ＩＳＳＮ１３４６－７８４０

港湾空港技術研究所
資料
TECHNICAL NOTEOFTHE PORT AND AIRPORT RESEARCH INSTITUTE

No.1320

June 2016

Sediment Transport and Near-Bed Dynamics by Currents and Waves in Muddy
Environments of Inner Bay
中川

国立研究開発法人康之

海上・港湾・航空技術研究所

National Institute of Maritime,
Port and Aviation Technology, Japan

CONTENTS
Synopsis ························································································································

3

1. Introduction ··············································································································

5

1.1 Background of the study ··························································································

5

1.2 Aim of the study and setup of the thesis ···········································································

6

References for Chapter 1 ·······························································································

7

2. Characteristics of muddy bed structure ········································································
2.1 Introduction ···········································································································

8
8

2.2 Specific features of muddy bed structures ··································································

8

2.3 Measurement of mud-water interface in Ariake Bay ····················································

92.3.1 Site description ···································································································

92.3.2 Field survey for mud density measurement ···························································· 10
2.3.3 Measurement results in Ariake Bay ······································································ 11
2.4 Sediment properties of muddy bed of study site in Tokyo Bay ····································· 13
2.4.1 Site description ·································································································· 13
2.4.2 Methods ············································································································· 14
2.4.3 Measurement results and discussions ···································································· 15
2.5 Conclusions ·········································································································· 18
References for Chapter 2 ······························································································ 19
3. Fine sediment dynamics under tide-dominated condition ············································ 20
3.1 Introduction ··············································································································· 20
3.2 Field observations ·································································································· 21
3.3 Estimation of suspended sediment concentration profiles with ADCP’s backscatter data ·· 21
3.3.1 Relationship between SSC and backscatter intensity ················································· 21
3.3.2 Estimated result of SSC by ADCP backscatter data ·················································· 22
3.4 Estimation of erosion rate parameter from field data ···················································· 24
3.4.1 Erosion rate formula for muddy bed ······································································· 24
3.4.2 Parameter acquisition procedure ············································································ 24
3.5 Simulation of temporal variation of total suspended load ············································· 25
3.5.1 A one-point TSL model ························································································ 25
3.5.2 Simulation of temporal variation of total suspended load ·········································· 26
3.6 Conclusions ·········································································································· 27
References for Chapter 3 ······························································································ 28
4. Data analysis and modeling of mud transport during storm event in Tokyo bay ············ 30
4.1 Introduction ············································································································ 30
4.2 Site description ······································································································ 30
4.3 Instrumentation ······································································································ 30
4.4 Data analysis ········································································································· 31
4.4.1 Estimation of bed shear stresses ············································································ 31
4.4.2 Erosion flux and bed elevation measurements ·························································· 32-1-

4.5 Observed data in the storm and flood event ································································ 33
4.6 Estimation of near-bed sediment fluxes ····································································· 34
4.6.1 Vertical fluxes ····································································································· 34
4.6.2 Fluid mud flow ··································································································· 35
4.7 Results ·················································································································· 36
4.7.1 Erosion flux estimation ························································································ 36
4.7.2 Sediment budget analysis near bed surface ······························································· 36
4.7.3 Derivation of fluid mud flux formula ····································································· 37
4.8 Conclusions ·········································································································· 38
References for Chapter 4 ······························································································ 39
5. Effect of wind wave disturbance on temporal variation of near-bottom dissolved oxygen in
inner Tokyo bay ··········································································································· 42
5.1 Introduction ············································································································ 42
5.2 Monitoring site and instrumentations ·········································································· 42
5.3 Measurement results and discussion ·········································································· 43
5.3.1 Salinity, temperature and DO concentration variations in summer of 2010··················· 43
5.3.2 DO concentration and wind events ········································································· 44
5.3.3 DO concentration and wind waves ········································································· 45
5.3.4 Near-bed processes of wave induced disturbance and DO concentration ······················ 47
5.4 Conclusions ·········································································································· 49
References for Chapter 5 ······························································································ 49
6. Application of fluid mud model with stochastic approach of bottom shear stress estimation
to storm event simulation ······························································································ 50
6.1 Introduction ············································································································ 50
6.2 Stochastic approach for estimation of wave induced bottom shear stress Monitoring site andinstrumentations ············································································································ 51
6.2.1 Characterization of wave induced instantaneous velocity ·········································· 51
6.2.2 PDFs of instantaneous orbital velocity and bottom shear stress ·································· 52
6.3 Stochastic formula for sediment transport rate ······························································ 54
6.3.1 Excess bottom shear stress ···················································································· 54
6.3.2 Estimation of fluid mud transport rates ··································································· 55
6.4 Application of the model to simulation of observed storm event Conclusions ················· 57
6.4.1 Simulation of current and wave field during storm event ··········································· 57
6.4.2 Simulation results of fluid mud transport ································································ 58
6.5 Conclusions ·········································································································· 60
References for Chapter 6 ······························································································ 61
7. Summary and conclusions ··························································································· 63
Acknowledgements ········································································································ 64-2-内湾域における波と流れによる底泥輸送および海底環境の動態に関する研究
中川

康之*

要

旨

内湾域沿岸部における効率的な海域利用や，適切な水域環境の保全を行っていくためには，海域特性の把握をふまえた環境変動の予測技術の向上が必要とされている．本研究で対象とする東京湾
および有明海における底質環境の特徴として，シルト・粘土分を主体とする泥質物が湾内の広い範囲に堆積し，地形変化をはじめとする水域環境の動態を評価するうえでは，底泥の堆積特性や外力条件に応じた挙動特性の把握が重要となる．本研究では，これらの海域を対象とした現地観測を通じて，現地底泥の堆積分布のモニタリング手法の構築と，潮汐流や波浪外力による底泥移動や底層の水質変動に関する実態の把握，さらに現地観測を通じて明らかとなった底泥の堆積および挙動特性を反映させた底泥輸送のモデル化を試みた．
堆積特性の把握においては，現地底泥のコアサンプル採取・分析や現地式密度計を新たに導入した底泥湿潤密度の現場計測を行い，底泥移動を評価するうえで重要となる底泥極表層における含水比等の鉛直分布構造について，各観測海域での特徴を明らかとした．特に東京湾深場においては，
含水比 300%以上の流動性の高い軟泥が 20cm 程度堆積していることが確認された．一方，超音波式流速計を用いた流動場の計測と，懸濁物濃度や溶存酸素濃度等の同時計測による底面境界観測シス
テムを導入した長期連続観測により，潮汐流や波浪等の作用外力に対する底泥移動や水質変動に関する応答特性の解明を試みた．各観測海域での物質輸送の評価に重要となる外力条件の特徴を配慮
し，有明海では潮汐流による底泥の巻き上げに伴う SS(浮遊懸濁物)の輸送現象を，また東京湾奥部
では高波浪時の波浪擾乱に伴う底泥挙動や水質変動に注目したデータ解析を行った．潮汐流によるSS 輸送現象に対しては，全水深を通じた SS 濃度の流速変動に対応する変動量の解析を基に，巻き上げ限界や巻き上げ速度等のモデル・パラメータの最適化を行い，懸濁物輸送量を適切にシミュレートする SS 輸送モデルを構築した．また，平常時には静穏な環境にある東京湾奥部においては，強風時に生じる風波擾乱の発達程度に応じて，底泥の顕著な移動や貧酸素期における DO(溶存酸素)濃度の回復が生じることを示し，波浪外力が海底環境に及ぼす影響の実態を明らかとした．
東京湾における記録的な高波浪イベント時の観測データからは，海底面直近での底泥輸送量の収
支解析を通じて，一般的な底泥輸送モデルで考慮されている巻き上げ・沈降過程以外にも，泥層内で生じる泥の水平輸送過程が重要であることを示し，さらに泥層内の鉛直構造を反映させた泥輸送モデルを新たに導出した．また，本モデルの実海域シミュレーションへの応用を通じて，波浪外力の時間変動の確率的評価を導入することにより，底泥輸送量の再現性が向上することを明らかとした．
キーワード：内湾，底泥，潮汐流，波浪，底質輸送，海底環境* 沿岸環境領域長（兼沿岸土砂管理研究グループ長）〒239-0826 横須賀市長瀬3-1-1 国立研究開発法人 海上・港湾・航空技術研究所 港湾空港技術研究所
電話：046-844-5051 Fax：046-844-1274
e-mail:nakagawa@pari.go.jp-3-Sediment Transport and Near-Bed Dynamics by Currents and Waves in Muddy
Environments of Inner Bay

Yasuyuki NAKAGAWA*

Synopsis
Understanding of dynamics of coastal morphology and sedimentary environment is critical topics
for sustainable use and management of coastal and estuarine area. The target area of the present
study, the Ariake Bay and the Tokyo Bay, are typical embayment in Japan, where mud or fraction ofsilt and clay is dominant in the bottom sediments, and the process of fine sediment transport is an
important factor for their water environment in the area. Several field monitoring were carried out
in the present study for capturing characteristics of spatial distribution of muddy sediment andnear-bottom processes including transport process of mud and water quality changes under theforces of waves and currents. Furthermore, the knowledge obtained through the field monitoring
were applied for modelling of fluid mud transport processes.
For the capturing sediment characteristics, besides the acquisition of key parameters of sediment
strength such as bulk density and water content from sediment cores, an in-situ device was also
newly introduced for direct measure of bulk density of bottom mud. In the Tokyo Bay site,
thickness of fluid mud layer is relatively higher in the offshore from the river mouth area with the
thickness of over 20 cm. Through the deployments of long-term monitoring in the fields, where
several acoustic velocimeters and synchronized measurements of water qualities including turbidity,
dissolved oxygen and so on, the present study examines sediment dynamics and water qualitychange due to waves and currents. Considering dominant forces in the study sites, suspended
sediment transport processes due to tidal current is examined and modeled with the data from the
Ariake Bay. On the other hand, sediment transport and water quality changes near the bed due towind waves are focused on for the study of the Tokyo Bay.
Dynamical processes during an extreme storm events due to a passage of typhoon weresuccessfully captured in the monitoring campaign of the Tokyo Bay and the data analyses revealed
the importance of fluid mud transport in the near surface mud layer. Model for the fluid mudtransport is newly derived and applied for numerical simulations of mud transport process under thestorm condition. Through the validation process of the model, it appears that stochastic approach iseffective for the evaluation of wave forces on the fluid mud transport simulation.
Key Words: Embayment, Mud, Tidal current, Wind wave, Sediment transport process, Near-bed
environment* Director of Coastal and Estuarine Environment Department
3-1-1 Nagase, Yokosuka, 239-0826 Japan
Phone：+81-46-844-5051
Fax：+81-46-844-1274
e-mail:nakagawa@pari.go.jp-4-decomposition of organic matters in the fine sediment.

1. Introduction

Resuspension of the mud results in high turbidity with
1.1 Background of this study

attenuation of light through the water column causing

Muddy sediments consists of silt and clay particles arewither of submerged aquatic vegetation.

mainly supplied through rivers discharge and commonly

Many studies have examined the mud behaviors indeposited in estuaries and coastal embayments during

fields such as the dynamics of wave induced

calm conditions of external forces on the bed such as

resuspension in an estuarine environment (Sanford, 1994,

currents and waves. After the deposition of the muds,

Shiraishi et al., 2000) and the fluid mud behaviors inhowever, they may be resuspended and transported

areas of continental shelves (Vinson and Mehta, 2003,

depending upon the extent of current and wave forces.

Traykovski et al. 2007). Difficulty of grasping the

Sincetransport

movement of the mud in the field is due to infrequent

processes cause changes not only in topography but also

but intense nature of the mud transport dynamics, which

in

the

dynamical

sedimentary

and

muddy

environments,

are under site specific and intermittent conditions ofunderstanding of the sedimentary process is crucial for

driving forces such as river flood and storm waves.

effective uses and sound managements of the coastal andHence, field monitoring is still a powerful tool toestuarine area.

elucidate unknown processes of mud behaviors in site,

From an economical view point, the prediction ofwhere it is required to properly choose in-situ

sediment movement is critical for the estimation of

instrumentation layouts for optimal acquisition of target

maintenance dredging cost for port and harbor

data.

operations because the sediment movement often results

Several types of numerical models have beenin shoaling or siltation of navigation channels and harbordeveloped

basins. The siltation problems are especially sever atprocesses in coastal and estuarine area. The models

ports and harbors locating near river mouth andclassified as so called process-based models typicallyextensive trials for minimizing siltation have been

consist of any 3-D hydrodynamic model and sedimentexperienced in many countries (PIANC・2008). In Japan,

modules (e.g. Amoudry and Souza, 2011). Sediment

several studies on muddy sediment have been carried out

modules implement different formulations depending on

extensively in 1980’s to solve engineering problems forthe sediment types of mud (cohesive) and sandthe port development at the shallow muddy coast of(non-cohesive), respectively. For muds, which is thesurrounded by the intertidal mud flat in the Ariake Bay

focus of the present study, the formulations for erosion(Port and Harbour Research Institute, 1990). In recent

with the power low (e.g. Ariathurai and Krone, 1976) are

years, while the deeper navigation channels are required

conventionally used, where considering erosion rate

due to increases in vessels draught, placement ofparameter and critical shear stress for erosion. Thesedredged materials in open water is strictly prohibitedparameters are highly site specific and depend on several

under the international regulation. Hence, there are

sediment properties such as particle size, water content

compelling reasons to execute the study of fine sedimentor consolidating state of muds, which is sometime ofdynamics that can be applied for minimize thevertical structure also. We need any efficient method formaintenance dredged volumes.

acquisition of the sediment parameters that reflecting the

To provide a better understandings of mud behaviors isstate of mud deposition in field for applying thenecessary also for assessing coastal and estuarine

numerical model.

environments

specific

Furthermore, formation of fluid mud layer near the bed

characteristics of the fine sediments. Muddy sediments

is also specific characteristics in muddy environments

are often include organic materials and contaminants

and leads to more complicated situation to be modeled

because pollutants are likely absorbed on to silt and clay

since the fluid mud behaves as Non-Newtonian fluid

particles. Water quality is also highly related to thedepending on the concentration (Mehta, 1991). Two

sediment condition and hypoxia is sometimes caused bylayer approach is one of typical ways to incorporate the

by

water

sediment

qualityelucidating

several-5-

forsimulatingthesediment transportfluid mud layer in numerical models, considering massanother view point and discussed effect of wave

and

the

disturbance on temporal change in near-bed water

non-Newtonian fluid (e.g. Odd and Cooper, 1989,

quality or dissolved oxygen concentration. Field data

Tsuruya et al. 1994). This approach has been stillshows the key role of high wind wave events in recovery

utilized (e.g. Deltaress, 2014), where density in the fluidof concentration of dissolved oxygen near the bed frommud layer is assumed as constant for simplicity. Because

hypoxia state.

the vertical structure of density or concentration profile

In Chapter 6, the fluid mud transport model derived inin the fluid mud layer is critical to determine theChapter 4 is applied to simulate mud transport dynamics

horizontal sediment flux, it is a challenge to properly

during the observed storm events around the monitoring

incorporate the vertical structure of the fluid mudsite coupling with 3D hydrodynamic and wave models.

density in the model.

In the simulation, bottom shear stress due to current andmomentumconservation

equationsforwaves are newly modeled as stochastic approach
1.2 Aim of this study and set-up of this thesis

considering irregularity of the wind wave instantaneous

The aim of the study is to enhance our understanding

velocity above the bed.

and modeling of mud behavior and near-bed dynamics

Finally, in Chapter 7 the previous chapters areunder current and wave forces. The set-up of the thesis issummarized and over all conclusions and future works

depicted in Figure 1.1.

are presented.

Chapter 2 presents observation results of verticalstructure of muddy sediment concentration in fields oftypical Japanese embayments. This chapter also discusscomparison of several methods for measurement of
muddy bed characteristics depending on the watercontent.
From Chapter 3 through Chapter 5, several analysis offield measured data are carried out to elucidate specificphenomena of muddy sediment transport processes andnear bed dynamics due to waves and currents. The target
phenomena that these chapter deal with are summarizedin Table 1.1. Chapter 3 describes dynamics of suspended
fine sediments under the tide dominated condition at amonitoring site with the water depth of around 8 m in ameso-tidal environment. Temporal change in suspended
sediment concentration profile is calculated using
acoustic back scatter signals through the water column
and they are applied for estimation pick up rate of thebottom sediment with 1DV analysis.
On the other hand, wave dominated resuspension
phenomena in a bay at intermediate water depth, aroundFigure 1.1 Set-up of the thesis

25 m, is demonstrated in Chapter 4. Field monitored data
are shown to reveal fine sediment transport processes
under an extreme storm event during a passage oftyphoon. Considering the characteristics of muddy bed
structure represented in Chapter 2, fluid mud transportmodel is newly derived in the chapter.
Chapter 5 is dedicated to near-bed dynamics from-6-

Table 1.1 Target phenomena of field monitoring for

No.1B, pp.148-165.

sediment transport and near bed dynamics discussed in

Shiraisi, S., M. Iijima, K. Nagano and K. Nakatsuji

Chapter 3, 4 and 5
Chapter#34Target depthof concern

8 ‐12mThrough the
all water
columnTidal
current

Suspended
sediment
dynamics

Near bed
and in mud
layer

Current
and Storm
waves

Suspended andFluid muddynamics

Near bed
water
columnWaves

Water quality(DO
concentration)change25 m

5(2000): Resuspension of muddy sediment by tidal

Water
depth atmonitoring
site

15 m

Forcingfactors

Targetphenomena

current and waves off Kansai International Airport,
Proc. of Coastal Engineering, Vol. 47, pp.1076-1080.
(in Japanese)
Traykovski, P., P. L. Wiberg, and W. R.Geyer (2007):
Observations and modeling of wave-supported
sediment gravity flows on the Po prodelta andcomparison to prior observations from the Eel shelf,
Continental Shelf Research, Vol.27, pp.375-399.
Tsuruya, H., K. Murakami, I. Irie, H. Sasajima and M.
Itoi (1994): 3-D numerical model for siltation
estimate considering fluid mud dynamics, Proc. of

RFERENCES for Chapter 1

Coastal Engineering, Vol. 41, pp.1011-1015. (inAmoudry, L. O. and A. Souza (2011): Derterministic

Japanese)coastal mor-phological and sediment transport

Vinzon, S. B. and Mehta, A. J., (2003): Lutoclines inmodeling: Areview and discussion, Reviews ofhigh concentration estuaries: Some observations at

Geophysics, 49, pp.RG2002-2011.

the mouth of the Amazon, Journal of Coastal

Ariathurai, R. and R.B. Krone (1976): Finite element

Research, Vol.19, No.2, pp.243-253.

model for cohesive sediment transport, Journal of
Hydraulics

Division,

ASCE,

ol.102,

No.3,

user

manual,pp.323-338.
Deltares(2014):

Delft3D-FLOW

ver:3.15.34158, 684p.
PIANC (2008): Minimising harbour siltation, PIANC
Report No.102, 75p.
Mehta, A. (1991): Understanding fluid mud in a
dynamic environment, Geo-Marine Letters, Vol. 11,
pp.113-118.
Odd,

N.V.M.

andA.J.

