Comparisons of Numerical Models on Formation of Sediment

Comparisons of Numerical Models on Formationof Sediment Deposition Induced by Tsunami Run-Up

Paper:

Comparisons of Numerical Models on Formation of SedimentDeposition Induced by Tsunami Run-Up

Ako Yamamoto∗1,†, Yuki Kajikawa∗2, Kei Yamashita∗3, Ryota Masaya∗4,

Ryo Watanabe∗4, and Kenji Harada∗5

∗1Forestry and Forest Products Research Institute

1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan†Corresponding author, E-mail: [email protected]

∗2Social Systems and Civil Engineering, Tottori University, Tottori, Japan∗3International Research Institute of Disaster Science (IRIDeS), Tohoku University, Miyagi, Japan

∗4Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Miyagi, Japan∗5Center for Integrated Research and Education of Natural Hazards, Shizuoka University, Shizuoka, Japan

[Received May 25, 2021; accepted August 31, 2021]

Tsunami sediments provide direct evidence of tsunami

arrival histories for tsunami risk assessments. There-

fore, it is important to understand the formation pro-

cess of tsunami sediment for tsunami risk assessment.

Numerical simulations can be used to better under-

stand the formation process. However, as the forma-

tion of tsunami sediments is affected by various con-

ditions, such as the tsunami hydraulic conditions, to-

pographic conditions, and sediment conditions, many

problems remain in such simulations when attempting

to accurately reproduce the tsunami sediment forma-

tion process. To solve these problems, various numeri-

cal models and methods have been proposed, but there

have been few comparative studies among such mod-

els. In this study, inter-model comparisons of tsunami

sediment transport models were performed to improve

the reproducibility of tsunami sediment features in

models. To verify the reproducibility of the simula-

tions, the simulation results were compared with the

results of sediment transport hydraulic experiments

using a tsunami run-up to land. Two types of ex-

periments were conducted: a sloping plane with and

without coverage by silica sand (fixed and movable

beds, respectively). The simulation results confirm

that there are conditions and parameters affecting not

only the amount of sediment transport, but also the

distribution. In particular, the treatment of the sed-

iment coverage ratio in a calculation grid, roughness

coefficient, and bedload transport rate formula on the

fixed bed within the sediment transport model are con-

sidered important.

Keywords: tsunami sediment transport, hydraulic exper-

iment, numerical simulations, model comparisons

1. Introduction

Tsunamis running up the coast or land carry a large

amount of sediment and seawater. This event was ob-

served as a “black tsunami” off the coastal areas of the

Sanriku region after the 2011 Tohoku Earthquake. In gen-

eral, the sediment contained in tsunami waves remains

after the seawater recedes and is widely distributed in

flooded areas [1]. The materials carried and deposited by

the tsunami are called tsunami sediments. They can be

preserved for a long period if the environment after their

deposition is not disturbed; thus they provide direct evi-

dence of the history of tsunami strikes in coastal areas [2–

4].

Several tsunami researchers have developed numerical

models. However, the formation of tsunami sediment fea-

tures is a complex physical phenomenon and is affected

by various factors, such as the characteristics of the in-

coming tsunami, topographic conditions, and condition(s)

of the sand(s) (grain size and specific gravity) comprising

the sediments. Owing to this, many challenges remain

in reproducing this phenomenon using numerical simula-

tions [5, 6].

Therefore, the purpose of the study was to clarify the

specific factors affecting the reproducibility of the spa-

tial distribution of sediment deposition. The investiga-

tion was based on various numerical simulations target-

ing hydraulic experiments on the formation mechanisms

of sediment deposits on land, as induced by tsunami

run-ups. This paper summarizes the contents of the

“Tsunami Hackathon” (https://tsnmhack.github.io/), held

by the Japan Society of Civil Engineers (JSCE) for three

days from September 1, 2020.

2. Method

Several teams participated in the project of “Tsunami

Hackathon,” and each team verified the reproducibility of

Journal of Disaster Research Vol.16 No.7, 2021 1015

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Yamamoto, A. et al.

Fig. 1. Experimental facility.

Fig. 2. Measurement points.

numerical simulations using their own models. This was

done based on the distribution of sand deposited in a hy-

draulic experiment that investigated the formation mech-

anism of sedimentary sand formed on land by tsunamis.

As preliminary public data, the teams were provided with

drawings of the channel, time series data of the water level

and flow velocity, and silica sand particle size distribution

data. No specification was made on how to input the ex-

ternal force conditions or how to handle the grain sizes of

the sand (uniform sand or mixed sand). Each team sub-

mitted the spatial distribution of the sand deposits, as ob-

tained from numerical calculations for comparison.

2.1. Experimental Setup

A schematic of the channel is presented in Fig. 1. The

experimental facility was a two-dimensional (2D) tsunami

wave-generating channel at Shizuoka University, Japan.

The channel consisted of a water storage tank and a wa-

terway section from the upstream side. A tsunami-like

bore was generated by rapidly opening the gate of the wa-

ter storage tank. The water storage tank was 4.5 m long,

1.4 m wide, and 0.5 m deep, and was connected to a hor-

izontal bed section of the channel across the gate. The

horizontal bed was 7.0 m long, 0.5 m wide, and 0.5 m

deep. The initial water depths in the tank and the water-

way were 0.3 m and 0.1 m, respectively. To stabilize the

flow from the water storage tank through the gate, fan-

shaped guides with a radius of 0.45 m were installed at

the two corners of the gate side in the water storage tank.

The waterway consisted of a horizontal, slope, and hor-

izontal sections from the upstream side, thereby assum-

ing the topographical conditions of a tsunami run-up to

land. The downstream end of the onshore horizontal sec-

tion was set upright with an impermeable wall surface. In

the fixed-bed experiment, the water level was measured

using an ultrasonic wave height meter, and the flow veloc-

ity was measured using an electromagnetic velocity me-

ter and propeller velocity meter. The locations where the

measurement devices were installed are shown in Fig. 2.

The velocity at Gate +9 m could not be measured in the

measurement range of the propeller velocity meter used in

the experiment; therefore, no data were available. In the

movable bed experiment, the water level and flow velocity

were measured immediately downstream of the gate. Sil-

ica sand with an adjusted grain size was used for the mov-

ing bed slope. The grain size distribution of the mixed

silica sand is shown in Fig. 3. Moreover, to capture the

deposited sand, a “Sand catcher,” as shown in Fig. 4, was

used to measure the amount of sand deposited on land.

The distribution of tsunami sediments was measured after

the tsunami had calmed down. The amount of sand was

1016 Journal of Disaster Research Vol.16 No.7, 2021


0.0

10.0

20.0

30.0

40.0

-1.0 0.0 1.0 2.0 3.0 4.0

Mass

(wt%

)

grain size,

(a) Each grain size distribution

0.010.020.030.040.050.060.070.080.090.0

100.0

0.01 0.1 1

Cu

mu

lati

veM

ass

(wt%

)

grain size(mm)

(b) Cumulative mass distribution

Fig. 3. Grain size distribution of silica sand.

measured as the amount of sand deposited per 0.2×0.2 m,

with a measurement section every 0.2 m. These data were

used as comparison data for numerical calculations.

