Estimation of longshore sediment transport along ...nopr.niscair.res.in/bitstream/123456789/42250/1/IJMS 46(7) 1307-13… · Estimation of longshore sediment transport along Puducherry

Indian Journal of Geo Marine Sciences

Vol. 46 (07), July 2017, pp. 1307-1319

*Corresponding Author

Estimation of longshore sediment transport along Puducherry coast,

Eastcoast of India; based on empirical methods and surf zone model

P. Mohamed Rajab1*

& K. Thiruvenkatasamy2

Department of Harbor & Ocean Engineering, AMET University, 135, ECR Road, Kanathur, Chennai-603112, India

[ E.Mail: [email protected], [email protected]]

Received 28 September 2016 ; revised 28 November 2016

Measured waves at 15 m water depth off Puducherry coast were used to estimate the longshore sediment transport along the

Puducherry coast based on empirical methods and surf zone model. Comparison of longshore sediment showed that transport

rate estimates gave wide variation among the empirical methods and surf zone model. Transport rate estimates using CERC gave

higher (factor 2.5) and Kamphuis gave factor 1.5 when compared to VanRijn transport estimates. Estimated littoral drift using

surf zone model was close agreement with estimated littoral drift using VanRijn method. Estimated annual longshore sediment

transport based on surf zone model along the Puducherry coast show that the highest northerly transport occurred in the month of

May, followed by September, July, June and August. Highest southerly transport was observed in December followed by

November. Net monthly transport was northerly from March to October and southerly during the remaining months. Transport

rate was found to be low in February. Volume of annual gross transport was estimated as 0.40 x 106 m3/year and the volume of

annual net transport was 0.13 x 106 m3/year (towards north).

[Keywords: Littoral Drift, Sediment transport formulae, LITDRIFT Module, Surf zone model]

Introduction

Waves breaking in the surf zone near the coast

mobilize the sediments around the breaker line

and currents generated by waves transport

sediments along and across the coast. When there

is variation in the supply of sediments or

obstruction to sediment movement along the coast

imposed by coastal structures, the long-shore

sediment transport is the major process governing

the long-term changes in the shoreline. While

dealing with long term coastline changes and

planning mitigation measures to prevent adverse

impacts due to human interference, particularly

along coasts with high littoral transport, the

prediction of along shore sediment transport rate

becomes important. Existing empirical formulae

relates longshore sediment transport rate with

wave parameters at the breaker point, sediment

characteristics and the sea bed slope, and they are

based on data measured in the field. Frequently

used empirical formulae for longshore sand

transport computation are those of CERC (1984),

Kamphuis (1991) and VanRijn (2002). The

reliable field data sets are too small to evaluate the

predictive abilities of different formulae over the

full range of parameters. Hence the sensitivity

studies are carried out to assess the influence of

different parameters on the transport rate

estimates, and to understand the relative

performance of different empirical formulae.

In the present investigation, two approaches

were followed for the estimation of longshore

sediment transport rates, viz., i) Empirical method

(CERC, Kamphuis and VanRijn formulae) and ii)

Process-based method (surf zone model). Surf

zone model (DHI-LITDRIFT module) was used a

mailto:[email protected]

INDIAN J. MAR. SCI., VOL. 46, NO. 07, JULY 2017

Wave height (m)

Above 3.00

2.75 - 3.00

2.50 - 2.75

2.25 - 2.50

2.00 - 2.25

1.75 - 2.00

1.50 - 1.75

1.25 - 1.50

1.00 - 1.25

0.75 - 1.00

0.50 - 0.75

0.25 - 0.50

Below 0.25

N

Calm0.03 %

5 %

process based method for the estimation longshore

sediment transport along the Puducherry coast.

Materials and Methods

The data collected during December 2010 by

Institute for Ocean Management (IOM), Anna

University, and National Institute of Ocean

Technology (NIOT), Chennai, on bathymetry and

sediment quality have been used in this study.

Directional wave data measured at 15 m water

depth, southeast of Ariyankuppam village,

Puducherry (Latitude: 11.87°N and Longitude:

79.84°E), recorded at 3 hourly interval for a

period of one year from July 2007 to June 2008,

by Indian National Centre for Ocean Information

Services (INCOIS), Hyderabad, was compiled and

was used for the estimation of littoral drift along

the Puducherry coast.

