NHESS - Investigating beach erosion related with tsunami sediment transport at Phra Thong Island, Thailand, caused by the 2004

NHESS - Investigating beach erosion related with tsunami sediment transport at Phra Thong Island, Thailand, caused by the 2004 Indian Ocean tsunami
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28 Oct 2020
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28 Oct 2020
Investigating beach erosion related with tsunami sediment transport at Phra Thong Island, Thailand, caused by the 2004 Indian Ocean tsunami
Investigating beach erosion related with tsunami sediment transport at Phra Thong Island, Thailand, caused by the 2004 Indian Ocean tsunami
Investigating beach erosion related with tsunami sediment transport at Phra Thong Island,...
Ryota Masaya et al.
Ryota Masaya
Anawat Suppasri
Kei Yamashita
Fumihiko Imamura
Chris Gouramanis
and
Natt Leelawat
Ryota Masaya
CORRESPONDING AUTHOR
ryota.masaya.r6@dc.tohoku.ac.jp
Civil and Environmental Engineering, Graduate School of Engineering,
Tohoku University, 6-6-06 Aoba, Aramaki-Aza, Aoba, Sendai 980-0845, Japan
Anawat Suppasri
International Research Institute of Disaster Science, Tohoku
University, 468-1 Aoba, Aramaki-Aza, Aoba, Sendai 980-0845, Japan
Kei Yamashita
International Research Institute of Disaster Science, Tohoku
University, 468-1 Aoba, Aramaki-Aza, Aoba, Sendai 980-0845, Japan
Fumihiko Imamura
International Research Institute of Disaster Science, Tohoku
University, 468-1 Aoba, Aramaki-Aza, Aoba, Sendai 980-0845, Japan
Chris Gouramanis
Department of Geography, National University of Singapore, 1 Arts
Link, Singapore 117570, Singapore
Natt Leelawat
Department of Industrial Engineering, Faculty of Engineering,
Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand
Disaster and Risk Management Information Systems Research Group,
Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand
Abstract
The 2004 Indian Ocean tsunami and the 2011 Tōhoku earthquake and
tsunami caused large-scale topographic changes in coastal areas. Whereas
much research has focused on coastlines that have or had large human
populations, little focus has been paid to coastlines that have little or no
infrastructure. The importance of examining erosional and depositional
mechanisms of tsunami events lies in the rapid reorganization that
coastlines must undertake immediately after an event. A thorough
understanding of the pre-event conditions is paramount to understanding the
natural reconstruction of the coastal environment. This study examines the
location of sediment erosion and deposition during the 2004 Indian Ocean
tsunami event on the relatively pristine Phra Thong Island, Thailand.
Coupled with satellite imagery, we use numerical simulations and sediment
transportation models to determine the locations of significant erosion and
the areas where much of that sediment was redeposited during the tsunami
inundation and backwash processes. Our modeling approach suggests that
beaches located in two regions on Phra Thong Island were significantly
eroded by the 2004 tsunami, predominantly during the backwash phase of the
first and largest wave to strike the island. Although 2004 tsunami deposits
are found on the island, we demonstrate that most of the sediment was
deposited in the shallow coastal area, facilitating quick recovery of the
beach when normal coastal processes resumed.
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Masaya, R., Suppasri, A., Yamashita, K., Imamura, F., Gouramanis, C., and Leelawat, N.: Investigating beach erosion related with tsunami sediment transport at Phra Thong Island, Thailand, caused by the 2004 Indian Ocean tsunami, Nat. Hazards Earth Syst. Sci., 20, 2823–2841, https://doi.org/10.5194/nhess-20-2823-2020, 2020.
Received: 07 Aug 2019
Discussion started: 26 Aug 2019
Revised: 29 Jun 2020
Accepted: 02 Sep 2020
Published: 28 Oct 2020
Introduction
The 2004 Indian Ocean tsunami and the 2011 Tōhoku earthquake and
tsunami caused large-scale geomorphologic changes in coastal areas during
the erosional phases of inflow and outflow (Pari et al., 2008; Goto et al.,
2011a; Tanaka et al., 2011; Haraguchi et al., 2012; Hirao et al., 2012; Udo
et al., 2013; Imai et al., 2015). In each tsunami event, the erosional
phases translocated sediments onshore and offshore and primed the coastal
zone for rapid (months to decades) recovery (Choowong et al., 2009; Ali and
Narayama, 2015; Udo et al., 2016; Saegusa et al., 2017; Koiwa et al., 2018).
However, little information exists to identify real-time sediment dynamics
during the erosional and depositional phases of tsunami events. In
particular, erosive phases mobilize sediments into the onshore (e.g., Jankaew
et al., 2008; Gouramanis et al., 2017) and offshore environments (e.g., Feldens
et al., 2009). Following the tsunami event, both offshore
environment and
coastal environments are primed for natural processes to resume and
redistribute sediments onshore to restore the coastal environment to similar
pre-tsunami configurations.
However, in many regions, such as the area affected by the 2011 tsunami,
extensive engineering interventions (e.g., levee construction and land level
raising) are affecting the natural recovery processes of the coastal zone.
In Japan, plans for coastal reconstruction and defenses are typically
formulated shortly after a tsunami, preventing natural recovery processes
(Suppasri et al., 2016), and many locations have not undergone or been
allowed to recover naturally (Udo et al., 2016). These political and
engineering interventions make it difficult to observe or predict the
natural recovery processes of coastal areas.
Before an understanding of the recovery processes of a tsunami-affected
coastal zone can be achieved, a thorough understanding of the sediment
budget must be determined. The relocation of sediments during the main
tsunami erosion and deposition phases establishes the pre-recovery or
baseline conditions upon which natural processes can act to facilitate the
recovery of the coastal zone. To determine the locations of sediment
deposition during a tsunami event, the sediment transport dynamics during
the tsunami must be defined.