2.2. Numerical Simulation

This study was conducted at the “Tsunami Hackathon,”

held online from September 1 to 3, 2021. The contes-

tants were divided into three calculation teams, and nu-

merical simulations were conducted for the experimen-

tal results (water level, flow velocity, and sediment dis-

tribution) described in Section 2.1. To evaluate the per-

formance of each model and make them more accurate

when reproducing the experiments, each team used dif-

ferent governing equations, numerical schemes, sediment

transport models, and calculation conditions for its sim-

ulations. The computational conditions for each team

over the three days are listed in Table 1. The non-

hydrostatic equations (fully three-dimensional (3D) and

2D non-hydrostatic) and nonlinear shallow water equation

were used as the governing equations for the tsunami flow.

Two teams mainly used the nonlinear shallow water equa-

tion, but used different numerical models, computational

conditions, and sediment transport models; they were des-

ignated as Team A and Team B (initially, Team A used the

fully 3D non-hydrostatic equation). The sediment trans-

port model (STM) proposed by Takahashi et al. [7] was

used by Team B. The third team, Team C, used the same

STM as Team B for the sediment transport model, but

used different governing equations (2D non-hydrostatic

equations) and other conditions. The details of each team

are presented below.

Fig. 4. Sand capture device (Sand catcher).

2.2.1. Team A

First, the 3D Reynolds-averaged Navier–Stokes equa-

tion model (3D RANS model), using the Cartesian coor-

dinate system, was adopted for the governing equations

of tsunami flow. Furthermore, the fractional area/volume

obstacle representation (FAVOR) method [8], which can

impose boundary conditions smoothly at complex bound-

aries, was introduced into the governing equations. The

standard k-ε turbulence model was adopted to evaluate

the eddy viscosity coefficient (the FAVOR method was

introduced therein). The effects of including suspended

sediments in a flow were not considered in the turbulence

model. At the wall boundaries, the frictional resistance

was given according to the logarithmic law, and the tur-

bulent quantities were given by the wall functions. The

bedload and suspended load were considered in the sed-

iment transport calculations. The bedload transport rate

per unit width qB was calculated using the equation pro-

posed by Ashida and Michiue, as follows [9]:

qB = 17τ∗32

(

1−Kcτ∗,c

τ∗

)(

1−√

Kcτ∗,cτ∗

)

√

sgd3, (1)

where s is the specific gravity of sand in water (= σ/ρ −1), σ is the density of the sediment, ρ is the fluid density,

g is the gravitational acceleration, d is the sediment diam-

eter, τ∗ and τ∗,c are the non-dimensional tractive and criti-

cal tractive forces, respectively, and Kc is the slope factor.

Yoshii et al. [10] reported that the prediction using Ashida

and Michiue’s formula was in good agreement with the

measured bedload data of a sediment transport experi-

ment based on a tsunami. The suspended load transport

rate considering the deposit rate per unit area qsu was es-

timated using the formula proposed by Itakura and Kishi,


Yamamoto, A. et al.

Table 1. Summary of model setups (RANS: Reynolds-averaged Navier–Stokes; WENO: weighted essentially non-oscillatory;

C-HSMAC: highly simplified marker-and-cell method on collocated grid system).

Team A B C

Case A1-1 A1-2 A2-1 A2-2 A3-1 A3-2 B1 B2 B3 C1 C2 C3-1 – C3-3

Governingequation

Non-hydrostaticEquation (3D

RANS)

Non-linear Shallow Water Equation(2D)

Non-linear Shallow WaterEquation (2D)

Non-linear Shallow WaterEquation (2D, NEOWAVE

without non-hydrostatic term)(Yamazaki et al., 2009 and 2011)

Numerical scheme Collocated grid Regular grid Staggered grid Staggered grid

WENO scheme,hybrid scheme,

C-HSMACmethod

WENO scheme, TVD Runge–Kuttamethod

Leap-frog methodMomentum Conserved

Advection scheme (Stelling andDuinmeijer, 2003)

Time step 0.001 s 0.001 s 0.005 s

Grid size 0.02 m 0.01 m 0.05 m 0.05 m

Sand grain size 0.25 mm mixed 0.25 mm 0.25 mm mixed 0.25 mm 0.25 mm

Manning’sroughnesscoefficient

0.0125 0.01370.0116 (B2: Set at each

grain size)Table 3

Transport formulafor bed- and

suspended loads

Ashida andMichiue (1972),Itakura and Kishi

(1980)

Ashida and Michiue (1972),Itakura and Kishi (1980)(A2-2: Ikeno et al., 2009)

STM (Takahashi et al.,2000)

B2: Takahashi et al. (2011)

STM (Takahashi et al., 2000)(C2-C3: Modified in Tab. 3)

Settling velocity Rubey (1933) Rubey (1933)Rubey (1933) with hinderingeffect (Richardson and Zaki,

1954)

Saturatedsuspended loadconcentration

No 0.01 Sugawara et al. (2014)

Critical frictionvelocity

Iwagaki (1956)modified

Egiazaroff (1972)

Iwagaki (1956)(A3-1: Iwagaki, 1956 and Tsuchiya,

1956)Iwagaki (1956) Iwagaki (1956)

Porous ratio 0.4 0.4 0.4

Incident wave Dam break Measured water level Dam break

as follows [11]:

qsu = K

(

α∗σ −ρ

σ

gd

u∗Ω−w0

)

−w0cb, . . . (2)

Ω =τ∗B∗

∫ ∞

α ′ξ

1√

πexp

(

−ξ 2)

dξ

∫ ∞

α ′

1√

πexp

(

−ξ 2)

dξ+2

τ∗B∗

−1. . (3)

Here, w0 is the settling velocity of the sediment as given

by Rubey’s formula [12], u∗ is the friction velocity, cb

is the suspended sediment concentration near the bed,

α ′ = B∗/τ∗− 2, α∗ = 0.14, K = 0.008, and B∗ = 0.143.

Itakura and Kishi’s formula has been widely used in river

research. Rubey’s formula was used to calculate the set-

tling velocity and is expressed as follows:

w0√sgd

=

√

2

3+

36ν2

sgd3−

√

36ν2

sgd3, . . . . . (4)

where ν is the kinematic viscosity of water. Moreover,

the FAVOR method was also introduced into the sedi-

ment continuity equations, and the saturated suspended

load concentration was not considered in the models of

Team A.