The directional wave data, measured at 15 m

water depth, southeast of Ariyankuppam village,

Puducherry (latitude: 11.87o

N and longitude:

79.84o E), recorded at 3 hourly interval for a

period of one year from July 2007 to June 2008,

by Indian National Centre for Ocean Information

Services (INCOIS), Hyderabad, were analyzed,

and the results are presented1. Measured wave

characteristics at 15 m water depth have been

presented in the form of annual wave rose

diagram is shown in Figure 1. The measured

wave characteristics at 15 m water depth is

observed that significant wave height vary

between 0.2 m and 3.56 m, the peak wave periods

are between 2.5 sec and 19.1 sec and the wave

directions are between 36.6° and 179.1° w.r.t.

North. The monthly variation of measured wave is

presented in Table 1 and show that the average

significant wave heights vary from 0.56m to

0.65m from February to April, June and August

and vary from 0.76 m to 0.88m in May, July and

from to September to November and January, and

vary around 1.14 in December. Zero crossing

wave periods vary from 3.5 s to 9.9 s over the

whole year. Predominant wave direction prevails

around 115° during March and October, 120° to

150° in April to September, and 95° to 100° from

November to February1.

Figure 1. Annual rose diagram for measured waves at 15 m water depth

1308

RAJAB & THIRUVENKATASAMY : ESTIMATION OF LONGSHORE SEDIMENT TRANSPORT

Table 1. Monthly measured wave characteristics off Puducherry

Month

Significant wave height Hs (m) Zero crossing wave

period Tz (s)

Wave direction

w.r.t. North (Deg. N)

Min. Max. Mean Min. Max Mean Min. Max Mean

January 0.34 1.45 0.82 3.7 7.0 5.3 47.8 153.3 106º

February 0.35 1.09 0.62 3.7 7.0 4.7 75.9 149.1 104º

March 0.26 1.24 0.63 3.4 7.1 4.8 54.8 164.5 117º

April 0.30 1.19 0.56 3.5 8.1 4.9 60.5 175.8 130º

May 0.48 1.36 0.80 4.0 6.5 5.2 68.9 161.7 147º

June 0.11 1.14 0.62 3.4 7.6 4.9 73.1 174.4 136º

July 0.40 1.39 0.76 4.6 9.4 6.7 101.3 160.3 122º

August 0.34 1.45 0.65 4.2 9.2 6.3 95.6 165.9 126º

September 0.41 1.57 0.76 4.2 8.5 6.3 102.7 165.9 127º

October 0.30 1.64 0.70 3.9 9.2 6.3 54.8 158.6 117º

November 0.32 1.62 0.88 3.5 9.9 5.8 36.6 160.3 96º

December 0.48 2.97 1.14 4.2 7.2 5.6 61.9 129.4 100º

Empirical Methods

Existing empirical formulae relate longshore

sediment transport rate with wave parameters at

the breaker point, sediment characteristics and the

sea bed slope, and they are based on data

measured in the field. Frequently used empirical

formulae for longshore sand transport

computation are those of CERC (1984), Kamphuis

(1991) and VanRijn (2002).

CERC (1984) transport formula

Based on dimensional analysis, and it relates

the immersed weight, wimQ , of the longshore

sediment transport to longshore wave power

(wave energy flux), P, per unit length of the beach

as2,

wimQ = K P (1)

where wimQ includes both the bed load and the

suspended load transports, K is a non-dimensional

calibration coefficient, and P is given by,

brbrgbr cossinCEP (2)

in which,

E = 2

br,rmsHg8

1 = Average wave energy at

breaker line

Hrms,br = Root mean square (rms) wave height at

breaker line;

Cgbr = nbr Cbr = wave group velocity at breaker

line, the coefficient

nbr ≈ 1 at the breaker line;

Cbr = Wave phase speed at breaker line;

θbr = Wave angle (in degree) at breaker line

(between wave crest line and coastline or between

wave propagation direction and shore normal

direction).

Recommended value of the calibration coefficient

in the CERC formula is K = 0.772&3

. For the

Puducherry coast, the recommended value for K =

0.80 was used in study4. The capability of the

CERC formula is predicting the littoral transport

rates due to high waves that occur during stormy

conditions has not been tested extensively.

