IRJET- Temporal Changes of Coastal Cliffs : A Case Study from Varkala

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 07 | July 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2821
TEMPORAL CHANGES OF COASTAL CLIFFS: A CASE STUDY FROM
VARKALA
Sruthy G S1, Dr. Sajinkumar K S2, Dr. Ramakrishnan K3
1M Tech Student, Environmental Engineering and Management, UKFCET, Kerala, India
2Assistant Professor, Dept. of Geology, University of Kerala, Trivandrum, Kerala, India
3Assistant Professor, Environmental Engineering and Management, UKFCET, Kerala, India
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Abstract - The coastal landforms along the Varkala is the
only place in Southern Kerala where cliffs are found
adjacent to the Arabian Sea and have undergone
remarkable change in terms of shape and disposition due
to marine and terrestrial processes and often by natural
and anthropogenic activities. Red laterite cliff fringing the
Papanasam beach is a famed tourist spot here. The
authorities, however, seem to be unaware of the need to
protect the cliff, which has geological significance. Fissures
have developed at several spots along the 2.5m pathway,
used by tourists to walk to the beach and the helipad
areas. Hundreds of local people staying on the northern
side of the cliff also use the pathway, dotted by many
shops. Huge chunks of earth precariously hang from the
top of the cliff at several places. Unauthorised
constructions and piling work atop the cliff and movement
of vehicles on the pathway are cited as the main reasons
for frequent landslips. The primary objective of this study
is to estimate the decadal changes caused in Varkala
Beach, India by comparing the Satellite Imageries (2003
and 2019) with Survey of India 1966 Toposheet. The new
shoreline was captured from the imageries using overlay
analysis techniques of GIS applications. Coastal erosion
may have a direct impact on the virtual quality of the
landscape. QGIS (3.6) has been used as a tool to delineate
the cliff erosion hazard for proper planning and
management of coastal developments.
Key Words: Coastal cliffs, cliff retreat, shore expansion,
shore widening, cliff erosion
1. INTRODUCTION
Sea cliffs are steep geological features with slopes typically
larger than 40° (Goudie, 2004), and around 80% of the
world’s oceanic coastlines have sea cliffs (Emery and
Kuhn, 1982). Their evolution occurs mainly by slope mass
movements of different types and sizes (Trenhaile, 1987;
Sunamura, 1992). Conflict between human activity and the
inherent instability of coasts cliffs has become a growing
problem in recent decades mainly due to the steady
increase of human occupation and activities in coastal
areas (Nunes et al., 2009; Teixeira, 2014).
Sea cliff morphodynamics, especially slope angle and
erosion rate, are influenced by marine processes (e.g.
waves and tide), subaerial processes (e.g. mass
movements, weathering, and bio-erosion), and
characteristics of rock materials (Masselink et al., 2011;
Emery and Kuhn, 1982). Sea cliffs are steep when marine
processes are pre-dominant and are gentle when subaerial
processes are predominant (Masselink et al., 2011).
Sunamura (1992) states that the resistance to cliff erosion
mainly depends on rock hardness.
Due to expansion of settlements and
infrastructure and due to the rapid population growth, the
land transformation on the natural land use and land
cover features has quickened (Mujabar and Chandrasekar,
2012). The changes in coastal land use and land cover due
to human activities have wrought in the earth’s life
support system causes major issues worried by many
people in recent decades (Luong, 1993; Jaiswal et al.,
1999; Misra et al., 2013). Such negative changes leads to
the vulnerability of places and people to climatic,
economic or socio-political perturbations of the regions
(Nicholls and Small, 2002; Yagoub and Kolan, 2006; Kaliraj
et al., 2014). Increasing of population and climatic
variability produces pressure on the land use and land
cover and cause the greatest environmental impact on
vegetative cover, shoreline change, landform degradation,
loss of biodiversity, seawater intrusion and groundwater
pollution, deterioration of soils and air along the coastal
regions (Chandrasekar and Kaliraj, 2012; Chauhan and
Nayak, 2005; Mahapatra et al., 2013; Kaliraj et al., 2014).
This will result in the total destruction of the ecosystem -
both land and aquatic. Even if the main cause of cliff
retreat is wave erosion, other processes contribute to the
total amount of cliff recession (Bird, 2016).
