, Di Geronimo et al. (2009) conducted a post-tsunami
field survey and created a detailed map of sediment distribution in Khao Lak area. According
to their survey, a shallow sandy seafloor (\10 m deep) with scattered basement outcrops
extended for 3–4 km offshore from the Khao Lak area. Except the coral reef surrounding the
Pakarang cape, the offshore area was basically covered by sand, mud and gravelly sand. We
used the topographical and sedimentological maps given in Di Geronimo et al. (2009) (Fig. 2
in their paper) and digitized the sand distribution extent. We roughly divided the simulation
domain into four types: Area_gravel, Area_sand, Area_mud and Area_rock (Fig. 3), and
specified the grain size D50 as 1.0, 0.2, 0.1 mm and non-erodible for these four types of
simulation areas, respectively. The thickness of the erodible bed can be assumed to be
unlimited, i.e., there is an adequate supply of sediment in the computation domain.
4 Simulation results
4.1 Influences of fault characteristics on tsunami height, tsunami deposit
and inundation limit
In this study, the seafloor displacement was calculated using Okada’s model (1985), which
assumes that the initial surface elevation will simply follow the sea floor deformation
instantaneously. The maps of the initial sea surface elevations corresponding to the four
rupture models used in this study are shown in Fig. 4. These four source models were used
to study the tsunami height, inundation extent, characteristics of tsunami deposits, and the
relationship among the aforementioned features.
4.1.1 Tsunami heights
Referring to Fig. 5, the four source models all predict that the northern tip of North
Sumatra and Thailand’s southwest Andaman coast are the two most affected areas, even
Table 2 Key characteristics of the four source models used in this study
Sourcemodel
Reference The numberof segments
Data used for inversion Data used for furtherconstraint
M1 (Mw 9.1) Piatanesi andLorito (2007)
16 Tide gauge data GPS Static offsets (near- andfar-field)
M2 (Mw 9.15) Chlieh et al.(2007)
661 GPS static offsets (near-and far- field) ? coralreefs uplift
Seismic data ? very far-fieldGPS data ? Jason-1 andTOPEX/Poseidon altimetrydata
M3 (Mw 9.08) DCRC 6 / Jason-1 altimetrydata ? satellite radarimageries after Tobita et al.(2006)
M4 (Mw 9.22) Grilli et al.(2007)
5 Offshore bathymetry ?initial seismicinversion model
Tide gauge data ? Jason-1altimetry data
Nat Hazards
123
though the severity differs significantly. Large tsunami heights ([4 m) predicted by M1
cover the scattered offshore islands north to 12�N and extend to the southern tip of Phuket
Island. M2 predicts a much smaller region of large wave height near Phra Thong Islands.
Compared with the tsunami heights predicted by M1, M3 gives a much more concentrated
area of large tsunami height, covering only the coastal region from Phra Thong Island to
Phuket Island. Similarly, the large tsunami heights ([4 m) predicted by M4 are also
concentrated in the coastal areas between Phra Thong Island and Phuket Island. Note that
the tsunami heights predicted by M4 are much larger than those predicted by M3. Figure 6
shows a comparison between the simulated tsunami heights with the measured data along
the Thai Andaman coast. Clearly, M4 produces much larger tsunami heights in this area,
followed by M3 and M1. M2 gives the weakest tsunami waves among the four source
models.
Fig. 3 Initial sand distribution in the computational domain
Nat Hazards
123
From the early studies on how earthquake-source parameters affect tsunamis, one might
naturally attribute the differences among these model results to two key factors of the first-
order importance in determining tsunami height: seismic moment and slip distribution
(Abe 1979; Geist 1998; Okal 1988). After analyzing a large number of seismic parameters
influencing tsunami generation, Okal (1988) suggested that tsunami height should have a
Fig. 4 The initial surface elevations derived from four fault models: a Piatanesi and Lorito (2007) (sourcemodel M1), b Chlieh et al. (2007) (source model M2), c Tohoku University Disaster Control ResearchCenter (DCRC) (Koshimura et al. 2009) (source model M3), d Grilli et al. (2007) (source model M4)
Nat Hazards
123
direct relationship with seismic moment M0. This conclusion was proved basically true by
our simulation results but with one exception. The tsunami heights along the Thai And-
aman coast are roughly correlated to the magnitudes of the selected source models, except
for M2, which ranks the second largest among the four fault models; however, much
smaller tsunami heights are generated compared to M1 and M3. The exceptional low
tsunami heights generated by M2 might be understood from the following two aspects: the
rupture length of the 2004 Sumatra–Andaman earthquake is extremely long and only the
northern portion of the rupture contributes significantly to the tsunami height along the
Thai Andaman coast. Since the seismic moment is linearly proportional to the slip value,
spatial variations in the amount of slip should be the most likely reason for the low tsunami
heights associated with M2.
The slip distribution of the northern part of the 2004 Sumatra–Andaman rupture, which
starts from the southern tip of Nicobar Islands and ends to the northern tip of Andaman
Islands, may directly affect the tsunami heights near the Thai Andaman coast. The fol-
lowing aspects may have contributed to the differences in the simulated tsunami heights in
this area:
Fig. 5 Maximum sea surface elevations during the whole life span of the tsunamis generated by the fourfault models: a Piatanesi and Lorito (2007) (source model M1), b Chlieh et al. (2007) (source model M2),c Tohoku University Disaster Control Research Center (DCRC) (Koshimura et al. 2009) (source model M3),d Grilli et al. (2007) (source model M4)
Nat Hazards
123
1. The rupture lengths in the four fault models are different and vary in the range of
1,155–1,300 km. The number of the sub-faults and the size of each sub-fault are
different among the four fault models. Along the rupture length, 16 sub-faults are
divided for M1, 661 for M2, 6 for M3, and 5 for M4. Except for M2, the other three
fault models assume that the slip is distributed uniformly over the sub-faults in the dip
direction. According to Geist and Dmowska (1999), this assumption may significantly
Fig. 6 The calculated tsunami heights (blue bars) and surveyed data (black dots) along Thai Andamancoast (the blue dots indicate the survey locations): a Piatanesi and Lorito (2007) (source model M1),b Chlieh et al. (2007) (source model M2), c Tohoku University Disaster Control Research Center (DCRC)(Koshimura et al. 2009) (source model M3), d Grilli et al. (2007) (source model M4). The red rectangularon the maps marks the Khao Lak coast shown in Fig. 7
Nat Hazards
123
underestimate the maximum tsunami height and the leading wave steepness of local
tsunamis in most cases, leading to an underestimation of local tsunami run-up.
