Shoreline changes in response to sea level rise along ...

Shoreline changes in response to sea level rise along Digha Coast, Eastern India: an analytical approach of remote sensing, GIS and statistical techniques Adarsa Jana, Arkoprovo Biswas, Sabyasachi Maiti & Amit K. Bhattacharya Journal of Coastal Conservation Planning and Management ISSN 1400-0350 J Coast Conserv DOI 10.1007/s11852-013-0297-5

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Author's personal copy J Coast Conserv DOI 10.1007/s11852-013-0297-5

Shoreline changes in response to sea level rise along Digha Coast, Eastern India: an analytical approach of remote sensing, GIS and statistical techniques Adarsa Jana & Arkoprovo Biswas & Sabyasachi Maiti & Amit K. Bhattacharya

Received: 24 July 2013 / Revised: 15 November 2013 / Accepted: 18 November 2013 # Springer Science+Business Media Dordrecht 2013

Abstract Shoreline is one of the rapidly changing linear features of the coastal zone which is dynamic in nature. The issue of shoreline changes due to sea level rise over the next century has increasingly become a major social, economic and environmental concern to a large number of countries along the coast, where it poses a serious problem to the environment and human settlements. As a consequence, some coastal scientists have advocated analyzing and predicting coastal changes on a more local scale. The present study demonstrates the potential of remote sensing, geospatial and statistical techniques for monitoring the shoreline changes and sea level rise along Digha coast, the eastern India. In the present study, multi-resolution and multi temporal satellite images of Landsat have been utilized to demarcate shoreline positions during 1972, 1980, 1990, 2000, and 2010. The statistical techniques, linear regression, end-point rate and regression coefficient (R2) have been used to find out the shoreline change rates and sea level change during the periods of 1972–2010. Monthly and annual mean sea level data for three nearby station viz., Haldia, Paradip and Gangra from 1972 to 2006 have been used to this study. Finally, an attempt has been made to find out interactive relationship between the sea level rise and shoreline change of the study area. The results of the present study show that combined use of satellite imagery, sea A. Jana (*) : A. Biswas : S. Maiti : A. K. Bhattacharya Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur 721302, West Bengal, India e-mail: [email protected] A. Biswas e-mail: [email protected] S. Maiti e-mail: [email protected] A. K. Bhattacharya e-mail: [email protected]

level data and statistical methods can be a reliable method in correlating shoreline changes with sea level rise. Keywords Shoreline change rate . Sea level rise (SLR) . Landsat . Linear regression . End-point rate . Regression co-efficient (R2)

Introduction Shoreline occurring between land and sea, is highly dynamic, and changes temporally and spatially in response to variations in influencing factors such as wind, wave tide, storm surge, sea level rise and land subsidence (Orford et al. 2002; Forbes et al. 2004; Cooper et al. 2004). It undergoes frequent changes, short term and long term, caused by hydrodynamic changes (e.g., river cycles, sea level rise), geomorphological changes (e.g., barrier island formation, spit development) and other factors (e.g., sudden and rapid seismic and storm events) (Scott 2005). Accurate demarcation and monitoring of shoreline changes are necessary for understanding and deciphering the coastal processes, coastal zone management planning, hazard zoning, erosion-accretion studies, regional sediment budgets, analysis and modeling the coastal morphodynamics. The conventional techniques for determining the rate of change of shoreline position include: field measurement of present mean high water level, shoreline tracing from aerial photographs and topographic sheets; comparison with the historical data using one of the several methods, (viz., end point rate (EPR) (Fenster et al. 1993), average of rates (AOR), linear regression (LR), and jackknife (JK) (Dolan et al. 1991). Recent advancements in remote sensing and geographical information system (GIS) techniques have led to improvements in coastal geomorphological studies, such as: semiautomatic determination of shorelines (Ryu et al. 2002;

Author's personal copy A. Jana et al.