Cooper(1989):

A

two-dimensional model of the movement of fluid
mud in a high energy turbid estuary, Journal of
Coastal Research, Vol.5, pp.185–193.
Port

and

HarbourEngineering

Research

guide

book

Institute(1990):

forsiltation

countermeasures, 257p. (in Japanese)
Sanford, L.P. (1994): Wave-forced resuspension of
upper Chesapeake May muds, Estuaries, Vol.17,-7-

2 CHARACTERISTICS OF MUDDY BED

2001). However, spatial distribution and structure of

STRUCTURE

sediment in field are determined by site specificconditions of sediment supply and forcing factors suchas wave and current. Therefore, better understanding of

2.1 Introduction

sediment characteristics in field is required forFine sediments including silt and clay often

modeling of sediment dynamics with more reliability.

accumulates and deposits on the sea bed in enclosed
bay and estuarine seas. Such fine materials sometimes

In the present chapter, after reviewing generalform very soft mud layer with high water content near

aspects of muddy sediment characteristics, some field

the surface of bottom sediments and it may makemeasured data taken at the target field of the present

difficult to identify the interface between the settledstudy in the Ariake Bay and the Tokyo Bay are

bottom sediments and sea waters with an intermediatedemonstrated to present actual situations of verticallayer so called fluid mud layer. The existence of thestructures of near bed surface at the typical muddy

fluid mud layer may cause the difficulty in defining theenvironments. Besides conventional sediment analysis

sea bed level and it has been one of the criticalmethod with taking core samples in the field, theproblems for the maintenance of navigation channels inpresent study apply other measurement methods

port and harbor facilities. From the environmental

including an in-situ bulk density measurement device

viewpoints, the understanding of existence

andand a vane strength meter etc. Through the data analysis,

movement of soft mud layer on the sea bottom isthe existences of fluid mud layer in the fields are

crucial for the estimation of the fate of various

confirmed

substances which are detached with fine sediments andconsidering the vertical structures.

and

their

thicknessarequantified

harmful to water environments.
Since erodibility of mud deposits depends on

2.2 Specific features of muddy bed structures

sediment parameters such as water content or bulk

One of the prominent properties of fine bottom

density (e.g. Whitehouse et al., 2000), spatial structure

sediment is the dependency of their dynamic behaviorsof the muddy sediment including vertical profile ason the water contents or sediment concentration as

mentioned above should be taken into consideration forsummarized in Figure 2.1. The concentration or dry

analysis of sediment dynamics (e.g. Sanford and Maa,density of mud, C, is ranging from the order of <～101

Bulk density : kg/m3
2650 2300 1500

1200

1100

1050

1027

Non‐Newtonian fluid

Newtonian fluid(Soulsby, 2000)

Consolidating settled bed

Fluid mud

Flocculated suspension
High concentration Low concentration
mud suspension
mud suspension

Fluid mud

(Winterwerp, 1999)

Fluid mud (van Leussen & Dronkers, 1988)
2650
0

1000
100

100
300

SSC : g/l

10

Water content : %
1000
10000

1
100000Figure 2.1 Classification of mud states depending on sediment concentration

-8-

0.1

∞

g/l as suspension state through the order of > 103 g/l as
consolidated state. Suspension with high concentration
around 101 g/l can affect the turbulent flow field andmay damping the turbulent energy in the overlying sea
water (e.g. Winterwerp, 1999), though it is treated as
Newtonian fluid. Between the two these states, there isa transient state or so called fluid mud with theconcentration of the order of a few 101 to 102 g/l,
though there are several definition for the range ofconcentration, where the mud behaves as laminar-flow
and non-Newtonian fluid. Behavior of the fluid mud issometime crucial process for causing siltation ofnavigational channels and harbor basins (e.g. PIANC,

Figure 2.2 Schematic vertical profile of fine sediment2008) and also for determining distribution of muddy

concentration (after Ross and Mehta, 1989)

sediments around the continental shelf during flood
dispersals (e.g. Kineke et al., 1996, Traykovsky et al.,some field data taken by the techniques combining

2000 ). The process of fluid mud formation is not clear

several methods.

enough for setting up universal model of them and may
sometimes be infrequent phenomena only during high

2.3 Measurement of mud-water interface in Ariake

concentration event. The feature of fluid mud isBayrequired to be captured by field monitoring.
2.3.1 Site description

Schematic profile of fine sediment concentration
bed isThe Ariake Bay is one of the largest estuaries in

depicted by Ross and Mehta (1989) as in Figure 2.2

Japan on the western coast of Kyushu Island (Figure

showing appearances of several mud states as2.3), with a surface area of 1,700 km2 and an averaged

mentioned

depth of 20 meters. Tidal effects are prominent all overfrom the water column through the settled

abovedependingonthesediment

concentration. Availability of information on the

Chikugo Riv.

vertical profile of sediment concentration may beＮcritical for estimating transport rates of sediment under
10

forces such as current and waves. In order to gain the
profile, both suspended andKikuchi Riv.

bottom sediments are
Japan seataken by any water sampler for suspension and by any

Deployment site
35 30'N

sediment core sampler for accumulated sediments,

20

20
10

Pacific ocean

respectively. As another method to directly measure theMidori Riv.

140 00'E

profile of density, there are several in situ devices
20

including tuning folk type densimeter and gamma ray

100

type system (e.g. Nichols, 1985, and PIANC, 2008). In

10

20ｋｍ

Figure 2.3 Location of Ariake bay and Port of

the following sections, characteristics of vertical

Kumamoto

structures of muddy environment are examined with-9-

the estuary and the range at spring tide varies from 4 m

Figure 2.5. As shown in the figure, four stations areat the mouth and increases to more than 6 m towards

selected to take sediment samples and measuring in-situ

the upper estuary. Annual mean rate of freshwater

bulk density profiles. The Stn. a and b are located at the3

supply is around 250 m /s flowing through main rivers

north and south side of the navigation channel of the

including Chikugo River into the north-east andKumamoto Port with the water depth of less than 4 m.

Kikuchi and Midori Rivers into east coast of the Bay,Of the stations on the navigation channel,

where intertidal mudflats are developed. The overall

located inside the dredged area with the water depth of

spatial distribution of surficial bottom sediments varies-7.7 m. St.d is also on the navigation channel but thedepending on the specific location in the Bay and finelocation is not dredged with the natural water depth of -materials (less than 75 m) are dominant off the

8.6 m.

St.c isnorth-west and middle-east coast of the bay as shown in
2.3.2 Field survey for mud density measurement

Figure 2.4.
The study site of the present study is off shore area

Field survey was carried out on December 11 andat the middle-east coast of the bay near the Kumamoto

12 in 2002 at the monitoring stations around thecity, where muddy sediments are dominant and interKumamoto Port. At these points, sediment samples

tidal mud flats are formed at the river mouth of thewere taken with core samplers with the diameter of 43

Shira River and the Midori River as mentioned above.

mm. The sediment core samples were sliced with the

Tidal range is around 4 m during spring tide period inthickness of 10 cm and analyzed for sediment

the area and semi diurnal component is dominant withproperties such as bulk density, particle size distribution,

the flood tide of onshore-ward current and the ebb tide

dry density, etc. Soundings were also carried out by anof offshore-ward current. In the embayment of the test

acoustic sounding with the frequency of 200 kHz and asite, a dredged navigational channel accesses to thelead line also. The weight of lead used for the sounding

harbor basin of the port of Kumamoto which is locatedis 6.2 kg and the area of base is 20.25 cm2. In addition

offshore of the intertidal mud flat area as shown into the taking core samples and the measurement of bed
level with the conventional techniques, an in-situ

Unit:%
Mud content(%)
110

densimeter was also applied to measure bulk density of

0 - 2020 - 40
40 - 60
60 - 80
80 - 100

100

90

80

70

60

50

40

30

20

Figure 2.5 Study site and sampling locations around

10

10

20

30

40

50

60

70

80

the port. (Bed elevations below the datum line; D.L.,

90

are indicated in brackets)

Figure 2.4 Distribution of mud content in Ariake bay- 10 -

sediment. All these sampling and measurements werecarried out at each station, where a research boat wasmoored as illustrated in Figure 2.6.
The density measurement device used in the present
study is one of the tuning folk type densimeter, aproduct of HR-Walingford (Hydramotion Co. presently)
and model XL-4 (Figure 2.7), with the mono folk typesensor of 10 cm length and 1.5 cm diameter. The
measurement system detects the vibration frequency ofthe sensor that varies with the density of the

Figure 2.6 Schematic of field measurement of mud bed

surrounding medium. Total weight of the sensor unit isstructure (a: In-situ densimeter, b: Sediment core

5 kg in the air and 4 kg in the water. It is connected

sampling, c: Lead method, d: Acoustic sounding withthrough a cable with a PC and both measured depth and

210kHz)density data are logged at the same sampling rate of 1
Hz. The measurement range of density is 0.9-1.5 g/cm3
and the resolution is 0.001 g/cm3. For the measurement
at the site, the sensor was suspended from the boat andtaken down at 10 cm pitch near the bottom level,
considering the previous measurement results of thesoundings. The density can be measured until the
sensor is buried in the consolidated mud layer.
2.3.3 Measurement results in Ariake Bay(1) Surface sediment properties

FigurePhysical characteristics of the bottom surface

2.7

Sensorofin-situmeasurement

sediments at the field in the Kumamoto bay are shown

Table 2.1 Surface sediment properties at the monitoring stations

Stn.a

Stn.b

Stn.c

Stn.d

24.9
24.8
46.5
28.7

2.5
4.7
37.3
58.0

2.0
3.6
33.3
63.1

<1.0
2.6
30.0
67.4

Density of Perticles(g/cm3)

2.697

2.682

2.662

2.674

Water content (%)

81.4
1.513

159.6
1.357

222.6
1.180

179.8
1.114

D50(µm)% of Sand(>75µm)% of Silt(5<d<75µm)% of Clay(d<5µm)

3Bulk Density(g/cm )- 11 -

muddensityin Table. 2.1. Most the surface sediments at the stations

study area, the south part of the navigation channel

consists of fine sediments with the median grain size of lessshows the relatively higher sand contents as shown inthan 2.5 m and with the mud content of over 97 %, except

the result of St.b in contrast to the muddy environment

the St.a where sand fraction is relatively high or mud content

at St.a in the north side of the channel. Though the mud(sum of silt and clay fractions in weight) is less than 80 %.
The higher mud content correlates with the higher watercontent is high at St.a, fluid mud is not observed and it

content, which is defined as following.

is considered to be because of current and wave

Wc  100 

wwws

disturbance for near surface sediment in the shallow(2.1)

area. On the other hand, the higher water content andwhere ww: mass of water and ws: mass of sedimentfluid mud are observed at the relatively deeper stations

grains. The highest value of the water content in theof St.c and St.d. It should be noted that Stn.c is locatedmonitoring stations is over 220 % at St.c. The dryin the navigation channel dredged up to -7.7 m from thedensity of the sediment particles are almost same values

original depth of less than 4 m. This fact indicates that3around 2.6 g/cm . However, the bulk density is ranging

the dredged channel accumulates fine particles with sofrom 1.11 through 1.51 g/cm3 and it decreases less thanhigh concentration that fluid mud layer may be formed.

3

1.2 g/cm in the high water content environment, which(2) Vertical structure of fluid mud layer

is classified as the fluid mud at St.c and d.

Bulk density distribution in the bottom sediment

As the spatial feature of sediment properties in thelayer can be obtained by analysis of sediment core

Figure 2.8 Measured density profiles by sediment core analysis and in-situ densimeter. (The vertical scaleis relative to D. L.) at Stn.a (a), Stn.b (b), Stn.c (c) and Stn.d (d) .

- 12 -

samples. The analyzed data for the core samples taken

the bed levels of the acoustic bottom and the lead linefrom the Kumamoto Bay are plotted by the circles indetection and it becomes as much as 50 cm in the mostFigure 2.8. The figure also shows the measured bulk

high water content mud case at Stn.c. This is considereddensity profiles with the in-situ device plotted by dots

to be caused by the settling and accumulation of finefrom water column through the near bottom sediment.

sediments in the dredged navigation channel. Vertical

The bottom level detected by the acoustic device andprofile of the bulk density with the in-situ device werethe lead method are also indicated by the dotted lines

measured several times at measurement point in thefor the acoustic and dashed lines for the lead line results,

navigation channel and they are almost same profile as

respectively.

shown in Figure 2.8.

At the Stn.a with the relatively higher sand content,
the bulk densities near the bottom surface are over 1.4

2.4 Sediment properties of muddy bed of study site

3g/cm all through the sliced layers of sediment corein Tokyo Bay

sample showing rigid sediment condition. The density
profile measured by the in-situ densimeter shows rapid

2.4.1 Site description

increase in the bulk density at the interface between the

Another study site is located at the Tama Riversea water with the density of around 1.02 g/cm3 and themouth in the Tokyo Bay, Japan (Figure 2.9). The

consolidated sediment with the density of over 1.4

surface area of the bay is approximately 1,500 km2 andg/cm3. In case of muddy sediment with relatively lower

averagedwater content at Stn.b, the densities obtained by thesemi-diurnal, meso-tidal condition with a maximum

core sample analysis shows almost uniformly distributespring tidal range of around 2 m in the upper bay area.

3

depth

is

around15mexperiencing

in vertical around 1.3-1.4 g/m , which is categorized as

The tributary of the bay has a high population of almost

consolidating

density

30 million people and the coastal area has been has

measurement result shows rapid increase at the

been highly developed artificially for industrial uses.

interface between the sea water and the mud layer. The

Several river flowing into the bay mainly at the northdifference between the detected bed levels by theeastern shore of the bay, including the Ara River, theacoustic device and the lead method is around 15 cm

Edo River and the Tama River, etc. One of the major

both for Stn.a and Stn.b.

concerns for the environment in the bay area is the

mud.

The

in-situbulk

In the cases of the mud with higher water content
at Stn.c and Stn.d, the vertical structures are different
from the above two stations and they shows gradualincrease on bulk density into the depth as shown in
Figure 2.8 (c) and (d). The measured profile of the bulk
density by the in-situ densimeter for these stations
shows the transition layer with the thickness of around
10 to 20 cm where the bulk density is between 1.02 and1.2 g/cm3. These detection of the density range with the
thickness around 10 to 20 cm means the existence of

Figure 2.9 Map of the Tokyo Bay and location of thefluid mud layer at these layers. Depending on the

study site

thickness of the fluid mud layer, the difference between- 13 -

recent years. A monitoring project was running at this○：Sediment sampling pointsite since 2007 in order to grasp the environmental
evolution of physical, chemical, and ecological aspects.

Stn.B3N

The present study, as a part of the monitoring project,

10m

focused on the sedimentary processes and carried out

Tokyo Int. Airport

analyses of sediment core samples to get information

Tama Riv.

on sediment properties and to monitor changes in

20m
Stn.A1
Stn.A2

sediment characteristics in the area.

Stn.B12.4.2 Methods

Stn.B2

Core samples were taken at the 25 points indicatedin Figure 2.10 around the Tama river mouth from May
0

30 through June 10, 2006. Undisturbed sediments were2km

taken by divers using core samplers with the diameterof 10.5 cm and the sediments in the depth of 30 cm

Figure 2.10 Core Sampling points in the study areafrom the surface were sliced into 4 layers of 0-1 cm,

deterioration of water and sediment qualities having

1-5 cm, 5-15 cm and 15-30cm, respectively. These

negative impact on benthic organisms (e.g. Kodama

sliced sediment samples were applied for the analyses

and Horiguchi 2010).

of sediment size, water content, ignition loss etc.

The site of the present study is off the mouth of the

The exposed sediment out of the core sampler was

Tama River, which discharges into the north-westernsliced off at the prescribed depth by pushing up theshore of the inner Bay with the total drainage basin areaof 6,000 km2. The Tama River has basin area of 1,240

sediment at the bottom end. During the slicing

km2 (the second largest of the bay) and carries aroundprocedures, all these exposed mud states were classified

20 m3 /s at typical background flow rates and 200-1,000

visually as “fluidized mud” or “consolidating mud”

m3 /s during flood events. The topography of the riveraccording to the condition when sediments were pushed
out of the core sampler at the top as shown in Figure

mouth area has been highly developed artificially for2.11. A handy vane which is an improved torque meterindustrial uses and a runway for the Tokyo International(e.g. Sassa and Watabe, 2007) was also applied for the

Airport has been newly extended in the water area for

Figure 2.11 Picture of fluid mud categorized by the visual classification- 14 -

measurement of sediment strength. The size of eachvane blade used here is 7.5 mm in the width, 30 mm in
the height, and the strength measured every 20 mm
pitch in the depth.
For determining the stratified fluid mud layer in the
muddy environment, acoustic device such as a
sub-bottom profiler can be utilized for capturing thevertical structures of bottom sediments. In order todetect the near-surface fluid mud layer and theconsolidating

mud

layer

in

the

deeperlayer,

Figure 2.12 Relationship between results of visual

multi-frequency system is often applied and the higher

classification of fluid or consolidated mud and theirfrequency acoustic signal around 200 kHz reflects at thewater content

interface between the sea water and fluid mud layer and：Sampling points

the lower frequency with around 30 kHz reflects at the○interface between the fluid mud layer and consolidating

10m

bed (eg. van Leussen & van Velzen, 1989). In thepresent study, a sub-bottom profiler which operates at
4-24 kHz (System3100-G, EdgeTek) was used forTama Riv.

monitoring the deposition of soft mud on the sea bed

20m

around the study site. Core samples of the bottom
sediment were also taken as ground truth data for
Dfm > 10cm

evaluations of the acoustic sounding images.