2. The values of slip vary among these fault models. The average slip value of the
northern portion of the rupture is 7.8 m for M1,\5 m for M2, 8.7 m for M3 and 12 m
for M4. The simulation results shown in Fig. 6 indicate that the calculated tsunami
heights are proportional to the average slip values in the corresponding rupture areas.
3. The locations of the rupture are not exactly the same. Compared with other fault
models, the main rupture zone of M3 is farther away from the Andaman and Nicobar
islands to the southwest. Even though the exact rupture location has limited influence
on far-field tsunami heights, it may pose considerable influence on local tsunami
height (Okal and Synolakis 2008).
In spite of a possible underestimation of the tsunami heights predicted by M1, M3 and M4
due to the assumption of uniform slip in the dip direction, these three models still generate
much larger tsunami heights in the coastal zones of interest, emphasizing the significance
of the slip value in determining tsunami heights.
In addition to seismic moment and slip distribution, other factors such as directivity,
focusing and defocusing effects may also affect tsunami heights. It has long been known
that the directivity plays an extremely important role in determining the tsunami height in
an area, especially in the far-field area (Okal 1988). The directivity effect suggests that
tsunami height is a strong function of the direction of tsunami path from its parent
earthquake. According to Kajiura(1972), the difference in azimuths for long rupture
sources is related to the geometric shapes of tsunami sources. In this study, the four fault
models all have similar geometries and fault orientations, suggesting that the directivity
effect is not the main reason for the discrepancy in the calculated tsunami heights.
Irregularity in bathymetry also plays an important role in determining the tsunami heights
in far fields for a tsunami generated with a given total energy (Satake 1988). It is because
the velocity of a tsunami (C ¼ffiffiffiffiffighp
under the shallow water approximation) varies with
the water depth h, a zone of reduced bathymetry (ridge, plateau) can spatially redistribute
wave energy (Okal and Synolakis 2008).
The average tsunami heights measured in the far-field regions seem to be more related
to the size of the parent earthquake, represented by its moment magnitude, than the exact
rupture location and slip distribution (Okal 1988; Okal and Synolakis 2008). Apparently,
directivity effect and wave focusing/defocusing by bathymetry during the propagation of a
tsunami play a more significant role in far-field than in near-field. By contrast, local
tsunami heights are controlled mainly by the magnitude and spatial variations of slip (Geist
1998). Along Thai Andaman coast, the tsunami heights are controlled mainly by the slip
value in the rupture area. As suggested by Okal (2008), the excitation of a tsunami should
grow linearly with the slip on the fault plane, and so should the final run-up, provided that
everything else are the same. When the rupture length is long, field survey data should
cover all the affected coastal areas since the constraints put on the magnitude and slip
values of the tsunami source by the tsunami heights are only limited to the near-field
regions.
4.1.2 Relationship between maximum extent of tsunami deposit and inundation limit
Figure 7 shows the inundation maps and the near shore tsunami heights along the Khao
Lak shoreline obtained by the four fault models. M4 gives the largest tsunami heights,
which are overestimated compared with the measured data. M1 and M3 give comparable
Nat Hazards
123
tsunami heights and similar inundation maps. Both the tsunami height and inundation limit
are significantly underestimated by M2.
Figure 8 compares the spatial distribution of the simulated tsunami deposit with the
corresponding inundation-limit line for each fault model. The simulation results indicate
that the tsunami deposits are roughly correlated to the inundation limit, which is consistent
with the observations of most post-tsunami field surveys along the low-lying coastal areas,
i.e. tsunami deposits commonly extend to over 90 % of the actual inundation limit (Gel-
fenbaum and Jaffe 2003; Moore et al. 2006; MacInnes et al. 2009). It should be pointed out
that the simulation results in Fig. 8 were obtained by assuming an unlimited sediment
supply in the simulation domain. In reality, the existence of coral reef and outcrops in
nearshore regions and the presence of concrete roads and coastal structures may make the
occupied area non-erodible, affecting the resultant distribution of tsunami deposits (the
influence of sediment supply will be discussed in Sect. 4.3). Recent field survey on the
inundation and tsunami deposits due to the 2011 Tohoku-oki tsunami from Sendai Plain
provides one exception (Abe et al. 2012; Chague-Goff et al. 2012; Goto et al. 2011, 2012).
After surveying seven shore-normal transects along the Sendai Coastal Plain, Abe et al.
(2012) found that sand layers of thickness [0.5 cm could extend to over 90 % of the
inundation distance in places where the inundation distance was\2.5 km. However, in the
places where the inundation distance was up to 4.5–5.0 km, sand layers of thickness
Fig. 7 The inundation maps, calculated tsunami heights (blue lines) and surveyed data (black dots) alongthe Khao Lak shoreline for the four source models: a Piatanesi and Lorito (2007) (source model M1),b Chlieh et al. (2007) (source model M2), c Tohoku University Disaster Control Research Center (DCRC)(Koshimura et al. 2009) (source model M3), d Grilli et al. (2007) (source model M4). The white line in eachmap indicates the measured run-up limit
Nat Hazards
123
[0.5 cm extended only to 3 km, which is only 57–76 % of the inundation distance;
beyond 3 km landward, the tsunami deposits continued as a mud layer to the inundation
limit. High concentrations of water-leachable chloride contained in the mud deposit
indicate that the geochemical markers may prove to be useful in identifying the maximum
inundation limit of paleotsunamis that could extend well beyond any preserved sand layer
(Goto et al. 2011). Therefore, the maximum landward extent of sand deposit can be only
assumed to represent the minimum inundation limit.