Yamano et al. 2006); identification of relative changes among coastal units (Jantunen and Raitala 1984; Siddiqui and Maajid 2004); extraction of topographic and bathymetric information (Lafon et al. 2002) and their integrated GIS analysis (White and El Asmar 1999). These techniques are attractive, due to their cost-effectiveness, reduction in manual error and absence of the subjective approach of conventional field techniques. Sea level rise is a major consequence of global warming, which threatens many low-lying, highly populated coastal regions of the world (Becker et al. 2012). Today the issue of shoreline changes due to sea level rise which caused by global warming/climate change has increasingly become a major issues in terms of impact on the population along the coastal area. Changes in mean sea level as measured by coastal tide gauges are called relative sea-level changes (Church et al. 2001). Sealevel has been rising at the rate of 1.7–1.8 mm/year over the last century and the rate has increased to 3 mm/year in the last decade (Church et al. 2004; Holgate and Woodworth 2004; Church et al. 2006). The tide gauge records at five coastal locations in India; Mumbai, Kolkata, Cochin, Kandla and Sagar Islands have reported an increase in sea level. The change in sea level appears to be higher on eastern coast compared to western coast. The average sea level rise for India has been reported as 2.5 mm/year since 1950’s (Das and Radhakrishna 1993; Kumar Danish 2001; Chand and Acharya 2010). In the present study, multi-temporal Landsat imageries (from 1972 to 2010) have been utilized to demarcate different shorelines. In comparison to previous works (Maiti and Bhattacharya 2009; Jana and Bhattacharya 2012), here we used transects with closed interval (250 m) with additional statistical techniques viz., end point rate, linear regression and regression coefficient (R2). Although relative sea-level frequently is influenced by local factors like sedimentation and tectonism, but studied temporal span and sedimentation rate is almost constant within chosen space. Therefore, choices of three nearby gauge stations were sufficient to represent the influence of sea level. Finally, an attempt has been made to find out the interactive relationship between the sea level rise and shoreline change in the present area. Study area The study area chosen in the present work is a 60 km long coastal stretch on the east coast of India, covering parts of Balasore and Midnapur littoral tracts occurring in Orissa and West Bengal states respectively, adjoining Bay of Bengal (Fig. 1). The western end of the study area is bounded by Subarnarekha River (Orissa), while Rasulpur River (West Bengal) forms the eastern boundary. It lies between 21° 30′ 0″ N to 21°48′ 0″ N latitudes and 87°24′ 0″ E to 57°54′ 0″ E longitude. The geological history of the coast is relatively short and the coast is still in its formative state. Geologically, this is the