2.4.3 Measurement results and discussions
Relationship between thickness of fluidized mud

0< Dfm<10cm

categorized by the visual classification and the water
depth of sampling locations are plotted in Figure 2.12,

Figure 2.13 Spatial distribution of fluid mud thickness

showing that the fluid mud exists only when the water(Dfm) according to visual classification of sediment

depth is over 10 m and the maximum thickness is 30

core samples

cm. Spatial distribution or contour of the fluid mud
thickness around the sampling area at the Tama River

m. In other survey result of sediment core sampling,

mouth is shown in Figure 2.13. The monitoring site

soft mud with the high water content of 300 to 400 % isinclude shallow area inside the Tama River with theobserved in the offshore and deeper area of the present

water depth of less than 5 m, which connected to thestudy site (Gomyo, 2001), indicating the soft mud

deeper offshore through a fore set slope with the depth

distribute in the deeper area.

between 5 and 10 m at the river mouth. The contours of

The results of classification as fluidized or

the fluid mud thickness correspond to the topography

non-fluidized mud are compared with the water content

and the thicker fluid mud over 10 cm distribute at theanalysis for the same samples in Figure 2.14. The figure

offshore of the river mouth with the water depth of 20

shows that the mud with the water content over 350 %- 15 -

does not exist as consolidated mud and all of them areclassified as fluid mud. There are several samples thatare classified as fluid mud, though their water contents
are less than 350 %. When the thickness of slicing
sample is over 10 cm, some sediments collapse itself by
self-weight, which is not able to keep the shape and areclassified as fluid mud. Therefore, in order to make thisvisualizing classification more general method, thethickness of the sediment slice should be unified as 1
cm for example.
Measurements of the handy vane torque meter arecompared with water content in Figure.2.15. The resultshows that mud with water content of over 350 % hasquite weak shear strength and the shear strength
become the larger with the decrease of water content

Figure 2.14 Histograms of fluid mud and non-fluid

less than around 350 %. This characteristics correspond

mud over water content

to the fact that critical water content for the existence of
fluid mud is also around 350 % as mentioned above.
Assuming dry density of sediment particle as 2.6 g/cm3,
bulk density is about 1.2 g/cm3 for mud with watercontent of 300%. The value of bulk density, 1.2 g/cm3,
is often applied to determine the navigable depth ornautical depth for port and harbor operation in muddy
environment (PIANC, 2008).
In the survey with sub-bottom profiler for taking
acoustic data, a research boat equipped with thetransducer was running on a survey line and was

Figure 2.15 Comparison between water content andmoored at several monitoring points also. An acoustic

shear strength measured by handy vane torquemeter

image taken on a survey line from the river mouthlines indicates monitoring points Stn. A1 and Stn.A2.

through the offshore is shown in Figure 2.16, and the

Figure 2.16 Acoustic image of sub-bottom profiler on survey line from river mouth to off shore.

- 16 -

Grey scale of the acoustic image is depend on thewith 1.2 g/cm3 at the surface and gradual increase into

extent of back scatter intensity of acoustic signal and it

the depth and corresponding profile of the water

becomes dark when the high intensity signal received

content shows high water content 300 % near thefrom sediment layer with the relatively higher density

surface, indicating existence of fluid mud at the surface

condition. Around the slope of foreset, the acoustic

layer. Under the depth of 60 cm at Stn.A2 the bulk

image shows relatively higher reflection from bottom

densities are over 1.4 g/cm3 with the decrease of water

surface. On the other hand, the image shows 2 layer

content down to around 110 %, showing consolidating

structure with the lower intensity near the surface andstate in the depth. These differences of vertical profiles

higher intensity layer appears beneath the low intensity

correlates with the sediment type constituent and thelayer at the offshore area.
In order to examine the spatial distribution of
acoustic image, the acoustic profile image wascompared with the analyzed data of sliced sedimentcore samples taken from Stn.A1 and Stn.A2. Acoustic
profile images at Stn.A1 and Stn.A2 are shown in
Figure 2.17, indicating that higher intensity layer
appears at the surface layer in case of Stn.A1. In case of
Stn.A2, low intensity layer covers on higher intensity
layer and thickness of the lower intensity layer reads
around 70 cm. Sedimentary profiles analyzed from coresamples taken from these stations are represented by
the parameter of bulk density and water content in
Figure 2.18. The profile of bulk density at Stn.A1
shows slight increase into the depth over 1.4 g/cm3 andwater content is almost uniform around 100 % throughFigure 2.17 Acoustic image of sub-bottom profiler atthe depth. In the data of Stn.A2, the bulk density start(a) Stn.A1 and (b) A2.

Figure 2.18 Vertical profiles of (a) water content and (b) Bulk density in the mud layers at Stn. A1 and Stn. A2.

- 17 -

sediment at Stn.A1 include 10 % of sand while the0

Sediment depth (cm)sediment at Stn.A2 consists of mud with 98 % of silt
and clay. Acoustic intensity images at the stations
correspond to the differences of sediment type andsedimentary structure. The high intensity reflection
from the surface layer at Stn.A1 represent the sedimentof sand mud mixture with low water content and the
low intensity near the surface layer in the acoustic

15
20
25

Water content (%)

For dynamics of muddy sediment transport, theFigure 2.19 Vertical profiles of water content at Stn.

vertical profile of bulk density or water content as

B1, B2 and B3.

represented in Figure 2.18 has a key role since the yieldtechniques in the present study, the vertical profiles andstrength depends on the water content. In otherthickness of fluid mud layer and their spatialadditional surveys, core samples were taken at Stn. B1,

distribution in the field were newly obtained and shownB2 and B3 and analyzed for water content profiles as

quantitatively as followings.

shown in Figure 2.19. Both of the off shore points at

At the study around the Kumamoto Port in theStn. B2 and B3 shows high water content over 300 % in

Ariake Bay, a tuning folk type of in-situ device for bulk

the surface layer and thickness of fluid mud layer

density measurement was also applied in addition to

exceeds 20 cm at Stn. B3. These characteristic oftaking core samples and thin fluid mud layers werevertical structure is incorporated to derive the transportobserved on the bottom surface at the deeper stations

formula of the fluid mud in the chapter 4.

with the water depth of 7 to 8 m. The relatively thicker
fluid mud is formed in the navigation channel with the

2.5 Conclusions

thickness of around 10 cm, indicating accumulation of

In order to reveal sedimentary structures near bed

fine sediment into the dredged deeper channel fromsurface in typical muddy environments in Japan, thesurrounding shallow and inter tidal mud flat area.

field surveys were conducted to measure verticalAt the Tokyo Bay site, formation of fluid mud layer

profiles of sediment properties in the Ariake Bay andis more apparently in the offshore area with the water

the Tokyo Bay. In the present study, core samples wereslicedStn.B310

mud on the surface.

andStn.B2530
0 100 200 300 400 500 600

result of Stn.A2 corresponds to the mud layer with fluid

taken

Stn.B1sediment

were

analyzed

depth over 10 m. According to visual classification offor

sediment core samples for fluid mud or consolidated

representing vertical profiles such as bulk density andmud, the muds with the water content over 350 % arewater content, which were compared with measurementclassified as fluid mud and these soft mud have only

results by other devices to examine muddy sediment

weak strength based on the measurement results with acharacteristics including existence of fluid mud in thehandy vane torque-meter. Thickness of fluid mud layer

fields. Especially in case of high water content ofis relatively higher in the offshore from the river mouth

muddy bed environments, the top surface of mud bed

area with the thickness of over 20 cm. The properties ofstructure may not be captured by any conventionalvertical structure observed in the field studies should beapproach with grab type samplers with the destruction

considered in modeling of mud transport dynamics

of near surface soft mud layer. By applying the several- 18 -

Journal of Coastal Research, Vol.SI-5, pp.1-22.

discussed in Chapter 4.

Whitehouse, R., R. Soulsby, W. Roberts and H.

References for Chapter 2

Mitchener (2000): Dynamics of esutuarine muds,

Gomyo, M., E. Yauchi and T. Ohtsuki (1990):

Thomas Telford Publications, 210 p.

Sedimentary characteristics of mud in Tokyo Bay,

Winterwerp,

Proc. of Coastal Engineering, JSCE, Vol. 37,

H.,(1999):

Onthedynamics

ofhigh-concentrated mud suspension, PhD Thesis

pp.848-852. (in Japanease)published as Report No.99-3, Communications on

Kineke, G.C., R. W. Sternberg, J. H. Trowbridge and
W.R. Geyer, (1996): Fluid-mud processes on thehydraulicandgeotechnicalengineering.

Delft

AmazonUniversityofTechnology,

Faculty

Civil

continental

shelf,

Continental

Shelf

Engineering and Geosciences, 172p.

Research, Vol.16, Issues5-6, pp.667-696.
Kodama, K. and T. Horiguchi, (2011): Effects ofhypoxia on benthic organisms in Tokyo Bay, Japan:
A review, Marine Pollution Bulletin, Vol. 63.
Pp.215-220.
Nichols, M. M., (1985): Fluid mud accumulation
processes in an estuary, Geo-Marine Letters, Vol. 4,
pp.171-176.
PIANC (2008): Minimizing harbor siltation, World
association for waterborne transport infrastructures,
Report No. 102, 75p.
Ross, M. A. and A. J. Mehta, (1989): On the mechanics
of lutoclines and fluid mud, Journal of Coastal
Research, SI. 5, pp51-61.
Sanford, L.P. and J. P.-Y. Maa (2001): A unified erosion
formulation for fine sediments, Marine Geology,
Vol.179, pp.9-23.
Sassa, S. and Y. Watabe (2007): Role of suction
dynamics in evolution of intertidal sandy flats: Field
evidence, experiments, and theoreticalmodel,

Journal of Geophysical Research, Earth surface, Vol.
112, F01003, DOI: 10.1029/2006JF000575
Traykovski, P., W. R. Geyer, J. D. Irish and J. ZF.
Lynch(2000):

The

role

of

wave-induced

density-driven fluid mud flows for cross-shelftransport on the Eel River continental shelf,
Continental Shelf Research, Vol. 20. pp. 2113-2140.
van Leussen, W. and E. van Velzen (1989): Highconcentrationsuspensions:Theiroriginandimportance in Dutch estuaries and coastal waters,- 19 -

ofexperiments

forelucidating

sediment

transport

characteristics were carried out in the Ariake Bay
3

FINE

SEDIMENTDYNAMICSUNDER

TIDE-DOMINATED CONDITION

3.1 Introduction
Modelling of fine sediment movement is notstraight forward because it involves several parametersdescribing the physical characteristics of sediment
properties such as erosion and deposition rate. To obtainsuch parameters, many laboratory studies (e.g. Mehta,
1988, Kusuda et al. 1989) and field tests with in-situ
erosion devices (e.g. Maa et al., 1993) have been madeon specific sediments. The dynamics of muddy
sediment transport process extensively depend on the
Figure 3.1 Location map of monitoring point off thesite specific conditions both of sediment parameters andforcing

conditions.

Therefore,

insteadofKumamoto Port. (The location is 32.45’23’’N/

the130.33’03’’E in WGS82 system)

deployment of laboratory or field experimental
instruments for directly measuring sediment parameters,
there is another possible approach whereby the
sediment parameters are obtained indirectly fromanalysis of time series data of suspended sedimentconcentration (SSC) measured in the field (Sanford andHalka, 1993, Hill et al. 2003, and Vinzon and Mehta,
2003).
The Ariake Bay, where is the study site in thischapter, experience relatively high tide with the

Figure 3.2 Schematic of instrumentations with bottom

maximum tidal range at spring tide of around 6 m in themounted sensors and moored system.

upper bay as mentioned in the previous chapter.

(Seguchi, et al. 1989, Kondo et al. 1996), the durations

Dynamics of suspended fine sediment driven by strongof their observations are restricted only to a one tidal

tidal current in the bay is considered to play a key role

cycle and there are few observations for the variation of

not only in the topographical change but also in thethe phenomena between spring and neap tidal cycle.

ecosystem evolutions in the bay. High turbid water withThis chapter describes resuspension and settlingsuspended fine sediments may control the sun light

dynamics of fine sediments under tidal current based on

penetration into the water column affecting the primary

a half month deployment of field monitoring in the field.

production and distribution of fine sediment has highIn the analysis of the monitoring data, acoustic

correlation with habitat of benthic species. Several

backscatter intensity data of an acoustic Doppler

research works have been carried out for assessing thecurrent profiler, ADCP, is applied to obtain temporalenvironment of the bay area. Although several field

variations of SSC profiles. From the time difference of- 20 -

total suspended load (TSL), the erosion and deposition

traps were mounted on the bottom so that the top of theparameters can be estimated.

sampler was located at 50 cm above the sea bottom.
Based on the bottom sediment analysis, silt and clay
particles are dominant as indicated by solid line in

3.2 Field observation
The field data presented in this section were

Figure 3.3 and particle size distribution of suspended

collected at a monitoring point off the Kumamoto port

particles is plotted by dots, showing almost samein south west of the Ariake Bay (Figure 2.3). The

cumulative curves between them.

monitoring site is indicated in Figure 3.1 locatedapproximately 5 km off the coastal line with the bottom

3.3 Estimation of suspended sediment concentrationlevel of -7.5 m from the local datum line (D.L.).

profiles with ADCP’s backscatter data

Instruments layout is illustrated in Figure 3.2, which
consists of bottom-mounted sensors and moored system,

3.3.1 Relationship between SSC and backscatterand the monitoring has been deployed for one monthintensityfrom mid-October through mid-November in 2001.

The use of acoustic backscatter intensity for the

Wave data were measured at a monitoring tower near

estimation of SSC profiles has become popular (e.g.

the study site where the bottom level is -5 m from theHill et al., 2003, Gartner, 2004) and the application of

D.L. which is operated by the Kumamoto port office,this technique is powerful tool for suspended sediment

Ministry of Land, Infrastructure, Transport and Tourism.

transport studies. In the present study, measured ADCPThe wave data during the deployment period of thebackscatter intensity was calibrated to SSC using thepresent study were provided by the port office as

estimated SSC from the OBS data measured at thesignificant wave height and period every 2 hours.

several levels. The relationship between the backscatterAn bottom mounted ADCP (Aquadopp 2MHz,

intensity of suspended particles at distance of D from

Nortek) measured velocity with 30 cm bins of the water

the ADCP transducers and SSC (in the unit of mg/l) is

column from 40 cm above the bottom throughassumed to be given by Eq. (3.1),

A log SSC  I  B  k log D  2D

approximately 1 m below the surface. The sample(3.1)

interval was 30 minutes and recorded 2 minutes

where I: measured back scatter intensity by ADCP (inaveraged velocity. Optical backscatter sensors (ATU5,

the unit of count) and B: back ground intensity. The last

JFE Advantec Co. Ltd.), OBS, were deployed to

two terms in the right hand side represent transmission

estimate suspended sediment concentration, SSC, andtwo sensors fixed at 20 cm and 50cm above the bottom
and another sensor was moored at 100cm below thesurface. The data of the OBS sensors were collected
every 5 minutes and converted to SSC in the unit of
mg/l by applying calibrations using sediment samples
taken from the monitoring site.
During the monitoring campaign, sea bed sedimentand suspended sediment were taken for particle size
distribution analysis. In order to take samples of

Figure 3.3 Size distributions of bottom sediment andsuspended sediments in the water column, sedimentsuspended sediment at monitoring site
- 21 -

Figure 3.4 Comparison of SSC estimated with OBS and back scatter intensity of ADCP.
loss of sound and k is attenuation coefficients bydiffusion and  is by absorption, respectively.

OBS
ADCP

The coefficient for attenuation by absorption, ,

200depends on frequency of acoustic device used for themeasurement and is set as 2.8 count/m in the present(a) 100cm below surface

100

SSC(mg/l)

study, considering the study by Suetsugu et al. (2002)
where the same type of ADCP was applied for the SSC
measurement. The parameters, B, k, and A are
calibrated so that Eq. (3.1) can well fit the relationship0200(b) 50 cm above bottom

100

between the recorded intensity, I, and SSC estimatedwith OBS. In the present study, the parameters are set0

as A=30, B=95 count and k=31, respectively.

10/16/0:00

10/17/0:00

10/18/0:00

Date

Comparisons between the OBS-derived SSC and thebackscatter intensities of ADCP at 100 cm below the

Figure 3.5 Time series of estimated SSC withsurface and at 50 cm above the bottom are shown in

ADCP comparing with OBS during spring tide.

Figures 3.4. The SSC measured near the water surface

during spring tide period are shown in Figure 3.5

are relatively low and the backscatter intensity iscomparing with OBS-derived SSC. The results show

ranging from 20 to 60 count only. In case of thethe estimates by ADCP data slightly over-estimate thenear-bottom measurement, SSC show more changeable

OBS-derived SSC but the temporal variation ofwith higher value and the backscatter intensity issuspended sediment due to tidal force can be reasonably

ranging from 130 to 170 counts. The fitted curves byreproduced

Eq. (3.1) with the calibrated parameters mentioned

by

the

method

with

ADCP.

The

discrepancies in the time series result near the surface

above are also shown in Figure 3.4 and it is showing

in Figure 3.5 (a) can be explained due to other sourcesthat they reasonably fit the data, especially near-bottomof suspend ed matters near the surface including

data.

phytoplankton etc. and air bubble during higher wave
condition with wave breaking, which affect response of

3.3.2 Estimated result of SSC by ADCP backscatter

acoustic signals differently from the suspended

data

sediments near the bottom.

The time series of SSC estimated by Eq. (3.1)

With the calibrated SSC from the back scatter data
- 22 -

50

4

8

Solid line: Hsig, Dots: Tsig

0.5

4

0

0

10/16/0:00

10/23/0:00

11/6/0:00

10/30/0:00

Tsig(s)01

SSC(mg/l)

108HSig(m)Distance from bottom（m)01211/13/0:00

Figure 3.6 Estimated SSC profiles with ADCP (contour in the upper figure) during the whole monitoring period
compering with wave conditions (the lower figure) monitored at wave tower.
of ADCP, temporal change in the vertical profile of
SSC at the site are shown in Figure 3.6 during thewhole monitoring period of one month. The figure also
shows significant wave height and period that wereobserved at the monitoring tower. It is apparent in the
figure that high concentration near the bottom occur
during every spring tide period and higher suspension
events are also indicated episodically according to therelatively rough wave condition as shown on November,

Figure 3.7 Estimated SSC profiles with ADCP during

6, with the significant wave height of over 0.7 m.

spring tide.