4.1.3 Thickness of tsunami deposit
Figure 9 compares the predicted thicknesses of tsunami deposit with those measured along
the four transects shown in Fig. 1. Three fault models (M1, M2 and M3) underestimate the
deposit thickness along all four transects. Only M4 gives comparable results, especially
along transects S2 and S3. The deposit thickness is roughly proportional to the tsunami
height along the coast, which determines the inundation depth and velocity inland. M2
predicts negligible tsunami deposits in the surveyed regions due to the low tsunami heights
and the limited inundation depth; M4 gives the largest tsunami heights and the thickest
deposit thickness. Figure 9 suggests that tsunami deposit thicknesses could be indicative of
nearshore tsunami heights, provided that other parameters (topography, initial sand dis-
tribution, etc.) are kept the same.
Figure 10 shows the spatial distributions of tsunami deposit along transects S1–S4
predicted by M4. A strong correlation between the topography and the deposit thickness is
demonstrated in Fig. 10. Thick deposits are found in the topographic lows but less deposit
in topographical highs. The distribution of tsunami-deposit thickness has been described in
many published papers, which have suggested that the thick deposits usually occur locally
in low-lying areas or in front of topographical highs (Peters et al. 2007; Moore et al. 2007;
Smith et al. 2007). The relationship between topography and deposit thickness underlines
that the deposit thickness is highly sensitive to the local topography in a studied area.
Therefore, the discrepancy between the simulated and measured deposit thicknesses is due
partly to the relatively coarse topography data we used in this study.
Fig. 8 The spatial distributions of tsunami deposit thickness: a Piatanesi and Lorito (2007) (source modelM1), b Chlieh et al. (2007) (source model M2), c Tohoku University Disaster Control Research Center(DCRC) (Koshimura et al. 2009) (source model M3), d Grilli et al. (2007) (source model M4). In each map,the blue line marks the run-up limit, and the red dots indicates the survey locations along the four transects.The color in the map indicates the deposit thickness and the unit of the color bar is meter
Nat Hazards
123
Characteristics of tsunami deposits are linked to tsunami source parameters through
tsunami heights and inundation limit in a study area. Aside from bathymetric variation and
the effect of directivity, tsunami heights along the coast of a study area are basically
controlled by two key source parameters: seismic moment and slip distribution. Local
tsunami heights are directly proportional to the slip value in the corresponding rupture
area. Far-field tsunami heights have a positive relationship with the seismic moment, which
determines the total volume of seawater displaced. Greater tsunami heights along a coast
could result in a larger inundation extent, giving a higher possibility that a larger portion of
the inundation area would be covered by thicker tsunami deposits. Our simulation results
also suggest that thicker deposits may correspond to larger earthquakes if multiple historic
or pre-historic tsunami-deposit layers are found in the same location, assuming that the sea
level hasn’t changed much between those events and all the deposits are well preserved.
From a comparison between the simulation results given by the four fault models and
the measured tsunami deposits and tsunamis heights along Thailand Khao Lak coast, it
seems that the fault model M4 is more reasonable. In the following discussion of possible
influences of other factors on tsunami deposits, only the fault model M4 is used.
4.2 Influence of multiple waves
As shown in Fig. 11a, the Khao Lak coast may see multiple waves according to the fault
model M4: the first peak is 13 m high, the second about 5 m high, the third \3 m, and
others are even smaller than the third wave. The time histories of tsunami height in
locations P1 and P2 (Fig. 11a) indicate that transect-S3 is mostly affected by the first wave.
According to our numerical simulations, the wave front reaches the inundation-limit line
0 2 4 6 80
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S1
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S2
Measured dataM1M2M3M4
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S3
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S4
Fig. 9 Comparisons of the simulated tsunami-deposit thicknesses with the survey data
Nat Hazards
123
around 2 h 40 min after the earthquake. We define the stage before 2 h 40 min as the up-
rushing stage and the rest the backwash stage. Figure 11b shows three snapshots of tsunami
deposit thickness along transect-S3; these snapshots clearly show that the tsunami deposits
along transect-S3 are formed mostly by the first wave during the up-rushing stage and
reworked by the receding flow during the backwash stage. Two large deposition zones (one
between 250 and 450 m inland, and the other between 500 and 700 m inland) are formed
0 200 400 600 800 1000 12000
4
8
12
16
Distance (m)
Ele
vatio
n (m
)Transect S1
0 200 400 600 800 1000 12000
10
20
30
Distance (m)
Dep
osit
thic
knes
s (c
m)
Model ResultsMeasurement
200 400 600 800 10000
4
8
12
16
Distance (m)
Ele
vatio
n (m
)
Transect S2
200 400 600 800 10000
10
20
30
Distance (m)
Dep
osit
thic
knes
s (c
m)
0 200 400 600 800 10000
4
8
12
16
Distance (m)
Ele
vatio
n (m
)
Transect S3
0 200 400 600 800 10000
10
20
30
Distance (m)
Dep
osit
thic
knes
s (c
m)
0 200 400 6000
4
8
12
16
Distance (m)
Ele
vatio
n (m
)
Transect S4
0 200 400 6000
10
20
30
Distance (m)
Dep
osit
thic
knes
s (c
m)
Fig. 10 Spatial distributions of tsunami deposits along the four surveyed transects. Left topographicprofiles, maximum tsunami heights, and tsunami deposits (with a 10-times exaggeration in the layerthickness) along the transects S1–S4 (see Fig. 1 for their locations). Right comparisons between thecalculated tsunami-deposit thicknesses and the survey data along the four transects S1–S4
Nat Hazards
123
during the up-rushing stage of the first wave, indicating that most sand in the deposits is
entrained locally by the wavefront and then transported inland. When the first wave reaches
the inundation limit, overland water starts to retreat, and part of the deposits in the large
deposition zone between 250 and 450 m are eroded and transported seaward to the coastal
area near the coastline, forming a smaller deposition zone near the coastline (between 10
and 200 m). The second and third waves are not large enough to modify the shapes of the
deposition zones. This is the case when the successive tsunami waves are smaller than the
first wave. In other cases where some of the successive tsunami waves are larger than the
preceding wave, pre-existing tsunami deposits could be completely removed by the large
successive waves, leaving no trace of tsunami deposits, as discussed by many previous
studies (Apotsos et al. 2011a; Dawson and Shi 2000; Li et al. 2012). These studies all
pointed out the possible changes in deposit thickness and distribution caused by backwash
flows or one of the subsequent multiple waves during a tsunami event. Due to the fact that
the tsunami-deposit thickness measured by geologists after an event records only the final
stage of sediment erosion–deposition process at a surveyed location, existing inverse
models (Jaffe and Gelfenbuam 2007; Soulsby et al. 2007) using deposit thickness as a main
parameter have to assume that successive tsunami waves are smaller than the first wave;
otherwise, the inverse models might underestimate the strength of the tsunami flow.