coastal stretch of the Indo-Gangetic plain and situated between tectonically active Ganga Delta and wave dominated Subarnarekha estuary. The area had formed due to out- building of the Subarnarekha delta through recession of the sea between 6,000 years B.P. and present day (Niyogi 1970; Dey et al. 2002). The area comprises of recent alluvial deposits belonging to Balasore-Contai coastal plain. Geomorphological studies of the area shows that this coastal region was made up of alternate lines of beach ridges or chennier plains and barriers islands, situated on silty or clayey marine terraces. The study area has overall uniform geomorphology with northern boundary (landward) comprising of older dune complex, followed by series of chenniers, beach ridges and intermediate mudflats; while the southern boundary (seaward) made up of recent remobilized sand and clay at various places (Chakrabarti 1991; Maiti and Bhattacharya 2009). Almost shore parallel formations of six ancient shoreline positions have been found over the 30 km wide coastal region which indicates the landward shifting of shoreline positions during different time periods. The studied zone experiences strong long shore current from SW to NE direction during the monsoon season and a less powerful long shore current from NE to SW direction during the winter season, due to seasonal variation in wind direction. The long shore current velocity recorded at Subarnarekha mouth and Digha are 1.2164 m/sec and 1.2620 m/sec respectively (Paul 2002). The present coastline is dominated by high energy macro-tidal environment (tidal range 4.5 m to 5 m) with predominantly southwesterly monsoon derived wave and bay-head cyclone prone areas (Paul 2002). The coast experiences semi-diurnal tidal fluctuations with a tidal range of 5 m to 5.8 m in the spring tide and 1.5 m or less in the nip tide (Paul 2006). The area falls within subtropical humid climate with three distinct seasons viz. pre-monsoon (March- June), monsoon (July - Oct), and post-monsoon (Nov- Feb). The maximum daily temperature ranges between 26.9 °C and 36.8 °C while the minimum temperature lies in between 5.7 °C and 24.7 °C. The range of average annual rainfall is 1,192 mm to 1,956 mm with relative humidity varying between 60 % and 90 % (GSI 1995). Seasonal changes in sea level have been known to occur for a long time in the coast. In March, sea level is lower than the mean in Northern Hemisphere. At this time the largest deviation occur in the Bay of Bengal, where values of −40 cm occur. In the month of June, the largest positive value of + 30 cm occurs in the Bay of Bengal and negative value of −26 cm occurs in the month of December in the same region (Paul 2002). The local mean sea level at Sagar Island is + 2.82 m above the Datum level. So far as tidal records are concerned the local mean water level gradually increased from 1956 to 1978 at the Hugli river tidal stations. The lowest recorded low water in 1940 was −0.21 m below the Datum level at Sagar Island tidal station.

Author's personal copy Shoreline changes in response to sea level rise along Digha Coast

Midnapur Balasore

Rasulpur Petua Ghat River Bankipur Junput Haripur Dadanpatra Mandarmani Shankarpur Digha Udaipur Subarnarekha Talsari River Kirtania

Fig. 1 Study area map showing important locations

Material and methods Three stages of work have been followed in this study to analyze the shoreline changes in response to sea level rise along Digha coast i) to analyze and interpret of satellite data (Landsat) for shoreline mapping; ii) use of statistical techniques for estimating shoreline change rates and sea level rise over four decades; and iii) to identify interactive relationship between sea level changes and shoreline change. Data used The study was carried out using five multi-temporal and multi-resolution images of Landsat MSS (Multispectral Scanner), TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper), which have been acquired on cloud free days of different time periods (Table 1), since same resolution data are not available over the chosen period (1972–2010). Landsat data has been used in coastal applications for decades

(Munday and Alfoldi 1979 Ritchie et al. 1990). Orthorectified Landsat MSS, TM and ETM+ images covering the study area for the years 1972, 1980, 1990, 2000, and 2010 were downloaded from USGS Global Visualization Viewer (2012). The data have been projected to the Universal Transverse Mercator (UTM) projection system with WGS-84 datum, zone-45. Monthly and annual means value of sea level data for nearby three stations; viz., Paradip, Haldia and Gangra have been retrieved from the database of the Permanent Service for Mean Sea Level (PSMSL). PSMSL is based at the Proudman Oceanographic Laboratory, Merseyside, United Kingdom, and contains annual mean values of sea level from almost 2000 tide gauge stations around the world. The PSML receives annual mean values of sea level from almost 200 national authorities, distributed around the world, which are responsible for sea level monitoring in each country or region. In this study, ERDAS Imagine and ArcGIS software have been used to carry out digital analysis, vector analysis and transect wise shoreline changes study.