A collocated grid system was adopted for the computa-

tional grids of the tsunami flow. The fifth-order weighted

essentially non-oscillatory (WENO) scheme [13] was ap-

plied to discretize the advective terms of the momen-

tum equations, and the hybrid scheme [14] was applied

to discretize the k-ε equations and suspended sediment

transport equation. The highly simplified marker-and-cell

method on the collocated grid system [15] was applied

to calculate the non-hydrostatic pressure field. Details of

this model are provided in another study [16]. Moreover,

in the calculation of the sediment transport on the fixed

bed, the sediment was assumed to be deposited on the en-

tire surface of the calculation grid if even a small amount

of sand existed in the grid. In addition, the sediment trans-

port rate was estimated to be no larger than the amount of

sediment in the grid. The calculations were performed

under uniform and mixed sand conditions, with the uni-

form sand having a grain size of 0.25 mm, and the mixed

sand having a distribution of five grain sizes (0.596, 0.421,

0.298, 0.212, and 0.150 mm), as shown in Fig. 3. The re-

sults for the uniform grain size and mixed sand are shown

as A1-1 and A1-2, respectively.

Next, the governing equations of the tsunami flow were

changed to a 2D nonlinear shallow water model (2D

model); the FAVOR method was introduced therein. Re-

garding sediment transport, the topography change model

considering the bedload and suspended load was used, as

in the first day. Moreover, in the calculation of the sus-

pended load transport rate, both Itakura and Kishi’s for-

mula and the formula of Ikeno et al. [17] were used. The

suspended load transport rate considering the deposit rate

per unit area qsu proposed by Ikeno et al. [17] is as fol-



lows:

qsu = Λ

(

ν2

sgd3

)0.2{

(

w0√sgd

)0.8

(τ∗− τ∗,c)

}2√

sgd

−w0cb. . . . . . . . . . . . . . . . (5)

Here, Λ is a coefficient. Ikeno et al. derived Eq. (5) from

a dimensional analysis and suggested that the coefficient

Λ = 0.15, in correspondence with their tsunami experi-

ment. The calculations were conducted under uniform

sand conditions. In the calculations using the 2D model,

an exponential distribution was assumed for the vertical

distribution of the suspended load concentration to eval-

uate the suspended sediment concentration near the bed

cb. A regular grid system was adopted as the computa-

tional grid, the fifth-order WENO scheme was applied to

discretize the advection terms of the governing equations,

and the third-order total variation diminishing Runge–

Kutta method [18] was applied to the time integration.

The details of the model can be found in the correspond-

ing reference [19]. The results obtained by using Itakura

and Kishi’s formula and the formula of Ikeno et al. are

shown as A2-1 and A2-2, respectively.

Finally, to solve the problem of estimating the sedi-

ment transport rate on a fixed bed, the calculation of the

non-dimensional critical tractive force and the formula for

the bedload transport rate on a fixed bed were modified.

Iwagaki’s formula [20], which is applicable to a mov-

able bed, was applied to a fixed bed area to estimate the

non-dimensional critical tractive force. Herein, Team A

attempted the calculation using Tsuchiya’s formula [21],

which can be applied to the smooth surface of a fixed bed.

In contrast, Ashida and Michiue’s formula has been ap-

plied to calculate the bedload transport rate, assuming that

there is a sufficient amount of sand in the calculation grid,

not only on the movable beds but also on the fixed beds.

Ashida and Michiue’s formula [9], which is applicable to

a fixed bed, is introduced as follows:

qB =β ′

µ ′f

f

(

u∗d

ν

)

τ∗

(

1−Kcτ∗,c

τ∗

)(

1−√

Kcτ∗,cτ∗

)

u∗d, (6)

⎧

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎪

⎩

u

u∗=

u∗z

ν:

u∗d

ν≤ 6.83,

u

u∗=

1

0.4ξ

(

1

2−

√

ξ 2 +1

4

)

+2.5 ln(

2ξ +√

4ξ 2 +1)

+6.83 :u∗d

ν> 6.83,

. . . . . . (7)

ξ = 0.4(u∗z

ν−6.83

)

, . . . . . . . . . . . (8)

where u∗ is the friction velocity, β ′ is the constant of pro-

portionality, µ ′f is the dynamic friction coefficient of sand

on a fixed bed with a smooth surface, β ′/µ ′f is the exper-

imental constant (≈ 1.0), u is the velocity at height z, and

f (u∗d/ν) is the value of u/u∗ at the height z = d.

The tsunami flow was calculated using the 2D model,

and the suspended load transport rate was calculated us-

ing Itakura and Kishi’s formula for uniform sand. Eqs. (1)

and (2) were applied to the movable and fixed bed ar-

eas, respectively. The results from using both Iwagaki’s

and Tsuchiya’s formulas for the estimation of the non-

dimensional critical tractive force are denoted as A3-1,

and the results from only using Iwagaki’s formula are de-

noted as A3-2.

2.2.2. Team B

First, the 2D model was used to calculate the tsunami

propagation, and the leapfrog method on a staggered grid

system was adopted for the discretization of the govern-

ing equations. The STM proposed by Takahashi et al. [7]

was used to calculate sediment transport. The bedload and

suspended load transport rate formulas derived by Taka-

hashi et al. [7] are expressed as follows:

qB = a (τ∗− τ∗,c)32

√

sgd3, if τ∗ > τ∗,c, . . . . (9)

qsu =

{

b (τ∗− τ∗,c)2√

sgd−w0C, if τ∗ > τ∗,c,

−w0C, if τ∗ ≤ τ∗,c.(10)

Here, a and b are the coefficients of the bedload and

suspended load transport rates, respectively. The non-

dimensional critical tractive force τ∗,c was estimated using

Iwagaki’s formula [20]. C is the depth-averaged concen-

tration of the suspended load. The calculation was con-

ducted under a single grain size condition from an estima-

tion of the average grain size, as shown in Fig. 3; the coef-

ficients were set to a = 21 and b = 1.2×10−2 [7]. More-

over, the saturated suspended load concentration was set

to 0.01 as a constant value. The water level measured im-

mediately downstream of the gate was used as the input

for the incident wave. The details are presented in Ta-

ble 2. This result was designated as B1.

Next, the condition of the sand grain size was changed

from a single grain size to a mixed grain size. Three grain

sizes were set, as shown in Fig. 3, and the grain sizes used

for the mixed sand were 0.250, 0.125, and 0.063 mm. The

coefficients a and b on the bedload and suspended load

transport rate formulas were interpolated from the results

of Takahashi et al. [22]. Moreover, the settling veloc-

ity, Manning’s roughness coefficient, and friction velocity

were set for each particle size. The settling velocity was

calculated using Rubey’s formula [12], and Manning’s

roughness coefficient was estimated from the Manning–

Strickler formula as follows [23]:

n =Hd

16

ψ√

g, . . . . . . . . . . . . . . (11)

ψ = 6.0+5.75 logHd

2.5dR

, . . . . . . . . (12)

where n is the Manning’s roughness coefficient, Hd is the

design water depth, and dR is the representative grain size

of the bottom material. The critical friction velocity u∗c


Yamamoto, A. et al.