Wav
e el
evat
ion(
m)
0 200 400 600 800 10000
5
10
15
20
Distance (m)
Dep
osit
thic
knes
s (c
m) t=2h 35min
t=2h 40mint=2h 45min
Before backwash
After backwash
Uprushing stage
2 2.5 3 3.5 4-10
01020
Point P2
2 2.5 3 3.5 4-10
0
10
20Point G
2 2.5 3 3.5 4-10
0
10
20Point P1
Time(hour)
(a)
(b)
Fig. 11 The influence of multiple waves on the tsunami deposit thickness along transect S3: a the timeseries of tsunami height at locations G, P1 and P2 (the exact locations are shown in Fig. 1); b threesnapshots of deposit thickness along transect S3
Nat Hazards
123
4.3 Influence of sediment supply
To further understand effects of different local sediment distributions, two more cases
were simulated with three different initial sediment distributions: sediment distributions
DA, DB and DC. The differences among these three distributions are summarized
below:
1. For the distribution DA, sand classes with grain sizes of 0.1, 0.2 and 1.0 mm were
specified for the muddy area, the sandy area, and the inland gravel area, respectively.
The rocky area was non-erodible.
2. For the distribution DB, the grain size in the inland gravel area was specified as
0.5 mm instead of 1.0 mm.
3. For the distribution DC, the inland area was assumed to be densely covered by
vegetation or buildings (i.e. non-erodible).
Figure 12 shows the maps of tsunami-deposit thickness, obtained by using the fault
model M4, for the three initial sediment distributions. The tsunami-deposit extents still
follow more or less the inundation extents even though the deposit thicknesses are sig-
nificantly different. Since the sediment distribution DB has finer sand available in the
inland area, it gives rise to a layer of tsunami deposits thicker than the other two distri-
butions. As shown in Fig. 13, the deposit thickness given by the sediment distribution DA
is 30–50 % thicker than those given by the sediment distribution DA in most of the
surveyed locations. The deposit thickness is significantly smaller for the sediment distri-
bution DC, and only in several surveyed locations can the deposits be observed. In other
words, if an inland region is non-erodible, the tsunami deposits may not be detectable in
most of the inundated areas.
It can be concluded from Figs. 12 and 13 that the initial sediment distribution, espe-
cially the inland sediment supply, can significantly affect the distribution of the resultant
Fig. 12 The predicted tsunami deposit thicknesses for different initial sand distributions : a distributionDA, b distribution DB, and c distribution DC. The color in the map indicates the deposit thickness and theunit of the color bar is meter
Nat Hazards
123
tsunami deposits. This conclusion is in agreement with what was observed during several
modern post-tsunami field surveys (Smith et al. 2007; Sato et al. 1995; Shi et al. 1995;
Richmond et al. 2012): the main source of tsunami deposits came from beach areas or local
sand. Similar conclusions were also mentioned by Apotsos et al. (2011b) who simulated
tsunami inundation and sediment transport in a sediment-limited embayment on American
Samoa; their simulations showed that the amount of sediment available for transport could
affect the onshore deposition thickness by more than 50 % by.
4.4 Influence of bottom roughness
Three distributions of bottom roughness in the inland areas, described by Manning’s
roughness coefficient, were simulated to understand the influence of bottom roughness on the
resulting tsunami deposits. The sediment distribution described in Sect. 3.4 was adopted here,
the fault model M4 was used to generate the initial tsunami, and a fixed value of n = 0.013
was chosen for the water area. The three distributions of bottom roughness in the inland area
are: n = 0.03 for Case NA, n = 0.06 for Case NB, and a viable value of n for Case NC. For
Case NC, the roughness was determined by considering the different land uses/covers (see
Fig. 14), and six different types of land uses/covers were identified in the inland region
according to the geo-referenced high-resolution satellite image Ikonos (CRISP 2004) in Khao
Lak area: grass land, young plantation, dense forest, urban area, mangrove forest, and water
areas. For each area, a different Manning’s coefficient was specified according to the
guideline for selecting the Manning’s coefficients for natural channels and floodplains (Ar-
cement and Schneider 1989). A large roughness value (n = 0.06) was chosen for the urban
0 2 4 6 80
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S1
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S2
Measured dataDADBDC
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S3
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S4
Fig. 13 Comparisons of the simulated tsunami-deposit thicknesses with the survey data for different initialsediment distributions
Nat Hazards
123
areas. Similarly, larger roughness values were also specified for dense forest (n = 0.065) and
mangrove forest (n = 0.08).
Referring to Fig. 15, which shows the maps of deposit thickness for the three distri-
butions of bottom roughness, the effects of bottom roughness on sediment transportation
and deposition are significant. As shown in Fig. 16, larger bottom roughness helps dissi-
pate wave energy more quickly, and consequently leads to a smaller inundation area, lower
inundation depth, slower flow velocity and thinner deposit zone. Although the deposit
thicknesses have been dramatically decreased by larger values of bottom roughness
Fig. 14 The map of bottom roughness based on the land-cover classification of a 2003 Ikonos image forKhao Lak
Nat Hazards
123
(n C 0.06), tsunami deposits still cover most part of the inundation area. If this thin deposit
layer is recognizable in urban areas or dense forest areas, the chance to identify tsunami
inundation extent still remains.