As for the relationship between the temporalvariation in near-bottom SSC and tidal current, the SSC

Snapshots of the vertical profiles of the SSC in theincreases during acceleration of flood and ebb current

accelerating phases of ebb and flood current are shownand decreases during deceleration and the minimum atin Figure 3.8. The highest concentrations are observed

slack water of high and low water at this coastal area

under the highest ebb current speed at 0:00 on Oct. 17(Nakagawa, 2002). The temporal change in the verticaland under the highest flood current at 6:00 on Oct. 17,

distribution of SSC during the spring tide period isrespectively, and these profiles are characterized by theshown in Figure 3.7 and the higher concentration

higher concentration around 70 mg/l near the bottom

appears during the ebb tide, from the high tide down toand exponential decay in the upward direction.

the low tide, and the flood tide, from the low tide up toFurthermore, these concentration profiles indicate thethe high tide. Considering these response of SSC profile

higher concentration under the flood current condition

to the tidal force condition, the temporal change in therather than the ebb current condition and thisconcentration of suspended sediment should beasymmetry is caused by the stronger current condition

dominated by the tidal currents mainly by local

during the flood period as is often observed around theresuspension around the monitoring site.

monitoring site (Nakagawa et al. 2007).

- 23 -

Figure 3.8 Change in the SSC profiles during accelerating period.
Typical values for M is usually taken constant
3.4 Estimation of erosion rate parameter from fieldtypically as 0.06 mg/cm2/min < M < 3 mg/cm2/min anddatae is ranging from 0.1 to 5 Pa (Winterwerp et al. 2004).
Because these variation of parameters depends on site
specific sediment characteristics, they should be taken

3.4.1 Erosion rate formula for muddy bed
There are several key parameters for modeling ofas values which corresponds to sediment characteristics

sediment dynamics and erosion speed and critical shearof target site in field for appropriate numericalstress of bed material is crucial for estimation ofsimulations. In the present study, these parameters weresediment transport rate under forces such as waves andevaluated with the field data of SSC estimated in thecurrent. These parameters are site specific since theyprevious section.

depends on sediment characteristics such as particle
size, water content or bulk density as mentioned in the

3.4.2 Parameter acquisition procedureprevious chapter. For cohesive sediments, the following
equation(socalledtheSeveral studies have been worked on the

Ariathurai-Partheniades

examination of erosion parameters from field measured

formulation) is often used for estimation of erosion flux,

data set of SSC profiles such as Sanford and Halka

E,(1993), Clarke and Elliott (1998) and Vinzon andeE  M ( c  1)

Mehta (2003). The parameters of M and e are, in the(3.2)

present study, estimated from the temporal variation of
total suspended sediment load (TSL), which is obtained

where M is erosion rate parameter, e is critical shear

as total weight of the SSC estimated with ADCP datastress for erosion, c is bottom shear stress by currents

through the water column. The TSL is calculated here as

and waves. Although there are other types of formula

total weight of suspended sediment from the bottom up

for erosion rate estimation including a power low

to the level of 7 m from the bottom, considering therelationship between erosion rate and shear stress (e.g.

otherFutawatari and Kusuda, 1993) and an exponential form

sourcesofsuspendedmattersuch

asphytoplankton near the surface.

(e.g Mehta, 1988), Eq. (3.2) has been widely applied to

As forcing parameter that is driving the temporal

numerical models for simplicity of the form.

variation of the TSL, bottom shear stress due to tidal- 24 -

shear stress so small values.

current is considered here. The bottom shear stress isestimated with the following quadratic law, c   wC f U 2

3.5 Simulation of temporal variation of total(3.3)

suspended load
where w is the density of sea water, Cf is dragcoefficient and U is current speed. The value of the

3.5.1 A one-point TSL modeldrag coefficient is the order of 10-3 for steady current

In general, the changes in SSC profile are affected(e.g. Soulsby, 1990) and ranging from 0.0016 to 0.0061

by advection and diffusive transport and local

depend on the roughness of the bed. For this study,

resuspension-deposition processes. However, the data

though the current is unsteady and tidally oscillate flow,

collected at the monitoring station of the present study,

the shear stress is simply estimated with a constant

as shown in Figure 3.7 and 3.8, indicates the higher

value of the drag coefficient as 0.0026 and U was

SSC events near the bed appear during ebb and floodtaken to be the current speed measured at 40 cm above

peak current for a spring tide period. The data suggests

the bed.

that the change in SSC profiles is strongly dominated

The rate of change in the TSL was calculated by theby resuspension and deposition due to the tidal current.

difference of the values every 30 minutes and they wereThis may simplify the phenomena and the temporalcompared with the bottom shear stress estimated aschange of the total suspended load was examined with amentioned above. Examples of the relationship between

simple 1D-model in the present study.

the rate of the TSL changes and corresponding bottom

Assuming the homogeneity of the field andshear stress are shown in Figure 3.10, which represents

neglecting the horizontal advection and diffusion terms,

the relationship during the same acceleration period of

SSin=SSout in Figure 3.9, temporal change in the total

the ebb and flood current examined in Figure 3.8. The

suspended load (TSL) through the water column can be

figure shows linear relationship between the bottom

simply expressed as balance between erosion rate, E,

shear stress and the increasing rate of the total

and deposition rate, D, at the bottom boundary.

suspended sediment load and regression equations ared (TSL)ED
dt

indicated in the figure.

(3.4)

Considering the same analysis of erosion parameters

For erosion rate, E, is estimated by Eq. (3.2) in thefor other acceleration periods (17 cases in total), thepresent study and the values of parameters in Eq. (3.2)

averaged erosion parameters are obtained as M=0.033

are set as 0.033 mg/cm2/min. for E and as 0.025 Pa for

mg/cm2/min. and e=0.025 Pa, respectively. The values

e which are estimated in the previous section. On theof both parameters M and e are smaller than theother term in the right hand side, deposition rate, D, isminimum of the ranges of the parameters mentioned

determined with the following expression,

above. Since the observed maximum bottom shear

D  wsCb p

stress is around 0.15 Pa, settled mud bed were not(3.5)

eroded considering the ordinal values as mentioned

where ws is settling velocity, Cb is the concentration ofabove. Therefore, it is conceivable that the suspended

suspended sediments near the bed, respectively. The

sediments are resuspended or picked up from fluff layer

probability function, p, is sometimes formulated as awith high water content just above the consolidated

function of bottom shear stress with a parameter of

mud bed and it makes the erosion rate and the criticalcritical stress for deposition, d. (e.g. Krone, 1993).

- 25 -  
p  1  c  d 

(3.6)

Use of the probability term for deposition is not able tosimulate the observed concentration of suspended
sediment (Sanford and Halka, 1993) and they applied acontinuous deposition formula (p=1) to reproduce
observed concentration change.

3.5.2 Simulation of temporal variation of totalsuspended load
Based on Eq. (3.4), the time variation of the TSL

Figure 3.9 Sediment budget in water column for

was simulated considering Eq. (3.2), Eq. (3.3) and Eq.

temporal variation of total suspended load (TSL)(3.5). The probability term is not included here but thebackground concentration. The figure shows that theresult is compared with that with Eq. (3.6) later. The

observed TSL variation dominated by tidal current issettling velocity was calibrated so that the observed

reasonably well simulated by the model with the

TSL variation can be best fitted visually and thecalibrated

calibrated settling velocity, ws’, in the simulation issettling

velocity

andthecontinuous

deposition formula. Since the model does notdefined as following.

incorporate the wave induced bottom shear stress, thereD  ws C(3.7)

are discrepancies between the model result andobserved TSL during the high wave event on Nov. 6,

where C is depth averaged suspended sediment2001. The good agreement of the numerical result

concentration.

means that the temporal variation of the TSL is

The estimated result of TSL variation with ws’= 0.18

dominated by the local resuspension around the

cm/s is plotted in Figure 3.11 and compared with

monitoring site driven by the tidal current and theseobserved

parameters can be applied to 3-D simulation for

TSL

data.

For

the

simulation,

theErosion rate (mg/cm2/min.)concentration during neap tide was considered as

sediment transport.

0.15●：period 1
E = 1.23c - 0.0341 (r2=0.997)○：period 2

0.1

E = 0.771c - 0.0374 (r2=0.974)

0.05

0
0

0.05

0.1

0.15

Bottom shear stress (Pa)

Figure 3.10 Relationship between erosion rates and bottom shear stress for the monitoring site

- 26 -

Current speed(cm/s)

TSL(mg/cm2)

40

(a)

20
0
20

Obs.

10

Cal.

0
10/16/0:00

10/20/0:00

10/24/0:00

10/28/0:00

TSL(mg/cm2)

Current
speed (cm/s)

Date
40

(b)

20
0
20

Obs.

Cal.

10
0
10/30/0:00

11/3/0:00

11/7/0:00

11/11/0:00

Date

Figure 3.11 Comparison between observed (indicated as “Obs.”) and simulated (indicated as “Cal.”) total suspended
load. ((a) Period from Oct. 16, 2001 through Oct. 29 , (b) Period from Oct. 30, 2001 through Nov. 13)
suspended sediment dynamics at the monitoring site.

In order to check the effect of the probability term
expressed with Eq. 3.6, another calculation was carriedConsidering time series of total suspended sedimentout for the TSL estimation, where d was set as d =eload (TSL) calculated from the estimated SSC profiles,

here. The computed result of TSL with Eq. (3.6) iserosion rate parameter, M, and critical shear stress forcompared with the observed and the computed result

erosion, e, are obtained as M=0.033 mg/cm2/min. andwith the continuous deposition (p=1) in Figure 3.12.

e=0.025 Pa, respectively. The values of both

According to the comparison, the result with Eq. (3.6)

parameters M and e are smaller than values inshows that deposition does not occur until c <d andliteratures and it is conceivable that the suspended

the estimated result overestimate the TSL in thesediments are resuspended or picked up from fluff layer

observed decreasing phase, while the continuous modelwith high water content just above the consolidated

is able to simulate well the pattern of TSL variation.

mud bed around the monitoring site. These parameters
may be applied for practical numerical simulations ofsuspended sediment dynamics in the bay.

3.6 Conclusionsfor

The temporal variation of the observed TSL ismeasurement of current and turbidity for one month off

highly influenced by the tidal current and is in phase

the Kumamoto Port in the Ariake Bay. Sedimentwith the tidal current speed. The estimated TSL withdynamics under the tidal dominated force condition

observed suspended sediment concentration can bewere examined through analysis of the field measured

simulated well by a simple 1-DV model with thedata. Transformation technique of ADCP acoustic

calibrated settling velocity as ws’= 0.18 cm/s. The

backscatter intensity into SSC has been applied throughmodel with the continuous deposition concept shows

the calibration with OBS derived SSC data. The

quite good performance to simulate the temporalobserved time series of SSC profiles indicates that local

variation of observed TSL, while the model witherosion and deposition processes are responsible forprobability function is not able to simulate the decrease

Monitoring

campaign

wasdeployed- 27 -

of the TSL during the decelerating current period.
These result supports for the indications by Sanford andHalka (1993) and Winterwerp and van Keseren (2004)
that

the

continuous

deposition

model

is

more

reasonable than the model with probability function fordeposition. The parameters obtained through thepresent study can be applied to 3-D simulation for
sediment transport dynamics.

References for Chpter3
Clarke, S. and Elliott, A. J., (1998): Modelling

Figure 3.12 Reproducibility of estimated TSL withsuspended sediment concentrations in the Firth ofcontinuous deposition formula (p=1) and Krone’s

Forth, Estuarine, Coastal and Shelf Science, Vol.47,

formula with Eq.(3.6).

pp.235-250.
Futawatari, T. and T. Kusuda (1993): Modeling ofKusuda, T., H. Yamanishi, H. Yoshimi and R.

suspended sediment transport in a tidal river. In:
Mehta, A.J. (Ed.), Nearshore and estuarine cohesive
sediment

transport,

Coastalestuarine

studies,

Mehta, A. J., (1988): Laboratory studies on cohesive
sediment deposition and erosion, In Dronkers, J. andDerivation of sediment resuspension rates fromW. van Leussen (Eds.) Physical processes inacoustic backscatter time-series in tidal waters,

estuaries, Springer-Verlag, pp.427-445.

Continental Shelf Research, Vol.23, pp.19-40.

Nakagawa, Y., S. Imabayashi and K. Suetsugu (2002):

Kondo, M., Y. Tohara, K. Hiramatsu, S. Shikasho and K.

Field data analysis of fine sediment transport process

Mori (1996): Characteristics of Erosion on cohesivein Ariake Bay, Proc. of Coastal Engineering, JSCE,

sedimentary materials by tidal current, Trans. ofVol. 49, pp.556-570. (in Japanease)

Japanease Society of Irrigation, Drainage and Rural

Nakagawa, Y., H. Yoshida, K. Tanaka and M. Oh-hataEngineering (JSIDRE), No. 182, pp.109-115. (in(2007): Field observation on resuspension of fineJapanease)sediments and turbulence structures in bottom

Krone, R. B., (1993): Sedimentation revisited, In Mehta
andestuarineandVol.115 (3-4), pp.271-287.

Hill, D.C., Jones S.E. and Prandle, D., (2003):

Nearshore

disturbed

studying sediment transport. Marine Geology,

Vol.211,

pp.169-187.

J.(Ed.)

of(1993): Vims sea carousel – a field instrument forby acoustic Doppler current profiler in San FranciscoA.

characteristics

onMaa, J.P.Y, L. D. Wright, C. H. Lee and T. W. Shannon,

concentrations from backscatter intensity measured
Geology,resuspension

studyJapanease)

Gartner, J. W., (2004): Estimating suspended solids

MarineExperimental

Engineering, JSCE, Vol. 36, pp.314-318. (inPp.504-519.

California,(1989):

undisturbed muddy sediments, Proc. of Coastal

American Geophysical Union, Washington, DC.

Bay,

Futawatariboundary layer, Proc of Coastal Engineering, JSCE,

cohesiveVol. 54, pp.445-450. (in Japanease)sediment transport, American geophysical union,

Sanford, L. P. and J. P. Halka, (1993): Assessing theCoastal and estuarine studies, pp.108-125.

- 28 -

paradigm ofmutually exclusive

erosionanddeposition of mud, with examples from Chesapeake
Bay, Marine Geology, Vol. 114, pp.37-57.
Seguchi, M., K. Watanabe and O. Katoh (1989):
Near-bottom current and turbidity in shallow coastof eastern-shore of Ariake Bay, Proc. of Coastal
Engineering, JSCE, pp.819-823. (in Japanease)
Suetsugu, T., K. Fujita, Y. Suwa and K. Yamamoto(2002):

Influenceof

sediment

transport

on

topography and bed material change at river mouth
estuary, Technical Note of National Institute forLand and Infrastructure Management, No.32, 169p.
Soulsby, R. L., (1990): Tidal-current boundary layers,
The Sea, Part2, pp.523-565.
Vinzon, S. B. and Mehta, A. J., (2003): Lutoclines inhigh concentration estuaries: Some observations at
the mouth of the Amazon, Journal of Coastal
Research, Vol.19, No.2, pp.243-253.
Winterwerp JC, van Kesteren W (2004): Introduction to
the physics of cohesive sediment in the marine
environment, Elsevier, 466p.

- 29 -

4 DATA ANALYSIS AND MODELING OF MUD
TRANSPORT

DURING

STORM

EVENT

IN

TOKYO BAY

4.1 Introduction
Sediment transport process at river mouths is highly
dynamic system including dispersion of sedimentparticles by waves and currents. Although there areseveral field measurement works which have figured

Figure 4.1 Location of Tokyo Bay and the Tamaout sediment dispersal at river mouths during floodRiverevents, understanding of sediment transport mechanism
is only limited because of difficulty of making longterm measurements to capture such highly intermittent
event. Since fine sediment transport process at rivermouth has a key role on variations in surrounding water
quality and ecological systems, better understanding of
the mechanism is critical to maintain and restore
estuarine and coastal environments in the area.
Study site of the present study is off the mouth of

Figure. 4.2 Fluid mud on surface layer

the Tama River in Tokyo Bay, Japan, where a newwith the total drainage basin area of 6,000 km2. The

runway has been developed and just opened in the fall

Tama River has basin area of 1,240 km2 (the secondof 2010 as a part of the Tokyo international airport. In

largest of the bay) and carries around 20 m3 /s at typicalorder to grasp any effects of the runway construction on

background flow rates and 200-1,000 m3 /s during flood

the surrounding environment of the water area,

events. The topography of the river mouth area has

comprehensive monitoring has been carried out since

been highly developed artificially for industrial uses

before the construction work started in 2007. This paper

and a runway for the Tokyo International Airport has

discuss observations of fine sediment transport process

been newly extended in the water area for recent years.

at the river mouth using measured data during the

The monitoring point of the present study is located off

extreme storm and flood event by a passage of athe river mouth with the mean water depth of about 25

typhoon in September 2007.

m. Bottom sediment is characterized by very soft mud(more than 99 percent of silt and clay) with high water

4.2 Site description

content over 300% according to the sediment analysis

The study site is located at the Tama River mouth inof samples of bottom sediment as shown in Figure 4.2.

the Tokyo Bay, Japan (Figure 4.1). The surface area ofSome examples of the vertical profiles of water content

2

the bay is approximately 1,500 km and averaged depth

are represented in Figure 4.3, indicating also theis around 15 m experiencing semi-diurnal, meso-tidal

sampling locations of the samples.

condition with a maximum spring tidal range of around2 m in the upper bay area. Several rivers, including the

4.3 Instrumentation

Tama River, flow into the north western part of the bay

Several acoustic and optical sensors were deployed- 30 -

(b)

(a)
10

Stn. B3
Tokyo Int. Airport
Tama Riv.
Stn. B1

20

Stn. B2km
02

4

Figure. 4.3 Monitoring sites of bottom sediments (a) and water content profiles of samples (b). The contour lines
represent the water depth in the map. (same figures as in Figure 2.13 and 2.19)
suspended sediment was measured with a LISST 100(laser in situ scattering and transmissometry) developed
by Sequoia Scientific Inc. and the measurement levelwas 10 cm above the bed.