Table 2. Calculation condition (Team B).

B1 B2 B3

Number of grids 52 × 2200 30 × 553

Grid interval 0.01 m 0.05 m

Time interval 0.001 s

Calculation time 60 s

Input dataMeasured water level (Fromthe gate +1 m)

Dam break

Grain size 0.250 mm0.250 mm0.125 mm0.063 mm

0.250 mm

Settling velocity 0.0332 m/s0.0332 m/s0.0117 m/s0.0035 m/s

0.0332 m/s

Manning’sroughness coefficient

0.01160.01160.01070.0098

0.0116

Critical frictionvelocity

0.0142 m/s0.0142 m/s0.0101 m/s0.0071 m/s

0.0142 m/s

was determined as follows:

u∗c =√

τ∗,csgd. . . . . . . . . . . . . (13)

As in the first experiment, the measured water level just

downstream of the gate was used as the input wave. This

result is presented in Section B2.

Finally, the condition of the grain size used in the sed-

iment transport model was changed back to the single

grain size of the first run, and the input wave was changed

to a dam break. In addition, the grid size was changed

because the computation became unstable owing to the

generation of waves with sudden changes. The details are

presented in Table 2 and the result is designated as B3.

2.2.3. Team C

First, to simulate tsunami and sediment transport, a

coupled model [24] was used. This model comprised

the NEOWAVE model [25, 26] based on the vertical in-

tegral non-hydrostatic equation [27] and the STM [7].

In general, the momentum conserved advection (MCA)

scheme [28] implemented in NEOWAVE approximates

wave-breaking and hydraulic jumps as bores and consid-

ers energy dissipation without using empirical indices to

conserve the flow. The volume and momentum are con-

served. The governing equations for fluid and sediment

transport, as defined on a spherical coordinate system, are

discretized on a staggered grid; however, in this study,

they were transformed into a Cartesian coordinate system

for the laboratory experiments. The hydrostatic flow was

calculated without considering the non-hydrostatic pres-

sure term. A dam break was used as the wave-making

method. The obtained results are denoted as case C.

In the model, the settling velocity, w0, in Eq. (10) was

replaced with w0(1−C/Cr)5 to consider the hindering ef-

fects [29], where Cr is the reference concentration. Man-

ning’s roughness coefficient ns for evaluating τ∗ could be

set separately from that for the tsunami flow, that is, n.

The roughness coefficients, n and ns, were set from the

Manning–Strickler equation based on the sand grain size.

A single grain size d50 was used. The equivalent rough-

ness ks was set to ks = 2.5d50 (cases C1, C2, and C3-1) or

ks = 5.0d50 (cases C3-2–C3-3). The critical friction ve-

locity was estimated using Iwagaki’s formula. Moreover,

the prediction formula proposed by Sugawara et al. [30]

was used to evaluate the saturated suspended load concen-

tration Cs in the sediment transport model, as follows:

Cs =1

s

esn2sU3

D43 w0 − esn2

sU3. . . . . . . . . . (14)

Here, D is the total water depth, U is the depth-averaged

horizontal flow velocity, and es = 0.025 is the efficiency

coefficient. The maximum value of the suspended load

concentration Cmax = 0.377 is based on observations by

Xu [31, 32]. The Cr mentioned above was assumed to be

Cmax.

As described below, the water level fluctuation and to-

tal amount of sand deposits simulated on the first day were

generally reasonable. Therefore, we considered that there

was an issue in modeling the sediment transport in the

fixed-bed section and examined the direction of improve-

ment by changing the parameter settings and algorithms

in the fixed-bed section. First, to improve the topographic

continuity at the connection between the movable bed and

fixed floor, we adjusted the connection shape to a slope to

make it closer to the actual experimental conditions. Next,

based on the Rouse number of the fixed-bed section, it

was determined that bedload transport was the predomi-

nant sediment transport mode, so the amount of sediment

entrainment from the fixed-bed section was changed to

zero to match the sediment transport mode in the experi-

ment. In addition, the effect of reducing the amount of bed

load sand was examined by focusing on the sediment cov-

erage of the fixed bed, as shown in the video. The cover-

age on the fixed bed A was assumed to be A = 0.2 and was

multiplied by the bedload rate formula in Eq. (9) as the co-

efficient. However, to avoid calculation instability caused

by discontinuous changes in the amount of bedload trans-

port between the movable bed and fixed bed sections, the

two sections were smoothly connected so that the cover-

age ratio A was set from 1.0 to 0.2. The simulated results

are denoted as C2.

Finally, to verify the effectiveness of the MCA scheme

in reproducing the tsunami run-up, the condition without

the MCA scheme was also examined (case C3-1). The

second validation of the sediment transport model (C2)

showed that it was able to reproduce the distribution trend

and total amount of sediment in the fixed bed, but the

result was an underestimation. Therefore, a sensitivity

analysis of the coverage and Manning’s roughness coeffi-

cient was conducted to investigate the effect of coverage

and/or terrestrial roughness on the sedimentation distri-

bution. The coverage (A) was varied from 0.15 to 0.3

(case C3-2), and the roughness coefficient (nsl) on the

land for the estimation of τ∗ was varied from 0.0 to 0.014

(case C3-3); however, n = 0.014 for tsunami flow and

ns = 0.014 on the movable bed. The details are presented

in Table 3.



Table 3. Calculation condition (Team C).

C1 C2 C3-1 C3-2 C3-3

MCA scheme√ √

No√ √

Manning’s

roughness

coefficient

n = ns = 0.012 n = ns = 0.014

n = 0.014

ns = 0.014 (< 11 m)

= 0−0.014 (≥ 11 m)

Sediment

entrainment

With sediment

entrainment

With sediment entrainment (< 11 m)

Without sediment entrainment (≥ 11 m)

Coverage A A = 1.0A = 1.0 (< 11 m)

= 0.2 (≥ 11 m)

A = 1.0 (< 11 m)

= 0.2 (≥ 11 m)

A = 1.0 (< 11 m)

= 0.15−0.3 (≥ 11 m)A = 1.0

n: for tsunami flow, ns: for τ∗ in sediment transport model

(a) Water level (b) Velocity

Fig. 5. Time series data of water level and velocity in the hydraulic experiment.

3. Result

3.1. Hydraulic Experiment

Figure 5 shows the results for the water level and flow

velocity at each measurement point in the fixed-bed exper-

iment. A rapid increase in the water level and velocity was

observed with the arrival of waves at each site. The water

level reached a maximum of approximately 0.08 m at the

junction of the fixed and movable beds, and the flow ve-

locity reached a maximum of approximately 2.5 m/s. The

run-up waves that reached the wall at the downstream end

of the channel were reflected by an upright impermeable

wall. Therefore, a return flow in the upstream direction af-

ter the arrival of the first wave was observed at each site.