Subaerial processes, biological weathering and
other marine processes can significantly increase the
recession rates. Weathering processes is more active on
the top of the cliffs, while erosion processes dominate the
cliff foot. Coastal landslides involve large masses of rocks,
earth or debris at the foot of a coastal slope. The instability
of a cliff can be due to the weight of a massive cap-rock
and develops with an increase of shear stress or a
decrease in shear strength (Bird,2010). The global
distribution of coastal cliffs is shown in the Fig-1:

Fig-1: Global Distribution of Coastal Cliffs
Cliffs and other landforms of rocky shores can be
eroded by many different interrelated processes, such as
hydraulic action, corrosion, attrition, solution and
quarrying and cavitation. Runoff processes also involve
sea cliffs, since rain and melting can generate water
flowing down a cliff slope. On soft rock outcrops it washes
away sediments producing rills and gullies. The materials
accumulated at the cliff foot are subsequently removed by
wave action and in some cases can protect the cliff from
wave attack (Furlani et al., 2011). Sea spray can also
generate runoff down the cliff. It contributes to
weathering promoting processes of wetting and drying,
when crystals of salt pluck the rock surface forming pits,
honeycombs and tafoni (Bird, 2016). Winds blowing
against a cliff can remove fine-grained particles from the
slope and create upto small caves (Bird, 2016). The water
coming from rainfall or melting snow percolates into the
rock mass through fractures, joints and cavities.
Groundwater seepage from a cliff face can wash out finer
particles leaving cracks and crevices up create an apron at
the cliff base (Bird, 2016).
1.1 Study Area
Fig-2: Location Map
Varkala is located 40 kilometres from
Thiruvananthapuram which is the capital city of Kerala.
The study area is shown in the Fig-2. Varkala is the only
place in southern Kerala where cliffs are naturally formed
adjacent to the Arabian Sea. Köppen-Geiger climate
classification system classifies Varkala's climate as tropical
monsoon. It has heavy rains during June–August due to a
southwest monsoon. In summer, the temperature rises to
a maximum of 32°C (90°F) and 31°C (88°F) in the winters.
Record high temperature in neighbouring
Thiruvananthapuram is 39°C (102°F). Annual average
rainfall is 3,100 mm (120 in).
Varkala is an important place as Kerala Geology is
concerned as it exposes sedimentary rocks found on
Cenozoic age, popularly known as the Warkalli formation.
The study area from Google Earth is shown in the Fig - 3
below:
Fig - 3: Study Area (Source: Google Earth)
The Warkalli formation of Mio-pliocene
age (type area is Varkala) is made up of alternating beds of
sand exposed along the Varkala cliffs. Thin seams of lignite
of the Warkalli formation suggests good vegetation at the
time of deposition of the clayey sediments. Vertical ridges
or speleothems can be formed because of local
precipitation of carbonates. The accumulation of
groundwater in permeable rocks can increase the
instability of rock masses. The increase shear stress due to
the additional loading of groundwater can result in cliff
collapse. As it has a unique geological feature, a national
geological monument has been declared by the Geological
Survey of India for their protection, maintenance,
promotion and enhancement of geotourism.
Varkala is a well-known tourist destination.
Around 20 plus resorts is situated in the municipality.
Tourism started thriving by the end of last century at the
Varkala beach (Papanasam), famous for Vavu Beli, a Hindu
custom performed at the beach. There are numerous
water spouts and spas on these cliffs. In 2015, Ministry of
Mines, Government of India and Geological Survey of India
(GSI) have declared the Varkala Cliff as a Geo-heritage site.
Human impact is very significant as it can modify,
directly and indirectly, sea cliffs and bluffs, such as
quarrying sand and rocks, loading the clifftop with
buildings, building roads, vehicular movement, removing
and weakening rock masses for fossils searching. This
study uses the application of GIS to assess the change in

cliff position over three particular years from satellite
images and topographical maps using QGIS software and
analyse the role of both natural and anthropogenic factors
likely to drive clifftop retreat. The collapsed images of cliff
is shown in the Fig- 4 below:
Fig - 4: Cliff image (Image taken from the study area)
2. LITERATURE REVIEW
A study by Young and Carilli (2018) uses two approaches
to capture information on the worldwide existence and
erosion of coastal cliffs: a detailed literature survey and
imagery search, and a GIS-based global mapping analysis.
The results suggest that the global coverage to estimate
cliff occurrence across 89% of the world vector shoreline
and coastal cliffs likely exist on about 52% of the global
shoreline. Ahmed (2011) describe the causes of major
changes in land use pattern of the coastal zone of
Bangladesh and identify the effects on the environmental
degradation obviously considered as a man-made disaster
in the area. The paper shows that the way of using the
lands in the coastal areas are gradually changing, i.e.,
diverse, competitive and alarming. An attempt is made by
Kaliraj et al (2017) to map the coastal landforms along the
coast using remote sensing and GIS techniques.