1309


Kamphuis (1991) transport formula

CERC transport formula does not take into

account the particle size of the beach sand and the

beach slope. the influence of beach sediment size

and the beach slope on littoral transport studied by

Kamphuis and formula for volume transport rate

can be written as5,

6.0

br

2

sbr

25.0

50

75.05.1

p

s

2vol )2(sinHd)(tanT

))(p1(

KQ

(3)

where,

Tp - Peak wave period (s)

tan β - Beach slope

d50 - Median grain size (m) of sand in

the surf zone

θo and θbr - Wave angel at breaker line in

degrees.

Using field data, assuming the sediment density,

ρs = 2650 kg/m3, and the sea water density, ρ =

1030 kg/m3, the calibration coefficient has been

determined as, K2 = 2.33, a dimensional

coefficient in the SI system.

Both CERC and Kamphuis empirical formulae

have been calibrated with data sets corresponding

to mild wave conditions, and their predictive

abilities are not extensively tested for wave

conditions observed during storms (i.e. for wave

heights > 2.0 m). Input data for these empirical

formulae are mainly wave parameters at the

breaker point. If the wave parameters are

specified in deep water, they have to be

transformed to the breaker point. While

transforming the wave parameters from deep

water to the breaker point, often seabed contours

are assumed straight and parallel in the nearshore

region and small amplitude wave theory is used5.

Kamphuis formula, in addition requires d50 and the

beach slope in the nearshore region.

VanRijn (2002) transport formula

Van Rijn tested CERC and Kamphuis

formulae using a relatively small (seven) high-

quality data sets and observed that the estimates of

transport rates based on CERC formula are larger

(by factor 2) compared with measured values for

storm conditions, but much too large (by factor 5)

for low wave conditions. Van Rijn also observed

that the estimates of the Kamphius formula were

found to be 1.5 times smaller than the measured

values for storm conditions, but larger (by factor

3) for low wave conditions.

Van Rijn proposed an alternative longshore

transport rate (in kg/s, dry mass) formula as a

product of wave-related stirring and wave-driven

longshore current3&6

. That is,

w

5.2

brs3mass VHKQ

and br

5.0

brs4w 2sin)Hg(KV ,

where wV is the longshore current velocity in the

mid-surf zone due to breaking waves. This

approach provides the facility to include the

influence of wind and tide induced currents in the

longshore transport formula. Van Rijn has also

reported that, K4 ≈ 0.3. Influence of particle size,

wave period and beach shape on longshore

transport rate were studied3, and introduced their

influence by a set of correction coefficients in the

above formula. Including all these correction

coefficients, Van Rijn (2002) developed a formula

for longshore sediment transport rate, which reads

as3,

eff

5.2

brsslopegrainswell3mass VHKKKKQ (5)

where

massQ = Longshore sediment transport rate (in

kg/s, dry mass);

swellK = Correction coefficients for swell

grainK = Correction coefficients for particle size

slopeK = Correction coefficients for bed slope

swellK = (Tswell/Tref), for swell waves of height <

2m,

= 1, for wind waves. Reference wave

period Tref= 6 s;

grainK = (dref/d50), for d50 < 2 mm. Reference

particle size dref = 0.2 mm,

= 0.1, for d50 > 2 mm;

slopeK = (tan β/tan βref). Reference slope, tan βref

= 0.01. max,slopeK = 1.25,

1310


and min,slopeK = 0.75. The overall slope is

defined as the average slope between the

waterline and 8 m depth contour;

and

effV = Effective longshore velocity at mid

surf zone,

= 5.02

tide

2

wave ]VV[ ,

where )2(sin)Hg(KVbr

5.0

brs4wave is the

wave induced longshore velocity in mid surf

zone, and 4K ≈ 0.3. tideV is the tide induced

longshore velocity in mid surf zone, and tideV =

0, 0.1, 0.3 and 0.5 m/s for non tidal, micro-tidal,

meso-tidal and macro tidal conditions

respectively. In the absence of tides Van Rijn

reported that K3K4 ≈ 40 in Equation (5) predicts

longshore transport rate close to measured

values3.