Author's personal copy A. Jana et al. Table 1 List of different satellite data used for shoreline change rate study with acquisition date and time, tidal heights at acquisition times, sea level (SL) shifts, and beach wise variations in shoreline shifts Sensor Time

Date of acquisition

(GMT +5:30)

TM ETM+ TM TM MSS MSS

4:28:29 4:27:56 3:38:00 3:38:00 3:52:22 4:08:00

February 13, 2010 December 10, 2000 November 21, 1990 November 14, 1990 January 17, 1980 December 12, 1972

SLa Shift from HTb Amount of shoreline shift (m)

Tide condition

Tidal height (ft) Condition (ft)

(m)

Digha (1:47)c Shankarpur Dadanpatra Junput (1:55)c (1:43)c (1:72)c

12.99 11.89 NA 11.71 15.69 11.60

0.69 1.55 NA 1.03 0.30 0.47

32.43 72.85 NA 48.41 14.1 22.09

Slack Slack NA Slack Slack Rising

a

Sea level from high tide

b

Tidal height as measured from Sagar Island tidal gauge

c

Beach slope for different locations (Paul 2002)

d

Not available

Shoreline mapping and change rate calculation In order to estimate and represent best shoreline positions considering the grid resolutions and effect of tides, we follow adopted methods by Maiti and Bhattacharya (2009). Band rationing method, involving ratio between bands 4 and 2 and other between bands 5 and 2 have been used in this study to demarcate shorelines of different years. Generally, the ratio b5/b2 is greater than one (1) for water, and less than one (1) for land in large areas of coastal zone. These results obtained from above rationing method are correct in coastal zones which are covered by soil, but not for land with vegetation cover, since it erroneously assigns some of the vegetation land to water due to aggregate background reflectance. To solve this problem, the two ratios are combined in this study. It gives the final binary image which represents the shoreline. At last to refine the overall results from above method the visual interpretation has been carried out to edit visually evident errors near the outlet of the river. For this purpose a color composite can be used and the best suited false color composite (FCC) band 432 in MSS and 543 in TM and ETM+ nicely depicts land-water interface. Finally, the shoreline along the study area was digitized

2.26 5.08 NA 3.39 0.98 1.56

37.95 85.25 NA 56.65 16.5 25.85

29.67 66.65 NA 44.29 12.9 20.21

using ArcMap 9.1 and ERDAS Imagine software using the on-screen digitization technique. In this study Digital Shoreline Analysis System (DSAS) version 3.2 programs is used to calculate rate of shoreline changes (Thieler et al. 2005). To carry out shoreline change study the digitized shoreline for the years 1972, 1980, 1990, 2000, and 2010 in the vector format were used as the input to the Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change. This extension (DSAS) contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, jackknife, etc.). Based on our setting, DSAS program generates 234 transects that are oriented perpendicular to the baseline at 250 m interval alongshore (tr. 1) to (tr. 234) (Figs. 2, 3, and 4). These transects cover the entire study coastline from Subarnarekha river to Rasulpur river (~60 km length). To assess the spatial and temporal migration trend of shoreline positions a baseline was created onshore which is parallel to general orientation to the present day coastline geometry. The measured distance between the fixed baseline point and the shoreline positions generated by the program provides a reliable record monitoring the changes of shoreline positions over Rasulpur River

Fig. 2 Shoreline position in different observation years (1972–2010) Pichhabani Inlet Jaldah Inlet Digha Inlet Talsari Inlet Subarnarekha River

49.68 111.6 NA 74.16 21.6 33.84

Author's personal copy Shoreline changes in response to sea level rise along Digha Coast

Polygon of land 1972

Polygon of land 1980 Union

Red- erosion Green- accretion Fig. 3 Erosion and accretion around Kirtania and Talsari area respectively during the period 1972–2010

the 38 years time frame of the generated vectors. The generated cross-shore transects together with the extracted 5 shoreline vectors are graphically depicted in Fig. 2. The data measured from each profile are then used to estimate the mean annual rate of shoreline change (m/yr) employing end point rate, linear regression and net shoreline change techniques. The end point rate is calculated by dividing the distance of shoreline movement by the time elapsed between the earliest and latest measurements (i.e., the oldest and the most recent shoreline). The net shoreline movement calculates a distance, not a rate. The NSM is associated with the dates of only two shorelines. It calculates the distance between the oldest and youngest shorelines for each transect. This represents the total distance between the oldest and youngest shorelines. If this distance is divided by the number of years elapsed between the two shoreline positions, the result is the EPR. NSM and EPR are essentially the same thing; however NSM gives us information on the absolute distance of shift as opposed to a rate. Linear regression method have been used since it is most commonly applied statistical technique for expressing shoreline movement and estimating rates of change (Crowell et al. 1997). The linear regression method is susceptible to outlier