4.4 Data analysis

4.4.1 Estimation of bed shear stresses
Figure.

4.4 Instrumentationlayout forbottomBed shear stress was calculated with a bottom

boundary measurementboundary model by Soulsby (1993) in the present study.

for the present study as shown in Figure 4.4. A Nortek

Mean current ( U  u 2  v 2 1 / 2 ) and representative wave

Vector acoustic Doppler velocimeter (ADV) measured

velocity ( U~ br ) were calculated from the ADV

three dimensional velocities at 10 cm above the bottom
every half hour at 8 Hz in about 2 minutes burst (1024measuring three dimensional velocities at 10 cm above

data per burst). For the measurement of verticalthe bottom. The mean current stress is expressed as the

structure of water velocity, an upward looking 600 kHz

following equation,

Workhorse ADCP of Teledyne RD Instruments wasused to record averages for 2 minutes every 10 minutes c   wC f U 2

with 30 cm range bins starting 1.62 m above the bed.

where w is the water density. The friction factor for

Opticalmean current, Cf, is set as 0.0041 referenced to thebackscatter

sensors(OBS),

using

Compact-CLW of JFE Advantech Co., were moored atmeasurement height (= 0.1 m) above the bed.

several levels near the bottom for the measurement of

Wave stress is given bysuspended sediment concentration (SSC) profiles.
Measured optical backscatter intensity

(4.1)

w 

was calibrated1~
 w f wU br2
2

(4.2)

using sediment samples taken from the site into SSC

with the following wave friction factor, fw (Soulsby,

with the unit of mg/l. Particle size distribution of1997)- 31 -

f w  1.39( A / z0 ) 0.52

with the frequency of 8 Hz during each 2-minute burst(4.3)

every 30 minutes. The data can be used to estimate thewhere the wave orbital amplitude, A=ubT/2 and T isvertical turbulent diffusion of suspended sediment as

the wave period. The bottom roughness length, z0, is

the Reynolds flux

assumed as 0.2 mm considering the bottom sediment
properties. The value of the wave friction factor fw

Fz  c ' w'

varies between 0.003 and 0.05 under the present

where c’ and w’ represent fluctuating components of theanalysis. The representative wave velocity, U~ br , issuspended sediment concentration and the verticaldefined as (e.g. Madsen, 1994),

fluctuations are defined as the deviation from the mean-~U br  2 u~ 2 rms  v~ 2 rms



(4.7)

velocity measured by the ADV, respectively. Theseand wave-band variations. In the results section of this

(4.4)

paper, temporal variation of the flux is used to validate~ and v~
where the root mean square velocities, u
rms
rms

the proposed formulation to estimate the resuspension~ and v~ , ofare calculated with wave band variation, u

flux.
Acoustic devices were used to measure bed

the ADV data over frequencies from 0.03 to 0.25 Hz.
The non-linear combined wave-current stress wascalculated by the following equations as the maximum
stress, max, and the mean stress,m (Soulsby, 1997) max   m   w cos  2   w sin  2 m   c 1  1.2(


 w 3.2 ) c w 



0.5

(4.5)

(4.6)

where is the angle between the mean current andFigure. 4.5 Comparison of suspended sedimentdominant wave direction.

concentration estimated OBS with ADV backscatter

4.4.2 Erosion flux and bed elevation measurements
The ADV system can be used to estimate thesuspended sediment concentration using the acoustic
backscatter signal (e.g., Hay and Sheng 1992,
Kawanishi and Yokoshi 1997, Fugate and Friedrichs
2002). In the present study, backscatter intensity wascalibrated with SSC data obtained by the OBS at thesame elevation in the vicinity of the ADV sensor. The
relationship between the measured SSC and the mean
acoustic backscatter intensity per burst is shown in
Figure 4.5. Based on the correlation, acoustic

Figure. 4.6 Typical example of backscatter intensity

backscatter measured by the ADV was calibrated to the

profile obtained by the ADV measurement with (a)SSC. The backscatter data of the ADV was recorded atecho from velocity measurement layer and (b) echo

the same sampling rate as the velocity measurementfrom the bottom- 32 -

elevation in several previous works (e.g. Andersen et al.

closest passage of the typhoon (Figure 4.7 (a), (d), (e)).

2006, Verney et al. 2007); acoustic backscatter records

According to statistics based on the long-term wave

obtained by the ADV were used for this purpose in therecords from 1983 to 1992 at a monitoring station in thepresent study. The instrument receives and records the

Bay (Japan Weather Association 1994), the recurrence

backscatterprobability of the wave event is less than 1% and thesignal

not

only

from

the

velocity

waves were the highest in the last 10 years.

measurement range but also from other surrounding
ranges (Nortek 2004) as shown in Figure 4.6. The

Coinciding with the wave event, there was also a

distance from the sensor to the bottom boundary can beprominent discharge of fresh water from the Tamaclearly detected in the profile of the backscatter data.

River, peaking at over 3,500 m3/s (Figure 4.7 (b)) andrecording the largest flood since 1982. The wind data
were measured by the Automated Meteorological Data

4.5 Observed data in the storm and flood event
While the instrument was deployed from August to

Acquisition System (AMeDAS) at the Haneda station,

September 2007, Tokyo Bay experienced an extreme

operated by the Japanese Meteorological Agency. The

storm and flood event due to the passage of a typhoon

wave gauge is located at the Tokyo Bay Light Housein early September. With strong southeasterly winds

station (35°33′58″N, 139°49′41″E) near Stn. C inreaching 25 m/s, the significant wave height exceeded

Figure 4.3 where the water depth is about 14 m, some 6

2.5 m and the wave period was over 5 s at the time of

km north from the long-term measurement site.

Figure. 4.7 Time series of (a) wind, (b) fresh water discharge of Tama River, (c) tide, and (d) significant wave
height and (e) period- 33 -

SSC at B + 0.0 m decreased, which was probably due toTemporal variations of SSC, turbulent diffusion flux

the limitation of the sensor range.

and bed level are shown in Figure 4.8 compared withthe force condition during the extreme storm and flood
event between September 5 and 12. During the period

4.6 Estimation of near-bed sediment fluxesof high shear stress caused by waves and currents on

The sediment concentration or density profile is

September 7, the near-bed SSC rapidly increased with acharacterized as shown in Figure 4.9, considering thehigh concentration of 2,000 mg/l just above the bottom

observed near-bed structures around the study site.

(B + 0.0 m), 1,000 mg/l at 10 cm, and 100 mg/l at 50

There is a clear gap between the sea water and the top

cm. During the high near-bed SSC event, the turbulentof the thin fluid mud layer over the consolidated mud.

diffusion flux of sediments calculated by Eq. (4.7) also

Key processes that should be estimated to model thisshowed higher values in the upward direction during

muddy sediment environment include vertical transport

the period. At the same time, the sea floor underwent

rate expressed as upward flux (Fz) and downward flux

slight erosion of 20 mm according to acoustic(FD). It is also necessary to model the advection process

measurements of the bed elevation.

of the sediment in the mud layer in horizontal directions.

The data shows a deposition of 50 mm following theThis paper examines methods of estimating these

erosion event, where the combined shear stressprocesses.

decreased to 0.3 Pa. During this deposition period, the
SSC at the lowest layer, B + 0.0 m, experienced an

4.6.1 Vertical fluxes

extremely high concentration of over 30,000 mg/l

The settling flux, based on the simultaneous erosion

indicating the existence of fluid mud near the bed. In

and deposition concept (e.g. Winterwerp and van

spite of the continuous deposition during the period, theKesteren 2004), is given by:

Figure. 4.8 Observed data during storm and flood event of (a) bottom shear stresses, (b) SSC, (c) erosion flux, and (d)bed elevation change- 34 -   w  C  s   w   s (4.12)

where w is the density of water and s is the density of
sediment particles.
In the present study, Eq. (4.9) was used to estimate
the upward flux between the fluid mud layer and theupper water column. The estimation was carried out fortheFigure. 4.9 Estimated contribution of sedimentobserved

forcingandsuspended

sediment

conditions during the storm event as described influxes on bed level change calculated by sedimentsection 4.5.

budget analysis near the bottom layer. (Zobs:
Observed bed level change, Z’: Bed level change by

4.6.2 Fluid mud flow

vertical fluxes, Zrsd: Residual bed level change

For estimating the advection flux in the fluid mud

estimated as contribution of horizontal flux in thelayer, an analytical solution for the velocity profile inpresent study.)

the mud layer was derived by assuming non-NewtonianFd  ws Cb

fluid behavior in the layer. Many previous works dealt(4.8)

with mud flow as a non-Newtonian fluid (e.g. Liu andwhere ws is the settling velocity of the suspended

Mei 1989, Huang and Garcia 1999, Knoch andsediment and Cb is the concentration. For the upward

Malcherek 2011). Here, the Bingham fluid model isflux in the water column, the following diffusion flux

used in which the relationship between stress and strain

formulation (e.g. Ross and Mehta 1989) can be applied:CFz   K z
zis expressed as:(4.9)

   y sgn

where Kz is diffusivity, which can be modeled using the

u
u m
 m
z
z

,

the neutral condition without stratification, K0:
K z  K 0 1  Ri 
(4.10)

u m
0 ,
z

if

 y
(4.13.b)

The parameters  and  in the equation are set atwhere  is the kinematic viscosity and y the yield

3.33 and 1.5, respectively, as proposed by Munk andstress of the fluid mud. These parameters depend on theAndersen (1948). The gradient Richardson number, Ri,bulk density and mineralogy. Analytical solutions foris the ratio of potential to kinetic energy gradients andis expressed in the following form,g  z 
Ri   u z 2

  y(4.13.a)gradient Richardson number, Ri, and the diffusivity inif

the vertical profiles of the horizontal current speed, um,
in the fluid mud layer are derived from an integral form

(4.11)

of Eq. (4.13):

where g is the acceleration due to gravity, is the local

u m ( z) fluid bulk density, and u is the current speed in the z    z dz
 
1

z

y

(4.14)

 hyhorizontal direction. The bulk density is related to the

The lower limit of the integration interval, −hy, issediment concentration, C, in the fluid as:

the yield surface level at which the external force, (z),is equal to the yield shear stress, y(z), in the mud layer.

- 35 -

The shear stress distribution in the mud layer can be

Figure 4.8 (c) compared with the directly measured

derived from momentum-balance equations.

resuspension flux calculated as Reynolds flux. The

Under a simplified condition with a flat bed andestimation delivers quite reliable results, except theequilibrium state in space and time, external shear

slight overestimates of resuspension during the earlystress in the mud layer becomes a constant andperiod of the storm event around September 7. This can

indicated as b. The external stress represents the shear

be improved in practice by introducing a critical Ri’ forstress at the boundary between the upper water column

erosion. This result implies that Eq. (4.9) is applicableand the mud layer. The kinematic viscosity is treated as

to the bottom boundary condition of the resuspension

constant through the depth considering the mud

flux in the case of an unconsolidated loose muddy

concentration as 0.5 Pa.s in the present study. On thebottom.

other hand, non-uniformity of yield shear stress in the

For the estimation by Eq. (4.9), the parameter K0 is

mud layer is taken into account in the present analysis.

calibrated and set at a constant value of 0.0044 m2/s for

The distribution profile was determined by considering

the result in Figure 4.8, considering the time series ofthe observed density profiles in the mud layers as

the directly measured flux or Reynolds flux by Eq. (4.7).

described precisely in the results section.

Note that the modified Richardson number Ri’ (Eq.
(4.15)) is not the same as the expression of flux
Richardson number; non-dimensional friction factors

4.7 Results

for current and waves are included in the bottom shear
stress term in Eq. (4.15). Therefore, the calibrated value

4.7.1 Erosion flux estimation
The erosion flux during the observed storm periodof the parameter K0 above is not equivalent to thewas estimated with Eq. (4.9) by using the field

diffusivity in the neutral condition. However, it ismeasurements of forcing and suspended sedimentremarkable that the diffusive flux expression dependentconditions described in section 4.4. For calculating theon the Richardson number and vertical gradient of the

diffusivity in Eq. (4.10), the modified Richardson

mean concentration can reliably reproduce the temporal

number Ri’ is introduced:

variation of the measured Reynolds flux after thegh( 10   b )
Ri  

 b _ maxadequate calibration of the coefficient.

(4.15)

where h is the distance between the bottom and the

4.7.2 Sediment budget analysis near bed surface

ADV measurement point (10 cm), 10 is the bulk

Considering the estimation results of verticaldensity of sea water with suspended sediment at 10 cm

sediment fluxes near the bed, sediment budget can beabove the bed, and b is that at the surface of the fluid

analyzed with the following sediment weight balance

mud layer. The bottom shear stress, b_max, is evaluatedequation.

by the velocity measurement data as non-linear dcombined wave-current stress as described in sectiondZ (t )
 Fz  Fd  Fr
dt

(4.16)

where d is dry density of sediment and Z(t) is the

4.4.
By substituting Eq. (4.15) into Eq. (4.10), theobserved bed level detected by the ADV’s backscattervertical flux was calculated with Eq. (4.9) for the period

signal. The sediment density was assumed as 1,300of the storm event in September 2007 shown in Figurekg/m3, which is equivalent to mud concentration of 300

4.8. The results estimated by Eq. (4.9) are plotted in

kg/m3, and it is relatively high concentration of fluid- 36 -

mud concentration range. In case that the mud
condition is assumed as consolidating mud with theconcentration of 500 kg/m3, the mud thickness becomes
60 % of the present results. Fz and Fd are upward anddownward sediment fluxes and they are calculated with
Eq.(4.7) and Eq.(4.8), respectively. The settling velocity
was assumed as 0.1 mm/s considering the sedimentparticle size. Fr is residual term in the analysis and it
deserves as the sediment flux that is contributed byhorizontal

flux

in

the

layer

under

the

Figure. 4.10 Schematic diagram of the transport processADVof mud bed with thin fluid mud layer

measurement height at 10 cm from the original bed
level.
The result of the analysis is shown in Figure 4.9 forthe duration of 24 hours under the storm event
condition. The solid line in the figure is the temporalchange in the bed level, which is already shown in
Figure 4.8 (d). Dashed black line is bed level change
calculated as the contribution of net vertical flux of Fz
and Fd, and Red line is residual terms. Based on theassumptions mentioned above, the bed level change by

Figure. 4.11 Definition of parameters for sediment

the residual contribution becomes 6 cm of deposition

concentration profile

through the storm event. The result means that thisKessel and Kranenburg (1996),

deposition should be compensated by the horizontal
 Cm ( z)  s 

flux and the horizontal transport should be taken intoc2

(4.17)

 y ( z )  c1 

account for near bed sediment transport dynamics as

where the empirical parameters c1 and c2 are set atshown in Figure 4.10.
In order to estimate the horizontal mud flux,

1,000 N/m2 and 3 respectively and s represents theestimation formula is newly derived in the following

density of sediment particles (= 2.6 g/cm3). For thesection.

sediment concentration profile of Cm(z), the following
function (e.g. Foda et al. 1993) is introduced:

C m ( z )  C 0  C  z D 

4.7.3 Derivation of fluid mud flux formula

0.25

Considering the observed near-bed structure of the(4.18)In Eq. (4.18), C0 is the concentration at the top of

muddy sediments, we propose a method for estimating

the fluid mud layer and C represents an increase in thehorizontal sediment transport flux in the mud layer. By

concentration at the depth of −D from the surface of theintegrating Eq. (4.14), we can obtain a vertical velocity

mud layer as shown in Figure 4.11. These parametersprofile in the fluid mud layer. In order to make theare chosen so that the approximate function, Eq. (4.18),

integration of Eq. (4.14) possible, we introduce theclosely fits the field data of sediment concentrations asfollowing relationship between the yield shear stress, y,shown in Figure 4.12 (a). Substituting the distribution

and the sediment concentration, Cm, proposed by van- 37 -

function of the yield shear stress, Eq. (4.17), into Eq.

q m ( x, y , z , t ) (4.14) allows us to derive a formulation of the velocityhb ( x , y ,t )

 C ( x, y, z, t )u ( x, y, z, t )dz
m

m

 hy

profile in the mud layer under the shear stress on thesurface of the mud layer, b, asum (z)   0 21

[  b   0 z  h y    0  1

5/4
5/4

 hy
4   z 

D
5/4


5 
D


3/2
3/2

  z 7 / 4  h y 7 / 4 
2   z   h y    4 D
]
D
0
3
3
/
2


3 
7 
D
D 7/4





9
9
5
D2 
 b   0  1 C 0 hy 2  16 Chy 4    0 1  4 C 0 hy 4  8 Chy 2 

45
25
 

2

9



11
5
11
D2 
16
4

4
2
3 
  0 2  C 0 h y 2 Ch y 4    0 3  C 0 h y 4  Ch y 
 
55
15


 11
5

(4.20)
where(4.19)

 h y  C 0    b 

hy 
 
D  C   c1 

4

where
C
C
C 2
C 3
 0  c1 ( 0 ) 3 ,  1  3,  2  3(
) , 3  (
)
s
C0
C0
C0

.

1/ 3


 1
C0


s

(4.21)

By using the above equation, horizontal sediment

The height of the yield surface, hy, is defined by themass transport rate in the fluid mud layer can be

relations between external shear stress and internal

estimated as a function of the external force or theyield shear stress. The results of computing velocity

bottom shear stress on the surface of the mud layer as

profiles using Eq. (4.19) are indicated in Figure 4.12 (c)demonstrated

in the two cases of b = 1.0 and 1.5 Pa, as examples.

in

Figure

4.13.

After

appropriate

validations of the results, the method could be applied

The parameters in the model used in the present study

for spatial mud transport simulations.

are indicated in the figure. In both shear stress cases,
the shear flows appear at the near surface of the mud

4.8 Conclusions

layer under the effect of the shear stress exerted by theThecurrent of the upper water layer. The mobility layers arepresent

study

examinedthetransport

characteristics of a mud bed with a thin fluid mud layer

limited up to the yield surface level.

observed in Tokyo Bay, where muddy sediments with

Furthermore, by considering the velocity profile inhigh water content appear at the bed surface. Field

the fluid mud layer and the fitted concentration profile,measurements captured a near-bed process with erosion

the horizontal sediment transport rate can be formulated

and deposition of the muddy sediments during a storm

asand flood event. As a preliminary work for modeling(a) Concentration(c) Velocity in mud layer(b) Yield stress

Sediment depth (m)0

tb =1.0Pa

0.05
-0.05

-0.1
0.10

tb =1.5Pa

0.15
-0.15
0.20
-0.2
0.25
-0.25
0.30

0

100 200 300 400

01(kg/m3)

2

0

0.2

(Pa)

0.4

(m/s)

Figure. 4.12 Profiles of modeled sediment properties: (a) concentration, (b) yield shear stress, and (c) estimatedcurrent speed in mud layer (Cb0 = 50 kg/m3, C = 250 kg/m3, D = 0.3 m,  = 0.47 Pa s)- 38 -

the entire muddy sediment transport process in Tokyo
Bay, estimation methods were examined for the key
processes.
A diffusion flux model was calibrated and used for
estimating the temporal variation of the vertical flux ofresuspended sediment near the bed. For the calibration,
field measurements of current and suspended sedimentconcentrations during a storm and flood event in Tokyo
Bay were used. The estimated result showed reliable

Figure. 4.13 Calculated sediment flux in mud layer

performance, implying that the diffusion flux type ofunder shear stress by overlying water current

formula can be applied for the bottom boundary
condition of the erosion flux from an unconsolidated

For application to more dynamic environment withloose muddy bed.

thick fluid mud layer often observed in the river mouth

The study also examined an estimation of total

area, the interaction should be considered in thehorizontal transport rate in the mud layer driven by

momentum equations or damping effect on theexternal shear stress. Flow velocity profiles in the mudhydrodynamics of water above the fluid mud layer.

layer were derived from a set of basic equations, where
the fluid mud layer was assumed to be a Bingham fluid.

