INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2013)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3776
Coastal vulnerability due to extreme waves at Kalpakkam
based on historical tropical cyclones in the Bay of Bengal
Sashikant Nayak and Prasad K. Bhaskaran*
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, India
ABSTRACT: The study reports the development of a coastal vulnerability index (CVI) based on extreme waves for the
Tamil Nadu coast. Region of interest is Kalpakkam, a coastal town located approximately 70 km south of the metropolis
Chennai in Tamil Nadu State, India. The CVI computation performed for a coastal stretch of about 250 km that covers ten
identified locations, with coastal Kalpakkam as the focal point. The study uses historical records of past cyclone tracks from
1945 to 2009 that had its landfall in Tamil Nadu State. There were 31 best cyclone tracks identified to construct the most
probable synthetic/hypothetical track for this region. This synthetic track used to conduct several numerical experiments
for cases of medium- and fast-moving cyclones. The extreme waves computed at these locations using a high-resolution
Simulating Waves Nearshore (SWAN) wave model particularly tuned for this region. Seven key parameters finally identified
in the computation of CVI. These include maximum significant wave height, maximum probable surge estimated from
50-year return period, and other geomorphologic characteristics at all ten stations. This is the metric indicator used in
the final estimation of CVI. The study signifies that metropolis Chennai and the adjacent region extending up to 57 km
northwards is a high-risk prone zone. The risk level due to extreme waves is low at Kalpakkam.
KEY WORDS
extreme waves; synthetic track; coastal vulnerability index; Tamil Nadu
Received 15 August 2012; Revised 15 May 2013; Accepted 25 May 2013
1. Introduction
In a global scenario, the western part of North Pacific
reported the maximum number of tropical cyclones
(average of 26 per year) followed by the eastern part of
North Pacific (average of 17 per year). The South Indian
Ocean and North Atlantic Ocean have an average of 10
per year, while the North Indian Ocean has an average
of about five cyclones per year (Niyas et al ., 2009).
The cyclone activity is quite predominant during midApril–June and October–December months in the North
Indian Ocean. The Bay of Bengal (BoB) experiences
higher cyclone frequency five times higher compared
with the Arabian Sea (AS). The favourable condition for
sustaining tropical cyclones has a direct bearing on the
sea-surface temperature (SST), i.e. higher in the BoB
(≈26–27 ◦ C) compared with the AS. In addition, the
remnants of cyclones that develop in western Pacific
Ocean basin are conducive for cyclogenesis activity in
the BoB (Niyas et al ., 2009). The closer proximity of the
BoB basin to the Inter-tropical Convergence Zone (ITCZ)
and the shift of ITCZ during monsoon activity enhance
the development of cyclogenesis into a tropical cyclone.
Singh et al . (2000) examined the frequency of tropical
cyclones using past data from 1877 to 1998 (122 years)
* Correspondence to: P. K. Bhaskaran, Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur,
Kharagpur 721 302, India. E-mail: [email protected]
 2013 Royal Meteorological Society
using a threshold wind speed greater than 48 knots,
highlighting that frequency and intensity of cyclones had
increased in the BoB. Srivastava et al . (2000) using the
data from 1891 to 1997 show that low-energy cyclonic
systems decreased in both BoB and AS in the past four
decades.
The rise in frequency and intensity of tropical cyclones
is therefore a risk to the coastal region. Hence, it is
important to assess the risk factor as densely populated
areas and coastal infrastructure have a direct bearing from
tropical cyclones. The term ‘risk’ is the probability of
expected loss from a given hazard that varies according
to the vulnerability of the region. The risk assessment
study involves four components, namely, environmental vulnerability, social vulnerability, hazard potential
and mitigation capacity. The risk index for each subcomponent formulated using the analytic hierarchy process. This study deals only with the aspects of environmental vulnerability. Cutter et al . (2003) describes about
the social vulnerability dealing with community experience and their ability to respond, cope, recover and adapt
to hazards. Poompavai and Ramalingam (2012) utilizing
satellite data reported on the risk assessment for a small
region in the north Tamil Nadu coast (area of 91.88 km2 )
covering Kottivakkam to Kovalam. A regional vulnerability map (scale of 1 : 50 000) was generated taking into
account the cyclones and storm surges.
Several researchers investigated the coastal vulnerability index (CVI) computation for different maritime
S. NAYAK AND P. K. BHASKARAN
states in India. Hegde and Reju (2007) developed CVI
for Mangalore (west coast of India) from Talapady to
Surathkal considering environmental variables such as
geomorphology, coastal slope, rate of shoreline change
and population as primary variables. Rao et al . (2008)
reported the CVI for Andhra Pradesh (east coast of
India) arising from sea-level rise using five variables,
namely, coastal geomorphology, coastal slope, shoreline
change, mean tidal range and significant wave heights.