The study apart from providing insight into the decadal
change of coastal settings also supplements a database on
the vulnerability of the coast, which would help the coastal
managers in future.
Ikeda and Testik (2019) investigated for eight
coastal sites near Santa Barbara, California on the
interactions among sea cliff morphology, beach
morphology, and coastal hydrodynamics. Among the study
sites, the widening of the buffer zone was larger for wider
beaches, while the cliff slopes were gentler. Their analysis
indicated that long-term cliff erosion in Southern
California may be estimated by using adequate wave-
induced cliff erosion models. Pian and Menier (2019)
combines the application of GIS with spatial and statistical
analysis to assess the role of both natural and
anthropogenic factors likely to drive clifftop retreat. This
approach aims at identifying the main factors associated
with differing rates of clifftop retreat in order to produce
an effective set of data for coastal managers. The study
focuses on two cliff systems located in South Brittany
(France): the sheltered and weathered low cliffs of the
Gulf of Morbihan, and the rocky cliffs of the Quiberon
Peninsula.
Since a range of process-based models have been
used the influence of varied vertical lithology has yet to be
quantified, Carpenter et al (2014) describes modifications
to the 2D SCAPE (Soft Cliff and Platform Erosion) model, to
explore interactions between vertical changes in cliff
resistive strength and prevailing coastal conditions. The
results have important implications for the management
of coastal cliffs exhibiting variable stratigraphy, combined
with the potential for future interactions with sea-level
rise.
Castedo et al (2012) developed a new model to
incorporate the behavioural and mechanical
characteristics of coastal cliffs which are dominated
geologically by over-consolidated clays (tills) and an
associated protective colluvial wedge. This model is
capable of providing precise and stable responses to some
of the inherent uncertainties in cliff recession processes
including those caused by different failure mechanisms
e.g. colluvium generation, groundwater and erosive tidal
cycles. Material strength is incorporated using the
unconfined compressive strength of the material that
composes the cliff. The model is thus validated through
profile evolution assessment at various locations of
coastline retreat on the Holderness Coast, UK. Higher
groundwater content also produces an increase in the
number and size of the slope failures. The results
represent an important step in linking material properties
to the processes of cliff recession.
Young et al (2018) used Airborne LiDAR data
collected in 1998 and 2009–2010 to measure coastal cliff
erosion and retreat between the Mexico/California border
and Bodega Head, California. Recent decadal-scale cliff
retreat is quantified for 595 km of the California coast.
Large magnitude historical and recent cliff retreat rates
were inversely correlated. Cliffs fronted by beaches
retreated 49% farther than cliffs without beaches. Cliff
retreat rates are used to detect cliff steepening and areas
prone to future cliff top failure. Vitousek et al (2017)
present a shoreline change model for coastal hazard
assessment and management planning. The model,
CoSMoS-COAST (Coastal One-line Assimilated Simulation
Tool), is a transect based, one line model which predicts
short term and long term shoreline response to climate
change in the 21
st
century. The proposed model
represents a novel and modular synthesis of process
based models of coastline evolution due to longshore and
cross shore transport by waves and sea level rise.
Young et al (2014) described a Modified sand
balance coastal retreat model for sea level rise. New
modifications include conditionally independent beach
and cliff retreat. The model includes subaerial processes
and external sand sources and deficits. Model validation

and application in southern California are explored. The
results underscore the influence of protective beaches on
cliff retreat. Understanding changing thresholds and
mechanisms for soft rock cliffs retreat is important under
changing climates.
This can be achieved through combining detailed
field observation, long-term process and morphological
monitoring and numerical modelling which is explained
by Brooks et al (2012). They quantify annual cliff retreat
for every year from 1993 to 2010 and retreat is grouped
high, intermediate and low. Process explanations are
sought using detailed archival data and numerical
modelling. Marine and terrestrial controls are identified.
Hence they suggest a model to explain different rates of
cliff retreat in soft rock cliffs.
Westoby et al (2018) used SfM-MVS methods in
which input photosets of coastal cliff faces can be acquired
by non-specialists using a consumer-grade digital
cameras. Placing GCPs along the cliff top and base at a
spacing approximate to cliff height produces acceptable
model accuracy. Correspondence between intersecting
TLS and SfM detected rockfall volumes improves beyond
threshold. Kilometre scale, TLS and SfM derived erosion
rates are comparable. Erosion patterns are spatially
variable and can locally exceed the background erosion
rate by over an order of magnitude.