Process based method – Surf zone model

Process-based methods consist of two major

parts, a wave model and a sediment transport

model.

The wave model includes, propagation,

shoaling and breaking of waves, computation of

the driving forces due to radiation stress,

momentum balance for the cross-shore and

longshore direction giving the wave setup and

the longshore current velocities. Based on this

formulation, there have been a number of

investigations on nearshore flow phenomena

such as wave set-up, set-down, longshore

current distribution, rip currents and nearshore

current field.

Waves approaching the coast are seldom

parallel to the shoreline, and these oblique

waves generate currents nearly parallel to the

shoreline close to the shore, called the longshore

current. This longshore current, which varies in

magnitude across this region, does not remain

parallel all along the coast due to variations in

the nearshore topography and the breaker height

along the coast. At certain locations along the

coast the longshore current turns seaward and is

referred to as the rip current. Offshore of the

breaker line, these rip currents diffuse and return

again to the surf zone to maintain the

conservation of mass. Longshore current with

rip current and the return flow form a two

dimensional flow pattern within and beyond the

surf zone, which is termed the nearshore wave

induced circulation.. These early studies are

based on principles of conservation of total mass

flux and total momentum flux or energy balance

through surf zone, and they estimate only the

average longshore current magnitude in the surf

zone5. In later investigations the depth averaged

equations of motion (continuity and momentum

equations) averaged over time are solved to

estimate the velocity distribution across the surf

zone. The different computational stages in the

hydrodynamic part of this method are,

i. Computation of the cross-shore

variation of wave height from the deep

water, up to the breaker point using the

small amplitude wave theory. In this

computation often the nearshore seabed

contours are assumed straight and

parallel;

ii. Computation of wave height distribution

after breaking using a surf zone model;

iii. Estimation of longshore current

distribution by solving depth averaged

momentum equations.

After computing the longshore current, the

longshore sediment transport rate that includes

bed load and suspended load transports, is

computed taking into account the local

conditions related to the energy dissipation, the

percentage of non-breaking waves and the rms

values of wave heights. This gives the

distribution of sediment transport across the

profile, which is integrated to obtain the total

longshore sediment transport rate. The annual

drift is evaluated from the contribution, iTiQ ,

of each of the incident wave occurring over the

year. Then the total annual drift Qan is

calculated as the sum of the contributions from

all incident waves7.

1311


NS

1iiian TQQ

Where NS is the total number of incident waves

and iT is the time intervals at which the wave data

sets are specified.

In the present study, surf zone model (DHI-

LITDRIFT module) was used a process based

method for the estimation longshore sediment

transport along the Puducherry coast. The DHI-

LITPACK-LITDRIFT model suite is a convenient

tool for studying the coastal processes related

sediment transport in the wave breaking zone7.

MIKE 21 LITDRIFT is a deterministic

numerical model which consists of two major

parts, viz. i) a hydrodynamic model and ii) a

sediment transport model (STP) (Figure 2). The

hydrodynamic model includes propagation,

shoaling and breaking of waves, calculation of the

driving forces due to radiation stress gradients,

momentum balance for the cross-shore and

longshore direction giving the wave setup and the

longshore current velocities. Having computed the

longshore current by the hydrodynamic module,

points are selected across the coast, which are

representative for the littoral drift estimation. The

sediment transport calculations carried out by the

STP module are made to reflect the local

conditions with respect to the energy dissipation,

the percentage of non-breaking waves and the rms

values of wave heights7.

Total sediment transport is dominated by

transport contributions from areas where wave

breaking occurs. In case of a bar-profile, the

sediment calculation points will have to be located

on the bars where waves are breaking. This gives

the distribution of sediment transport across the

profile, which is integrated to obtain the total

longshore sediment transport rate. By considering

the variation in the hydrodynamic climate (e.g. the

yearly wind, wave, tide, storm surge and profile

conditions) it is possible to determine the net

gross littoral climate at a specific location. Annual

drift is evaluated by the contribution of transport

from each of the incident wave occurring over the

year. When calculating the annual drift, the wave

climate in LITDRIFT is described at specified

intervals in a time series file where each set of

items describes the characteristics of incident

waves. The duration of the wave incident is given

as a fraction of a year. The 3 hourly wave data off

Puducherry coast measured at 15 m depth are used

as input to the module.