Fig. 4 Continuous occurrences of spits around Haripur-Junput area during the period 1972–2010

Fig. 5 Flow chart for determining the zones of accretion and erosion from polygons of lands for different years (1972 and 1980)

effects, and also tends to underestimate the rate of change relative to other statistics, such as EPR (Dolan et al. 1991). In Linear regression, rate of change statistic can be determined by fitting a least-squares regression line to all shoreline points for a particular transects. The rate is the slope of the regression’s line (Thieler et al. 2003).

Fig. 6 Cross-plot of time versus amount of shoreline shift with respect to 1972 shoreline position and 95 % upper and lower confidence limits were marked along transect 58 (depositional) and 162 (erosional)

Author's personal copy A. Jana et al. Fig. 7 Rate of shoreline changes in different time interval: a Net shoreline change b/w 1972–1980; b Net shoreline change b/w 1980– 1990; c Net shoreline change b/w 1990–2000; d Net shoreline change b/w 2000–2010; e Net shoreline change b/w 1972–2010

Author's personal copy Shoreline changes in response to sea level rise along Digha Coast Table 2 Statistics of shoreline changes in different time interval EPR

NSM

Year (m/y) Mean S.D. Max Min Year (m)

1972–1980 −8.14 28.54 57.78 −147.42 1972–1980

1980–1990 −0.68 18.05 91.43 −79.69 1980–1990

1990–2000 −5.65 20.38 68.83 −102.67 1990–2000

2000–2010 8.14 19.85 114.4 −43.88 2000–2010

1972–2010 −1.25 8.68 25.73 −41.9 1972–2010

Mean S.D. Max Min

−65.14 228.29 462.24 −1179.36

−6.77 180.46 914.3 −796.9

−56.48 203.82 688.3 −1026.7

81.36 198.45 1144 −438.8

−47.61 330.01 977.74 −1592.2

Erosion-accretion (Net areal change) mapping Net areal change or erosion accretion zones were determined by overlaying (union) two polygons of lands for different periods using ArcGIS software. The schematic flow chart of steps is

Fig. 8 Shoreline erosion and accretion along the study area: a Net changes b/w 1972–1980; b Net changes b/w 1980–1990; c Net changes b/w 1990–2000; d Net changes b/w 2000–2010; e Net changes b/w

shown in Fig. 5. Later derived polygons were manually marked with erosion and accretion. The polygons also provide quantification in a real change. The obtained results were later discussed in details in next sections referencing Fig. 8 for consecutive years (1972–1980, 1980–1990, 1990–2000, 2000–2010).

1972–2010; f Bar graph showing erosion, accretion and net areal changes (Km2) in different time intervals

Author's personal copy A. Jana et al. Fig. 9 a Accretion processes in front of the seawall at Old Digha, b Beach erosion and damaged of artificial construction at Bankipur, c Beach accretion and development of new dunes at Talsari

Sea level changes Sea-level rise is an important consequence of climate change, both for societies and for the environment (Kumar et al. 2010). Sea-level rise can be a product of global warming through two main processes: thermal expansion of seawater and widespread melting of land ice. Global warming is predicted to cause significant rises in sea level over the course of the twenty-first Fig. 10 a Average mean sea level variation from 1972 to 2006 and b Net shoreline change, over the period from 1972 to 2010

century. Thus it becomes necessary to study the effect of sealevel rise on the coastal areas. In the present world due to the buildup of global warming mainly there is a general tendency comes that shoreline fluctuate in response to sea level fluctuation. But this sometimes may not be the exact reason of shoreline shifting. Beach erosion through wave action, long shore drift and rip current that moves the beach sediment perpendicular to the shoreline is some other ways of altering the shoreline