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the sediments in the study site and incorporated aand high-resolution measurements of bed level

distributionchanges in a temperate, microtidal coastal lagoon.

function,

which

fits

the

sediment

Marine Geology 226, pp 115–125.

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Ariji R, Yagi H, Nadaoka K, Nakagawa Y, Ogawa H,

under an arbitrary shear stress condition assuming an

Shimosako K, Shirai K (2011): Temporal and spatial

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Fan S, Swift DJP, Traykovski P, Bentley S, Borgeld JC,

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and

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California.

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Vol.

JournalofGeophysicalK, Shirai K (2011): Field measurement of erosion

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C09025,and deposition processes of muddy bed sediment
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Hay AE, Sheng J (1992): Vertical profiles of suspended

Nobre AM, Ferreira JG, Nunes JP, Yan X, Bricker S,

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GeophysicalLan D, Lencart e Silva JD, Pascoe P, Telfer T, Zhang

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X,

Hsu T-J, Ozdermir CE, Traykovski PA (2009):

Zhu

M(2010):

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Sohma A, Sekiguchi Y, Kuwae T, Nakamura Y (2008):

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- 41 -

5 EFFECT OF WIND WAVE DISTURBANCE ON

gauge respectively. DO concentration measured at 50

TEMPORAL VARIATION OF NEAR- BOTTOM

cm above the bottom intermittently increased especially

DISSOLVED OXYGEN IN INNER TOKYO BAY

after relatively higher wave events. The data also showsthat the DO concentration near the bed is highly
correlated with suspended sediment concentrations.

5.1 Introduction
In addition to the bottom sediment qualities, there
are several water quality parameters that are crucial for

5.2 Monitoring site and instrumentations

the benthic ecosystem biodiversity and they may be

The data analyzed in the present chapter wasaffected by physical conditions by waves and current.

obtained through the monitoring campaign at the site inAmong them, dissolved oxygen near the bed is quite

the north east of the Tama River mouth shown in Figure

important indicator for estuaries and inner bays where

5.1 for 3 month from July through September in 2010.

hypoxic state have often occur. Tokyo Bay also has

As shown in Figure 5.2, surface mooring for thechronic occurrence of hypoxia or anoxia especially inmeasurement of water quality at several depths andthe inner bay area in summer season and thebottom mounted instrumentation for current and wave

mechanisms of variation in space and time have beenmeasurements were deployed at the monitoring station.

studied in the previous studies (e.g. Fujiwara et al.,2000). From the hydro dynamical viewpoints, theimportance of meteorological condition including windand fresh water discharge has been pointed out on the
movement of hypoxic water in the lower layer aroundthe inner bay (Ueno et al, 1993, Nakayama et al. 2007,
Yagi et al., 2008). Although the wind waves also may
have an important role on the variation of the spatial
distribution of dissolved oxygen through the verticalmixing, the waves are not inferred in the previous
literatures.
In the present chapter, the field observations of

Figure. 5.1

Location of monitoring sitewater quality in the summer of 2010 reveal that thewind wave is one of key factors for temporary recoveryof near bed dissolved oxygen (DO) concentration fromhypoxic state in the inner Tokyo bay. The observation
was a part of monitoring campaign to elucidate thebehavior of the hypoxic water in the area. Basicparameters of water quality such as dissolved oxygen,salinity, temperature and turbidity were measured by amooring system for 3 month from July throughSeptember in 2010. Physical parameters were also
obtained for current profile by a bottom mounted

Fig. 5.2

ADCPs and for wind waves by an ultrasonic wave

current and water qualities.

- 42 -

Instrument set up for monitoring of wave,

Measurement items of water quality includes dissolved
oxygenconcentration,

turbidity

withsalinity,

Compact-CT,

temperature

andCompact-DOW

and

5.3 Measurement results and discussion

5.3.1 Salinity, temperature and DO concentration

Compact-CLW of JFE Advantech Co. Ltd., which

variations in summer of 2010

recorded the data every 10 minutes. These sensors were

Temporal variation of the vertical profiles of waterlocated at the 4 depths of 0.5 m, 3.6 m, 7.2 m and 10.8

temperature, salinity and density expressed by t arem from the sea surface and were recovered on a boat

shown in Figure 5.3, which are monitored under thefor checking and cleaning the sensors to prevent from

depth of 3.6 m from the water surface. The water near

biofouling twice a week during the monitoring period.

the bottom experienced hypoxic condition from July

Checking operation of the bottom mounted instruments

through early September with density gap developed.

for

wave

After that, the density gap was cleared and it is foundmeasurement by Wave Hunter (IO Technic) wereincreasing of water density and recovery of thecarried out by scuba divers once a week during thedissolved oxygen in the end of September. It is noted

whole monitoring period. Besides the mooring andthat even during the hypoxic state period from Julybottom mounted instrumentations, vertical profile data

through early September, there are several temporaryof water quality were measured by hanging a water

recoveries of dissolved oxygen concentration as

quality profiler (AAQ-1183 of JFE Advantech Co. Ltd.)

indicated triangles in Figure 5.3. This chapter focuses

every 4 or 5 days at the monitoring stations as shown inon

Figure 5.1.

concentration and relationship between the event andcurrent measurement

by ADCP andthetemporalrecoveryofnear-bottomDOphysical conditions such as wind waves and currents.

Figure 5.3

Temporal variations of (a) Salinity, (b) Water temperature, (c) density (t) and (d) concentration ofdissolved oxygen during whole monitoring period. Triangles in (d) indicate typical recovery events of near bed
dissolved oxygen concentrations.

- 43 -

water.

During the monitoring period, there were only slight

Observed density variation in the water column isprecipitation with the maximum of 30 mm/hour on Sept.

compared with wind velocity and DO in Figure 5.4,

8 and less than 10 mm/hour on Jul. 29 and Aug. 9.

which shows also significant wave height, period andthe depth of t=20 as an indicator of density interface

5.3.2 DO concentration and wind events
The temporal variation of DO concentration in the

between upper light and lower dense water. It is foundinner Tokyo Bay is owing to dynamics of hypoxic waterthat downward shift of the density interface happen(e.g. Fujiwara et al. 2000, Nakayama et al. 2008). Yagi

during southerly or northward wind conditions for a

et al. (2008) indicated that the advection of hypoxic

few days as indicated by the arrows, period-1, -2 and -3,

water is affected not only by intrusion of higher density

in the figure. During the terms of these higher southerlywater through the mouth of the bay but also by wind

wind periods, the DO concentrations are increased

driven current with field measurements and numerical(Figure 5.4 (a)) and the concentration at 7.2 m from thesimulations. Sequential southerly wind blowing, whichsurface, the half level of the total water depth, is raised

direction is from mouth of the bay to the upper bay,

up to almost the same concentration as the near surface

makes the density interface layer to be shiftedlayer at 3.6 m from the surface.

downward by the effect of wind drift of the surface
①
(a)DO

②

If wind is continuously blowing with the speed of③
Measurement depth of DO and t(25hr running mean)
3.6 m from surface
7.2 m from surface
10.8 m from surface
0.5m above bed(mg/l)

10
5(kg/m3)

0

bt

25
20
15

(m/s)

(d)H1/3

5
0
-5
1

(m)

(c)WindSpeed 10

0.5

Northward comp.
Eastward comp.

0
4

(s)

(e)T1/3

3
2
0

(m)

(f)Depth-5

Deep water wave depth-10

Depth of density interface

-15

24

28

4

8

12

16

20

Date in 2010
Figure 5.4

Temporal variations of DO (a), t, (b), wind speed (c), significant wave height and period (d,e) and depths

of deep water wave condition and density interface (f). Red dots in (a) means row (every 1 hour) data of DO measured
at 0.5 m above bed.

- 44 -

Figure 5.5

Vertical distributions of DO at Stn. 1, 2 and 3 during hypoxia state on Jul. 26, 2010 and recovery state on

Jul. 31, 2010.
10 m/s on a water surface of two density layer systemand (e), respectively. It is pointed out also by Sato et al.

in a rectangular shaped embayment with the length of(2012) that the sudden recovery of near-bottom DO

40 km from an open end to another closed end, theconcentrations from hypoxic state in summer season ofdepression of the density interface by wind drift of

the Tokyo Bay correlates with events of strong wind

water body in the upper layer would be approximatedfrom the south with the speed of over 10 m/s. They

as around 7 m. Based on the simple estimates, it isdiscuss the effect of wind event on the circulation of theconceivable that the recovery of DO concentration at

water body at inner bay. In the present study, in addition

the middle layer at the inner bay is caused by the wind

to the circulation by the wind stress, effect of verticaldrift of the upper layer with relatively higher DO

mixing by the wind wave disturbance on theconcentration.

near-bottom DO concentrations is also discussed in thefollowings.

DO concentration measured at the nearest heightfrom the bottom or 0.5 mab shows temporary increases
from the almost anoxia condition, which prominently

5.3.3 DO concentration and wind waves

occur corresponding to the higher southerly wind

While the downward shift of the density interface

events of the period-1 and -3. Also at the surrounding

occur during the period-1 and -3 where sudden recovery

stations at Stn.1, 3 and 4, for example, comparison ofof the near-bottom DO concentration occur, verticalthe vertical profiles of DO concentration measured

density gradient in the water column is decreased bybefore the period-1 on Jul.26, 2010, and during themixing of waters between upper and lower layers asperiod on Jul. 31, 2010, clearly shows the increase inshown in Figure 5.4 (b). In order to evaluate the extent

the DO concentrations in the lower layer as shown inof the mixing of water, we can introduce the parameter

Figure 5.5. During both these periods where the rapid

as the relative density between the upper and lower

increase of the DO concentrations near the bottom

layer as indicated by the following equation.

occur, by the effect of the higher southerly wind, the   2   1  /  2wind waves are also developed with the longer wave

(5.1)

In the present study, for the upper layer density, 1,

period at the inner bay area presented in Figure 5.4 (d)- 45 -

and the lower layer density, 2, the water density at theacceleration. With the longer wave period almost of 4 s

depth of 3.6 m from the surface and 0.5 m above theduring the period-1 and -3, the penetration depthbottom, respectively. The density was calculated

becomes larger than 10 m as shown in Figure 5.4 (f).

considering the temperature and salinity measured at

The comparison of penetration depth, h’, and the windeach depths. The relative density difference clearly

speed during the period-1 is presented by Figure 5.6 (b)shows negative correlation with the wind speed during

which shows the sudden increase of the penetration

the period-1 as shown in Figure 5.6 (a) and the relative

depth under the condition of wind speed of over 10 m/s.

density rapidly decrease when the wind speed is over

Both dependencies of the density difference and the

10 m/s. This wind condition coincides with thepenetration depth on the wind speed are very similar as

stochastic analysis on the relationship between the windshown in Figure 5.6 and it indicates that the wave

condition and recovery of the DO concentration innerdisturbance works for vertical mixing of the water orTokyo Bay by Nakayama et al. (2010), where winddecreasing of density difference. The penetration depth

speed exceeding 10 m/s contribute the recovery of thedepends on the wave period and orbital excursion of sea

near bed DO.

water depends on the wave height also. The

On the other hand, considering the deep water wave

dependency of these parameters on the wind speed are

condition of the linear wave theory, penetration depth

also shown in Figure 5.6 (c) and (d), respectively. It canof the wave motion from the surface, h’, is estimatedbe seen that both of these parameters also increased

with the following equation,

rapidly after the wind speed of around 10 m/s. As theh  0.5gT12/ 3 /(2 )mechanism of enhancement of vertical mixing due to(5.2)

surface wave propagations, several processes may

10

x 10
(a)

develop turbulence such as breaking waves at the

-3

8
6
4
2
2

4

6

8

10 12

Deep water wave depth；h’(m)Relative density diｆference；

where T1/3 is significant wave period, g is the gravity

6

(b)

8
10
12
2

4

4

(c)

3.5
3
2.5
2

4

6

8

10 12

0.8

8

10 12

(d)

0.6
0.4
0.2
2

Wind speed (m/s)

Figure 5.66

Wind speed (m/s)
Significant Wave Height (m)Significant Wave Period (s)Wind speed (m/s)

4

6

8

10 12

Wind speed (m/s)

Dependency of relative density difference between upper and lower layer (a), deep water depth (b),significant wave period (c), and significant wave height (d) on wind speed.

- 46 -

surface, bottom shear stress and wave induced verticalu rms  u 2 diffusion (Qiao et al. 2004, Mori et al. 2010). In thef2 S ( f )df
uu

,

f1

following chapter, the wave induced disturbance near
the bottom is focused on and the effect of it on thevrms  v 2 

recovery of DO concentration during hypoxic conditionf2

 S ( f )df
vv

.

(5.4)

f1

is discussed comparing measured wave induced orbital

In the analysis, the velocity amplitudes werevelocities and the temporal variation of the DOanalyzed for the higher frequency components with theconcentration.

wave period of 3 to 8 s, which represents mainly wind
wave induced velocity, and for the lower frequency

5.3.4

Near-bed

processes

of

wave

inducedcomponents with the period of 30 to 300 s representing

disturbance and DO concentration

the effect of infra gravity waves.

Measured wave orbital velocity of eastward andTemporal variation of the wave velocity amplitudes

northward components are shown in Figure 5.7(a) andof the wind wave component and the longer wave

the data were measured at the height of 0.5 m above thecomponent during the period-1 are shown in Figure 5.8,

bed with the sampling frequency of 2 Hz. The datacomparing with variation of DO concentration near theapparently detects wave orbital velocity due to surface

bottom. According to the comparison between them, thewind wave propagation with the peak frequency of 0.2

DO concentration recovered from the hypoxic state justHz or wave period of 5 s as indicated in the power

after the peak of the wave velocity amplitude of thespectrum of these velocity time series presented inwind wave component. This time lag between the peakFigure 5.7(b). From these data set measured with 20

of wave velocity amplitude and the recovery of the DO

minutes bursts at 60 minutes interval, intensity of theconcentration is observed also in the event during thewave orbital motion during each burst is estimated asperiod-3. The reason of this time lag is considered to be

representative wave velocity amplitude, considering therelated with the consumption of dissolved oxygen nearfrequency domain, by the following equations.

the muddy bed. Even if the oxygen is supplied from the

Uw  u2
rms

v

upper layer, it may be consumed in the near-bed

(5.3)

2
rms

hypoxic water and DO may not rise up for a while

where

under the wave disturbance.

(b)

U,V(cm/s)

-10
-20
0

20

40

60

80

100

120

Time(sec)

Suu,Svv(cm2/s)

V:Northward U:Eastward

(a)
0

4

10

‐:Eastward‐:Northward

2

10

0

10

-2

10

0.01

0.1

1

Hz
Figure 5.7

Measured near-bed current speeds (U: Eastward component, V: Northward component) and their power

spectra (Suu and Svv) during rough wave condition at 4pm on Jul. 29, 2010.

- 47 -

The characteristic of the near-bed water can becharacterized by the water temperature, T, and salinity,
S, as shown in T-S diagram of Figure 5.9 where colorsof plots means the measurement depths and the redplots are the data at 0.5 m above the bed. The data withDO concentration of less than 2 mg/l plotted by circle
in the figure, the T-S relationships apparently differ
from other water in the above layers. However, the T-S
relationship of the near-bottom water, when the DO isover 2 mg/l, is almost consistent with the above layer
and it is considered to represent the dependency of therising of near-bottom DO concentration on the verticalFigure 5.9

Relationship between salinity andmixing process.

temperature

considering

oxygenconcentration (Black dots: data taken at 3.6 m below

On the other hand, during the temporal recovery ofwater surface, Blue: at 7.2 below water surface,

the near-bottom DO concentration, sudden drop of theGreen: at 10.6 m below water surface and Red: atconcentration as indicated by the arrows in Figure 5.8

Figure 5.8

dissolved

0.5 m above bed)

Temporal variations of SSC (a), DO (b), orbital velocity amplitudes of wind wave component (c) and longwave component (d) and averaged current speed (d) measured at 50 cm above the bed(b). These falls of DO concentration correspond to theincrease of the orbital velocity amplitude of the lower- 48 -

frequency

componentandsuspended

oceanographic society of Japan, pp.303-310. (insediment

Japanease)concentration too. It is conceivable that the resuspended

Mori, N., T. Suzuki and N. Kihara (2010): A study on

sediment by the longer wave disturbance consume

air-sea mixing due to wind and wave under strong

dissolved oxygen.

wind condition, Journal of Japanese Society of Civil
Engineers, B2(Coastal Engineering), Vol.66, No.1,

5.4 Conclusions

pp.311-315. (in Japanese)

In order to elucidate the roles of wave induced
disturbance on the near-bottom environment, field

Nakayama, K., M. Sivapalan, C. Sato and K. Furukawa

monitoring was carried out at inner Tokyo Bay in the(2010): Stochastic characterization of the onset ofsummer of 2010 under hypoxic condition in theand recovery from hypoxia in Tokyo Bay, Japan:

near-bottom layer. The monitoring consists of a

Derived distribution analysis based on “strong wind”

mooring system of instruments for water quality

events, Water resource research, Vol.46, W12532,

measurements including salinity, Temperature, DO, anddoi: 10.1029/2009 WR 008900.

SS concentrations at several depths and also a set of

Ueno, S., K. Nadaoka, H. Katsui and H. Ohtani (1993):

bottom mounted current and wave measurement

Field observation of sudden change in density,

devices. Measurement results shows that the temporalcurrent and water quality field and internal wave

change in vertical profiles of water density clearly

under stratified condition in Tokyo Bay, Proc. of

responds to the wind event from the south with

Coastal Engineering in Japan, Vol. 40, pp.246-250.

downward shift of density interface in the water column,

Yagi, H., T. Pokavanich, S. Yasui, K. Nadaoka, R. Ariji,

which provides the vertical mixing and recovery of DO

S. Matsuzaka, N. Suzuki, K. Morohoshi, R. Oda andconcentrations in the lower layers. Furthermore, theY. Nihei (2008): Temporal and spatial variations ofeffect of wave disturbance on the water quality near the

hypoxic water mass in Tokyo bay governed by thebottom has been revealed by comparison of temporal

coastal upwelling at bay mouth and wind-drivenchange in the near-bottom DO with wave orbital

current, Proc. of Coastal Engineering in Japan, JSCE,

velocities amplitudes analyzed from measured data with

Vol.55, pp.1081-1085.(in Japanese)high time resolution. The wind wave disturbance on thebottom has a key role as a trigger of recovery of thenear-bottom DO concentration from hypoxic condition.