Fig. 6 shows the amount of sand deposited at the horizon-

Fig. 6. Distribution of the amount of sand deposited in the

hydraulic experiment.


Yamamoto, A. et al.

Fig. 7. Time series data of water level (Team A).

tal section of the terrestrial area in the movable bed exper-

iment. The distribution of the deposited sand exceeded

40 g near the shoreline and decreased slowly toward the

downstream wall. The total amount of sand deposited in

the land area was 1041.4 g.

3.2. Numerical Simulation of Hydraulic Experi-

ments

3.2.1. Team A

The results for the water level and flow velocity from

A1 are shown in Figs. 7 and 8, respectively. The arrival

time of the first wave at each measurement point was gen-

erally reproduced by the 3D RANS model for the water

level and current velocity. The wave height was larger

than the experimental value at the arrival time of the first

wave at the measurement point near the downstream end

wall; however, it was generally consistent at the other

points. As shown in Fig. 9, a large amount of sand was

deposited near the downstream end wall, and a smaller

amount of sand was deposited from the shoreline to the

middle of the channel. By comparing the results from the

A1-1 uniform grain size and the A1-2 mixed sand, it can

be confirmed that the A1-2 mixed sand condition tends

to deposit sediments slightly closer to the middle of the

channel.

The calculated results for the water level and veloc-

ity by the 2D model of A2 are shown in Figs. 7 and 8,

in which the results from the 3D RANS model are al-

ready indicated. Comparing the results of each model,

although some differences were observed after the con-

vergence of the first wave among the models, no signifi-

cant differences were observed in the arrival time of the

Fig. 8. Time series data of velocity (Team A).

Fig. 9. Comparison of the distribution of the amount of sand

deposited (Team A).

first wave and wave height. In other words, the numer-

ical simulations of this phenomenon showed that the 3D

flow did not develop in most areas, except near the down-

stream end wall, where the vertical flow was dominant.

Regarding the amount of sediment deposition, as shown

in Fig. 9, even after changing both the flow model and the

suspended sediment transport equation, more sand tended

to deposit near the downstream end wall, and the sediment

deposition from the shoreline to the middle of the channel

could not be reproduced. However, it can be confirmed

that the result from A2-1 (using Itakura and Kishi’s for-

mula) tended to indicate deposition of more sand in the



middle of the channel than the result from A2-2 (using

the formula of Ikeno et al.).

As shown in Fig. 9, similar to A2, both A3-1 and A3-2

did not show any improvements in the tendency of sedi-

ment deposition near the downstream end wall. However,

it was possible to reproduce the tendency for sand to ac-

cumulate in the middle of the channel, where it had pre-

viously not accumulated. By comparing the results from

A3-1, in which Iwagaki’s and Tsuchiya’s formulas were

used for the non-dimensional critical tractive force, with

the result from A3-2 in which only Iwagaki’s formula was

used, it can be seen that the reproducibility of A3-2 was

improved. This is because Tsuchiya’s formula is applica-

ble to smooth surfaces, although it can be used in fixed

beds. The smooth surface condition did not completely

satisfy the conditions of the present experiment. There-

fore, A3-2 was considered to have better reproducibility

than A3-1. Moreover, from a comparison of A2-1, which

uses Eq. (1), and A3-2, which uses Eq. (1) and Eq. (2), it

can be seen that it is necessary to apply the sediment trans-

port rate formula according to the fixed bed, as shown in

Eq. (2) when estimating the sediment transport rate in a

fixed bed. However, the situation in which sediment de-

position did not appear near the shoreline could not be im-

proved despite various examinations. Although the calcu-

lation of amount of sediment deposition with mixed sand

instead of uniform sand under the condition of A3-2 was

conducted, there was no significant difference in the cor-

responding results. When the results of Team A were ex-

amined, reproduction of the amount of sediment deposi-

tion near the shoreline and near the downstream end wall

remained an issue.

Moreover, it is not fully clear why this phenomenon

could not be reproduced by the analysis using the 3D

model. Although not taking the saturated suspended load

concentration into account in the model might be one of

the causes, the most likely cause is the setting of grid

spacing in the vertical direction. In calculations of A1-1

and A1-2, the vertical grid spacing was set to 0.005 m.

However, the flow depth at the time of tsunami run-up on

the slope was quite low, and the vertical grid spacing of

0.005 m may have made the grid too coarse for the 3D

model to calculate the friction velocity from the near-bed

velocity. Therefore, it is necessary to improve the sedi-

ment transport model as well as the 2D model and exam-

ine the setting of the vertical grid spacing in more detail

for the 3D model.

3.2.2. Team B

The results for the simulated water level and velocity

from B1 are shown in Figs. 10 and 11. It was confirmed

that the calculated arrival time of the first wave and wave

height generally coincided with the experimental data in

terms of the water level and current velocity. However,

after the passage of the first wave, a large, reflected wave

was generated from the tank side upstream of the chan-

nel, and a run-up phenomenon of the reflected wave to

the land area occurred. A comparison of the distribution

of the weight of the deposited sand is shown in Fig. 12.

The weight of the deposited sand was almost the same, ex-

cept near the downstream end wall. However, large scour-

ing (which was not observed in the experiment) occurred

near the horizontal and slope areas of the land area at the

transition point between the movable and fixed beds. The

time series of the topography at this point showed that a

strong return flow occurs at the convergence of the first

wave, resulting in significant scouring. One of the rea-

sons for this was that the scouring depth limit was not set

in the model. In addition, the amount of sand deposited

near the downstream end of the wall was larger than the

actual amount. In the experiment, the amount of deposited

sand decreased as it approached the downstream side wall.

However, in the numerical calculation, it increased as it

approached the wall. This was because the model used

for the calculation was a 2D model, and therefore, verti-

cal turbulence was not reflected in the calculation. The

velocity of the flow at this point decreases rapidly with

the wall impact and is considered to have accelerated the

sand deposition.

In B2, only the sand grain size condition in the sedi-

ment transport model was changed to mixed sand. There-

fore, the results for the water level and flow velocity did

not change significantly from the results of B1, as shown

in Figs. 10 and 11. The results for the amount of sand de-

posited in B2 are shown in Fig. 12. The amount of sand

deposited was more consistent with the experimental data

from near the shoreline to the horizontal area relative to

the amount of sand deposited in B1. However, the amount

of sand near the downstream end of the wall was not im-

proved by the calculation considering the mixed sand.

The distribution of each grain size shows that medium-

to coarse-grained sand is abundantly distributed. In con-

trast, the amount of fine-grained sand, which is relatively

easy to transport, is small along the shoreline and near the

wall. This is because strong reflected waves are generated

at the downstream wall, and the waves carry away easily

transportable fine-grained sand.