Figure 2. Flow diagram for littoral drift estimation - surf

zone model

(Source: DHI-LITPACK manual, 2010)

The surf-zone model setup is shown in Figure 3.

The orientation of the profiles is taken as 105 deg.

N (shore normal off Puducherry coast). The grid

spacing, which on the basis of the length of the

profile is selected to be 10 meters. Following

inputs are given to the model7,

i) The cross-shore profile from the

measured bathymetry has been

incorporated in the model.

ii) The measured wave field at 15 m

water depth for the period of one year

is given as input to the model.

iii) The measured d50 =0.25 mm as

median size of nearshore seabed

sediments is used.

SEDIMENT

DATA

WAVE, WIND &

CURRENT DATA

BATHYMETRY

DATA

INCIDENT

WAVES

LONGSHORE CURRENT

TOTAL LITTORAL

DRIFT

STP

POINT SELECTION TO

TRANSPORT

CALCULATIONS

TRANSPORT ESTIMATION

1312


Figure 3. Model setup – LITPACK Module

Initial coastline

Baseline

Seawall

Breakwaters

Pile Jetty

Groins

Cross shore

profile

UTM X – AXIS (m)

UT

M Y

– A

XIS

(m

)

Sensitivity studies

Sensitivity studies on empirical formulae

Sensitivity studies are carried out to assess the

influence of different parameters on transport rate

estimates, and to understand the relative

performance of different empirical formulae. All

the empirical transport formulae are essentially

developed by relating the immersed weight of the

longshore transport rate to the longshore wave

energy flux, which is proportional to [Hs br2.5

sin

(2θbr)]. In CERC formula, the transport rate is

1313


expressed as a function of wave height and wave

incident direction at the breaker point. In the other

two formulae, the transport rate is expressed as a

function of wave period, sediment particle size

and beach slope in surf zone, in addition to wave

height and direction at the breaker point. Typical

range of parameters normally encountered in

alongshore transport formulae are:

Wave height at the breaker point: 0.5 - 3 m

Wave direction at the breaker point: 0 - 25º

Wave period: 4 – 10 s

Sea bed slope, tan β: 0.01 – 0.1

Beach material size (fine sand to coarse sand)

0.1 – 1.0 mm

Transport rates estimated using three empirical

formulae for selected wave parameters (HB and θB

) are presented in Table 2. The variations of long

shore transport rate as a function of wave height

and wave angle at the breaker point are shown in

Figures 4a-4b. The beach slope = 0.019 and

median particle size = 0.25 mm observed at

Puducherry are used in these estimates. Figures

4a&4b shows the long shore transport rate as a

function of wave height and wave angle at the

breaker point. . From the Figures 4a and 4b, it

observed that that VanRijn estimates falls in

between CERC and Kamphuis estimates for

increase in breaker wave heights. And also CERC

and Kamphuis show higher estimate than VanRijn

estimates for low breaker wave heights. And also

it is observed that while increasing wave breaker

height and breaker angle, the longshore transport

rate increases significantly.

Sensitivity studies on empirical formulae & Process

based method (DHI-Litdrift model)

In the present study, using the DHI - LITDRIFT

model, a process based method, the longshore

sediment transport rates for a typical range of

offshore wave heights with offshore wave

direction, αo = 45° and wave period, T = 6 s are

estimated and compared with the predictions

based on CERC, Kamphuis and Van Rijn

empirical formulae.

Table 2. Transport rates estimated using three empirical

formulae

HB

(m)

H2.5 *

sin 2θB

Qt, CERC

(kg/s)

Qt, Kam

(kg/s)

Qt, Van

(kg/s)

Breaker wave angle θB =5° (Deg.N)

0.25 0.01 0.69 0.5 0.11

0.50 0.03 3.93 1.99 0.87

0.75 0.08 10.83 4.47 2.93

1.00 0.17 22.23 7.95 6.95

1.25 0.30 38.83 12.42 13.57

1.50 0.48 61.25 17.89 23.44

1.75 0.70 90.05 24.35 37.23

2.00 0.98 125.73 31.8 55.57

2.25 1.32 168.79 40.25 79.12

2.50 1.72 219.65 49.69 108.53

2.75 2.18 278.75 60.12 144.45

3.00 2.71 346.48 71.55 187.54

Breaker wave angle θB =1° (Deg.N)