Unfortunately, real-time data from observations have not been possible to
establish quantitative estimates of sediment erosion and deposition during a
tsunami event, though qualitative spatial patterns of the sediment process (Udo
et al., 2016; Yamashita et al., 2016) have been examined through analysis of
video footage. Prior studies have mainly estimated sediment transport
dynamics such as erosion and sediment deposition through remote sensing
(e.g., Fagherazzi and Du, 2008; Choowong et al., 2009; Liew et al., 2010) and
sedimentological and stratigraphic analysis (e.g., Paris et al., 2007; Hawkes
et al., 2007; Switzer et al., 2012). However, the information obtained
regarding the final results of the sediment transport process is limited. It
is difficult to obtain information on where sediment has eroded and
deposited (e.g., Pham et al., 2018) and whether topographic changes caused
by the local sediment runoff or deposition are the results of action from
inflow or backwash (e.g., Choowong et al., 2009; Paris et al., 2007; Switzer et
al., 2012). This information determines the sediment budget in the system
before and after the tsunami and is therefore important for considering
geomorphic recovery.
Numerical simulations using wave dynamics of an area can reproduce
spatial–temporal variations in the sediment mobility and deposition and can
effectively model the sediment transport process using the wave and sediment
characteristics of the natural system. In recent years, numerical
modeling of tsunami sediment transport has been developed (e.g., Takahashi et
al., 2000), improved (e.g., Takahashi et al., 2011; Apotsos et al., 2011a; Li
and Huang, 2013; Morishita and Takahashi, 2014; Yamashita et al., 2018) and
applied in the field (e.g., Gelfenbaun et al., 2007; Takahashi et al., 2008;
Apotsos et al., 2011b, c; Gusman et al., 2012; Li et
al., 2014; Arimitsu et al., 2017; Yamashita et al., 2017), and
reproducibility has been confirmed by comparison between the calculated and
measured values (e.g., Li et al., 2012; Ranasinghe et al., 2013; Sugawara et
al., 2014a; Yamashita et al., 2015; Yamashita et al., 2016).
An important consideration in the sediment dynamics during catastrophic
marine events (e.g., typhoon and tsunami) is the degree of development and
human modification of the coastal zone prior to the event. Artificial
structures, such as sea walls, roads and buildings interfere with washover
processes, and these areas are often targeted from reconstruction and
rehabilitation through rapid engineering reconstruction. Little is known
about the recovery processes in sparsely developed and unpopulated areas. As
such, the largely anthropogenically undisturbed Phra Thong Island, western
Thailand, is an ideal location to model the sediment dynamics, coastal
erosion and deposition following a major tsunami event.
The main objective of this study is to investigate the short-term conditions
of sediment transport such as erosional and depositional process and
establishes the baseline sediment conditions that led to further
investigation of the long-term recovery of the Phra Thong Island coastline
after the 2004 Indian Ocean tsunami. We used tsunami sediment transport calculations to
spatiotemporally reproduce the sediment transport processes occurring
during the tsunami and identify zones of sediment deposition in the offshore
and onshore areas and validate these modeling results with published
observational data of the 2004 Indian Ocean tsunami deposits on the island. Due to the
largely natural environment, Phra Thong Island is a rare case that is useful
for verifying tsunami sediment transport models where few artificial
features can generate model uncertainties.
Examining the sediment transport processes on Phra Thong Island is also
expected to elucidate phenomena and improve numerical calculation models for
the future and is applicable to other areas. Furthermore, at least three
paleotsunami deposits were identified in areas impacted by the 2004 Indian Ocean tsunami on
Phra Thong Island (Jankaew et al., 2008; Sawai et al., 2009; Fujino et al.,
2009; Fujino et al., 2010; Prendergast et al., 2012; Brill et al., 2012a, b;
Gouramanis et al., 2017; Pham et al., 2018). Thus, clarifying the sediment
transport conditions of the 2004 tsunami will also be important for future
estimations of history, scope and cause of older tsunamis on Phra Thong
Island and elsewhere in the coastal areas of the Indian Ocean.
Setting and method
2.1
Phra Thong Island, Thailand
During the 2004 Indian Ocean tsunami, a wave of approximately 7 m inundated the northern
portion of Phra Thong Island (Fig. 1) and measurements up to 20 m were
recorded from the southernmost tip of the island (Jankaew et al., 2008). Over
70 people were lost and a village of 100 households disappeared.
Geomorphologically, the western coast of the island has a beach ridge sequence trending parallel
to the coast, which formed during the sea level regression following
mid-Holocene sea level highstand about 6000 BP (Brill et al., 2015). The
eastern shore of the island is extensively covered by mangroves along the
shores of tidal channels. The island has a tropical climate. Additionally,
paleotsunami deposits are preserved in swales in the beach ridge system
along the western coast of Thailand (e.g., Jankaew et al., 2008; Gouramanis et
al., 2015, 2017). Furthermore, although local beaches were lost in the 2004
tsunami, satellite photography showed rapid natural recovery within 18
months (e.g., Choowong et al., 2009).
Figure 1
Location of Phra Thong Island.
2.2
Topography and bathymetry data
The topography and bathymetry data used for the tsunami sediment transport
calculations were created based on various water depths and elevations.
Figure 2 shows the terrain data that were created. Topographic data were
downscaled from Region 1, which includes the Andaman Sea, to Region 6, which
includes all of Phra Thong Island. The grid spacing decreases from Region 1
(the spatial grid size
=1215
m) to Region 6 (
=5
m). In the tsunami sediment transport calculations, UTM zone 47N was used to geospatially constrain the horizontal modeling coordinates
of Phra Thong Island. Region 1 is the projection of depth data of the
30 s grid provided by GEBCO (2014) on the Cartesian coordinate system
UTM 47N. Regions 2–4 use a digital marine chart with 300 m resolution based
on a survey by the Royal Thai Navy. Regions 5 and 6 use an original 5 m
(terrain data) and 15 m (sea depth data) grid spacing to create mean terrain
and water depth data based on analysis of satellite images by EOMAP and
elevation data provided by the Land Development Department of Thailand (LDD,
2018). The terrain data of Region 4, created from the digital marine chart
of 300 m resolution, showed discontinuity at the boundary with Region 5,
which had a higher resolution. The discontinuity was therefore removed to
the extent possible by interpolation with an inverse distance weighting
method using all terrain data.