Rajawat et al . (2006) investigated the hazard line along
Indian coast using the data from shoreline displacement,
tide, waves and elevation. Dinesh Kumar (2006) reported
the effect of climate-induced sea-level rise and consequences of potential vulnerability at Cochin (southwest
coast of India). Kumar et al . (2010) studied the coastal
vulnerability for Orissa coast (east coast of India). In
context to Kalpakkam, a location of strategic national
importance, a comprehensive study of CVI has not been
reported. Hence, the motivation of this study deals with a
detailed study of environmental vulnerability aspects for
Kalpakkam and adjoining coastal areas.
The historical record of past cyclone tracks for North
Indian Ocean reveals that cyclones over the BoB moved
west, northwest or northwards direction prior to its landfall (Shrestha et al ., 1998). Along the northeast coast in
the BoB, the cyclone NARGIS, a very severe cyclonic
storm, had its landfall on 2 May 2008. It resulted in
the worst natural disaster along the densely populated
Irrawaddy delta of Myanmar. NARGIS was a Category4 cyclone with sustained wind speed of 210 km h−1
according to the Unisys Weather report. Extensive flooding occurred along coastal plains accompanied with a
huge loss of life and property. The extent of damage
was extremely severe in the Ayeyarwady province of
Myanmar. The SAARC Meteorological Research Centre (SMRC) reported that in a century (1891–1991) about
1009 cyclones formed in the BoB with landfall in the east
coast of India (Shrestha et al ., 1998). The wind speed
for these cyclones ranged from 31 to 119 km h−1 . The
SMRC report also mentions that cyclones having landfall in Tamil Nadu State occurred during the months of
May, October and November. This attributes because of
the presence of little or no vertical wind shear. The associated storm surges from cyclones in the BoB are very
devastating, and reported in several studies (Murty et al .,
1986; Rao et al ., 1994, Dube et al ., 1997, Dube et al .,
2000, Chittibabu et al ., 2004).
Location-specific high-resolution numerical models for
storm surges at different maritime states bordering the
BoB and the AS were first developed at Indian Institute
of Technology Delhi (IITD) (Johns et al ., 1985, Dube
et al ., 1994, Chittibabu et al ., 2000, Dube et al ., 2004),
referred to as IITD storm surge model. Storm surge ranging from 3 to 6 m along with inland penetration of up
to 8 km was reported by Mani (2000) for cyclones that
occurred from 1952 to 1993 in the Tamil Nadu coast.
The Building Materials Technology Promotion Council
(BMTPC), a unit under the Ministry of Urban Development, Government of India, developed a vulnerability
 2013 Royal Meteorological Society
atlas for India (BMTPC, 1997). This atlas reports the
probable maximum surge height along coastal Chennai,
a metropolis in the Tamil Nadu State as 5.45 m. The
report by Ministry of Environment and Forests (MoEF,
2004), Government of India to the United Nations Framework Convention on Climate Change studies (UNFCCC),
states that Chennai has a high exposure level to cyclones
in terms of population density. This report mentions
that Chennai ranks first, for cyclones normalized by district area. The National Disaster Management Authority
(NDMA) under the Government of India in a recent
report categorized various maritime states along east and
west coast of India vulnerable to cyclonic winds and
coastal flooding. The study mentions that 14 districts in
Tamil Nadu State and Puducherry are prone to cyclone
disasters (Table I). As seen from Table I, ten districts are
highly prone to wind and cyclone disasters and four districts are highly vulnerable to coastal flooding. Table II
shows the district-wise classification of cyclone parameters having landfall in the Tamil Nadu coast.
The area of interest in this study is Kalpakkam, a
small coastal town located 70 km south of Chennai,
located in the Kanchipuram district (Figure 1). As per
Table II, the Kanchipuram district had the maximum
number of severe cyclones, as well the total number of
cyclones compared with any other districts of Tamil Nadu
coast. The probable maximum storm surge and maximum
precipitation were 3.5 m and 68 cm, respectively. The risk
level of probable maximum storm surge from cyclones is
moderate, and a high risk of flooding can occur owing to
precipitation. The Tamil Nadu State is mostly dependent
on monsoon rains, and thereby drought condition prevails
when the monsoon fails. The climatic system of Tamil
Nadu ranges from dry sub-humid to semi-arid. The area
of interest, Kalpakkam, is a region of strategic national
importance located in the east coast of India (12◦ 30 N
latitude and 80◦ 10 E longitude). The coastline is nearly
linear and oriented in the northeast–southwest direction.
Its elevation is 5 m above mean sea level (MSL) at
the coast that gradually increase to 100 m above MSL
approximately 100 km across the coast. In this study,
Kalpakkam location is the focal point, and the coastal
vulnerability is determined for a distance of ±100 km
along the coast from impacts of extreme waves and
storm surges. The subsequent section deals with the
methodology followed by results and discussion covering
various case studies using hypothetical tracks generated
from past track history.