Author's personal copy Shoreline changes in response to sea level rise along Digha Coast Table 3 Statistics show the interactive relationship between shoreline changes and sea level rise (SLR) Year

Avg. shoreline shift (m)

% of Tr. showing erosion

% of Tr. showing accretion

SD

Year

Avg. MSL (m)

SD

R2

1972 1980 1990 2000 2010

0 −65.14 −6.77 −56.48 81.36

0 29.06 61.54 80.34 61.54

0 70.94 38.46 19.66 38.46

0.00 228.29 180.46 203.82 198.45

1972 1980 1990 2000 2006

6.923 6.987 7.026 7.072 6.993

5.000 21.67 40.50 33.00 16.51

0.31

R-statistics was measured using Eq. (1)

position over the time. A number of processes can cause mean sea level (MSL) to fluctuate such as thermostatic expansion, tidal effect, atmospheric pressure, isostatic adjustment, melting of polar ice shelf and sea ice. However, such fluctuation in MSL leads to significant shifting of shoreline. Mean sea level at the coast is defined as the height of the sea with respect to a local land benchmark, averaged over a period, such as a month or a year-long. Changes in mean sea level as measured by coastal tide gauges are called relative sea-level changes (Church et al. 2001). To assess the average MSL of the coastal region three sets of point data have been taken from PSMSL (Permanent Service of Mean Sea Level) for Paradip (located at the West) and Haldia (located at East), Gangra (located between Paradip and Haldia) from 1972 to 2010. The data set shows the monthly average which is transformed into yearly average. The standard deviation and co-efficient of variation of the entire period have been calculated for these stations. In order to assess the shoreline changes in response to sea level rise, yearly average MSL data of the stations have been filtered so that shoreline changes can be compared with the MSL data of same year or to its corresponding one. The MSL records of the target years for these stations are averaged again in order to get grand average MSL of the same periods for the entire coast under study.

Results and discussion Shoreline change analysis All measurements along the same transect are plotted in a cross-plot, with ‘year’ plotted along the X-axis and the corresponding shoreline shift with respect to 1972 shoreline position plotted along Y-axis (Fig. 6). In the cross-plots, positive trends indicate accretion, whereas negative trends as erosion. Figure 6 represents typical cross-plots with a positive trend for transect (tr. 58) and negative trend for transect (tr. 162). The plot also represents the linear regression equation as a measure of shoreline change rate and Regression coefficient (R2) as a measure of uncertainty. Figure 3 represent the overall erosion and accretion in specific areas, namely Kirtania and Talsari respectively during the period from 1972 to 2010. Figure 4

represent the occurrences of three spits between Haripur and Junput areas during 1972–2010. Result shows that 30.63 % transect fall in the category of advancing shoreline during the time span of 1972–2010. Where 69.36 % transect experience retreating shoreline during the same time span. The entire stretch of the coastline under study, the transect-wise information of shoreline change rate has been calculated and plotted (Fig. 7). Table 2 summarizes the shoreline change and change rate for the EPR and NSM method. Total four consecutive time span (1972–1980; 1980–1990; 1990–2000; and 2000–2010) have been grouped in order to summarize interpretation. Various time groups are shown in Fig. 7a to e. Among all four time intervals, the period of 1990– 2000 has experienced highest change rate of shoreline position followed by the period of 2000-2010. Records of MSL over the years reveals that 1990–2000 was the period when sea level along this shoreline experienced a substantial increase in its height which in association with strong long shore current and wave action shift the shoreline back at a greater magnitude. Erosion-accretion (Net areal change) In the present study net areal change in km2 was calculated and erosion and accretion zone have been mapped for different time interval (Fig. 8a–e). The results of the study shows that the negative change (erosion) (−3.93 km2), (−2.11 km2), and (−1.63 km2) was observed during the period of 1972– 1980, 1980–1990, and 1990–2000 respectively. While positive change (accretion) (3.32 km2) was found only during the period of 2000–2010. The results also revealed the overall negative change (erosion) (−4.08 km2) during the period from 1972 to 2010 (Fig. 8f). Field reports during the month of December 2011, shows that accretion processes was occurred in front of the seawall at Old Digha, beach erosion and damaged of artificial construction at Bankipur and beach accretion and development of new dunes at Talsari (Fig. 9). Sea level rise and shoreline changes In order to assess the shoreline changes in response to sea level rise, yearly average MSL data of three stations namely, Haldia, Gangra and Paradip have been calculated during the period