Figure 2
Terrain data. The black frame shows Region 1 to Region 6, and the black line in Region 6 shows the cross section where calculation was
performed. Dashed squares are the beach where erosion was confirmed from
satellite images.
2.3
Tsunami source model
The tsunami source model proposed by Suppasri et al. (2011) was used as the
tsunami source of the 2004 Indian Ocean tsunami as the model focused on the
coast of Thailand and accurately reproduced the inundation area and surveyed
trace height of the 2004 Indian Ocean tsunami. The fault model is divided into six small
faults from satellite image analysis and survey results, and it is assumed
that each small fault slides simultaneously and instantaneously. For the
tsunami source, the vertical tectonic displacement in each fault was
calculated according to Okada (1985). Table 1 shows the fault parameters of
each fault and Fig. 3 shows the initial water level.
Table 1
Earthquake fault parameters for calculating initial water level (Suppasri et al., 2011).
Download Print Version
Download XLSX
2.4
Tsunami sediment transport calculation
2.4.1
Tsunami propagation and run-up model
Tohoku University's Numerical Analysis Model for Investigation of Near-field
tsunamis, No. 2 (TUNAMI-N2) is based
on the nonlinear long wave theory and
was used as the tsunami propagation and run-up model (Imamura, 1996).
(1)
(2)
(3)
Here,
is the change in water level from the still-water surface,
is
the total water depth from the bottom to the water surface and
is the
acceleration of gravity. The bottom friction is expressed according to the
Manning formula, where
is Manning's roughness coefficient (
=0.025
s m
).
and
are the total flow fluxes in the
and
directions,
respectively, and are given by integrating the horizontal flow velocity
from the water bottom
to the water surface
. It is assumed that the
horizontal flow velocity is uniformly distributed in the vertical direction.
Figure 3
Initial water level after earthquake occurrence.
The nonlinear long wave theory consists of a continuous equation that is
derived from (1) the principle of conservation of mass (continuity equation)
and (2) the conservation of momentum (equation of motion). These two
equations are obtained by vertical integration from the seabed to the
water surface.
When the water depth is about 50 m or less, the effects of the second, third
and fifth terms of the advection and seabed friction terms (Eqs. 2 and
3) are reduced; therefore wave theory that omits these terms is often used at
depths shallower than 50 m. Meanwhile, the Message Passing Interface
(MPI)
parallel was implemented in the model for highly efficient calculations.
Both the advection term and the bottom friction term were therefore
considered in the calculations without reducing accuracy in deeper waters.
The reproducibility of the calculated results is based on the tsunami height
data (IUGG; available at
, last access: 19 October 2020) for the 2004 Indian Ocean tsunami and is discussed using the geometric
mean
and geometric standard deviation
proposed by Aida (1978).
(4)
log
log
(5)
log
log
log
Here,
is the number of points,
is the tsunami height at the
th point,
is the calculated value at the
th point and
2.4.2
Sediment transport model
For the tsunami movable bed model, we used the numerical sediment transport
model (STM) proposed by Takahashi et al. (2000), which solves the time
evolution of sediment transport considering the exchange sediment volume of
the bed and suspended load layers according to the flow conditions of the TUNAMI-N2 model based on
nonlinear long wave theory. For each time step in the
finest calculation region (region 6), the STM receives the total flow fluxes
from TUNAMI-N2 and calculates the change of seafloor and land surface and
feeds this to the next time step of the TUNAMI-N2 model.
In this model, tsunami sediment transportation is divided into two layers,
bed load layer and suspended load layer. In the bed layer, the sediment
particles are transported through rolling, sliding or saltating. In the
suspended load layer, the sediment particles are uplifted and transported by
the dynamic of flowing suspension. The governing equations consist of
continuous equations for the bed load layer and the suspended load layer:
(6)
ex
(7)
ex
Here,
is the porosity of the sand particles,
is the bottom
height from the reference plane,
is the amount of bed load sediment,
is the average suspended load layer concentration,
is the suspended
load layer thickness (equal to total water depth) and
are the water
discharges in the
direction and
direction.
is the settling velocity
of the sand particles.
Equation (6) is a continuous equation for within the bed load layer. The
first term is the exchange sediment volume with the bottom, the second term
is the balance of sediment flow volume moving in a tractive form in the flow
direction and the third term defines the balance of suspension flux, caused
by diffusion, and sedimentation flux, caused by gravity, as the exchange
sediment volume between the bed load layer and the suspended load layer.
Equation (7) is a continuous equation for within the suspended load layer.
The first and second terms are bed load sediment moving in a suspended state
in the flow direction, the third term is the exchange sediment volume
between the bed load layer and the suspended load layer, and the fourth term
is the increase or decrease in the sediment flow in the suspended load
layer.
In Eqs. (8) and (9), the equations defining the bed load sediment
volume
and the equation defining the exchange sediment volume
ex
of the bed load layer and suspended load layer are necessary, but
according to Takahashi et al. (2000), they are obtained by the following.
(8)
crit
(9)
ex
crit
(10)
Here,
is the coefficient of the bed load sediment volume equation,
is the coefficient of the suspension volume equation,
is the
density ratio of the sand particles (
and
are the
density of sand particles and water, respectively;
2650 kg m
−3
and
=1000
kg m
−3
is the
acceleration of gravity,
is the grain diameter,
is the settling
velocity of the sediment grains,
is the Shields parameter
(Eq. 10),
crit
is the critical Shields parameter,
is the friction velocity obtained from Manning's law
).
is the roughness coefficient for STM. It is worthwhile to
mention that in the tsunami calculation, roughness coefficient
is the
parameter to calculate the shear stress from ground surface which differs
from
, the parameter for calculating the shear stress which exerts
force on the surface of sand particles.