2. Methodology
The impact on any coastal belt from natural hazards like
cyclones can lead to risk of human lives, property and
damage to coastal structures. The calamity associated
with cyclones in nearshore coastal environment results
from damage associated with high wind speed, storm
surges and coastal flooding. To evaluate and quantify
the risk assessment from cyclones in any coastal region,
Int. J. Climatol. (2013)
COASTAL VULNERABILITY AT KALPAKKAM DUE TO EXTREME WAVES
Table I. List of vulnerable districts for cyclone wind and coastal/inland flooding in the state of Tamil Nadu.
S. No.
District
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Thanjavur
Cuddalore
Kanchipuram
Thiruvallur
Tiruvannamalai
Viluppuram
Ramanathapuram
Puducherry and Karaikal
Nagapattinam
Pudukkottai
Sivaganga
Thoothukudi
Tirunelveli
Kanyakumari
Wind and cyclone
Coastal/inland flooding
Very high
Very high
Very high
Very high
Very high
Very high
Very high
High
Very high
High
High
Very high
Very high
High
Flood zone
Flood zone
–
–
–
–
–
–
Flood zone
–
–
Flood zone
–
–
Source: Cyclone prone districts in India, National Disaster Management Authority, Government of India.
Table II. District-wise cyclone parameters in the state of Tamil Nadu touching the coast.
S. No.
1
2
3
4
5
6
7
8
9
10
11
12
Districts
Kanchipuram
Cuddalore
Tiruvarur
Nagapattinam
Chennai
Viluppuram
Ramanathapuram
Thoothukudi
Tirunelveli
Thanjavur
Thiruvallur
Kanyakumari
Cyclone parameters
Number of
severe
cyclones
Total
number of
cyclones
Wind
speed
(m s−1 )
8
4
3
3
0
3
1
1
3
1
0
0
13
6
6
10
0
3
2
1
3
2
5
0
39–50
39–50
47
39–47
50
39–50
39
39
39
47
39–50
39
Probable,
maximum storm
surge (m)
3.5
3.5
5.5
4.5
3.5
3.5
12
7
7
5.5
4
3
Probable
maximum
precipitation (cm)
68
68
60
68
52
68
48
52
48
48
56
40
Source: Cyclone prone districts in India, National Disaster Management Authority, Government of India.
many factors require consideration. Interestingly, every
cyclone track is unique, and no two tracks are exactly
similar in terms of cyclone parameters and trajectory.
Scheitlin et al . (2010) demonstrated a novel way using
an archive of 383 tropical cyclones to articulate historical
tropical cyclone activity across space. This resulted in
the generation of a probable pathway by averaging the
analogue tracks. Similar study was reported by Hall and
Jewson (2005) leading to the development of a statistical
hurricane model with an objective to model the tracks
and genesis of hurricanes.
To understand and quantify the risk with cyclones, it
would be worthwhile to generate a synthetic track, considering the history of all cyclone tracks in the BoB that
had landfall in the Tamil Nadu State. Hence, this study
attempts to construct a synthetic track for Tamil Nadu
based on past track history using a well-established statistical method coupled with several numerical experiments.
The Joint Typhoon Warning Centre (JTWC) provides the
message files of past cyclone tracks and cyclone parameters that developed in the Indian Ocean for the period
from 1945 to 2009. The cyclone tracks documented by
 2013 Royal Meteorological Society
JTWC provide vital information about cyclone movement
in the Indian Ocean for nearly past six decades.
2.1.
Analysis of past cyclone tracks
The past cyclone tracks in the North Indian Ocean were
extracted from the best track archive of JTWC and Unisys
Hurricane Database for the period from 1945 to 2009.
From the composite available tracks, a set of tracks
was identified and selected based on its proximity to
Kalpakkam location. The resultant tracks were filtered
based on seasons into two groups, namely, summer and
winter tracks. For summer, the grouping was based on
all the cyclone tracks that occurred during the months
of March–June in all years; and remaining period of
year comprised the winter tracks. On the basis of the
analysis noticed the numbers of summer tracks were
quite a few, and thereby excluded from the preparation
of synthetic track. Therefore, all available cyclone tracks
during winter months were considered to construct the
synthetic track. This synthetic track was used to perform
several numerical experiments in final estimation of CVI.
Int. J. Climatol. (2013)
S. NAYAK AND P. K. BHASKARAN
Figure 1. Study area—location of Kalpakkam, southeast coast of India.
2.2.
Generation of probable synthetic track
For the analysis, 49 cyclone tracks were available during
the winter season. The criterion is to select those tracks in
winter season having close proximity to the Kalpakkam
region. This finally resulted in 31 tracks (Figure 2(a))
subsequently used to construct the probable synthetic
track. A cyclone track comprises the cyclone eye, identified in terms of its latitude, and longitude coordinates
along the track. To generate a synthetic track, these eye
locations are the vital parameter taken into consideration. The JTWC best track data archive comprises the
cyclone eye location at regular intervals of every 6 h.