References for Chapter 5
Qiao,F. , Y. Yuan, Y. Yang, Q. Zheng and C. Xia (2004):
Wave-induced

mixing

in

the

upper

ocean:

Distribution and application to a global ocean
circulation model, Geophys. Res. Lett., 31, L11303,
doi: 10.1029/2004GL019824
Fujiwara, T., T. Takahashi, Y. Yamada and A. Kaneko(2000): Response of hypoxic water mass in Tokyo
Bay to the variation in hydrographic condition
outside of the bay, Oceanography in Japan, The- 49 -

6 APPLICATION OF FLUID MUD MODEL WITH

vertical. As represented in Chapter 4, however, theSTOCHASTIC APROACH OF BOTTOM SHEAR

vertical profiles of sediment concentration taken fromSTRESS ESTIMATION TO STORM EVENT

the sediment cores shows rapid increases in the

SIMULATION

sediment concentration toward the deeper layer
especially in the fluid mud layer near the bottom
surface with the thickness of a few decimeters and these

6.1 Introduction
Numerical simulations of sediment transport invertical structure is considered in the present study forcoastal and estuarine area by process-based approachthe derivation of the equations to estimate horizontal

are generally used for understanding of sedimentfluid mud transport rate.

dynamics and morphological evolution of study site

Another critical issue for providing reliable(e.g. Harris et al. 2008). Process-based model consists

estimates of sediment transport rates in numericalof several modules which represent relevant processes

simulations is computation of the bottom shear stresstoincludingdue to waves and currents. Wave induced bottom shearhydrodynamic module for determining force conditions

stress is often evaluated by representative bottom

in current and wave fields and sediment transport

orbital velocities, which can be estimated by themodule in which transport rates for vertical andstatistic parameters of surface waves, such as

horizontal

considering

representative wave height and period. Since thesediment properties such as sediment size andinfluence of wave propagation on the sedimentcohesiveness (e.g. Amoudry and Souza, 2011).

dynamics depends on the characteristics of wave

sediment

transport

directionsaredynamics,

calculated

For cohesive sediment or muddy environment on

frequency, near-bottom orbital velocities can bewhich the present study focus, the processes are moreobtained more accurately considering surface wave

complicated than non-cohesive or sandy case and key

spectra for relatively deeper water condition (e.g.

parameters for determining critical condition ofWiberg & Sherwood, 2008). Furthermore, sedimentinitiation of movement include not only sediment

dynamics beneath waves involves strongly nonlinear

particle size but also water content or bulk density ofphenomena including critical stresses for the initialmoisture mud (e.g. Whitehouse et al. 2000). Because

movement of sediments and importance of wave groups

bottom boundary condition for sediment movement

or fluctuation of wave amplitude in the movement ofdepends on sediment properties as mentioned above, it

sediment has previously been confirmed from fieldis crucial to consider information on spatial distribution

observations (e.g. Williams et al. 2002, Kularatne &of sediment properties in target field for getting

Pattiaratchi, 2008). Although time averaged bottom

reasonable simulation result. Another characteristic

shear stress is generally calculated with representative

feature that should be taken into account in numericalwave parameters such as orbital velocity amplitude insimulations of muddy environment is formation andsediment transport modeling, the use of time averagedtransport process of fluid mud layer above underlying

stress may underestimate transport rate because of theconsolidating mud layer. Due to lack of information on

above mentioned reasons.

the fluid mud process in detail, dynamics of the layer is

The aim of this chapter is to apply the derived

often simplified by 2-layer approach in practical models

formula of fluid mud transport rate in Chapter 4 to(e.g. Tsuruya et al. 1994, Deltares, 2014), where thenumerical simulation of sediment dynamics during thedensity of the fluid mud layer is assumed as constant in

observed storm event in the Tokyo Bay by coupling- 50 -

with a 3-D circulation and wind wave models,data are recovered without error during the storm

incorporating the irregularity of wave induced bottom

period.

shear stress. Before the numerical simulation, a

The temporal variations in the representative wave

stochastic approach for evaluating bottom shear stressorbital velocity amplitude, U~br , and mean velocity,

due to irregular waves is proposed, where probability

U  u 2  v 2 , for each burst are shown in Figure 6.1.

distribution of bottom shear stress is derived through astochastic characterization of field measured data of

The representative wave orbital velocity is defined withwave induced instantaneous velocities. These numericalthe following form (e.g. Madsen, 1994),





~
~
U br  2 u~ 2 rms  v~ 2 rms  2U rms ,results are compared with those calculated byconventional estimations of time averaged bottom shear
where

stresses.

(6.1)

u~rms and v~rms are the RMS values of theinstantaneous

wave

orbitalvelocity

components,

6.2 Stochastic approach for estimation of wave

u~(t), v~(t) , that are determined as deviations from theinduced bottom shear stressmean velocity of each component,

6.2.1

Characterization

of

wave

u~ (t ), v~ (t )   (u (t )  u , v(t )  v ) .

induced(6.2)

These procedures are the same as Eq. (4.4) described ininstantaneous velocity

Chapter 4.

The field measured data used in this study wereobtained at the same location at Stn.B in Figure 4.3 inDuring the period indicated in Figure 6.1, theChapter 4 during the storm event in September 2007. A

maximum velocity of the representative wave orbital

bottom-mounted

anvelocity is 0.2 m/s at 5:00 am on Sep. 7, while theelectromagnetic current meter was installed for a monthmaximum mean current speed is 0.3 m/s at 6:00 am on

at the station as shown in Figure 4.4. The surface

Sep. 7. During this event, the maximum significant

elevation and near-bed (30 cm above the bed) current

wave height and period were observed at the Tokyo Bay

speed were recorded with the sampling rate of 5 Hz forLight House Station as 2.5 m and 5 s, respectively as

a 20-minute burst every hour. There are errors in wave

represented in Figure 4.7. The wave event was caused

height measurements by acoustic device during theby the passage of a near-by typhoon, whereas the highstorm caused by air bubble generated with wave

current speeds were mainly caused by fresh water

breaking near surface but near-bottom current speed

influx during the storm from rivers flowing into theacoustic

wave

gauge

with

~

Figure 6.1 Measured Ubr and U during the storm event- 51 -

inner bay. The main direction of the averaged current

shown in Figure 6.3, where the instantaneous orbital

was south west ward direction at the monitoring station

velocity is normalized by the RMS value of U~  . The(Nakagawa et al. 2009).

normalized robability function, P(x), is expressed in theform,

6.2.2 PDFs of instantaneous orbital velocity and1
 x2 
P  x   2  2 exp    ,
 2 bottom shear stress
The components of the instantaneous wave orbital

(6.3)

where

velocity in the mean current direction, U~  , wereduring the high wave event are shown in Figure 6.2. All

~
U
.
x  ~

U rms

of these distributions are characterized by the shape

If we can obtain the relationship between thewith a peak that indicates the averaged current overinstantaneous current speed and corresponding bottom

each burst sampling and symmetrical distribution ofshear stress, the PDF of the instantaneous orbital

wave induced fluctuations around the peak. Histograms

velocity can be transformed into the PDF of bottom

of instantaneous orbital velocities are often assumed as

shear stress. For example, Tanaka and Samad (2002)

the Gaussian distribution (e.g. You, 2009) and

derivedprobability density function (PDF) of the instantaneous

instantaneous orbital velocity into bottom shear stressvelocities observed in the present study can be also

for irregular waves by the following equation,

computed and resulting histograms for each burst

represented by the normalized Gaussian distribution as
600

aformulation t  (a) 02:00


2

for

0.035 (6.4)

transformation

0.16~~ 0.68U t U t  ,

from

(6.5)

where  is the representative angular frequency, and

N 400

and are the density and viscosity of the sea water. By

200using Eq. (6.5), the probability density function of  can

0-0.4 -0.2
600

N

400

0

0.2

0.4

0.6

0.8

be obtained from the probability distribution of the(b) 03:00

instantaneous velocity.
However,200
0-0.4 -0.2
600

N

400

their

model

consider

only

wave

fluctuation of velocity field without uniform current0

0.2 0.4

0.6

0.8

0.2 0.4

0.6

0.8

0.2 0.4 0.6

0.8(c) 04:00

200
0-0.4 -0.2
600

N

400

0

(d) 05:00

200
0-0.4 -0.2

0~U (m / s )Figure 6.3 Probability density function of the~
Figure 6.2 Histograms of instantaneous velocity, U  , ininstantaneous orbital velocity assumed by a Gaussian

the mean current direction during the storm event of 7

distribution (solid line) and measured data (dots) from

September 2007.

22:00 on 6 September to 09:00 on 7 September 2007.

- 52 -

and the friction coefficients used in Eq. (6.5) are thefollowing, 0 . 52~
 U  t  
f w t   1 . 39 ( A t  / z 0 )  0 .52  1 . 39  rms z0 numbers for smooth turbulent conditions as mentionedby the authors, while the bed current observed in the(6.10)

field is basically under rough turbulent condition.

where =T/2 and T is the wave period and

Therefore, in the present study, the transformation ofrepresentative period was used in the analysis.

instantaneous bottom shear stress from orbital velocity

An example of the transformation of the PDF of theconsidering uniform current as the following equation,

wave orbital velocity into the bottom shear stress with cw t   1    c t    w t 

Eq. (6.6) is shown in Figure 6.4. The probability(6.6)

distribution of bottom shear stresses has a peak at thewhere  is introduced to combine the shear stresses by

bottom shear stress of the uniform current andcurrent, c, and waves, w, in order to incorporate theasymmetrically decreases in both sides of the peak.

non-linear effect considering the instantaneous velocity,

This is caused by the linear combination of current and~U t   U  U t  . The parameter,  is expressed as thewave induced bottom shear stress expressed with Eq.

following equation.

(6.6). There is nonlinear interaction between waves and2~ U rms




 
~ 
 
 U  U rms

current and the PDF of bottom shear stress combined(6.7)

waves and current may distorted around the peak, butreasonable way of transformation of instantaneous

This parameter indicates relative strength of the wave

velocity does not exist for co-existence condition of

disturbance to the averaged current. If wave disturbanceis relatively weak with small value of the root mean

P (u)
4

square of wave orbital velocity, U~ rms , the parameter 

3becomes zero and the bottom shear stress is expressed2

only by the stress due to current effect.

1

The bottom shear stress due to current is the same

0
-0.2
P ()
1.5

expression as Eq.(4.1) and the velocity term is replacedwith the instantaneous velocity here.
 c   w C f U t 2

0

0.2

0.4

m rms

max

0.5

1

0.6

0.8

1.5

2

(6.8)

1
The friction factor for mean current, Cf, is set as 0.003
referenced to the measurement height (= 0.2 m) above

0.5

the bed in this case. The bed shear stress due to waves

0is expressed in the same form of Eq. (4.2) in Chapter 40 (Pa)and the velocity terms are replaced with the
instantaneous velocity here also.
 w t  2

f wU t U t 

Figure 6.4 Probability Density Functions of the(6.9)

instantaneous velocity in the direction of the meancurrent (upper figure) and the converted bottom shear

For the friction coefficient, fw, the same form as Eq.

stress (lower figure). The wave and mean current(4.3) in Chapter 4 and was modified here, considering

conditions arewave orbital velocity, U~ rms t  , as expressed by the- 53 -

U =0.3 m/s and U~br =0.15 m/s.

current and wave. Therefore, the simplified expression

any type of sediment transport model that is expressed

is introduced in the present study to obtain theas a function of bottom shear stress. Same concept has

probability distributions and they are considered tobeen applied for estimating sediment bed load by

estimate sediment transport rate in the following

Tsujimoto (1990) and effect of surface wave is also

sections. The time averaged bottom shear stressconsidered. But the wave effect is incorporated merely

considering the wave-current interaction calculated byas the fluctuation of the uniform current in his model

the method of Soulsby (1997) is also indicated in theand the fluctuation by the surface waves is not

figure as m, with the root mean square value (rms) andincorporated as shear stress due to waves. In the present

the maximum (max) values of the combined wave andstudy, the bottom shear stresses due to current andcurrent bottom shear stress. The rms value is defined aswaves are separately evaluated as described in therms=(m +0.5w )previous section and the probability density function is

2

2 0.5

and the maximum value is defined

obtained considering both contribution of them.

as Eq. (4.5).

For the sediment transport rate function, qm(), in Eq.
(6.11), arbitrary model can be substituted into the

6.3 Stochastic formula for sediment transport rate
Forsediment

transport

phenomena,

criticalequation. In the present study, the newly derived fluid

conditions are often assumed for the initial movement

mud transport model described in the Chapter 4 is usedof sediment particles and these can be represented byfor computation of fluid mud transport rate in the field.

the yield stress in case of cohesive sediments. Based on
this assumption, sediment would move only when the

6.3.1 Excess bottom shear stressbottom shear stress exceeds the yield stress, as

For estimation of sediment transport rate underindicated in Figure 6.5. If we introduce a probability

bottom shear stress, excess shear stress is generally

density function for the bottom shear stress, considering

introduced for cohesive bed materials as an indicator ofa sediment transport rate that is modeled as the function

strength of muddy sediments. In order to represent theof the bottom shear stress, qm(cw), the total sedimentfundamental characteristics of the stochastic modeltransport rate, Q(cw), may be estimated with the form,

introduced in the present study, the excess shear yield yieldQ( )   P( )q m ( )d 

stresses were calculated. For the stochastic model, the P( )qm ( )d .(6.11)

excess shear stress was calculated by the following
equation,

The equation (6.11) itself shows universal form and ex_present  bP    yield d   yieldP    yield d(6.12)

yieldyield yield

The results was compared with the other excess
shear stress that is obtained with the conventionaltconstant shear stress, which is calculated as, excess  m   yield .

Figure 6.5 Schematic diagram of the relationshipbetween fluctuating bottom shear stress and yield stress.
(Sediment movement occurs only when the bottom
shear stress exceeds the yield stress indicated in grey.)- 54 -(6.13)

Assuming that the wave and current conditions areparameterized with a representative wave orbital
velocity and a mean current, the excess shear stresses
can be computed as in Figure 6.6, where the results forthe case of U =0.3 m/s and U~br =0.15 m/s are indicated.
The horizontal axis in the figure corresponds to theyield stress of the muddy sediment and the curves show
the excess shear stress over the yield stress computedwith Eqs. (6.12) and (6.12). The probability density
function for  in Eq. (6.12) is obtained by theprocedures outlined in the previous section, considering
the bottom shear stress estimated by the instantaneous

Figure 6.6 Comparison of excess shear stresses

velocity and the Gaussian distribution for thecomputed with the stochastic model (Eq. (6.7)) andinstantaneous velocity. For the time averaged constantthe constant shear stress model (Eq. (6.8)). Case forbottom shear stress due to waves and currents in Eq.

~
U =0.3 m/s and U br =0.15 m/s.

(6.13), and the time averaged form, m, is expressed

but the excess shear stress by the stochastic approach iswith Eq. (4.6) formulated by Soulsby (1997).

between the values estimated by the root mean squareCalculated excess shear stress results with theseand the maximum from around yield=0.4 Pa throughequations are shown in Figure 6.6, showing theyield=1.0 Pa. The difference between the estimation

dependency of the excess shear stress on the yield shear

results between the stochastic model and the time

stress, which is indicated in the horizontal axis in the

averaged constant model also depends on the extent of

figure. In the case of the constant stress model with thewave orbital velocity. Their dependency on thetime averaged shear stress, the excess shear stressvariation of wave orbital velocity conditions arelinearly decreases to zero when m=yield. This means

indicated in Figure 6.7. The figure shows estimatedthere is no movement of sediment under conditions

results of excess bottom shear stress and the rms values

where m<yield. In contrast, the curves for the stochastic~
of wave orbital velocities, U rms , are set as 0.01 m/s

approach decreases relatively slower than the case forthe constant model and the value of excess shear stress(Case1), 0.05 m/s (Case2) and 0.1 m/s (Case3) with theis not zero even when yield >m. For the same yieldsame current condition ( U =0.3 m/s). The effect ofshear stress condition, the excess shear stress estimatedwave velocity variation is already prominent fromwith the stochastic model is larger than the value of theCase2, where the rms value of wave velocity is abouttime averaged constant shear stress model and this

15 % of the current speed.

corresponds to the under estimate of the sedimenttransport rate by the time averaged model.

6.3.2 Estimation of fluid mud transport rates

For reference, the excess shear stress estimated by

Applying the fluid mud transport formulation

the root mean square and maximum values are also

derived in Chapter 4, sediment transport rate wasindicated in Figure 6.6 and these values are higher thancomputed following the stochastic approach of Eq.

the stochastic model in the case of smaller value of yield,(6.11), considering the wave and current parameters,- 55 -

Figure 6.7 Relative differences of estimated excess shear stress by the stochastic model (ex_present) and the constantshear stress model (ex_m) depending on variation of wave orbital velocity.

~U brand U , observed during the wave event asshown in Figure 6.1. Computation of the fluid mudtransport rate by Eq. (4.20), with a constant bottom
shear stress, was also undertaken and compared withthe stochastic model result. For the constant stress case,the bottom shear stress due to waves and currents, m, is~estimated by Soulsby’s formulation with the U br andU , as shown in Figure 6.1.

In figure 6.8 (a), calculated bottom shear stresses
considering the observed current and wave parameters~of U and U br are indicated as m, rms and max,

Figure 6.8 Force condition during the observed storm

respectively. The temporal variation of the time

the stochastic model and the constant shear stressaveraged bottom shear stress,m, is basically coincide

model (b).

with the variation pattern of the current speed and itwith the max. In the simulation, the yield stress ofexperiences maximum value of around 0.4 Pa at 6:00

muddy sediment is assumed to be 0.24 Pa and theam on Sep. 7. Other constant bottom shear stresses havetransport rate estimated by the constant stress model

much higher values than the time averaged and the

become zero when the estimated shear stress is lessmaximum values of rms and max are 0.7 Pa and 1.0 Pa,

than the yield shear stress, which means that there is norespectively.

movement of fluid mud. On the other hand, even under

event (a) and Simulation of fluid mud transport rates by

Computed results of the sediment transport rates

force conditions where m is less than yield, the transport

using these constant bottom shear stresses are shown inrate can be calculated as non-zero value with the

Figure 6.8 (b), comparing with the results of thestochastic approach by the contribution of the bottom

stochastic model. The estimation of the constant stressshear stress over the yield stress in the probability

model with the time averaged shear stress is extremely

distribution. The amount of the transport rate in the

low value and the result with stochastic model is closestochastic approach is strongly owing to the sediment

to the estimation by the constant shear stress modeltransport model, qm().