The grain-size-dependent parameter for bed load (
and exchange
rate (
in Eqs. (8) and (9) is derived from Eqs. (11) and
(12) based on the hydraulic experiments by Takahashi et al. (2011).
(11)
9.8044
3.366
(12)
0.0002
6.5362
However, the functions should not be applied when
is outside the 0.166 mm
to 0.394 mm range as the validity of extrapolated
values may produce
erroneous results.
(13)
36
36
Equation (13) is a settling velocity of the sand particles by Rubey (1933).
Here,
is the kinematic viscosity coefficient (
1.39
10
−1
Considering the effect from the bed slope (Watanabe et al., 1984), the
formulation of the bed load,
, Eq. (6), is rewritten as
as shown in Eqs. (14) and (15):
(14)
(15)
where
is the parameter which is related to the diffusion
coefficient of the sediments (
=2.5
; Sugawara et al.,
2014a).
Sediment transport during tsunami largely occurs as suspension (Takahashi et
al., 2000). In such situations, suspended sediments are maintained in the
water column by turbulence while the energy of the turbulence is dissipated
due to the increased suspended sediment concentration. This induced an
equilibrium state in which no further sediment supply from the bottom
occurs. The resulting concentration is called the saturated sediment
concentration. The expression for saturation concentration of suspended
sediments
is applied as (Van Rijn, 2007; Sugawara et al.,
2014a)
(16)
where
is the efficiency coefficient
=0.025
; Bagnold, 1966). Note that in the sediment
transport calculation, the saturation concentration of suspended sediments
given by Eq. (16) is applied entrainment of sediment from the bottom
layer. Namely, sediment supply from the bottom to the water column
(suspended load layer) by
ex
is not permitted if
. However, supersaturation (
due to sediment advection, or sudden decrease in
due to the change of flow parameters, is permitted. In this
calculation, when
exceeds maximum concentration
max
was
set to 37.7 %, based on the observed value (Xu, 1999a, 1999b).
In Eqs. (6) and (7), the bottom height (
) is determined
from the reference plane, and the average suspended sediment concentration
) is the initial values before the tsunami and the flow flux (
). Because
suspended sediment thickness (
) is given by the equation of motion of
a fluid and the continuous equation, sea level fluctuation can be determined
over time. Further, the MPI parallel was implemented to enable relatively
efficient wide-area calculations (e.g., Yamashita et al., 2016).
2.5
Calculation conditions
The initial conditions for the numerical simulations used the terrain data
(Fig. 2) and tsunami source (Fig. 3). The simulations were performed
using a
3:1
nested grid that increased the resolution from a 1215 m
grid
to a 5 m
grid. Additionally, the target region of the sediment
transport calculation was limited to Region 6, with a grid spacing of 5 m
The simulations were calculated over a 0.05 s increment with a 6 h
period in which the test case with a 12 h period showed the suspended
sediment concentration in the vicinity of the shoreline decreased and
stabilized. Therefore, the 6 h simulation was used for the reproduction of
the 2004 tsunami as well as further sensitivity analysis of the grain size
and roughness coefficient.
Table 2
Set parameters for sediment transport calculations.
Download Print Version
Download XLSX
For the bottom conditions of STM, the roughness coefficient was fixed at
=0.030
s m
, and the entire area of Region 6 was
considered the movable bed. In general, when simulating tsunami sediment
transport, it is necessary to determine the roughness coefficient according
to land use. However, since there is no land use map before the tsunami on
Phra Thong Island, a fixed value was used, similar to previous studies (e.g.,
Sugawara et al., 2014a, b; Yamashita et al., 2015, 2016).
However, Sugawara et al. (2014a) showed that the variation in Manning's
roughness coefficient for the sand beds may affect the general distribution
pattern of sediment deposits and erosions across the artificial topographic
features with much higher roughness coefficient such as artificial canals,
roads and populated residential areas. Therefore, a sensitivity analysis on
the roughness coefficient was performed. Phra Thong Island has no such
artificial topographic features and using the single roughness coefficient
should sufficiently capture the overall roughness. However, to ensure robust
conclusions, a sensitivity analysis for two bottom conditions was performed
at
=0.025
s m
and
=0.035
s m
, which
are within the range of previously used estimates of roughness (e.g.,
Sugawara et al., 2014a, b).
The grain size was based on one sediment dataset (Gouramanis et al., 2017)
from the locally eroded region and was considered a representative value
for all of the tsunami sediment grain sizes. A uniform grain size of
=0.127
mm was used. The critical Shields parameter
crit
in
Eqs. (9) and (10) was obtained using Eqs. (17) and (18) according
to Iwagaki et al. (1956).
(17)
crit
crit
(18)
crit
8.41
11
32
Here,
crit
is the critical friction velocity and
is
the density of water. Table 2 shows each parameter used for the sediment
transport calculations in this study.
The numerical model used in this paper can only consider a single grain
size, so the model cannot resolve the grading observed in the sand layers
(e.g., Gouramanis et al., 2017). Additionally, initial bed grain size can
have a large effect on erosion and deposition (e.g., Apotsos et al., 2011b;
Sugawara et al., 2014a; Jaffe et al., 2016). Furthermore, the sediment data
we used to set the grain size are from a single location in the north of the
island and are assumed to be a representative grain size for the tsunami
deposits. As such we performed a sensitivity analysis on the grain size.
Pham et al. (2018) investigated the surface grain size offshore (water depth
15
m),
nearshore (water depth
15
m) and onshore on Phra Thong Island,
which they considered to be the source of sediments that formed the tsunami
deposits. Pham et al. (2018) recorded a mean grain size of 0.314 mm in the
offshore area, 0.129 mm in the nearshore area and 0.285 mm in the onshore
area. Based on these mean grain sizes, we conducted a sensitivity analysis
for two grain sizes (0.285 and 0.314 mm representing the offshore and
onshore sediments).