Amongst these 31 tracks, it was noticed that the average
number of cyclone eye positions (considering the position from source to landfall) at regular interval of 6 h was
 2013 Royal Meteorological Society
13. Therefore, as a first guess the synthetic track should
comprise at least 13 positions of cyclone eye at a regular interval of 6 h. The synthetic track was constructed
using the inverse distance weight (IDW) approach. The
31 tracks were assigned weights based on their closeness
in terms of great circle distance from Kalpakkam location
(80.16◦ E; 12.56◦ N). The weights were mathematically
computed using the expression:
wk =
1
, where k = 1, 2, 3 . . . , 31
d (e, tk )
(1)
In the above equation, d (e,t k ) refers to the nearest great
circle distance from Kalpakkam location to the track
(tk ). Once the weights are assigned to the respective
past tracks, the 13 cyclone eye coordinates for the
Int. J. Climatol. (2013)
COASTAL VULNERABILITY AT KALPAKKAM DUE TO EXTREME WAVES
(a)
(b)
Figure 2. (a) Composite cyclone tracks during winter months in coastal Tamil Nadu. (b) Synthetic/hypothetical track for coastal Tamil Nadu.
synthetic track can be estimated using IDW method, and
mathematically expressed as
31
Xi =
31
wk xki
k =1
31
k =1
, Yi =
wk
wk yki
k =1
31
k =1
, where i = 1, 2, . . . , 13
wk
(2)
where (Xi , Yi ) corresponds to the i th coordinate of the
synthetic track, (xki , yki ) corresponds to the i th coordinate
of the k th track, and wk is the weight of the k th track.
Figure 2(b) shows the synthetic track obtained using the
IDW approach.
2.4. Simulating Waves Nearshore (SWAN) wave
model
The SWAN is a third generation state-of-art spectral
wave model. It describes the evolution of wave action
density (N ) which is the ratio of variance density to
the intrinsic frequency (Booij et al ., 1999). The action
balance equation is mathematically expressed in the
form:
∂
∂
Stot
∂N
+ ∇. cg N +
(4)
(cθ N ) +
(cσ N ) =
∂t
∂θ
∂σ
σ
The right-hand side term (S tot ) denotes the total wave
energy. The expansion of S tot is expressed as:
Stot = Sin + Swc + Snl4 + Sbot + Sbrk + Snl3
2.3. Generation of wind field for the synthetic track
The well-accepted formulation of Jelesnianski (1965) was
used to generate the wind field for the synthetic track, and
mathematically expressed in the form:
The terms on the left-hand side of Equation (4) represent the change of wave action density in time and the
propagation of action density in geographical space. The
depth- and current-induced refraction with cθ and cσ rep-


Vx
2 Rr

r
1 − (x − x0 ) sin ϕ − (y − y0 ) cos ϕ

,0 ≤ r ≤ R

 r+R V + Wr 1+( r )2 r
(x − x0 ) cos ϕ − (y − y0 ) sin ϕ
R
y
Wx
=
Wy

Vx
2 Rr

R
1 − (x − x0 ) sin ϕ − (y − y0 ) cos ϕ

+
W
,r ≥ R

r
2
 r+R V
1+( r ) r
(x − x ) cos ϕ − (y − y ) sin ϕ
y
R
where (x, y) are the coordinates of the computed point
and (x 0, y 0 ) is the location of cyclone eye. (Wx , Wy ) are
the components of wind velocity at the computed point
and (Vx , Vy ) are the components of wind velocity at the
eye of cyclone; ϕ is the angle between wind direction at
sea surface and direction of the gradient wind; γ is the
distance between the computed point and eye of cyclone
and R is the radius of maximum winds. As mentioned
above, the synthetic wind fields were generated at regular
intervals of 6 h using different combinations of maximum
sustained wind speed (V ) and radius of maximum wind
(R) as shown in Table III.
 2013 Royal Meteorological Society
(5)
0
(3)
0
resents the propagation velocities in the θ and σ space,
respectively. The total energy in Equation (5) refers to
the transfer of energy from wind to waves (S in ); the dissipation of wave energy due to white-capping (S wc ) and
the nonlinear energy transfer due to quadruplet interaction (S nl4 ). These three processes are important for wave
propagation in deep waters. In addition to these three
terms, for shallow waters, the dissipation due to bottom
friction (S bot ), depth-induced breaking (S brk ) and triad
wave–wave interaction (S nl3 ) should also be accounted.
The nonlinear interaction process in shallow water governed by the triad wave–wave interaction based on the
Int. J. Climatol. (2013)
S. NAYAK AND P. K. BHASKARAN
Table III. Numerical experiments with synthetic track.
Classification
based on
forward
motion
Mediummoving cyclone
(≈16 km h−1 )
Maximum
sustained
wind speed
(V ) in m s−1
30
60
63
Fast-moving
cyclone
(≈30 km h−1 )
30
60
63
Radius of
maximum
winds (R)
in km
30
40
45
30
40
45
30
40
45
30
40
45
30
40
45
30
40
45
Acronym
V1
V1
V1
V2
V2
V2
V3
V3
V3
V1
V1
V1
V2
V2
V2
V3
V3
V3
R1
R2
R3
R1
R2
R3
R1
R2
R3
R1
R2
R3
R1
R2
R3
R1
R2
R3
lumped triad approximation theory of Eldeberky (1996)
and the bore-based model of Battjes and Janssen (1978)
for depth-induced breaking dissipation. Many versions
of SWAN model are available since its inception, and
the recent version 40.85 was used in this study. The
SWAN model runs in both stationary and non-stationary
modes. The default propagation scheme for stationary
computation is the second-order upwind scheme in geographic space and mix of central and first-order upwind
scheme for both frequency and direction propagation.