Fig. 15 The predicted tsunami-deposit thicknesses for different types of land coverage: a case NA; b caseNB; c case NC. The color in the map indicates the deposit thickness and the unit of the color bar is meter
0 2 4 6 80
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S1
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S2
Measured dataNANBNC
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S3
0 2 4 6 8 10 120
10
20
30
Sample location
Dep
osit
thic
knes
s (c
m)
Transect S4
Fig. 16 Comparisons of the simulated tsunami-deposit thicknesses with the survey data for different typesof land coverage
Nat Hazards
123
5 Discussion
5.1 Uncertainties associated with inverting tsunami source parameters
from the characteristics of tsunami deposits
The characteristics of tsunami deposits such as landward extent and deposit thickness have
been used to infer the recurrence intervals and magnitudes of great tsunami earthquakes,
slip distributions of historic or prehistoric tsunami earthquakes in some specific areas
(Bourgeois et al. 2006; Macinnes et al. 2010; Martin et al. 2008; Nanayama et al. 2003;
Satake et al. 2005). From the extents of prehistoric tsunami deposits, Nanayama et al.
(2003) inferred that large tsunamis unusually occurred about every 500 years on average
over the past 2,000–7,000 years along the Kuril trench. Using tsunami-deposit distribution,
Macinnes et al. (2010) further determined the magnitude and slip distribution of the 1952
Kamchatka great earthquake. Martin et al. (2008) untangled the 1969 Ozernoi and 1971
Kamchatskii tsunamis using a combination of field mapping of tsunami deposits and
tsunami modeling; they differentiated these two tsunamis in some localities, and elucidated
the earthquakes’ focal mechanisms and rupture areas. The commonality of the approaches
used in these studies is the use of forward numerical models: a series of hypothetical
rupture models are proposed based first on known information such as tectonic setting of
the source region, existing catalogs of the earthquake, paleoseismological evidences in the
stratigraphic record, and then forward numerical models are used to simulate the tsunami
propagation and inundation in the interested coastal areas based on these hypothetical
rupture models. The simulated inundation extents are compared with the observed deposit
extents to see whether the initial tsunami heights suffice to inundate the whole area covered
by tsunami deposits. Obviously, the trial-and-error procedure could be totally hopeless and
questionable when we have no clue or little information on the location and the geometry
of the source earthquake. However, when the characteristics of tsunami deposits are used
as proxies to infer the tsunami source parameters, we should always bear in mind the
following points:
1. In some instances, the evidence for tsunami deposits on a coast simply may not be well
preserved due to subsequent flooding or bio- and cryoturbation (Bourgeois et al. 2006).
2. The characteristics of tsunami deposits are very site-specific and may be
significantly affected by local conditions. It is necessary to have a good
understanding of the local conditions such as topography, land uses and sediment
availability. According to the aforementioned numerical experiments, the thickness
of tsunami deposits is more easily affected by local conditions. Compared with
deposit thickness, the deposit landward extent is more robust: if a thin deposit still
could be detected, its extent would be indicative of the tsunami inundation area,
thus the tsunami size.
3. Information on the local sea-level and geomorphic history of a coastline under
investigation is essential when reconstructing a tsunami history for a specific
location. On a stable coast, it is possible to take the current elevation and extent of a
tsunami deposit as an indicator of run-up and inundation; however, on an unstable
coast, changes in relative sea level and shoreline location must be taken into
account. Even for young tsunami deposits, run-up estimates might be inaccurate if
there had been uplift or subsidence associated with recent earthquakes (Bourgeois
et al. 2006).
Nat Hazards
123
5.2 Forward numerical models versus inversion models
The application of forward numerical models described in Sect. 5.1 relies heavily on the
availability of the location and geometry of a source earthquake, which could be
unavailable for pre-historic tsunamis. In addition to forward numerical models, there are
other types of inverse models that could directly calculate the slip distribution of a fault
model from available tsunami data. These types of inverse models are briefly reviewed
here, and followed by a discussion of possible difficulties in using inland tsunami-height
data to infer slip distribution.
The characteristics of a tsunami wave, including wave heights recorded by DARTs in
deep sea, waveforms recorded by tide gauges, and runup heights measured during post-
tsunami surveys, are useful to constrain some fault parameters (Geist and Dmowska 1999;
Okal and Titov 2007; Piatanesi and Lorito 2007; Pires and Miranda 2001; Satake 1987; Wu
and Ho 2011; Abe 1973). Different methods for tsunami-waveform inversion have been
proposed in the past. Various types of data are required by these methods. Abe (1973)
employed a backward ray-tracing technique to locate the boundaries of a tsunami source.
In this method, only the tsunami arrival time at each observation point is required to start
the backward tracing computation. This technique applies only to linear long waves and
can only locate the tsunami source, without giving any information about its shape and
amplitude. Satake (1987) proposed a method to invert the slip distribution using tsunami
waveforms recorded by tide gauges. In this method, the fault area is divided into sub-faults,
and synthetic waveform is calculated at each tide gauge for each sub-fault. By using the
technique of Green’s function and the waveforms recorded by available tide gauges, they
treated the observed waveform as a linear superposition of the Green’s functions so that the
displacement on each sub-fault can be determined by solving a linear equation (Satake
1987; Johnson and Satake 1993). Piatanesi et al. (1996) proposed a very similar approach
to retrieve the information on slip distribution. They used Green’s function technique and
solved shallow water equations using a finite-element method instead of finite-difference
method; their results showed that the local run-up heights collected during the post-event
field surveys could be used for the inversion when tide-gauge records are not available/
sufficient. Instead of applying a least-square procedure to minimize the difference between
the recorded tide-gauge waveforms and the calculated synthetic waveforms, they applied a
least-square procedure to minimize the difference between the observed run-up values and
the computed maximum water levels along the coast.
For methods based on linear long waves, using surveyed tsunami-height and run-up data
in inland areas or some tide-gauge data in very shallow areas can be problematic. It is
evident that the assumption of linear waves holds only in deep seas where tsunami heights
are far less than water depth. However, linearity of water waves is no longer valid in an
inundated area or in nearshore waters where the water depth is usually comparable to
tsunami height. In reality, the nonlinearity of tsunami waves and the effects of local
bathymetry and topography cannot be neglected. To address this difficulty, an adjoint
method proposed by Pires and Miranda (2001) may be very promising. As an alternative to
the approaches based on linear long waves, the joint method has the advantage of being
able to use either linear or non-linear forward propagation models that can account for non-
linear advection and run-up effects. This method is particularly useful for pre-historic
tsunamis or even some historic and modern tsunamis for which the tide-gauge records are
not available or scarce. For pre-historic tsunamis, the available information could only be
tsunami deposits, which may be scattered in some coastal lowland areas. With this method,
the tsunami heights must be first inferred directly or indirectly from the characteristics of
Nat Hazards
123
tsunami deposits before they are used as input data to the inversion model. However, the
adjoint method has only been tested using tide-gauge records for the tsunami caused by the
28th February 1969 Gorringe Bank earthquake. The capability of using tsunami heights or
run-ups as the only input data to an inversion model is still yet to be proved.