- 56 -

For the constant stress model, the use of otherparameters representing the force conditions such as the
maximum shear stress, max, instead of the time
averaged one,m, provide closer

result with thestochastic model in the present case. In the case of a
relatively narrow distribution of bottom shear stresses,
any representative constant shear stress could result in areasonable approximation. However, as indicated in theprevious section, larger wave orbital velocities or U~brcorrespond to larger U~ rms , and result in a wider
distribution of instantaneous orbital velocities andFigure 6.9 Computational domain and distribution ofbottom shear stresses. Therefore, the stochastic model

water content (WC) of mud (%). “MP” is monitoringis considered to be a more reasonable approach forpoint of field observation during storm event.

sediment transport modeling in storm conditions.

every one hour are utilized for the simulation of thefluid mud transport in the present study. Computation

6.4 Application of the model to simulation of

domain of the present study is shown in Figure 6.9 andobserved storm eventthe spatial resolution is 20 to 100 m. Considering thenumerical results of current and wave parameters on the

6.4.1 Simulation of current and wave field during

grids of the computation domains, fluid mud transport

storm event

rate on the grids are calculated with Eqs (4.10) andIn order to reproduce the sediment dynamics(6.11). The spatial distribution of water content

process during the storm event observed in the Tokyo

observed in the field was applied to estimate yield shear

Bay, a numerical simulation was carried out with thestress distribution in space, though it is assumed as

newly derived fluid mud transport model and theconstant in time in the present simulation. The target ofstochastic wave force model as represented in thethe simulation is to explain the residual sediment influx

previous section. Current and wave field was simulatedby transport of fluid mud flow in the fluid mud layer.

with an integrated model of a circulation model and a

The monitoring point in the field is indicated as “MP”

wave model, which is specially developed for assessing
theenvironmental

impacts

byain the figure and model result is validated comparing

topographicalwith the observed data at the monitoring point.

developments around Tama river mouth in the Tokyo

The present study proposed computation method

Bay (Nadaoka et al. 2015). In the integrated model, thewith a stochastic approach of wave force estimation for

Princeton Ocean Model, POM, (Mellor, 2004) issimulation of sediment transport rate, which may

applied for the circulation and the SWAN model

reproduce more correctly than any conventional time

(SWAN team, 2006) for wave field simulation,

averaged approach for bottom shear stress due torespectively. Calculated results of horizontal velocities

current and waves. In the present model, it is requiredat the nearest layer to the bottom boundary andthat the wave parameters of wave induced velocity nearsignificant wave height and period of surface waves atthe bottom are given through the numerical simulations- 57 -

of wave field. Temporal variations of the computed

orbital velocities were corrected in the present study as

results of near-bottom current and wave parameters aredescribed in the following section.

shown in Figure 6.10, comparing with field data
observed at MP. In the observed data, there are some

6.4.2 Simulation results of fluid mud transport

loss data of wave parameters during the high wave

In the present study, representative wave orbitalenergy period, where air bubbles generated by breaking

velocity amplitudes at all grids in the computational

waves may influence acoustic measurements, thoughdomain were corrected multiplying the numericalnear-bottom velocity data is collected without errors all

results by the factor of the ratio between of thethrough the monitoring period including the extreme

observed and numerical values at the monitoring station

storm period. Simulated significant wave heightof MP in Figure 6.9. Computational result of spatial

reasonably agree with the observed data before and
0.6

there are slightly difference in the result of significant

0.4

U(m/s)

after the missing data of high wave energy period but
wave peod, where the calculated period is relatively
longer before the storm and relatively shorter after theandperiodisHsig(m)height0.2

3

representative wave orbital velocity amplitude with the
wave

Obs.
Cal.

0
0

storm event. Because of these discrepancies, calculated
simulated(a)slightlyunderestimated after the storm event.
Representative orbital velocity can be estimated

12

(b)

12

height, considering frequency dependence of the5

Tsig(s)(c)6amplitudes were calculated from simulated wave

20

frequency spectrum by the SWAN model and they are

0.2

U’br(m/s)

3

larger

than

those

estimated

directly

from

24

36

48

24

36

48

36

48

Obs.
Cal.

4

the bottom. The representative wave orbital velocity

amplitude from surface wave spectrum are slightly

48

1

reasonably from power spectrum of surface wave

plotted in Figure 6.10 also. The estimates of velocity

36

Obs.
Cal.

2

0
0

transfer characteristics of surface wave motion down to

24

No wave data

0.15
0.1

the

0.05

representative surface wave parameter of significant

0
0

(d)

12

Obs.
Cal. (by Hsig
and Tpeak)Cal. (by power
spectrum)12

24

wave height and period. However, the calculated value

Hour from 0:00 on Sept. 6, 2007

from the wave spectrum are still underestimated the

Figure 6.10 Comparison of calculated results andobserved wave orbital velocities. It should be noted thatobserved data for (a) Averaged current, U , (b)the estimated results are significantly smaller thanSignificant wave height, Hsig, (c) Significant wave

observed values during the storm event, which means

period, Tsig, and (d) Representative wave orbital

the wave height and period are not correctly estimatedvelocity ( U~br ). Numerical results are indicated asby the wave model. This issue of wave model should be“Cal.” and field data as “Obs.”, respectively.

improved for getting more reliable wave field data as a
future work. Therefore, the computed results of wave

distribution of fluid mud flux for the time step of 5:00

- 58 -

am on Sep. 7 during the storm event is shown in Figure

were also calculated by a mass balance considering the6.11 (a), representing horizontal movement of the fluid

computed results of sediment flux at every one hour.

mud around off the Tama River mouth. Mud with

Spatial distribution of the bed level change due to fluid

higher water content distributes off the Tama River

mud flow after 24 hour experiencing the storm event ismouth area are forced to move by the current and waves.

in Figure 6.12, which represents the results with theAnother result obtained with the time averaged bottom

stochastic approach and the conventional time averaged

shear stress, m, is also shown in Figure 6.11 (b),model, respectively. The both results show change inindicating the area of fluid mud movement isthe bed level around the river mouth area with a spatial

apparently narrower than the result with the stochastic

pattern of erosion at the near shore and deposition at theapproach in Figure 6.11 (a) under the same force

off shore area. The spatial distribution of the bed level

condition.

change is due to the dominant or averaged current

Temporal variations of the bed level at each griddirection because the present model assume that fluid

FigurespatialFigure 6.12 Distribution of bed level change due to

distribution of fluid mud transport rate for time step at

fluid mud transport during the storm period by the

5:00 am on Sep. 7 by the stochastic model (a) andstochastic model (a) and averaged shear stress model

averaged shear stress model (b).

(b).

6.11

Computational

resultsof- 59 -

(a) at 4:00AM on Sep. 7(kg/s/m)(b) at 6:00AM on Sep. 7

MP

0(kg/s/m)(c) at 7:00AM on Sep. 7

MP

MP

5 km

(kg/s/m)

5 km

0

0

5 km

Figure 6.13 Computational results of spatial distribution of fluid mud transport rate with the stochastic model during thestorm. (Result for time step at 5:00 am on Sep. 7 is in Figure 6.11(a))
mud is transported in the direction of dominant current,
which is flowing southward through the storm event.
The results in Figure 6.12 show wider area of bed
level change in the case of stochastic bottom shear
stress model rather than the time averaged model inaccordance with the result of the sediment flux
distribution in Figure 6.11. Furthermore, change in the
bed level during the storm event observed at the MP is
deposition of around 4 cm and the contribution of the

Figure 6.14 Temporal change in bed level due to fluid

fluid mud transport to the bed level change is estimatedmud transport calculated with the present model

as deposition of around 6 cm as discussed in Chapter 4.

compared with estimated bed level change from

The present model considers only horizontal fluid mudobserved data at the monitoring point, MP.

transport and the numerical results with the stochasticstorm event. Furthermore, the temporal variation of themodel represent deposition of 5.5 cm in the vicinity of

bed level simulated by the present model is also

MP, though the simulation with the time averaged

compared with the bed level change estimated by themodel results in deposition of only less than 0.1 cm.

observed data as in Figure 6.14. There is slightly

Although the number of monitoring point is restricteddifference in the time evolution between the modeland the validation should not be enough with only one

result and observed data and it would owe the accuracy

station, the present model reproduces more reasonably

of force condition simulated with the numerical models.

the observed bed level change than the conventional

It means that there are still work to improve thetime averaged shear stress model. Spatial distributions

reproducibility of force conditions for near bedof mud sediment fluxes computed with the stochastic

dynamics in numerical modeling.

model at several time steps are also indicated in Figure
6.13 and they shows that the distribution of calculated

6.5 Conclusions

flux varies depending on the force conditions during the

Numerical simulation was carried out to examine- 60 -

fluid mud transport during the storm event observed incoastal mor-phological and sediment transport

the Tokyo Bay. The newly derived formulations formodeling: Areview and discussion, Reviews of

fluid mud transport rate in Chapter 4 was applied forGeophysics, 49, pp.RG2002-2011.
Deltaresthe numerical simulation. In the present study,(2014):

Delft3D-FLOW

user

manual,ver:3.15.34158, 684p.

furthermore, a stochastic approach was introduced forthe estimation of wave induced bottom shear stress toHarris, C. K., C. R. Sherwood, R. P. Signell, A. J. Beverincorporate the effect of irregularity in the wave forces

and J. C. Warner (2008): Sediment dispersal in theacting on a bed.

northwestern Adriatic sea, Journal of GeophysicalResearch,

For the formulation of the stochastic model for

Vol.

113,

CS11S03,doi:

10.1029/2006JC003868.

bottom shear stress, it was confirmed that theprobability density function of instantaneous wave

Kularatne, S. and Pattiaratchi, C., (2008): “Turbulent

orbital velocities can be approximated well by akinetic energy and sediment resuspension due toGaussianwave groups”, Continental Shelf Research, 28, pp.

distribution,

where

the

distribution

is726-736.

parameterized with the representative wave orbital
velocities or the standard deviations of the orbital

Madsen, O.S., (1994): “Spectral wave-current bottom

velocities. The probability function of bottom shear

boundary layer flows,” Coastal Engineering 1994:

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Meller,instantaneous orbital velocity.

G.

L.

(2004):

Users

guide

for

a

three-dimensional, primi-tive equation, numerical

The present model was validated through a storm
event simulation, where calculated results of current

oceanmodel, Program on Atmos-pheric andfield by the POM and wave field by SWAN model were

Oceanic Sciences, Princeton University, Prince-ton,utilized as force condition for the sediment transport

NJ 08544-0710.

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Nadaoka, K., H. Yagi, Y. Nakagawa, Y. Nihei, R.

level change during the storm with the present model

Yamanaka, N. Uchikawa, K. Kawasaki and T.

and those with a conventional time averaged bottom

Mishimashear stress model shows an underestimation by theenvironmental impact assessment model and its

time averaged model, representing more reliable results

application to Tama Rivr estuary around Haneda

by the present model based on the validation with the

Airport, Journal of Japan Society of Civil Engineers,

observed data.

Ser. B2 (Coastal Engineering), Vol.71, No.1, (inJapanease),

Since the present model focused on the transport of(2015):Developmentof

integrated

in press.

Nakagawa, Y., R. Ariji, S. Matsuzaka, K. Morohoshi, H.

fluid mud observed offshore of the river mouth andtheir modeling, it is considered as future works that the

Yagi,

K.

NadaokaandS.

Tanaka(2009):

model incorporate another transport factors erosion and

Observations of fine sediment transport event

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International Conference on Asian and Pacific
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Williams, J.J., Rose, C.P., and Thorne, P. D., (2002):“Role of wave groups in resuspension of sandy
sediments”, Marine Geology, 183, pp. 17-29.
You, Zai-Jin. (2009): “The statistical distribution of
nearbed wave orbital velocity in intermediate
coastal water depth”, Coastal Engineering, 56(8), pp.
844-852.

- 62 -

velocity profiler to estimate total suspended sediment

7 SUMMARY AND CONCLUSIONS

fluxes, the suspended sediment load under tidal current
Muddy

sediment

transportinestuary

was formulated.

andembayment is dynamical process under various forcing

In Chapter 4, observed data of muddy dynamics

terms such as current and waves. Response of theunder extreme storm event was demonstrated and theirmuddy sediment to the forces depends on sediment

process was modeled. The observed data successfully

properties and their vertical structure near the bottom.

captured the near-bottom process during high wave

In the present study, field monitoring to grasp

energy condition due to a typhoon event in the Tokyo

sedimentary characteristics were carried out in fields

Bay. A new formula for estimation of fluid mud

providing spatial distribution and vertical structures oftransport rate was derived based on the Bingham model

muddy sediment. Sediment transport dynamics wereconcept.

also examined through implementations of monitoring

In Chapter 5, in order to elucidate the effect of wave

campaigns to elucidate how tidal current and wave

disturbance on the other near-bottom environment thanforcesnear-bottom

sediment transport process, observed temporal change

environment including muddy sediment dynamics andin dissolved oxygen near the bottom was examinedwater quality change. Based on the informationcomparing wave force condition by using fieldobtained from the field data analysis, a formula formonitored data monitored in summer at the inner Tokyo

estimating fluid mud transport rate was newly derived

Bay. The data shows that the recovery of dissolved

and a stochastic model for evaluating bottom shear

oxygen from hypoxic condition near the bottom

stress under irregular waves was also proposed in theapparently occur just after the peak of wind wave event.

present study. These methods were applied to numerical

In Chapter 6, the newly derived fluid mud model

simulation of fluid mud dynamics during a storm event

was applied to numerical simulation of sedimentand verified with observed data. Summary anddynamics during the observed storm event in the Tokyo

conclusions of each chapter is as followings.

Bay. A stochastic approach was also developed for

drive

temporalvariationof

In Chapter 2, sediment characteristics in the target

estimating bottom shear stress under irregular wave

field of the present study in the Ariake Bay and thecondition. From the field measured data of near-bottom

Tokyo Bay were represented with the field monitored

velocity

data. In the Ariake Bay, sediment core sampling andinstantaneous velocity induced wind waves are welltheir analysis results were carried out and in-situ device

approximated by the Gaussian distribution and thefor bulk density measurement was introduced in thepresent study proposed transformation from thefield monitoring. The monitoring in the Tokyo Bay

probability function of instantaneous velocity into theincluded extensive core sampling and fluid mud layer

probability function of bottom shear stress. It is verified

does stationary exist and the thickness of the fluid mud

through comparison of numerical results and observed

layer is around 10 cm.

data that the present model can estimate the bed level

shows

that

probability

functionof

In Chapter 3, characteristics of muddy sedimentchange during the storm event well rather than atransport under tidal force was examined based on theconventional time averaged bottom shear stress model.

field data obtained in the Ariake Bay where is

Based on all the studies of this thesis, we cancategorized as meso-taidal estuary with maximum tidesummarize as followings. 1) Practical methods areof around 5 m. By applying the acoustic Doppler

proposed to simulate muddy sediment transport process,- 63 -

depending on the transport conditions of mud,

ACKNOWLEDGEMENTS

including suspended state and fluid mud state, and new

I would first like to express my deep appreciation toformula is provided for fluid mud transport rate

Dr. Kazuo Nadaoaka, Prof. of Tokyo Institute ofestimation. 2) The study shows the wave has a key role

Technology (TIT), for his valuable suggestions,on the near bed environment of the embayment andguidance and continuous encouragements through theproposed new approach for evaluation of wave forces

study. I am also thank Profs. Toshihiro Osaragi (TIT),

on the bottom as a stochastic model considering

Hiroshi Yagi (National Defense Academy), Shinjiro

variation of instantaneous wave orbital velocity of

Kanae (TIT), Yasuo Nihei (Tokyo University ofirregular waves. This approach can be applied not only

Science), Asso. Prof. Naoki Kagi (TIT) and Dr. Takashi

to fluid mud transport but also other near bed dynamic

Nakamura (TIT) for reviewing my dissertation and theirprocess such as pick up of sediments. 3) All knowledge

valuable and helpful comments to improve the thesis.

obtained through the studies can be applied to improve

I want to express my gratitude to Dr. Hiroich

the assessment works for sedimentary and near bed

Tsuruya, who was the Head of Hydrodynamics

environments in embayment, which needs the more

Laboratory when I started to work for Port and Harbour

reliable simulation techniques.

Research Institute (PHRI), for his guidance to the

As future works, the following aspects could beinteresting research world of muddy sediment dynamics.

considered for extending the knowledge obtained

I also would like to thank Dr. Yoshiaki Kuriyama,

through the present study. The model developed forDistinguished Researcher of Port and Airport Research

simulating fluid mud dynamics in the present study can

Institute (PARI), for his continuous encouragements

be coupled with other sediment transport process toand valuable comments on my study works.

establish over all sediment dynamics model. For

I would like also to thank the ports and harbours

application to more dynamic environment with thickbureau and the both regional development bureaus for

fluid mud layer often observed in the river mouth area

Kanto and Kyushu regions of the Ministry of Land,in the south east Asian countries, the interaction

Infrastructure, Transport and Tourism (MLIT) for

between the sea water and mud behavior should be

providing funding to carry out this research.

considered in the momentum equations, although the

Furthermore, I would like to extend my gratitude tointeraction is neglected in the present study because theall of former and current colleagues of PHRI and PARI,

thickness of the fluid mud layer is relatively small as

including Profs. Tadashi Hibino (Univ. of Hiroshima),

the order of few decimeter compared with water depth

Hirofumi Hinata (Univ. of Ehime), Yusuke Uchiyama

of 20 to 30 m. Another challenge with development of(Univ. of Kobe) and Dr. Tomohiro Kuwae (PARI), forthe present work is coupling the physical process suchtheir

as resupension of muddy sediment and chemical

encouragements through many open discussions in theprocesses to forecast the water quality change including

institute and personal discussions.

near bed dissolved oxygen concentration, which isimportantparameterforevaluating

the

water

environment.
(Received on Jan 25, 2016)- 64 -

valuableandhelpfulcomments

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