0.25 0.001 0.14 0.19 0.02

0.50 0.006 0.79 0.76 0.17

0.75 0.017 2.18 1.71 0.59

1.00 0.035 4.47 3.04 1.40

1.25 0.061 7.80 4.74 2.73

1.50 0.096 12.31 6.83 4.71

1.75 0.141 18.10 9.29 7.48

2.00 0.197 25.27 12.14 11.17

2.25 0.265 33.92 15.36 15.90

2.50 0.345 44.14 18.97 21.81

2.75 0.437 56.02 22.95 29.03

3.00 0.544 69.63 27.32 37.69

1314


For Breaker wave angel θB = 5°

Figure 4a. Variation of longshore transport rates with breaker wave height

(For θB = 5° data from Table 2)

For Breaker wave angel θB = 1°

Figure 4b. Variation of longshore transport rates with breaker wave height

(For θB = 1° data from Table 2)

1315


Figure 5. Variation of longshore transport rates with offshore wave height

(For offshore wave direction αo = 45° and wave period = 6 s)

Among the parameters influencing longshore

transport, only the influence of wave parameters

at 15 m water depth (Wave height and Wave

direction = 45º) on the transport rate is examined

(Figure 5). In this study, the offshore wave

parameters are defined at a water depth of 15 m,

because waves are generally measured in 15 – 20

m depth of water using directional wave rider

buoys. In these studies, for the Puducherry coast,

the beach slope is measured at 5m water depth as,

tan β = 0.019, bed material size, d50 = 0.25 mm

and wave period T= 6 s. The variations of

longshore sediment transport rates with offshore

wave height are shown in Figure 5.

Figure 5 show that the longshore transport rates

increases with increasing offshore wave height.

The computed longshore transport based on

CERC formula always gives higher estimates

whereas Kamphuis formula gives lower estimates

for the offshore wave height greater than 0.5 m.

The transport rate estimates based on LITDRIFT

module has close agreement with the transport

rate estimates based on VanRijn method.

Sensitivity studies- Surf zone model (DHI-Litdrift

model)

Sensitivity studies are carried out by varying the

parameters in the surf zone model and their

relative performances with bed roughness and

median particle size examined as shown in

Figures 6a & 6b. The orientation of Puducherry

coast in surf zone model was given as nearly

straight and oriented in N15°E direction. The

INCOIS measured wave data have been given as

input to surf zone model and the littoral drift has

been estimated. Figure 5a-b shows that the annual

gross and net transport increases with reducing the

bed roughness length and decreases with

increasing in median particle size.

1316


Figure 6a. Variation of gross and net drift with median particle size - Surf Zone model

Figure 6b. Variation of gross and net drift with bed roughness length - Surf Zone model

Validation of surf zone model results

In the absence of field measurements, the

estimated annual littoral drift obtained from this

surf zone model was compared with the estimated

annual littoral drift based on CERC, Kamphuis,

VanRijn empirical formula. Moreover, the

estimated annual net and gross longshore

sediment transport rates based on module is

validated with earlier estimates of annual net and

gross longshore sediment transport rates based on

ship reported wave data8 and based on measured

data for Puducherry coast9. It is observed that

estimated annual net and gross transport rates

based on the module has good agreement with

earlier estimated annual net and gross transport

rates for the Puducherry coast.

1317


Results and Discussion

In the present study, the measured waves at 15

m water depth off Puducherry coast were used to

estimate the longshore sediment transport along

the Puducherry coast. Two approaches were

followed for the estimation of longshore sediment

transport rates, viz., i) Empirical method (CERC,

Kamphuis and VanRijn formulae) and ii) Process-

based method (surf zone model). Monthly

volume of littoral drift using empirical methods

(CERC, Kamphuis and VanRijn empirical

formulae) and process based method (DHI-

LITDRIFT module) are estimated and presented

in Table 3. It is observed that the highest

northerly transport occurs in the month of May,

followed by September, July, June and August.