An analytic model is proposed by Finzi and Harlev
(2016) for evaluating regional cliff retreat rate based on a
DEM and GIS analysis. Cliff height and inclination were
used to establish relative retreat rates of cliffs within the
Makhteshim Country, Israel. Further improvement of the
model is achieved by addressing scarp-talus interactions
evident from the DEM. Known retreat rates of two cliffs
enabled model calibration and derivation of retreat rate
estimates for many cliffs. The model reveals significant
variations in current retreat rates along the cliffs in the
study area. To assess the sea cliff failure susceptibility of
low retreat rate cliffs Queiroz and Marques (2019) states
logistic regression as an effective method. They states that
anthropogenic influence on cliff instability has changed
along time and proved to have some relation with cliff
failures. The number of large cliff top failures increased
along time, suggesting that the triggering factors have also
changed.
Stanchev et al (2017) applied DSAS to shoreline
changes and cliff retreat, Shabla, Northeast Bulgaria. The
goal was to provide reliable data and useful information
for the development of a pilot marine spatial plan for
Shabla Municipality. The study was focused on the
analysis of shoreline movement and cliff retreat utilizing
GIS. Cliff erosion hot-spots with high priority were
identified. Recommended strategy to the pilot MSP for
Shabla Municipality.
Three shoreline prediction methods have been
used and compared for Holderness coast by Castedo et al
(2015). Empirical models predict recession values with
high variability and uncertainty. Process-response models
(PRM) predict reasonable recession values with low
uncertainty. PRM results are less sensitive to the change of
one parameter than empirical models. PRM can be used to
inform when/where to intervene along rapidly eroding
coasts. Brooks and Spencer (2012) test five shoreline
response models to accelerating sea level rise. The SCAPE
model is the best predictor of shoreline response in soft
rock cliffs. Future shoreline positions are found to 2050
and 2095 on a rapidly retreating coast. They quantify
considerable sediment release to the nearshore zone in
future.
A study was made by Kaliraj et al (2017) on the
coastal land use and land cover features in the South West
coast of Kanyakumari which are dynamically regulated
due to marine and terrestrial processes often controlling
by natural and anthropogenic activities. The primary
objective of this study is to estimate the decadal changes
and their transformations of land use and land cover
(LULC) features under Level II category of USGS-LULC
Classification System using Landsat ETM+ and TM images
using Maximum Likelihood Classifier (MLC) algorithm for
the time period 2000 – 2011.
This study delivers the basic pre-requisite
information for understanding the trends in landuse and
land cover changes and transformation in the coastal area.
A composite fall-slippage model is proposed in an another
study by Sajinkumar et al., (2016) for the tertiary
sedimentary coastal cliffs of Varkala in the Western
coastal tract of Peninsular India which are retreating
landwards due to several factors. Slippage in this area
affects all the litho-units and hence their geologic
characteristics are considered for developing the slippage
model. This mathematically derived model can be used in
other cliffs exhibiting the same morphology as well as the
one controlled by the same influencing factors.
3. METHODOLOGY
The landforms of the coast are highly sensitive to
marine and terrestrial forces to maintaining equilibrium
and stability to the morphological structures, and hence
analysis of the changes in coastal landforms are important
for coastal zone management. The assessment of shoreline
changes and coastal cliff retreat is usually carried out by
field and aerial surveys combined most recently with high
resolution satellite or orthophoto images (Morton and
Miller, 2004; Hapke and Reid, 2007; Perez-Alberti et al.,
2012; Hapke et al., 2016). Shoreline positions can be
marked by several different features such as the
vegetation line, the high water line, the low water line, or
the wet/dry line (Thieler et al., 2009).

Earlier studies have demonstrated that the GIS
approaches is an effective tool for analysis especially by
incorporating time-line data to inquire into morphological
change of the coastal landforms. Thus in the present study,
GIS technique employed for extracting the coastal
geomorphological landforms at high resolution. The
various types of spatial data sources such as topographical
map (scale 1:25,000) published by Survey of India in the
year 1966, high resolution image of Google Earth acquired
for 2003 and 2019 are used for mapping the coastal
landforms through a series of systematic geo-processing
operations with QGIS 3.6 software. They were scanned,
geo-referenced and plotted. Four corner points were taken
and they were geo-referenced for four coordinates.
After geo-referencing, shape files have been
created for three images and thus a path along the cliff
was drawn using line feature for three images each. For
interpreting the extent of shifting of the cliff, the 1000m
long cliff area is divided into ten equal segments at 100m
each (ie., 100m to 1000m) on the shape file of 1966
topographical map.