Figure 4
Comparison of calculated and measured maximum tsunami height.
Results
3.1
Verification of reproducibility
3.1.1
Tsunami height
Figure 4 shows the results of the calculation of the maximum tsunami heights
and the seven measured tsunami heights on Phra Thong Island. From Eqs. (4) and (5),
=1.16
and
=1.40
are obtained. The JSCE (2002) consider
0.95
1.05
and
1.45
as guides for evaluating reproducibility of tsunami numerical calculations. Although the
value is slightly higher than
the guideline, this is because of an uncertain 19.6 m measured in the
southern part of the Island. Additionally, the source model used in this
calculation gives
=0.84
and
=1.30
for reproducibility of
tsunami height in the wide area along the coast of Thailand (Suppasri et
al., 2011). Therefore, it can be said that this calculation has the same
tsunami reproducibility as the previous study.
Figure 5
Topographic change and shoreline position caused by the tsunami
(solid and dashed lines show the coastline after and before the
tsunami in the simulation; P and Q are the points of confirmed local beach
erosion in regions (a) and (b); blue and red map erosion and
deposition after the tsunami in the simulation).
3.1.2
Shoreline changes
Our sediment transport models identify the locations of significant sediment
erosion, which are confirmed from post-tsunami satellite images. Figure 5
shows the pre-2004 Indian Ocean tsunami topographical and geomorphological features (dashed
line) and the modeled changes caused by the tsunami (solid line). Erosion
typically occurs locally where small tidal channels breach the youngest
beach ridge system (Fig. 5a and b). Comparison with the satellite
image shows that the position of erosion in both regions is consistent
(Fig. 6). Although the actual amount of erosion is unknown, this indicates
that the planar spread of the eroded component can be well reproduced by the
calculation. Region (a) was further investigated in detail, as the area
corresponds to the point where sediment outflow occurred (Jankaew et al.,
2008).
Figure 6
Comparison of observed shoreline position from Fig. 5 regions (a)
and (b) derived from satellite images before and after the tsunami (20 December
2004 and 30 January 2005), which is overlain by the modeled extent of erosion
showing that the modeled results closely match the observed changes. The
red line is the calculated shoreline after the tsunami, and the yellow line
is the shoreline before the tsunami (© CNES, 2019, Distribution
Airbus DS”).
(a1)
Satellite image before the tsunami in region (a).
(a2)
Satellite image
after the tsunami in region (a).
(b1)
Satellite image before the tsunami in
region (b).
(b2)
Satellite image after the tsunami in region (b)
Figure 7
Comparison of field-measured and simulated tsunami deposit
thickness using a representative grain size of
=0.127
mm. The black point
shows the measured thickness by Jankaew et al. (2008) and Gouramanis et al. (2017), and the white point shows the simulated thickness. The blue and red lines show
the cumulative curves of measured data and simulated data.
3.1.3
2004 Indian Ocean tsunami onshore sediment deposition
In addition to the erosional features, the model simulated the deposition of
2004 Indian Ocean tsunami sediments across the island. The thickness of these simulated
deposits is compared with 133 measured 2004 Indian Ocean tsunami deposit thicknesses (Jankaew
et al., 2008; Gouramanis et al., 2017). Figure 7 shows a comparison of layer
thicknesses at each site (black circles for measured results and white
circles for simulated results), which shows that most of the sites are
overestimated within 1 km from the shoreline and underestimated at distances
greater than 2 km from the shoreline. The model specification and
topographical data can be considered the major causes of this error.
First, considering the overestimation within 1 km of the inundation
distance, it is found that the STM has a setting of the maximum suspended
concentration,
max
as 37.7 % (Xu, 1999a and b). The computed
suspended concentration in this area is higher than
max
. Therefore, the
surplus sediment is forced to be deposited in this zone, causing
overestimation. Pham et al. (2018) found that the source of tsunami deposits
in Phra Thong Island is mainly the sediment from the nearshore zone. In
other words, the first wave, which had the highest wave height, eroded a
large amount of sediment in the nearshore and transported a large amount of
sediment inland. Therefore, it is considered that the maximum concentration
was reached during the first wave run-up because of the very high
concentration of suspended sediment, which led to the overestimation of the
forced sedimentation in the simulation.
Second, considering the underestimation of the deposition in inundation
distances of 2 km or more, the most likely reason is the computational grid
and the model specification. Previous studies have shown that tsunami
deposits are highly affected by locality features (e.g., Sugawara et al.,
2014a; Watanabe et al., 2018). As shown in three locations with the actual
measured deposit thickness (dashed boxes) in Fig. 8, it can be seen that
most of the measured thickness is zero, which indicates and supports the reasons of localized deposition from the 2011 tsunami (Goto et al., 2011b; Abe et al., 2012; Chagué-Goff et al., 2012). Although the computational grid is very
fine (
=5
m), it is difficult to reproduce local
sedimentation with averaged elevation data. It is worth noting that STM
adopts only single grain size and can only perform deposits which consist of
sand. Sugawara et al. (2014a) conducted a tsunami sediment transport
simulation on the Sendai Plain and discussed the transportation possibility
of finer-grained sandy and muddy sediment. Muddy sediments were also found
in the Sendai Plain at a distance of 2 km or more
from the 2011 tsunami.
The STM-based tsunami sediment transport simulation of Sugawara et al. (2014a)
could not be reproduced for Phra Thong Island. This can be attributed to the
limitation of sandy sediment and single grain size model. Therefore, it is
possible that muddy or very fine-grained sediment was deposited at the three
sites but was underestimated in the simulations using the current model.
Figure 8
Spatial distribution of measured and simulated thickness of tsunami
deposits. The black dotted lines indicate that the calculated values are
underestimated at distances greater than 2 km.