More details on the physics and numeric of SWAN are
available in the technical documentation of SWAN model
(version 40.85).
In this study, multi-scale modelling approach was used
to simulate wave conditions using the synthetic track. To
obtain realistic wave estimates in the area of interest (the
coastal belt of Kalpakkam), the study area was divided
into three zones, namely D1, D2 and D3 (Figure 3). The
outer domain D1 covers the geographical area extending
up to 70◦ S, the area encompassing most of the Southern Ocean. The Southern Ocean is a potential source for
swell generation in the global oceans attributed from fastmoving strong synoptic wind systems. Swells developed
in this area propagate quickly along the great circle arc
and can reach the Indian mainland in about 3–4 d after
its generation. The latest version of WAM model (version
4.5.3) is used to simulate the wave conditions in the outer
domain (D1). The intermediate domain (D2) utilizes the
time varying 2D-variance density spectrum along lateral
boundaries that covers the BoB domain having a spatial grid resolution of 16 × 16 km. The innermost domain
(D3 in Figure 3) is a fine resolution flexible finite element grid, with spatial resolution of one order less than
the intermediate domain. The flexible unstructured grid is
used for the inner domain account for the complex coastline geometry, an essential prerequisite for reliable wave
 2013 Royal Meteorological Society
Figure 3. Multi-scale domains for realistic wave estimate in coastal
Tamil Nadu.
estimates. The authors believe that the multi-scale modelling approach that utilizes three varying domain sizes
can resolve distant swells reaching the Tamil Nadu coast.
The interaction of swells with local wind waves is a topic
of interest investigated separately, and not a scope of this
study. The wind field used to simulate wave climate for
domain D1 by WAM model is climatologically averaged
blended European Centre for Medium-Range Weather
Forecasts (ECMWF) winds for the winter months. The
blending algorithm utilizes remotely sensed retrievals
from ECMWF analysis, ensuring a good quality wind
product. The temporal and spatial resolution of wind data
for domain D1 is six hourly zonal and meridional components of surface winds gridded in 0.25◦ × 0.25◦ box. The
blended ECMWF winds have proven accuracy and the
correlation coefficients are in the range from 0.80 to 0.90
for global oceans. As wave models are very sensitive
to input wind forcing, the authors believe that blended
ECMWF product suffices the best quality for D1 domain.
The wind fields for domains D2 and D3 were generated from synthetic track as described in Section 2.3..
Several numerical experiments were carried out with synthetic track, and the resultant maximum significant wave
heights from these experiments were analysed to assess
the coastal vulnerability surrounding the Kalpakkam location.
2.5. Indicators of vulnerability in coastal Tamil Nadu
2.5.1. Geomorphology
Kumanan et al . (2010) generated a geomorphology map
for the entire coastal region of Tamil Nadu. Their study
Int. J. Climatol. (2013)
COASTAL VULNERABILITY AT KALPAKKAM DUE TO EXTREME WAVES
Table IV. Geomorphologic classification of coastal Tamil Nadu.
S. No.
Classification
1
Very low
2
Low
3
Moderate
4
High
5
Very high
Table VI. Classification based on shoreline changes for coastal
Tamil Nadu.
Features
Rocky coasts and cliffed
coasts
Uplands, pediments,
medium cliffs and
indented coasts
Low cliffs, alluvial
plains and beach ridges
Estuary, lagoons, creeks
and backwater
Barrier beaches, sand
beaches, salt-marshes,
mud-flats, deltas and
mangroves.
Table V. Classification based on slopes for coastal Tamil Nadu.
S. No.
Classification
Slope (%)
1
2
3
4
5
Very low
Low
Moderate
High
Very high
>0.81
0.61–0.80
0.41–0.60
0.21–0.40
<0.20
S. No.
1
2
3
4
5
Classification
Accretion/erosion
potential (m year−1 )
Very low
Low
Moderate
High
Very high
>+2
1.0 to 2.0
−1.0 to +1.0
1.1 to −2.0
<−2.0
of this study reveals that shoreline changes are associated
with accretion and erosion of sediments. Accordingly, the
coastal belt of Tamil Nadu grouped into five classes that
range from very low to very high and listed in Table VI.
2.5.4. Mean tidal range
The information of mean tidal range elevation data is
also available in the report of Kumanan et al . (2010). It
was prepared based on the data obtained from National
Hydrographic charts for 13 different locations covering
entire Tamil Nadu coast. The tidal range elevations varied
between 0.6 and 1.2 m. In this study, appropriate tidal
range elevations for the study area are divided into five
classes.
was based on interpretation of raw and digitally processed
IRS P6 LISS 3, AWIFS and LANDSAT satellite data.