The distance from these base points to the shape file path
of 2003 and 2019 are measured using measuring scale on
QGIS software. The shape file thus obtained is thus shown
in the Fig - 5:
Fig - 5: Segmented line
The variation of cliff retreat over 2003 and 2019 from the
year 1966 measured and a graph plotted showing the
deviations on cliff retreat by making the shape file path of
topographical map of 1966 (scale 1:25,000) published by
Survey of India as data.
4. RESULTS AND DISCUSSION
The spatial distribution of cliff top retreat values
along the Varkala cliff among the years 1966, 2003 and
2019 is represented. The lines drawn using each shape
files over each geo-referenced maps are then compared
for shifting of the coastal cliff for those three years (1966,
2003 and 2019). The extent of shifting of the cliff is
interpreted by dividing the shape file path into ten equal
units at 100m each on the shape file of 1966 topographical
map. The corresponding distance from these base points
to the shape file path of 2003 and 2019 are measured
using measuring scale on QGIS software and the datas
obtained are listed in the Table - 1:
Table -1: Shifting of cliff on study area
A graph is plotted showing the variation of cliff retreat
over the years 2003 and 2019 from the year 1966 and is
shown in the Chart - 1:
Chart - 1: Variations in retreat over 2003 and 2019
From the Chart, it is interpreted that deviation has
occurred from 1966 to 2003 and from 2003 to 2019.
There was a huge shift in cliff within 37 years ie., an
average shift of 1.47m/year in the cliff than for 16 years
ie., from 2003 to 2019 itself, as it shows an average
deviation of only 0.67m/year. The sinusoidal graph

represents the varying nature of the cliff surface over
years. Somewhat uniform shifting is seen in between the
years 2003 and 2019. Finally, the shifting of layers for the
years 1966, 2003 and 2019 are separately prepared for
geo-database with the attributes using QGIS software.
Furthermore, these classified images are involved
for accuracy assessment using post field verification
technique (Kaliraj et al., 2017).
The shifting of cliff may be due to natural as well
as anthropogenic reasons. The impact of anthropogenic
reasons may be due to increase in human activities above
the cliff surface. Constructions as well as vehicular
movement on the cliff top is increasing year by year. The
increasing number of buildings over the cliff top from
2003 to 2016 as per the data collected from Varkala
Municipality is listed in the Table - 2:
Table -2: Newly constructed buildings (Unpublished
report; Source: Varkala Municipality)
The number of constructions are increasing
yearly over the cliff top. Both legal and illegal
constructions are there at the cliff top. There are certain
regulations for constructing a building at the cliff top, but
illegal construction activities are taking place. At 2003,
there were only 460 buildings, but at present there
are1299 buildings. The datas presented here are only
about legal constructions, i.e., more than these numbers,
there are unauthorised buildings also.
4. SUMMARY AND CONCLUSION
Coastal zones are interaction zones between land and sea,
and thus many environmental, economic and social issues
will lead to coastal erosion. An estimation of the decadal
changes caused in Varkala Beach, India by comparing the
Satellite Imageries (2003 and 2019) with Survey of India
Toposheets 1966 has been made in this study in order to
analyse the cliff retreat over a stretch of 1km extending
from Black Beach to Helipad on the Varkala cliff area. With
the aid of GIS technique the change in the cliff was clearly
brought out. Deviation has occurred from 1966 to 2003
and from 2003 to 2019. There was a huge shift in cliff
within 37 years ie., an average shift of 1.47m/year in the
cliff than for 16 years ie., from 2003 to 2019 itself, as it
shows an average deviation of only 0.67m/year. Coastal
processes involving wave, current wind, tide, rainfall,
gradient and anthropogenic activities mainly influence the
characteristics of landforms especially coastal cliffs. GIS
provides an effective platform for assessment of cliff
changes and transformations over time. In this study area,
it is observed that area in beach face landforms are
converted into settlements and built-ups and it is
increased from 1966 to 2019 due to human encroachment
and urban expansion activities. The pressure on the cliff
due to illegal construction and free movement of vehicles
along the cliff edge were the reasons for cliff retreat.
Under the rule, only temporary constructions can be
undertaken two metres from the pathway bordering the
cliff. But it is seldom followed and the authorities do not
initiate action against those violating the rule. Thus
regulations for construction activities and vehicular
movements should be followed as well as shore protection
techniques should be implemented for preventing further
erosion of coastal cliff.
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