For all above-mentioned reasons, it is more practical to evaluate the
simulation results by the overall trend of the tsunami deposit rather than
comparing the thickness point by point. In Fig. 7, the line of
“cumulative volume” shows the cumulative deposition expressed at each point
by the sediment thickness multiplied by the area of the computational grid.
In general, the tsunami deposits are greatly affected by local
micro-topography (Sugawara et al., 2014a; Jaffe et al., 2016), and it is
difficult to fit the modeled layer thickness with the observed layer
thickness using DEM averaged in a computational grid. Therefore, we
introduce the concept of cumulative sedimentation and evaluated the scale
of the amount of sediment movement generated. Although the modeled layer
thickness typically overestimates the observed layer thickness by
+7
%, such low variation
suggests a relatively successful reproduction of the observed dataset (Fig. 7). The modeled overestimation
is likely due to the assumption that the entire exposed land area would act
as a movable bed. In reality, this is an oversimplification of the true
ground surface, which contains vegetation that binds and traps the soil and
wet regions (i.e., in swales) that would have higher degrees of sediment
cohesion, reducing the area that would be eroded. In addition, the model
also reproduces the inland thinning of the 2004 Indian Ocean tsunami deposit. Based on these
results, comparison of the sediment layer thickness of the 2004 tsunami
shows that the scale and the overall sediment transport trend are
comparable, and therefore, the results are sufficiently reproducible with
confidence to evaluate the actual sediment transport.
Figure 9
Topographic change and shoreline position caused by the tsunami for
each grain size.
Table 3
Volume of erosion and deposition in regions (a) and (b) for each
grain size (percentage shows the ratio to reference).
Download Print Version
Download XLSX
Figure 10
Comparison of field-measured and simulated tsunami deposit
thickness for each grain size.
Figure 11
Topographic change and shoreline position caused by the tsunami
for each roughness coefficient.
Table 4
Volume of erosion and deposition in regions (a) and (b) for each
roughness coefficient (percentage shows the ratio to reference).
Download Print Version
Download XLSX
Figure 12
Comparison of field-measured and simulated tsunami deposit
thickness for each roughness coefficient.
3.1.4
Sensitivity analysis for grain size and roughness
Figure 9 shows the topographical changes and the thickness of the sediment
layers used in this calculation for each grain size, and Table 3 shows the
volume of erosion and deposition in regions (a) and (b). These figures show
that the smaller the grain size is, the greater the topographic change. This
can be understood as the smaller the grain size, the larger the Shields
parameter in Eq. (10), which indicates the ease of sediment transport, and
the greater the amount of bed load in Eq. (8). However, Fig. 9 suggests
that the qualitative characteristics of sediment transport are the same in
the three cases, due to the fact that the local erosion position of the beach in regions
(a) and (b) did not change for any grain size. And then, comparing the
tsunami sediment thickness in Fig. 10 the errors of the cumulative volume
of
=0.314
mm and
=0.285
mm are
−63
% and
−55
%. Therefore, the
grain size of
=0.127
mm is considered to show the better
reproducibility.
Figure 11 shows the topographical changes and thickness of sediment layer in
this calculation for each bottom roughness coefficient, and Table 4 shows
the volume of erosion and deposition in regions (a) and (b). These figures
show that the larger the value of roughness coefficient
is, the
greater the topographic change. This can be understood as the larger the
roughness, the larger the Shields parameter in Eq. (10) because the friction
velocity is proportionate to
. Therefore, an increase in the
roughness coefficient indicates the ease of sediment transport and the
greater the amount of bed load in Eq. (8). However, Fig. 11 suggests
that the qualitative characteristics of sediment transport are the same in
the three cases, due to the local erosion position of the beach in regions
(a) and (b) not changing for any bottom conditions. And then, comparing
the tsunami sediment thickness in Fig. 12, the errors of the cumulative
volume of
0.025 s m
and
0.035 s m
are
−8
% and 13 %. Therefore, the roughness
coefficient of
0.030 s m
is considered to show
the better reproducibility.
3.2
Sediment transport process
Although the model reproduces the zones of sediment erosion and deposition
well, the sediment transport processes during the tsunami event are further
examined in regions (a) and (b) in Fig. 5. The modeled time series of the
changes of water height and elevation at point P in region (a) and point Q
in region (b) are shown in Fig. 13. The modeling results show that the
first wave arrived 2 h 40 min after the earthquake, and backwash was
generated 10 min later (Fig. 13). In addition, the ground surface
elevation increased by about 30 cm through sediment deposition during the
first inflowing wave, and more than 1.5 m was eroded during the backwash
transporting sediment towards the ocean, so beach loss in both regions is
considered to be a result of erosion during the backwash (red line in Fig. 13).
In addition, no major topographic changes occurred on the beaches in both
areas after the second wave backwashed; most of the sediment movement on the
eroded beaches is considered to have been completed by the second wave
drawback. In other words, the sediment transport processes during this
period are the most important to examine the shoreline changes that occurred
during the tsunami and set up the primary conditions for beach recovery
post-tsunami. As such, there are two narrow time periods that highlight the
key factors for establishing the initial conditions of the recovery
process. First, why was the beach not eroded by the inflowing waves? Second,
how did the sediment flowing seaward in the first wave move?
Based on the waveform (which assumes a flat surface), a shore-normal cross-section calculation was carried out along the transect in Fig. 2. The
transect covers the region (b) from 1000 m offshore across the shoreline
and 1000 m inland. Beyond these distances the planar effect was considered
to be negligible. Figure 14 shows the changes in ground level, water level,
suspended sediment concentration and saturation of suspended sediment
concentration on the transect at each unit of time as waves washed in and
out.
3.2.1
Why was the beach not eroded by the pushing wave?
As shown in Fig. 14, prior to the first wave, the ocean receded to below
approximately 8 m below mean sea level. As inflow of the first wave began,
sediment was eroded from the sea floor at about 5–10 m below mean sea level.