As a result of B3, although the generation of the re-

flected waves is not completely suppressed, as shown in

Figs. 10 and 11, the wave height of the second wave,

which had been a problem thus far, is slightly improved.

As shown in Fig. 12, the amount of sand deposited in the

horizontal section of the land area increased significantly

near the downstream end of the wall, and the sand near

the shoreline was discharged with the convergence of the

waves. This is in contrast to the previous results. Fig. 11

indicates that the discharge of sediment near the shoreline

in this case was also caused by the predominance of nega-

tive velocities owing to the development of local scouring

at the time of the convergence of the first wave at the tran-

sition between the fixed and movable beds.

During the three-day verification, the reproduction of

the water level and velocity in the second wave was im-

proved by changing the input wave to the dam break.

However, the phase tended to be delayed toward the tip of

the run-up. Therefore, further improvement is needed to

accurately reproduce the amount of deposited sediment.


Yamamoto, A. et al.

Fig. 10. Time series data of water level (Team B).

This is important to address the wall and the switching

point from the fixed bed to the moving bed, where ver-

tical turbulence is especially likely to occur. Moreover,

the calculation time for B3 was shorter than that for B1

and B2, which were conducted under the condition of a

smaller grid size. In this context, it can be confirmed that

the calculation of B3 can be performed stably. As the dis-

tribution of fine sediment at each site was obscured by

increasing the grid size, it was necessary to use different

grid sizes, depending on the required accuracy.

3.2.3. Team C

From C1, it can be seen that the reproductions of the

water level and flow velocity at each location were gener-

ally consistent, as shown in Figs. 13 and 14. However, as

shown in Fig. 15(a), the amount of sand deposited tended

to be unevenly distributed around the upper end of the

wall, indicating that the depositional distribution differed

greatly from the experimental results.

As a result of C2, it was confirmed that the distribution

and total amount of sand deposited in the terrestrial area

were generally good, as shown in Fig. 15(a). The Rouse

number of the fixed bed at 15 m from the gate was gener-

ally 1 during the rising period of the upwelling wave and

exceeded 2.5 in the subsequent flow. In other words, it

was inferred that the flow field was dominated by bedload

transport. Although the experimental video image of the

flow over the fixed bed shows suspended transport, it cap-

tured the advection of the sediment entrainment from the

moving bed at the beginning of the run-up, which implies

that occurrence of resuspension from the bottom was dif-

ficult. To make the model consistent with this sediment

Fig. 11. Time series data of velocity (Team B).

(a) Experiment, B1, B2, and B3

(b) Three grain size in B2

Fig. 12. Comparison of the distribution of the amount of

sand deposited (Team B).

transport field, the hoisting rate on the fixed bed was set

to 0. From the experimental images released on the first

day, the coverage of the deposited sediment was clearly

smaller than 1. Considering that there is a correlation



Fig. 13. Time series data of water level (Team C).

between the coverage ratio and the reduction rate of the

amount of bedload rate [33], the effect of the coverage ra-

tio was investigated by assuming that the coverage ratio

of the sediment in the fixed bed was 0.2. As a result, it

was found that the amount of deposited sand reproduced

the trend of the sand volume decrease from the shoreline

to the wall, as well as the experimental results. The re-

sult of C2 suggests that the sedimentation distribution can

be improved by considering the sediment transport at the

fixed bed.

As a result of C3, it was confirmed that the phase of the

wave was significantly delayed compared to the experi-

mental value when the MCA scheme was not considered

(case C3-1), as shown in Figs. 13 and 14. In addition, the

phase difference became larger at the top of the run-up.

It was shown that the MCA scheme can reproduce waves

close to the experimental values, even on a dry bed. How-

ever, the model with the non-hydrostatic pressure term

did not show any significant changes (the test case is not

shown in Tables 1 and 3). This suggests that the influ-

ence of the non-hydrostatic pressure term was small in

this case. From the results of C2, it can be seen that sedi-

ment deposition is affected by varying the coverage of the

sediment in the fixed bed area. Therefore, in addition to

C2 in Fig. 15(a), which was conducted with a coverage ra-

tio of 0.2, the results from varying the coverage ratio in the

range of 0.15 to 0.3 (C3-2) show that the overestimation

of the amount of sand deposited near the berm decreases,

and the amount of sand deposited near the wall tends to

increase. However, there are still some problems in re-

producing the amount of sand deposited near the halfway

point and downstream end wall. In addition, the effect

Fig. 14. Time series data of velocity (Team C).

(a) Experiment, C1, C2 and C3-3 with nsl = 0.0065 (nsl :

roughness coefficient on the land (mixed bed) for τ∗)

(b) Sensitivity analyses of nsl in C3-3 (nsl : roughness coeffi-

cient on the land (mixed bed) for τ∗)

Fig. 15. Comparison of the distribution of the amount of

sand deposited (Team C).


Yamamoto, A. et al.

Fig. 16. Comparison of the total amount of sand deposited

in each case (C3-2: A = 0.2, C3-3: nsl = 0.0065).

of Manning’s roughness coefficient on the land nsl on

the sediment distribution in the sediment transport model

was investigated, as shown in C3-3 (coverage ratio of 1).

As a result, when the roughness coefficient was set to 0,

the tendency of sediment accumulation near the shoreline

and decreasing inland could be reproduced, as shown in

Fig. 15(b). By gradually increasing the roughness, the

amount of sand near the shoreline decreased to a value

close to the experimental value, and the sand distribution

gradually shifted to the downstream end wall. Based on

the bedload formula, the relationship between coverage A

and the roughness coefficient of the mixed field consist-

ing of the fixed and movable beds, αns, is appropriated

to O(A1/2) ∼ (α), where α is the correction coefficient.

In other words, the results of the sensitivity analysis in

Fig. 15(b) reflect coverage A, and the reduced roughness

coefficient αns is interpreted to represent the spatiotem-

porally averaged coverage A. The sensitivity analyses im-

ply that an appropriate reduction of Manning’s roughness

coefficient, which corresponds to an equivalent roughness

coefficient, could be an effective method for obtaining a

simple estimation of sediment transport in a mixed field

of fixed and movable beds.

4. Discussion

In the examinations, although there were some cases

with a phase delay of the first wave, in general, all teams

were able to reproduce the experimental waves. Fig. 16

shows the total amount of sediment deposited as per the

results of each model over three days. All team models

were able to reproduce the total amount of sediment de-

posited on land over three days. However, the distribu-

tion of sediment in the land area remained an issue for

each team. Although the distribution of sediment near the

transition between the movable and fixed beds showed a

tendency to improve, it was difficult for all teams to repro-

duce the amount of sediment near the downstream wall.