Compared with the trial-and-error approach used in forward numerical models, the
reliability of inversion models depends mostly on the quality and quantity of surveyed
tsunami data. To derive reasonable inversion results, a large number of tide-gage stations
with good azimuthal coverage is desirable. For pre-historic tsunamis, the inference from
tsunami deposits to tsunami heights could be promising but very challenging.
6 Conclusions
In this paper, numerical experiments were conducted for the Thailand’s Khao Lak region to
understand the uncertainties associated with inverting tsunami source parameters from the
characteristics of tsunami deposits. Two key characteristics of tsunami deposits were
examined: deposit extent and thickness. The influences of tsunami source parameters,
multiple waves, sediment supply and bottom roughness on deposit extent and thickness
were discussed. Our main conclusions are summarized as follows:
1. For near-field tsunami deposits (close to the source area), the deposit extent and
thickness are more indicative of the slip values close to the tsunami deposit area. For
far-field tsunami deposits, the deposit extent and thickness are more indicative of the
magnitude of the source earthquake.
2. Compared with deposit thickness, the deposit landward extent is more robust: if a thin
deposit layer can be detected, the extent is indicative of the tsunami inundation area,
thus the tsunami size.
3. The extent and thickness of tsunami deposits may be further used to quantify some
parameters of the corresponding source earthquake through two possible approaches:
forward numerical models based on a large number of hypothetical rupture models,
and direct inverse models. Forward numerical models require considerable compu-
tational resources, and existing direct inverse models are for linear long waves. There
is a need to extend direct inverse models to handle nonlinear effects.
4. To reconstruct a tsunami history for a specific location, it is important to know the
local conditions, the preservation of tsunami deposits, the local sea-level and
geomorphic history of the coastline.
Acknowledgments The authors would like to thank Dr. Shigehiro Fujino for generously sharing with usthe detailed tsunami-deposit data using in this study. This work was supported by the Earth Observatory ofSingapore, Nanyang Technological University, Singapore, through the project ‘‘Understanding TsunamiSources from Surveyed Tsunami Heights and Sediment Deposits’’. This is EOS Contribution No. 63.
References
Abe K (1973) Tsunami and mechanism of great earthquakes. Phys Earth Planet Inter 7(2):143–153Abe K (1979) Size of great earthquakes of 1837–1974 inferred from tsunami data. J Geophys Res
84(B4):1561–1568. doi:10.1029/JB084iB04p01561Abe T, Goto K, Sugawara D (2012) Relationship between the maximum extent of tsunami sand and the
inundation limit of the 2011 Tohoku-oki tsunami on the Sendai Plain, Japan. Sediment Geol282:142–150
Nat Hazards
123
Ammon CJ, Ji C, Thio HK, Robinson D, Ni S, Hjorleifsdottir V, Kanamori H, Lay T, Das S, Helmberger D,Ichinose G, Polet J, Wald D (2005) Rupture process of the 2004 Sumatra–Andaman earthquake.Science 308(5725):1133–1139
Apotsos A, Gelfenbaum G, Jaffe B (2011a) Process-based modeling of tsunami inundation and sedimenttransport. J Geophys Res 116:20. doi:10.1029/2010JF001797
Apotsos A, Gelfenbaum G, Jaffe B, Watt S, Peck B, Buckley M, Stevens A (2011b) Tsunami inundation andsediment transport in a sediment-limited embayment on American Samoa. Earth-Sci Rev107(1–2):1–11. doi:10.1016/j.earscirev.2010.11.001
Arcement GJJ, Schneider VR (1989) Guide for selecting manning’s roughness coefficient for naturalchannels and floodplains. Water Supply Paper 2339. Washington, DC
Banerjee P, Pollitz F, Nagarajan B, Burgmann R (2007) Coseismic slip distributions of the 26 December2004 Sumatra–Andaman and 28 March 2005 Nias earthquakes from GPS static offsets. Bull SeismolSoc Am 97(1 A Suppl):S86–S102
Bourgeois J (2009) Geologic effects and records of tsunamis. In: Bernard EN, Robinson AR (eds) The sea:tsunamis, vol 15. Harvard University Press, London, pp 53–91
Bourgeois J, Pinegina TK, Ponomareva V, Zaretskaia N (2006) Holocene tsunamis in the southwesternBering Sea, Russian Far East, and their tectonic implications. Bull Geol Soc Am 118(3–4):449–463
Chague-Goff C, Andrew A, Szczucinski W, Goff J, Nishimura Y (2012) Geochemical signatures up to themaximum inundation of the 2011 Tohoku-oki tsunami—implications for the 869AD Jogan and otherpalaeotsunamis. Sediment Geol 282:65–77
Chlieh M, Avouac JP, Hjorleifsdottir V, Song TRA, Ji C, Sieh K, Sladen A, Hebert H, Prawirodirdjo L,Bock Y, Galetzka J (2007) Coseismic slip and afterslip of the great Mw 9.15 Sumatra–Andamanearthquake of 2004. Bull Seismol Soc Am 97(1 A Suppl):S152–S173
CRISP (2004) IKONOS images of Aceh Besar district, Aceh, Sumatra, Indonesia, captured on 29 December2004. Centre for Remote Imaging, Sensing and Processing. http://www.crisp.nus.edu.sg/tsunami/tsunami.html. Accessed 16 March 2013