This nearshore erosion increased the suspended sediment concentration as the
first wave propagated onshore. At the shoreline, the suspended sediment
concentration saturated and sedimentation could begin at the shoreline. In
other words, it is estimated that sediment eroded the nearshore (5 m
depth
10
m) environment during the first inflowing wave,
and much of this sediment was transported shoreline and inland.
Figure 13
Chronological change of flow depth and land surface at points P and
Q in regions (a) and (b) (the blue line shows the flow depth and the red line shows
the land surface).
It should be noted that there will be no increase in suspended sediment
when the suspended sediment is saturated in the model and is the likely
reason that the beach was not eroded by the inflowing first wave. Although
there is a possibility that the beach was actually eroded, the numerical
results suggest that the erosion in shallow coastal waters (deeper than 5 m but shallower than 10 m) resulted in a very high concentration of suspended
sediment when the inflowing first wave entered
−5
m to the beach section of
the coast and sediment ceased to be entrained. Pham et al. (2018) found that
the source of the 2004 Indian Ocean tsunami deposits on Phra Thong Island was from the
nearshore (depth
15
m). This means that large-scale erosion in
shallow water has occurred and a large amount of sediment has
been
transported inland, which agrees with the simulation results. Therefore, it
is highly likely that the sediment concentration was very high when it
reached the beach during the first inflowing wave. Takahashi (2012)
showed that when the suspended sediment is in a high concentration state,
turbulence is suppressed and the ability to retain suspended sediment may
decrease. Therefore, it is highly probable that the same phenomenon occurred
on Phra Thong Island and the beach erosion during the inflowing wave was
suppressed.
3.2.2
How did the sediment flowing seaward in the first wave move?
In Fig. 14, at the initiation of backwash, the suspended sediment
concentration is low. As backwash flows towards the ocean, the velocity
increases, which increases erosion and causes the suspended sediment
concentration to increase. This finding is consistent with the changes
recorded in Fig. 13. Beach erosion due to backwash has also been confirmed for the 2004 Indian Ocean tsunami in Sri Lanka and the 2011 tsunami along the Sendai Plain
and at Rikuzentakata (e.g., Tanaka et al., 2007, 2011; Yamashita et al., 2015, 2016). On the Sendai Plain, the estuary section of
the old river tends to increase the return flow due to the tsunami (Tanaka
et al., 2007, 2011). Therefore, there is a possibility that regions (a) and (b) (Figs. 2, 5 and 6) where local beach erosion of the
backwash occurred on Phra Thong Island are the old river part.
Conversely, the entire beach was eroded by the return flow in Rikuzentakata
(Yamashita et al., 2015, 2016), but no erosion was observed along the entire
beach on Phra Thong Island and the Sendai Plain. Yamashita et al. (2015,
2016) suggested that the difference between Rikuzentakata and the Sendai
Plain may be related to the horizontal distance of the
plains. On the Sendai
Plain, the inland topographic gradient is small, the inundation distance is
long and the inland inundation depth tends to be small. Therefore, the
potential energy that the inundation depth changes to kinetic energy during
the backwash (return flow) becomes relatively small. The Sendai Plain and
Phra Thong Island are flooded plains over 2 km inland and have similar
topographical features.
Figure 14
Change in water level (WL), land surface (EL), topographic change
(TC), suspended sediment concentration (CS) and saturation suspended
sediment concentration (CS_Sat) by section calculation along
the survey line in region (b). (I) before the first inflowing wave,
(II) Advance of first leading wave in shallow water. (III) Start of first
leading wave run-up. (IV) Maximum of first leading wave. (V) Advance of
second backwash. (VI) Maximum of second backwash.
For the above reasons, the local beach erosion due to the return flow on
Phra Thong Island occurred at the mouths of tidal channels and within tidal
channels, and minimal erosion occurred across the wider beach ridge
strand plain. As backwash of the first wave ended, the water still contained
a high suspended sediment concentration and this was deposited in the
nearshore environment at less than 5 m water depth (Fig. 15). After that,
no significant topographic change was found. Thus, modeling shows that
most of the sediment that eroded from the onshore area was deposited in the
shallow nearshore zone.
Discussion
4.1
Sediment transport process and beach erosion
Regions (a) and (b) were selected for detailed investigation of the
simulation results and discussed.
On Phra Thong Island, the 2004 Indian Ocean tsunami wave was large enough to expose the
nearshore sediments and entrained most of its sediments from the shallow
offshore region (below 5m). The wave ran up the exposed nearshore area while
retaining sediment from the shallow offshore region. The sediment
concentration gradually increases as the wave runs up the relatively long
distance of the exposed nearshore zone and becomes sediment-saturated as the
wave reaches the shoreline, making it difficult for new sediment to be
eroded further. This explains why there was little erosion of the beach
during the inflowing wave and may be a characteristic sediment transport
property of shallow beaches like those on Phra Thong Island. The numerical
simulation results suggest that there is little transportation of sediments
from the beach by the first inflowing wave and that inland tsunami deposits
originated from the nearshore environment. This finding validates the observation of Sawai et
al. (2009) that the 2004 Indian Ocean tsunami entrained diatoms from shallow
offshore waters at Phra Thong Island, and the observation of Pham et al. (2018)
that sediment grain sizes and mineralogy were most similar to those of
nearshore sediments. Figure 15 shows the results of the calculated sediment
deposition both onshore and offshore of Phra Thong Island. From the modeling
results, most of the eroded sediment was deposited in shallow nearshore
environments in water less than approximately 5 m deep.
The simulations show that the eroded sediments were deposited in the
nearshore zone during backwash (Fig. 15), which primed the coastal zone for
rapid coastal recovery. The removal of sediment from the onshore coastal
zone also generated accommodation space that may have contributed to the
coastal recovery process. Future studies can build on these findings to
determine the extent of sediment transport and deposition and identify the
processes of coastal recovery on Phra Thong Island.
Figure 15
Sediment distribution derived from the simulation (showing depth
contours at 5 m intervals in the sea area).