To discuss the cause of the increased sediment depo-

sition near the downstream side wall, the calculation re-

sults were examined separately for the bedload and sus-

pended load. All teams examined the bedload and sus-

pended load separately, and bedload deposition was found

to be dominant near the transition between the movable

and fixed beds. In addition, it was confirmed that the

sediments around the wall were formed by bed- and sus-

pended loads. This was also confirmed by the results

for C1, C2, and C3-3 of Team C. C2 without sediment

entrainment significantly improved the distribution trend

compared with C1, which overestimated the amount of

sand deposited near the wall. The variation of nsl in C3-3

plays a role in controlling the bedload rate on the fixed

bed. The result (Fig. 15(b)) implied that the entrained

sediment at the slope settled before reaching the down-

stream end wall and that the bedload transport drove the

sediment to the wall side. As nsl can be associated with

the coverage, it is expected that the amount of sand de-

posited near the wall would be smaller because nsl is

larger around the shoreline area where the coverage and

the amount of sand deposited are larger, and nsl is smaller

near the wall where they are smaller. Therefore, the accu-

racy of the distribution may be improved if coverage A can

be expressed as a function of the amount of sand deposited

at each calculation grid. Moreover, in the 2D calculation,

a comparison was performed between a case in which the

distribution of suspended load concentration in the verti-

cal direction was assumed to be an exponential function,

and a case in which the distribution was assumed to be

constant. The results showed that the amount of deposited

sediment near the wall tended to increase more when as-

suming a constant distribution. This result suggests that it

is difficult to reproduce the phenomenon induced by 3D

flow in 2D calculations.

In addition, our study suggests that the reproducibility

of the total amount of sediment in the run-up area can be

improved by properly setting the sediment coverage ratio

in the calculation grid, roughness coefficient, and bedload

transport rate formula, even in the 2D numerical model.

As can be seen from the calculated result C2 indicated in

Fig. 15(a) by Team C, the result of the sediment distribu-

tion is dramatically improved by considering the sediment

coverage ratio in a grid. Therefore, we concluded that

the most important factor in reproducing this phenomenon

with numerical simulations is the consideration of the sed-

iment coverage ratio in a calculation grid. Next, from the

analyses by Team C, in which the sediment coverage ratio

was fixed and the roughness coefficient on the fixed bed

was changed, as shown in Fig. 15(b), and the results in

Section 3.2.3, it can be seen that the change in the rough-

ness coefficient on the fixed bed also affects the calculated

results. In particular, the result for nsl = 0.0065 was the

closest to the experimental data. However, nsl = 0.0065

was too small, even if the experimental channel bed had

a smooth surface. Therefore, we concluded that the set-

tings of the roughness coefficient on the fixed bed can-

not affect the calculated results as much as the considera-

tion of the sediment coverage ratio. Finally, as mentioned

in the results of Section 3.2.1, from the simulated result

A3 shown in Fig. 9 from Team A, it was found that the



change in the bedload transport rate formula on the fixed

bed also improved the calculated result. However, no dra-

matic improvement was achieved, and the experimental

data could not be sufficiently reproduced. Therefore, we

have identified that the effect of changing the bedload

sediment transport formula for the calculated results was

smaller than the effect of setting the roughness. More-

over, the simulated result B2 indicated in Fig. 12(a) from

Team B shows the mixed sand result, but there was no

dramatic change from B1, that is, the uniform sand result.

Therefore, the effect of the mixed sand was considered to

be small within the scope of the experimental conditions

covered in this study. According to the above results, to

reproduce this phenomenon by numerical simulation, the

following factors are considered to have an impact on the

results: (1) whether or not the sediment coverage ratio in a

calculation grid is taken into consideration, (2) the setting

of the roughness coefficient on the fixed bed, and (3) the

selection of the bedload transport rate formula.

Furthermore, as the computational load for the 2D cal-

culations was small, the results can be shown and dis-

cussed based on a relatively short time (e.g., three days

in this study). As this advantage is effective in emergency

situations requiring immediate results, it is necessary to

continue working on improving the reproducibility of the

phenomenon. Therefore, it is expected that a new 2D

model for accurately reproducing this phenomenon will

be developed in the future. This can be achieved by lo-

cally incorporating a 3D model approach into a 2D model

while simultaneously examining the above three factors

and the vertical grid spacing of the 3D model.

5. Conclusion

In this study, three teams conducted several numeri-

cal simulations using various numerical models, aiming

to reproduce sediment deposition on land, as induced by

a tsunami run-up. With respect to the tsunami flow, all

teams were able to reproduce the experimental data. How-

ever, in terms of the distribution of the sediment deposits,

the initial calculations showed that each team had prob-

lems near the switching point between the movable and

fixed beds and near the downstream wall. Therefore, to

solve these problems, each team conducted various ex-

aminations by changing the bedload transport rate for-

mula, sediment coverage ratio in a grid, roughness, and

other parameters. Consequently, it was found that the phe-

nomenon could be reproduced with some accuracy by a

2D model into which the above changes had been intro-

duced. However, the sediment deposition near the down-

stream end wall generated by the 3D flow could not be

reproduced by a 2D model.

A detailed investigation using a 3D flow model will

be necessary to clarify the phenomenon of sedimentation

near the downstream end wall. However, from the view-

point of practicality, the reproduction of the phenomenon

using a 2D model is desired. Hence, we expect to de-

velop a new 2D model for accurately reproducing this

phenomenon by locally incorporating a 3D model into a

2D model.

Acknowledgements

This study was made possible with the support of the Coastal En-

gineering Committee of the JSCE. The experimental work was

supported by JSPS KAKENHI Grant Number 17H02060. We

would also like to express our gratitude to Dr. Yoshinori Shigihara

of the National Defense Academy for his advice on writing this

paper and thank him for this opportunity.

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[26] Y. Yamazaki, K. F. Cheung, and Z. Kowalik, “Depth-integrated,non-hydrostatic model with grid nesting for tsunami generation,propagation, and run-up,” Int. J. Numer. Methods Fluids, Vol.67,pp. 2081-2107, doi: 10.1002/fld.2485, 2011.

[27] G. S. Stelling and M. Zijlema, “An accurate and efficient finite-difference algorithm for non-hydrostatic free-surface flow with ap-plication to wave propagation,” Int. J. Numer. Methods Fluids,Vol.43, No.1, pp. 1-23, doi: 10.1002/fld.595, 2013.

[28] G. S. Stelling and S. P. A. Duinmeijer, “A staggered conservativescheme for every Froude number in rapidly varied shallow waterflows,” Int. J. Numer. Methods Fluids, Vol.43, No.10, pp. 1329-1354, doi: 10.1002/fld.537, 2003.

[29] J. F. Richardson and W. N. Zaki, “Sedimentation and fluidization:Part I,” Trans. Inst. Chem. Eng., Vol.32, pp. 35-52, 1954.

[30] D. Sugawara, H. Naruse, and K. Goto, “On the role of energy bal-ance for numerical modelling of tsunami sediment transport,” AGU2014 Fall Meeting, 2014.