The satellite data subjected to various image-processing
techniques and blended with shuttle radar topography
mission (SRTM)-based digital elevation model (DEM)
provided a better representation of geomorphic features
and subsequent interpretation (Kumanan et al ., 2010).
The analysis revealed that coastal belt of Tamil Nadu
comprises diverse geomorphologic features such as flood
plains, palaeochannels, river beds, sand bars, fluviomarine, deltaic plains, beach ridges, tidal flats, mangroves, creeks, beaches, etc. On the basis of vulnerability
to rising sea level and possible inundation, the geomorphology map for Tamil Nadu was divided into five classes
(Kumanan et al ., 2010) as shown in Table IV.
2.5.5. Storm surge
2.5.2. Coastal slopes
The Intergovernmental Panel on Climate Change (IPCC)
as well the World Food Programme (WFP) had quite
recently stressed on the importance of vulnerability of
natural systems for policy matters on environmental risks.
As discussed above, the coastal environment experiences
potential impact due to rising sea levels, extreme weather
events such as cyclones associated with storm surges and
coastal flooding. These physical mechanisms result in
changing the geomorphologic setting of a coastal belt.
Therefore, proper assessment of coastal vulnerability is
important for a decision support system in an integrated
coastal zone management (ICZM) programme, considered very essential at present. A well-accepted methodology to assess coastal vulnerability is the CVI, a method
reported by Gorintz et al . (1997). This study uses this
approach for assessment of CVI due to extreme waves
The coastal slopes expressed in percentage for the coastal
belt of Tamil Nadu are available in the report of Kumanan
et al . (2010). These slopes were computed using the
digitally processed SRTM data, which mention that about
80% of coastal belt in Tamil Nadu lies within 1%. In
this study, the vulnerability expressed in terms of coastal
slopes shows that Tamil Nadu coast grouped into five
classes as shown in Table V.
2.5.3. Shoreline changes
The erosion and accretion along the coastal belt of Tamil
Nadu analysed using the satellite data from LANDSAT
TM 1990, LANDSAT ETM 2000 and IRS P6 LISS 3 are
available in the study of Kumanan et al . (2010). Analysis
 2013 Royal Meteorological Society
The analysis of cyclone track data for the period from
1891 to 2007 shows that the number of cyclones that
made landfall is highest in Chennai (with 15 cyclones),
making it the most vulnerable coastal district. The
analysis of probable maximum surge [total water level
(TWL)] based on 50-year return period reveals that
Ramanathapuram district of south Tamil Nadu has the
highest TWL that could attain 6.0 m (Jain et al ., 2010).
The other coastal districts have less impact ranging from
2 to 3 m surge due to their geographical location, low
values of pressure deficit and tidal range.
3.
Computation of CVI
Int. J. Climatol. (2013)
S. NAYAK AND P. K. BHASKARAN
Table VII. Assigned ranks for the six parameter model.
Coastal locations
Assigned ranks
Key parameters
a
Pulicat
Chennai
Mamallapuram
Kalpakkam
Kovalam
Thazhankadu
Puducherry
Kirumampakkam
Raasapettai
Parangipettai
a
b
Geomorphology
Coastal
slopea
Relative
sea-level
ratea
Rate of
shoreline
changea
Mean tidal
rangea
Maximum
significant wave
height
Storm Surgeb
5
5
5
3
5
2
5
1
5
5
4
5
3
2
2
3
3
4
5
5
5
3
3
3
3
5
4
4
5
5
5
5
5
3
5
1
3
4
3
3
5
5
3
3
5
4
4
3
4
2
2
4
4
5
3
3
2
1
1
3
5
4
3
2
2
1
1
1
1
1
Data Source: Status of Tamil Nadu Coast in context to Global warming and related sea-level rise (Kumanan et al ., 2010).
Data Source: Maximum Probable Storm Surge (Jain et al ., 2010).
in coastal Tamil Nadu. The following variables are considered in this study: (1) coastal geomorphic setting; (2)
coastal slope with susceptibility to inundation by flooding and shoreline retreat; (3) relative sea-level change or
subsidence; (4) tendency for shoreline to retreat/advance
due to eustatic rate in sea-level rise; (5) tidal range linked
with both permanent and episodic inundation hazard; (6)
wave height based on maximum significant wave height
for each coastal destination and (7) associated probable
maximum storm surge based on 50-year return period.