Geomorphologically, the Sendai Plain, which was inundated by the 11 March 2011 Tōhoku tsunami, is similar to the beach ridge plain on Phra
Thong Island (Tanaka et al., 2011), but most of the tsunami sediment deposited onshore came from terrestrial sources (Goto et
al., 2012; Szczuciński et al., 2012; Takashimizu et al., 2012; Sugawara
et al., 2014b). However, the Tōhoku tsunami differed from the 2004
Indian Ocean tsunami as the Japanese event had a much smaller receding wave (Nationwide Ocean
Wave information network for Ports and HArbourS, NOWPHAS; available at
, last access: 19 October 2020). As such the Japanese tsunami may not
have achieved sediment saturation as the wave approached the shoreline,
thereby containing a lower sediment concentration and allowing large volumes
of sediment to be entrained from the beach for subsequent formation of
inland deposits. The different sources of deposited sediment in the two
areas reflect contrasting sediment transport mechanisms on shallow beaches
and may be useful for identifying paleotsunami from coastal recovery and
geological records.
4.2
Limits of calculation results
This study analyzed tsunami sediment transport on Phra Thong Island using
numerical calculations and assumed that the island was unvegetated and
lacked topography. However, the western half of the island has an undulating
surface caused by the beach ridge and swale system and is extensively
vegetated with trees and dense grasses on the ridges and thick grasses
within the swales. The eastern half of the island has wide tidal channels
and an extensive fringing mangrove system. Both topography and differing
vegetation types add complexity to the inundation and backflow sediment
transport models not captured here. In the future, it is necessary to consider
the influence of vegetation and topography on tsunami sediment transport.
Another potential limitation of the model is the selection of a single
(median) grain size for the sediments. As shown in previous studies (e.g.,
Sugawara et al., 2014a, b), the assumption of transport of single-grain-sized sediment differs from actual situations because of the distribution of
grain sizes mobilized and deposited by tsunami. Therefore, it is important
to set representative grain sizes and fully study how grain size affects
tsunami sediment transport. Future modeling may consider simulating the
suite of grain sizes individually or simulating a population of grain sizes
that are identified in the modern environment and in preserved tsunami
deposits.
Furthermore, although the calculation was performed considering the entire
area a movable bed, the existence of fixed beds, such as rocky areas, should
be considered. We consider this a minor component of this research as the
rocky headlands that serve as fixed beds are relatively small in area and
would contribute little to the overall simulations in our models.
Sugawara et al. (2014b) consider the simulation result of sediment layer
thickness using the tsunami sediment transport calculation to be affected by
grain size, bottom conditions and topographic data. Their study showed that
the layer thickness increases as grain size becomes finer, and the layer
thickness distribution tendency was unchanged regardless of grain size.
Similar results were obtained in this study.
Conclusions
Because of insufficient knowledge about the topographic recovery process
after a tsunami, this study used sediment transport modeling to identify
the erosional and depositional processes affecting the coastal zone at Phra
Thong Island, Thailand, during the 2004 Indian Ocean tsunami.
First, it was confirmed by comparing simulated results of the shoreline and
sediment layer thickness that the location of beach runoff identified on
Phra Thong Island was reproducible and consistent with sediment transport
results (Figs. 6 and 7). Based on the sediment transport results, we conclude
that the processes of sediment erosion and deposition on Phra Thong Island
are characterized by the following sequence:
erosion caused by the inflowing waves occurred at a relatively shallow
location in the offshore area, and the transported sediment was deposited
near the shoreline;
the inflowing waves caused minimal erosion of the shoreline; and
erosion of the shoreline was largely caused by backwash resulting in onshore
sediments deposited in the shallow nearshore zone.
These erosional and depositional processes demonstrate the locations of
sediment removal and subsequent deposition during the different phases of
the first tsunami wave on Phra Thong Island which will serve as an important
baseline of sediment sources for further study of the recovery process. The
simulations also show that the zones of erosion and deposition across the
island and offshore coastal zone are non-uniform. In particular, the zones
of erosion and deposition highlighted in the simulations establish the
environmental conditions that existed in the transitional phase between
catastrophic tsunami and normal coastal processes that facilitated coastal
recovery.
Acknowledgements
We would like to express our gratitude for the support from Dr. Panon
Latcharote of the Faculty of Engineering, Mahidol University; Supot
Teachavorasinskun, Dean of Faculty of Engineering, Chulalongkorn University; Pitcha Jongvivatsakul, Department of Civil Engineering, Chulalongkorn
University; Sorot Sawatdiraksa, Thai Meteorological Department; Ratchaneekorn Thongthip, International Tsunami Museum (Thailand); and the Royal Thai Navy for data. This work is a contribution to IGCP project 639, “Sea-level
Change from Minutes to Millennia”.
Financial support
This research has been supported by the JSPS Grant-in-Aid for Scientific Research (A) (grant no. 17H01631), the JSPS Bilateral program for joint research with the National Research Council of Thailand (NRCT), the NUS start-up grant (grant no. R-109-000-223-133), and the Ratchadapisek Sompoch Endowment Fund (2019), Chulalongkorn University (grant no. 762003-CC).
Review statement
This paper was edited by Maria Ana Baptista and reviewed by Pedro Costa and four anonymous referees.
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Articles
Abstract
Introduction
Setting and method
Results
Discussion
Conclusions
Acknowledgements
Financial support
Review statement
References
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Short summary
This study examines the sediment transport during the 2004 Indian Ocean tsunami event on Phra Thong Island, Thailand. We use numerical simulations and sediment transportation models, and our modelling approach confirms that the beaches were significantly eroded predominantly during the first backwash phase. Although 2004 tsunami deposits are found on the island, we demonstrate that most of the sediment was deposited in the shallow coastal area, facilitating quick recovery of the beach.
This study examines the sediment transport during the 2004 Indian Ocean tsunami event on Phra...
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Sections
Abstract
Introduction
Setting and method
Results
Discussion
Conclusions
Acknowledgements
Financial support
Review statement
References