[31] J. Xu, “Grain-size characteristics of suspended sediment in theYellow River, China,” Catena, Vol.38, No.3, pp. 243-263, doi:10.1016/S0341-8162(99)00070-3, 1999.

[32] J. Xu, “Erosion caused by hyperconcentrated flow on the LoessPlateau of China,” Catena, Vol.36, No.1-2, pp. 1-19, doi:10.1016/S0341-8162(99)00009-0, 1999.

[33] G. Tanaka and N. Izumi, “The bedload transport rate and hy-draulic resistance in bedrock chanels partly covered with gravel,”J. Jpn. Soc. of Civil Eng., Ser. B1 (Hydraulic Engineering), Vol.69,No.4, pp. I 1033-I 1038, doi: 10.2208/jscejhe.69.I 1033, 2013 (inJapanese with English abstract).

Name:Ako Yamamoto

Affiliation:Assistant Professor, National Defense Academy

Address:1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan

Brief Career:2016- Graduate School of Society Sciences, Kansai University

2018- Forestry and Forest Products Research Institute

2021- National Defense Academy

Selected Publications:• A. Yamamoto, T. Takahashi, K. Harada, M. Sakuraba, and K. Nojima,

“Hydraulic experiment on tsunami deposits formation related with sand

grain and bore wave,” J. Jpn. Soc. of Civil Eng., Ser. B2 (Coastal

Engineering), Vol.73, pp. 367-372, 2017 (in Japanese).

• A. Yamamoto, T. Takahashi, K. Harada, M. Sakuraba, and K. Nojima,

“Validation of Sediment Transport Model Using Hydraulic Experiment

Data to Assess the Influence of Grain Size and Reflection Wave on

Tsunami Deposit,” Safety Science Review, Vol.9, pp. 3-19,

http://hdl.handle.net/10112/00017148, 2019.

Academic Societies & Scientific Organizations:• Japan Society of Civil Engineers (JSCS)

• Sedimentological Society of Japan (SSJ)

• Japanese Society of Coastal Forest (JSCF)

• Japan Geoscience Union (JpGU)

• American Geophysical Union (AGU)

Name:Yuki Kajikawa

Affiliation:Associate Professor, Social Systems and Civil

Engineering, Tottori University

Address:4-101 Minami, Koyama-cho, Tottori 680-8552, Japan

Brief Career:2006- Research Associate, Faculty of Engineering, Tottori University

2008- Assistant Professor, Graduate School of Engineering, Tottori

University

2018- Associate Professor, Social Systems and Civil Engineering, Tottori

University

Selected Publications:• “Effect of Check Dam System on Water Redistribution in the Chinese

Loess Plateau,” J. of Hydrologic Eng., Vol.18, No.8, pp. 929-940, 2013.

• “Three-Dimensional Numerical Analysis of Tsunami Runup-Induced

Local Scouring around a Cylinder,” J. Jpn. Soc. of Civil Eng., , Ser. B2

(Coastal Engineering), Vol.76, No.2, pp. I 487-I 492, 2020 (in Japanese).

Academic Societies & Scientific Organizations:• Japan Society of Civil Engineers (JSCE)

• Japan Society for Natural Disaster Science (JSNDS)



Name:Kei Yamashita

Affiliation:Researcher, Division of Research for Earthquake

and Tsunami Regulatory Standard and Research

Department, Nuclear Regulation Authority

Address:1-9-9 Roppongi, Minato-ku, Tokyo 106-8450, Japan

Brief Career:2014- Postdoctoral Researcher, International Research Institute of Disaster

Science (IRIDeS), Tohoku University

2015- Assistant Professor, IRIDeS, Tohoku University

2018- Visiting Researcher, University of Hawaii

2018- Researcher, Tokushima University

2018- Associate Professor, IRIDeS, Tohoku University

2021- Researcher, Nuclear Regulation Authority

Selected Publications:• “Development of a tsunami inundation analysis model for urban areas

using a porous body model,” Geosciences, Vol.8, Issue 1, No.12, 2018.

• “Numerical simulations of large-scale sediment transport caused by the

2011 Tohoku Earthquake Tsunami in Hirota Bay, Southern Sanriku Coast,”

Coastal Engineering J., Vol.58, No.4, Article No.1640015, doi:

10.1142/S0578563416400155, 2016.



Name:Ryota Masaya

Affiliation:Telecom Business Division, Telecom and Utility

Business Sector, NTT DATA Corporation

Address:3-3-3 Toyosu, Koto-ku, Tokyo 135-6033, Japan

Brief Career:2018- Graduate School of Engineering, Tohoku University

2021- NTT DATA Corporation

Selected Publications:• “Investigating beach erosion related with tsunami sediment transport at

Phra Thong Island, Thailand, caused by the 2004 Indian Ocean tsunami,”

Natural Hazards and Earth System Sciences, Vol.20, pp. 2823-2841, 2020.

Name:Ryo Watanabe

Affiliation:Civil and Environmental Engineering, Graduate

school of Engineering, Tohoku University

Address:6-6-06 Aza-Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-0845, Japan

Brief Career:2020- Civil and Environmental Engineering, Graduate school of

Engineering, Tohoku University

Selected Publications:• “Topographic changes caused by the 1964 Alaska Tsunami in Discovery

Bay, State of Washington, USA,” J. Jpn. Soc. of Civil Eng., Ser. B2

(Coastal Engineering), Vol.76, No.2, pp. I 433-I 438, 2020.

Academic Societies & Scientific Organizations:• Japan Society of Civil Engineering (JSCE)

Name:Kenji Harada

Affiliation:Associate Professor, Center for Integrated Re-

search and Education of Natural Hazards,

Shizuoka University

Address:836 Ohya, Suruga-ku, Shizuoka 422-8529, Japan

Brief Career:2003- Postdoctoral Fellow, Disaster Prevention Research Institute, Kyoto

University

2005- Research Scientist, Disaster Reduction and Human Renovation

Institution

2008- Assistant Professor, Saitama University

2011- Associate Professor, Shizuoka University

Selected Publications:• A. Kitamura, Y. Yamamoto, K. Harada, and T. Toyofuku, “Identifying

storm surge deposits in the muddy intertidal zone of Ena Bay, Central

Japan,” Marin Geology, Vol.426, Article No.106228, 2020.

• T. Noda, K. Yamori, and K. Harada, “Development of Disaster Response

Applications and Improvements in Regional Disaster Prevention Capacity

Based on Collaborative Information Use,” J. Disaster Res., Vol.14, No.2,

pp. 375-386, 2019.


• Japan Society for Natural Disaster Science (JSNDS)

• Japan Association for Earthquake Engineering (JAEE)

• The Seismological Society of Japan (SSJ)

• Japan Society for Disaster Information Studies (JASDIS)


• Asia Oceania Geoscience Society (AOGS)

• Japan Geoscience Union (JpGU)


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Comparisons of Numerical Models on Formation of Sediment