The study by Kumanan et al . (2010) considers only
four environmental variables, namely, geomorphology,
coastal slope, shoreline change and mean tidal range
to compute the CVI at various coastal belts in Tamil
Nadu. In this study, seven variables are used to assess
CVI within ±100 km considering Kalpakkam as the
nodal point. The authors believe that seven-parameter
CVI model for Tamil Nadu coast was not reported
in the literature. Hence, this study should be more
comprehensive as compared to the report of Kumanan
et al . (2010). The seven risk variables used to formulate
the CVI can identify coastal areas more prone to risk
levels. The CVI computation is mathematically expressed
in the form:
(6)
CVI = [X1 .X2 .X3 .X4 .X5 .X6 .X7 ] /7
where X 1 , X 2 , . . . , X 7 are the ranks for geomorphology,
coastal slope, relative sea-level rate, shoreline change
rate, mean tidal range, maximum significant wave height
and storm surge for each coastal location, respectively.
The ranks assigned to these seven parameter models are
listed in Table VII. The numbers 1–5 shown in this table
follow the characterization of very low, low, moderate,
high and very high, respectively.
4.
Results and discussion
Figure 4(a) shows the synthetic track along with the
locations of ten stations used in this study. The station
 2013 Royal Meteorological Society
at northward side is Pulicat, located approximately 55
and 135 km north of Chennai and Kalpakkam Township,
respectively. The station located at southward limit of
study area is Parangipettai, which is about 139 km
south of Kalpakkam. The shortest distance between
Pulicat and Parangipettai is about 200 km. Two sets of
numerical experiments are conducted with the synthetic
track: (1) medium-moving cyclone and (2) fast-moving
cyclone. The classification of medium- and fast-moving
cyclones is in accordance with the directive of India
Meteorological Department (IMD). According to this
classification, medium-moving cyclones should possess
a forward motion speed of ≈16 km h−1 , and for fastmoving cyclones, the translation speed is ≈30 km h−1 .
Table III shows the details of numerical experiments
performed using the synthetic track.
Overall, a set of 18 numerical experiments performed
using the synthetic track, 9 each for medium- and fastmoving cyclones. All the experiments use varied combination of maximum sustained wind speed (V ) and radius
of maximum winds (R). The maximum sustained wind
speed varied from 30 to 63 m s−1 for both medium- and
fast-moving cyclones. In Table III, the range of values
used for parameter ’V ’ is in accordance with the message files of JTWC best track. According to JTWC record
from 1945 to 2009, the maximum sustained wind speed
never exceeded 63 m s−1 at coastal Tamil Nadu and set
as the upper limit in numerical experiments. Sustained
maximum wind speed of 30 m s−1 is a common occurrence evident from JTWC best tracks. The numerical
experiments use three different combinations for maximum winds (R): 30, 40 and 45 km. Majority of tropical
cyclones and severe cyclonic storms (about 99%) that
develop in the Indian Ocean has ’R’ varying between
30 and 45 km that justifies the limits used in our analysis. The wind field along synthetic track under different
combinations of ‘V ’ and ‘R’ is generated using Jelesnianski formulation mentioned in Section 2.3.. The list of
Int. J. Climatol. (2013)
COASTAL VULNERABILITY AT KALPAKKAM DUE TO EXTREME WAVES
(a)
(b)
(c)
(d)
Figure 4. (a) Locations of ten coastal stations for vulnerability analysis. (b) Maximum significant wave height (in metres) for Locations 1 and 2.
(c) Maximum significant wave height (in metres) for Locations 3 and 4. (d) Maximum significant wave height (in metres) for Locations 9 and
10.
acronym in last column of Table III refers to each case
study at all ten locations as shown in Figure 4(a).
The computed maximum significant wave heights (in
metres) varied between 2.5 and 7.0 m within ±100 km
window from Pulicat to Parangipettai. The location2 corresponding to Chennai as shown in Figure 4(b)
represents the highest maximum significant wave height
of 7.0 m. Factors that contribute to maximum wave
heights at Chennai result from the broad and shallow
continental shelf and favourable direction of onshore
winds. This is evident at all five stations from Pulicat to
Kovalam (Figure 4(a)). A cyclonic system in the Northern
Hemisphere has anti-clockwise winds with onshore wind
direction on right side of the track, wherein the radius
of influence felt for locations from Pulicat to Kovalam.
The stations located south of Kovalam to Parangipettai
experience offshore wind direction during the course of
cyclone landfall. The maximum significant wave heights
at Kalpakkam location were about 5.8 m (Figure 4(c)).
The width of continental shelf for all the stations from
Kalpakkam to Parangipettai is very narrow. This has a
direct bearing on approaching waves, as the offshore
depth from shelf break is relatively deep. In context to
the synthetic track, the wind system south of Kalpakkam
is towards offshore resulting in an unlimited offshore
fetch. The locally generated wind waves at these coastal
 2013 Royal Meteorological Society
stations, left of the synthetic track, have their mean
wave direction directed offshore. The narrow width of
continental shelf together with offshore wind direction
leads to low wave heights for coastal stations from
Kalpakkam to Parangipettai (Figure 4(a)). The maximum
computed significant wave height for Parangipettai ranges
between 5 and 6 m (Figure 4(d)). This attributes to the
nonlinear interaction mechanism between the local windwaves-directed offshore and approaching swells from the
opposite direction. These swells generate from Southern
Ocean and traverse the steep slope off Parangipettai. The
numerical experiments with medium- and fast-moving
cyclones under different combinations of ’V ’ and ’R’
reveal that differences in maximum significant wave
height do not exceed 0.5 m. This is evident for the coastal
station Kirumampakkam (having steeper slope) and south
of the synthetic track. The maximum difference observed
for Parangipettai location is about 0.25 m. The remaining
eight stations have a mild bottom slope. The resultant
maximum significant wave height with medium- and fastmoving cyclone shows only a marginal difference.