Dawson AG, Shi S (2000) Tsunami deposits. Pure Appl Geophys 157(6–8):875–897Dawson AG, Shi S, Dawson S, Takahashi T, Shuto N (1996) Coastal sedimentation associated with the June
2nd and 3rd, 1994 tsunami in Rajegwesi, Java. Quat Sci Rev 15(8–9):901–912Di Geronimo I, Choowong M, Phantuwongraj S (2009) Geomorphology and superficial bottom sediments of
Khao Lak Coastal Area (SW Thailand). Pol J Environ Stud 18(1):111–121Fujii Y, Satake K (2007) Tsunami source of the 2004 Sumatra–Andaman earthquake inferred from tide
gauge and satellite data. Bull Seismol Soc Am 97(1 A Suppl):S192–S207Fujino S, Naruse H, Matsumoto D, Sakakura N, Suphawajruksakul A, Jarupongsakul T (2010) Detailed
measurements of thickness and grain size of a widespread onshore tsunami deposit in Phang-ngaProvince, southwestern Thailand. Isl Arc 19(3):389–398
Galappatti G, Vreugdenhil CB (1985) A depth-integrated model for suspended sediment transport. J HydraulRes 23:359–377. doi:10.1080/00221688509499345
Geist EL (1998) Local tsunamis and earthquake source parameters. Adv Geophys 39:117–209Geist EL, Dmowska R (1999) Local tsunamis and distributed slip at the source. Pure Appl Geophys
154(3–4):485–512Gelfenbaum G, Jaffe B (2003) Erosion and sedimentation from the 17 July, 1998 Papua New Guinea
tsunami. Pure Appl Geophys 160(10–11):1969–1999Goto K, Imamura F (2007) Numerical models for sediment transport by tsunamis. Quat Res 46(6):463–475Goto K, Chague-Goff C, Fujino S, Goff J, Jaffe B, Nishimura Y, Richmond B, Sugawara D, Szczucinski W,
Tappin DR, Witter RC, Yulianto E (2011) New insights of tsunami hazard from the 2011 Tohoku-okievent. Mar Geol 290(1–4):46–50
Goto K, Chague-Goff C, Goff J, Jaffe B (2012) The future of tsunami research following the 2011 Tohoku-oki event. Sediment Geol 282:1–13
Grilli S, Ioualalen M, Asavanant J, Shi F, Kirby J, Watts P (2007) Source constraints and model simulationof the December 26, 2004, Indian Ocean tsunami. J Waterw Port Coast Ocean Eng 133(6):414–428.doi:10.1061/(asce)0733-950x(2007)133:6(414
Hori K, Kuzumoto R, Hirouchi D, Umitsu M, Janjirawuttikul N, Patanakanog B (2007) Horizontal andvertical variation of 2004 Indian tsunami deposits: an example of two transects along the western coastof Thailand. Mar Geol 239(3–4):163–172
Jaffe BE, Gelfenbuam G (2007) A simple model for calculating tsunami flow speed from tsunami deposits.Sediment Geol 200(3–4):347–361
Johnson JM, Satake K (1993) Source parameters of the 1957 Aleutian earthquake from tsunami waveforms.Geophys Res Lett 20(14):1487–1490. doi:10.1029/93gl01217
Nat Hazards
123
Kajiura K (1972) The directivity of energy radiation of the tsunami generated in the vicinity of a continentalshelf. J Oceanogr Soc Jpn 28(6):260–277
Kihara N, Matsuyama M (2010) Numerical simulations of sediment transport induced by the 2004Indian Ocean tsunami near Kirinda Port in Sri Lanka. In: Proceedings of 32nd conference on coastalengineering, Shanghai, China
Koshimura S, Oie T, Yanagisawa H, Imamura F (2009) Developing fragility functions for tsunami damageestimation using numerical model and post-tsunami data from Banda Aceh, Indonesia. Coast Eng J51(3):243–273
Li LL, Huang ZH (2013) Modeling the change of beach profile under tsunami waves: a comparison ofselected sediment transport models. J Earthq Tsunami 7(1):1350001. doi:10.1142/S1793431113500012
Li LL, Qiu Q, Huang ZH (2012) Numerical modeling of the morphological change in Lhok Nga, west BandaAceh, during the 2004 Indian Ocean tsunami: understanding tsunami deposits using a forward mod-eling method. Nat Hazards 64(2):1549–1574
Liu PLF, Yong-Sik C, Briggs MJ, Kanoglu U, Synolakis CE (1995) Runup of solitary waves on a circularisland. J Fluid Mech 302:259–285
MacInnes BT, Bourgeois J, Pinegina TK, Kravchunovskaya EA (2009) Tsunami geomorphology: erosionand deposition from the 15 November 2006 Kuril Island tsunami. Geology 37(11):995–998
Macinnes BT, Weiss R, Bourgeois J, Pinegina TK (2010) Slip distribution of the 1952 Kamchatka greatearthquake based on near-field tsunami deposits and historical records. Bull Seismol Soc Am100(4):1695–1709
Martin ME, Weiss R, Bourgeois J, Pinegina TK, Houston H, Titov VV (2008) Combining constraints fromtsunami modeling and sedimentology to untangle the 1969 Ozernoi and 1971 Kamchatskii tsunamis.Geophys Res Lett 35(1):L01610. doi:10.1029/2007gl032349
Meltzner AJ, Sieh K, Abrams M, Agnew DC, Hudnut KW, Avouac J-P, Natawidjaja DH (2006) Uplift andsubsidence associated with the great Aceh–Andaman earthquake of 2004. J Geophys Res111(B2):B02407. doi:10.1029/2005jb003891
Moore A, Nishimura Y, Gelfenbaum G, Kamataki T, Triyono R (2006) Sedimentary deposits of the 26December 2004 tsunami on the northwest coast of Aceh, Indonesia. Earth Planets Space 58(2):253–258
Moore AL, McAdoo BG, Ruffman A (2007) Landward fining from multiple sources in a sand sheetdeposited by the 1929 Grand Banks tsunami, Newfoundland. Sediment Geol 200(3–4):336–346
Morton RA, Gelfenbaum G, Jaffe BE (2007) Physical criteria for distinguishing sandy tsunami and stormdeposits using modern examples. Sediment Geol 200(3–4):184–207