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from 1972 to 2006. The average MSL for the entire coastline shows the decreasing linear trend that somewhere strongly oppose our finding of shoreline shifting over a period of 38 years because of the sea level rise (SLR). Average shoreline shift also calculated from transect lines spreading throughout the part of the Digha coast and that shows a retreating linear trend of shoreline (Fig. 10b). The degree of association between mean sea level and shoreline shift can be displayed by Pearson’s “r” statistics Eq. (1), where x = shoreline shift for a 10 year period, y = average MSL change during that same 10 year period. For chosen temporal unit of analysis 10 years, the result shows the value 0.31 (Table 3). This indicates a lower degree of relation between two parameters. r ¼ CovðX ; Y Þ=σX σY

ð1Þ

The graph shows that MSL height was in an increasing trend during the period of 1990–2000 (Fig. 10a) which in fact the same time span when shoreline also experience high magnitude of shifting from its earlier position (Fig. 10b). But the corresponding between the shoreline shift and sea level rise suggests the shift was more. Therefore, other than SLR, storm surge, dune stability, monsoonal dynamics etc. are important controlling factors.

Conclusion The present study aimed at the applicability of satellite data along with different statistical techniques to find out interactive relationship between shoreline changes and sea level rise and to study the dynamic nature of shoreline which is very valuable in coastal erosion hazard assessment. Transect-wise shoreline changes analysis study shows that an extensive part of the shoreline is affected by the shore zone erosion during last 38 year. However, in some areas accretion had taken place, while in a few areas, both erosion and accretion had occurred with formation of spits. From the interactive relationship between sea level rise and shoreline changes, a generalized overview can be made between those parameters. However statistical correlation is insignificant dues to complex interactions of many other factors, not considered in this study. For examples, the mean sea level height was with increasing trend during the period of 1990–2000 where shoreline also experience high magnitude of erosion. Similarly, during the time of onset as well as retreat of Monsoon, several depressions are used to occur over the Bay of Bengal that in fact causes sea level rise when it moves towards land as an impending storm. The potential energy of the atmosphere is transformed into enormous kinetic energy which is transferred into the sea surface produces surge of sea water in association with elevated sea waves that engulf the shore zone and make a positional shift of the shoreline from its earlier position. So, all these controlling factors should be

taken into account alongside the sea level change in order to capture the exact mechanism of shoreline dynamics over time. In order to carry out the analysis to study the interactive relationship between sea level rises and shoreline changes at high precession level, it is necessary to quantify the net sediment budget flux and its tuning with the sea level change at maximum accuracy level over the years and that in fact needs integration of rigorous field data collection and high resolution satellite images. It also needs some further enhancements related to continuous monitoring of sea level rise along the coast of India. Unfortunately in recent framework, such long-term, high resolution data are unavailable to researchers for sound conclusive statements. Acknowledgments This work forms a part of Ph. D work of the first author. The authors are thankful to anonymous reviewer for the constructive suggestions which has improved the manuscript. The authors also thank USGS Global Visualization Viewer (GLOVIS) for providing free satellite data. Authors also thank Permanent Service for Mean Sea Level (PSMSL) for providing free tide gauge data.

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