Jain et al . (2010) computed the 50-year return period
of maximum probable water level (TWL) using local tide
and wind-wave setup on storm surge amplitudes along
the east coast of India. Their study uses synthesized
tracks, composites from observed tracks and composites
Int. J. Climatol. (2013)
S. NAYAK AND P. K. BHASKARAN
Figure 5. Probable maximum surge along Tamil Nadu based on 50-year return period.
from theoretical tracks for each district along the Tamil
Nadu coast. The synthetic tracks were used for numerical
simulation of storm surges with IITD storm surge model.
Their methodology (Jain et al ., 2010) uses the maximum
pressure deficit (P ) for each cyclonic event. Using
P as input, a suitable statistical analysis based on
extreme value analysis was performed to obtain probable
maximum P for return periods of 2, 5, 10, 25 and
50 years. Although mathematical projections are possible
beyond 50 years, the effect of climate change and other
anthropogenic effects can bring in more uncertainties.
Therefore, a return period of 50 years would suffice to
understand the coastal vulnerability. Figure 5 shows the
probable maximum storm surge based on a 50-year return
period.
The CVI computed at all ten stations for Tamil Nadu
is shown in Figure 6, and this varied in a scale from 5
to 65. The lowest and highest CVI of 5 and 65 corresponds to coastal destinations of Chennai and Kirumampakkam, respectively. The location of interest, i.e. coastal
Kalpakkam has a CVI of 15. The computed CVI classified into four categories: CVI < 20 is classified as low;
 2013 Royal Meteorological Society
≥20 and ≤35 as moderate; ≥35 and ≤50 as high; and
≥50 and <65 as very high. According to this classification, Chennai has a very high risk of coastal disaster
followed by Pulicat, approximately 55 km north of Chennai. The coastal locations in the immediate north–south
vicinity of Kalpakkam, i.e. Mamallapuram and Kovalam
show a moderate CVI. The risk level at Kalpakkam location records in the upper percentile of low category CVI.
The study signifies that for a total coastline length of
approximately 250 km between Pulicat and Parangipettai,
the coastal region north of Kalpakkam located approximately 57 km (between Pulicat and Chennai) is a very
high-risk zone. The risk levels for other coastal regions
varied from low to moderate.
5. Summary and conclusions
An ICZM requires a precise knowledge on the complex
interaction mechanism that occurs in a coastal region.
The problem becomes more complex and diversified
when the coastal belt is vulnerable and prone to natural
disasters. The coastal belt is a fragile environment and
Int. J. Climatol. (2013)
COASTAL VULNERABILITY AT KALPAKKAM DUE TO EXTREME WAVES
Figure 6. Coastal vulnerability index for selected ten coastal stations in Tamil Nadu.
the impact from marine hazards such as cyclones and
associated storm surges is significant. There is a need to
study and understand a coastal belt from these hazards
subjected to varying degree of coastal vulnerability.
The integrated effects from various physical components
such as geomorphic setting, coastal slope, rate of sea
level and shoreline changes, mean tidal range, maximum
significant wave height, and associated storm surges
need consideration for CVI computation. The location
of interest in this study is coastal Kalpakkam, a strategic
location of national importance. Coastal vulnerability at
a distance of ±100 km along the coastal belt of Tamil
Nadu with Kalpakkam as the focal point is determined.
The historical cyclone tracks in the BoB for the Tamil
Nadu region during the winter months from 1945 to 2009
were used to construct the most probable synthetic track.
The synthetic track comprises 13 points from the source
to the landfall location. The Jelesnianski model provides
wind field for the synthetic track. Several numerical
experiments with different combinations of maximum
sustained wind speed (V ) and radius of maximum winds
(R) were used to assess the possible maximum significant
wave height along ten selected coastal destinations. The
final computation of CVI was performed for these ten
coastal locations with medium- and fast-moving cyclone
cases. The computed maximum significant wave heights
varied from 2.5 to 7.0 m at these ten locations. Further
study was conducted on the role of bottom topography,
continental shelf width and radial directions of wind
fields along left and right side of synthetic track on
wave characteristics. The resultant maximum significant
wave height and probable maximum storm surge at these
ten locations were assigned appropriate ranks for input
to the seven parameter coastal vulnerability model. The
computed CVI varied from 5 to 65, with the maximum
 2013 Royal Meteorological Society
noticed at Chennai. The study signifies that Kalpakkam
is at low risk whereas Chennai is the highest risk prone
zone.
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