Highest southerly transport was observed in

December followed by November. The net

monthly transport is northerly from March to

October and southerly during the remaining

months. Transport rate is found to be low in

February. The estimated littoral drift along the

Puducherry coast using surf zone model show that

the volume of annual gross littoral transport was

0.40 x 106 m

3/year and the volume of annual net

littoral transport was 0.13 x 106 m

3/year (towards

north).

Table 3. Monthly volume of alongshore sediment transport using three Empirical methods and process based method (DHI-

LITDRIFT module)

Month

Longshore sediment transport (m3/month)

Empirical method Process based method

CERC (1984) Kamphuis (1991) VanRijn (2002) DHI-LITDRIFT

Southerly Northerly Southerly Northerly Southerly Northerly Southerly Northerly

January 70441 -24440 29394 -14398 40265 -12256 25425 -11550

February 13086 -11968 7030 -8170 5196 -5858 4575 -5339

March 6248 -27236 2914 -16333 2509 -12092 2330 -12429

April 6027 -40408 3606 -29533 3236 -21546 2542 -23602

May 3635 -103332 2405 -38928 2171 -44992 1505 -54785

June 4273 -48021 4525 -21563 2489 -19844 2035 -26958

July 56 -76264 189 -65549 62 -56341 24 -31983

August 69 -63477 155 -47249 42 -41094 42 -28273

September 27 -81436 41 -60046 14 -55038 11 -35260

October 21936 -43046 11976 -32777 12752 -25812 8697 -20157

November 102855 -18605 47684 -17419 61280 -12968 41647 -9261

December 124681 -19895 46365 -10696 65925 -10692 44231 -5581

ANNUAL 353333 -558129 156283 -362661 195942 -318532 133065 -265178

Annual

Gross 911462 518944 514474

398243

Annual Net - 204796

(Northerly)

-206378

(Northerly)

-122590

(Northerly)

-132113

(Northerly)

(+) = Sediment transport towards Southerly direction

(-) = Sediment transport towards Northerly direction

1318


Conclusion

The comparison of sediment transport rates

using measured data shows that transport rate

estimates gives wide variation among the

empirical methods and process based method.

Transport rate estimates using CERC gives higher

(factor 2.5) and Kamphis gives factor 1.5 when

compared to VanRijn transport estimates.

Estimated littoral drift using surf zone model is

close agreement with estimated littoral drift using

VanRijn method. While dealing with long term

coastline changes and planning mitigation

measures to prevent adverse impacts due to

human interference, particularly along coasts with

high littoral transport, the prediction of along

shore sediment transport rate becomes important.

Prediction of this complex phenomenon on a

regional scale demands a complete understanding

of the underlying processes. However, the MIKE-

21 suites of models bring out these phenomena in

a regional scale and it is suitable in the application

of prediction for management decision9-10

.

Acknowledgements

Author express his sincere thanks to Dr. P.

Chandramohan, Managing Director, Indomer

Coastal Hydraulics (P) Ltd., Chennai, for

supporting data for completion of this work.

References 1. Mohamed Rajab, P., Mahadevan R., and

Chandramohan, P., Comparison of Hind-Cast Wave

with measured wave off Puducherry Coast, Eastcoast

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2. CERC- Shore Protection Manual, US Army Corps of

Engineers, Coastal Engineering Research centre, Vols.

I to III, US Govt. Printing Office, Coastal and Ocean

Division, vol. 105, WW 4, 1984.

3. Van Rijn, L.C., Longshore sediment transport, Report

Z3054.20, Delft Hydraulics, Delft, The Netherlands,

2002

4. Ranga Rao, V., Ramana Murthy, M. V., Manjunath

Bhat and Reddy, N. T., Littoral sediment transport and

shoreline changes along Ennore on the southeast coast

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7. Danish Hydraulic Institute (DHI), An Integrated

Modelling System For Littoral Process And Coastline

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Software, Copenhagen, 2010.

8. Chandramohan, P. and Nayak, B. U., Longshore

sediment transport along the Indian Coast, Indian

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9. Panigrahi, J. K., Sathish Kumar V., and Tripathy J. K.,

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research, Elsevier publications, 2010.

10. Sanil Kumar, V., Pathak, K.C., Pednekar, P., Raju, N.

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91(4), 530-536, 2006.

1319

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