Nanayama F, Satake K, Furukawa R, Shimokawa K, Atwater BF, Shigeno K, Yamaki S (2003) Unusuallylarge earthquakes inferred from tsunami deposits along the Kuril trench. Nature 424(6949):660–663
Nelson AR, Kelsey HM, Witter RC (2006) Great earthquakes of variable magnitude at the Cascadiasubduction zone. Quat Res 65(3):354–365
Okada Y (1985) Surface deformation due to shear and tensile faults in a half-space. Bull Seismol Soc Am75:1135–1154
Okal EA (1988) Seismic parameters controlling far-field tsunami amplitudes: a review. Nat Hazards1(1):67–96
Okal EA (2008) Excitation of tsunamis by earthquakes. In: Bernard EN, Robinson AR (ed) The sea: ideasand observations on process in the study of the seas, vol 15. Harvard University Press, pp 137–177
Okal EA, Synolakis CE (2008) Far-field tsunami hazard from mega-thrust earthquakes in the Indian Ocean.Geophys J Int 172(3):995–1015
Okal EA, Titov VV (2007) M TSU: recovering seismic moments from tsunameter records. Pure ApplGeophys 164(2–3):355–378
Peters R, Jaffe B, Gelfenbaum G (2007) Distribution and sedimentary characteristics of tsunami depositsalong the Cascadia margin of western North America. Sediment Geol 200(3–4):372–386
Piatanesi A, Lorito S (2007) Rupture process of the 2004 Sumatra–Andaman earthquake from tsunamiwaveform inversion. Bull Seismol Soc Am 97(1 A Suppl):S223–S231
Piatanesi A, Tinti S, Gavagni I (1996) The slip distribution of the 1992 Nicaragua earthquake from tsunamirun-up data. Geophys Res Lett 23(1):37–40
Pires C, Miranda PMA (2001) Tsunami waveform inversion by adjoint methods. J Geophys Res C: Oceans106(C9):19773–19796
Rajendran CP, Rajendran K, Anu R, Earnest A, Machado T, Mohan PM, Freymueller J (2007) Crustaldeformation and seismic history associated with the 2004 Indian Ocean earthquake: a perspective fromthe Andaman–Nicobar Islands. Bull Seismol Soc Am 97(1 A Suppl):S174–S191
Richmond B, Szczucinski W, Chague-Goff C, Goto K, Sugawara D, Witter R, Tappin DR, Jaffe B, Fujino S,Nishimura Y, Goff J (2012) Erosion, deposition and landscape change on the Sendai coastal plain,Japan, resulting from the March 11, 2011 Tohoku-oki tsunami. Sediment Geol 282:27–39
Nat Hazards
123
Roelvink D, Reniers A, Dongeren Av, Vries JvTd, Lescinski J, McCall R (2008) XBeach model descriptionand manual. XBeach Webpage hosted by Deltars. http://oss.deltares.nl/web/xbeach/documentation.Accessed 16 June 2012
Satake K (1987) Inversion of tsunami waveforms for the estimation of a fault heterogeneity: method andnumerical experiments. J Phys Earth 35:241–254
Satake K (1988) Effects of bathymetry on tsunami propagation: application of ray tracing to tsunamis. PureAppl Geophys 126(1):27–36
Satake K, Nanayama F, Yamaki S, Tanioka Y, Hirata K (2005) Variability among tsunami sources in the17th–21st centuries along the Southern Kuril Trench. In: Satake K (ed) Tsunamis, vol 23. Advances innatural and technological hazards research. Springer, Netherlands, pp 157–170. doi:10.1007/1-4020-3331-1_9
Sato H, Shimamoto T, Tsutsumi A, Kawamoto E (1995) Onshore tsunami deposits caused by the 1993Southwest Hokkaido and 1983 Japan Sea earthquakes. Pure Appl Geophys 144(3–4):693–717
Shi S, Dawson AG, Smith DE (1995) Coastal sedimentation associated with the December 12th, 1992tsunami in Flores, Indonesia. Pure Appl Geophys 144(3–4):525–536
Smith DE, Foster IDL, Long D, Shi S (2007) Reconstructing the pattern and depth of flow onshore in apalaeotsunami from associated deposits. Sediment Geol 200(3–4):362–371
Soulsby RL, Smith DE, Ruffman A (2007) Reconstructing tsunami run-up from sedimentary characteris-tics—a simple mathematical model. Coast Sediments 7:1075–1088
Spiske M, Weiss R, Bahlburg H, Roskosch J, Amijaya H (2010) The TsuSedMod inversion model applied tothe deposits of the 2004 Sumatra and 2006 Java tsunami and implications for estimating flowparameters of palaeo-tsunami. Sediment Geol 224(1–4):29–37
Srisutam C, Wagner JF (2010) Tsunami sediment characteristics at the Thai Andaman Coast. Pure ApplGeol 167(3):215–232
Tanioka Y, Yudhicara, Kususose T, Kathiroli S, Nishimura Y, Iwasaki SI, Satake K (2006) Rupture processof the 2004 great Sumatra–Andaman earthquake estimated from tsunami waveforms. Earth PlanetsSpace 58(2):203–209
Tobita M, Suito H, Imakiire T, Kato M, Fujiwara S, Murakami M (2006) Outline of vertical displacement ofthe 2004 and 2005 Sumatra earthquakes revealed by satellite radar imagery. Earth Planets Space58(12):e1–e4
User manual for COrnell Multi-grid COupled Tsunami model-COMCOT V1.7 (2009) http://ceeserver.cee.cornell.edu/pll-group/doc/COMCOT_User_Manual_v1_7.pdf. Accessed 11 Oct 2011
Van Rijn LC (1993) Principles of sediment transport in rivers, estuaries and coastal seas. Aqua Publications,The Netherlands
Wang X (2009) User manual for Cornell multi-grid coupled tsunami model-COMCOT V1.7. COMCOTwebsite hosted by Cornell University. http://ceeserver.cee.cornell.edu/pll-group/doc/COMCOT_User_Manual_v1_7.pdf. Accessed 18 Jan 2013
Wu TR, Ho TC (2011) High resolution tsunami inversion for 2010 Chile earthquake. Nat Hazards Earth SystSci 11(12):3251–3261
Nat Hazards
123