Coastal erosion monitoring using multi-temporal remote sensing and sea surface temperature data in coastal districts of Gujarat state, India

ABSTRACT

This study was carried out to monitor the shoreline changes and associated erosion along the coastal districts using multi-temporal Landsat digital data over the period from 1978 to 2020. The High Tide Line was delineated using Landsat Satellite data from 1978, 1998, and 2020 based on various geomorphologic and land use/cover types. The extent of the coastline erosion at two continuous decades intervals, and the total during 42 years from 1978 to 2020 was delineated, and in the coastline, erosion was monitored. The analysis indicated that the highest coastal erosion took place in the Kachchh district. The results of this study indicate that about 723.6 km is subjected to erosion, which is almost 45.9% of the total Gujarat coastline. A gradual increasing trend was observed in SST from 1860 to 2020 along with the increase in CO2 emission for the period of 50 years (1960-2010). The analysis of variation in the normalized SST with annual mean sunspot activity from 1960 to 2020 revealed that the peak of SST follows the peak of annual mean sunspot activity till 2000. However, the solar activity declined significantly since 2000 and the annual mean sunspot number decreased until 2020, while on the contrary, the SST showed an increasing trend.

KEYWORDS:

• Sea surface temperature (SST)
• Sea-level rise
• coastal erosion
• solar activity
• sunspot number (SSN)

1 Introduction

Sea-level rise is the most significant effect of climate change. High projected rates of future sea-level rise have captured the attention of the Gujarat state, particularly for those which are located in low-lying areas as well as small islands. Such areas are concerned in view of reduction in their land areas due to inundation and coastal erosion. The sea surface temperature (SST) rises due to the fact that ocean absorbs most of the excess heat from greenhouse gas emissions. Therefore, the rising SST causes sea-level rise followed by erosion of islands and low-lying areas. Currently, Gujarat coastline is not well studied in view of SST, sea-level rise, and annual mean sunspot activities due to the unavailability of these very specific data related to coastal areas of Gujarat State and lack of geo-referenced cadastral data at village level. Patel et al., 2020, analyzed the Shoreline changes along the south Gujarat coast using multi-temporal Remote-Sensing Satellite data from 1972, 1990, 2001, and 2011 in USGS Digital Shoreline Analysis System (DSAS). The shoreline was delineated using the high tide line (HTL) derived from satellite data based on various geomorphology and land-use and land-cover features. Further, it is very important to address the questions whether the SST and hence the sea-level rise are influenced by CO₂ emission or solar and galactic activity. Therefore, in this study, the potential of remote-sensing technique for monitoring the coastline changes and sea-level rise using SST as well as the ground- and space-based observations of the sunspots and GCR was analyzed. The impact of SST and sunspot activity on sea-level rise along the coastal districts of Gujarat State was studied using multi-temporal Landsat data. Currently, Gujarat coastline is not well studied in view of SST, sea-level rise, and annual mean Sunspot activities due to unavailability of these very specific data related to coastal areas of Gujarat State and lack of geo-referenced cadastral data at village level.

Therefore, currently, following are the most priority scientific issues connecting influence of solar and cosmic rays on oceans and climate conditions on the Earth. Currently, Gujarat coastline is not well studied in view of SST, sea-level rise, and annual mean Sunspot activities due to unavailability of these very specific data related to coastal areas of Gujarat State and lack of geo-referenced cadastral data at village level. The present study, therefore, is motivated in view of above dangers and demonstrates the potential of remote-sensing technique for monitoring the coastline changes and sea level rise using SST, annual mean Sunspot activities in the coastal districts of Gujarat State.

1.1 Coastal vulnerability to future sea-level rise

A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise, which highlights those regions where the physical effects of sea-level rise might be the greatest. The approach by (Pendleton et al., 2004) combines the coastal system’s susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park’s natural vulnerability to the effects of sea-level rise. Rao et al. (2008) studied the vulnerability level of Andhra Pradesh (AP) coast was studied using five physical variables, namely, coastal geomorphology, coastal slope, shoreline change, mean spring tide range, and significant wave height. The CVI prepared by integrating the differentially weighted rank values of the five variables was used to classify the coastline into low, moderate, and very high-risk categories. The results indicated that about 43% of the 1,030-km-long AP coast is under very high-risk, followed by another 35% under high-risk if the sea-level rises by ~0.6 m displacing more than 1.29 million people living within 2.0 m elevation in 282 villages in the region. Manik Mahapatra et al. (2015) carried out the coastal vulnerability assessment of the entire Gujarat coast on 1:50,000 scale considering five physical parameters, namely, coastal geomorphology, coastal slope, shoreline change rate, mean spring tidal range, and significant wave height. The CVI was computed integrating the five physical parameters using the additive method and relative ranking was assigned to various coastal segments based on the vulnerability level. Based on the CVI values Gujarat coast was categorized into four risk-level classes and the results indicated that 785 km (45.67%) of the Gujarat coast is under high to very high-risk category and 934 km (54.33%) of the Gujarat coast is under moderate to low-risk category due to an anticipated rise in sea level. The coastal regions under very high-risk category are along north-western parts of the Gulf of Khambhat, the northernmost parts of the Gulf of Kachchh, and western parts of the Kachchh coast.

1.2 Sea surface temperature (SST)

SST and upper ocean heat content (OHC, upper 700 m) in the tropical Indian Ocean underwent rapid warming during 1950–2015, with the SST showing an average warming of about 1°C. The SST and OHC trends are very likely to continue in the future, under different emission scenarios. The studies carried out by (Roxy et al., 2020) indicated that the SST in the tropical Indian Ocean underwent rapid warming during 1950–2015, and it was observed that the average warming of about 1°C based on SST and also as per the climate model projection, a rise in tropical Indian Ocean SST by 1.2–1.6°C during the period of 2040 to 2069. Beal et al. (2019) observed that over the last two decades, the Indian Ocean is warming fastest as compared to tropical oceans and accounts for about 25% of the increase in global oceanic heat content. The relationships between solar activity and surface temperature, rainfall, sea level pressure (SLP), SST, upper ocean temperature, and other factors were reported by several researchers (e.g., White et al., 1997; Van Loon et al., 2007; Meehl & Arblaster, 2009; Zhou & Tung, 2010; Gray et al., 2010; Semeniuk, et al., 2011; Gray et al., 2013; Hood et al., 2013; Kuchar et al., 2014). The 11-year cycle in sunspot numbers which is strongly related to changes in solar radiation and the variation in sunspot numbers is used as an indicator of variation in solar activity was reported by (Miyahara et al., 2010). The analysis of SST data of 150 years from 1854 to 2007 by Zhou and Tung, 2010, indicated that a robust signal of warming over solar max and cooling over solar min, with high statistical significance in the time domain. Yamakawa et al. (2016) analyzed the global distribution of correlation coefficients between annual relative sunspot numbers (SSN) and SST from July to December over a 111-year period from 1901 to 2011. The results of this analysis indicated that sunspot numbers and SST were positively correlated in wide areas, with statistically significant positive correlations in many regions. The long-term variations revealed a link with solar activity, although SST did not exhibit periodicities.

1.3 Coastline/shoreline change monitoring using remote-sensing and GIS

Krunal et al. (2021) monitored the shoreline changes during the last 40 years near the Mahi estuarine belt in Gulf of Khambhat region using multi-temporal Landsat data from 1978 to 2018. The results of the decadal shoreline change indicated that shoreline has continuously changed from 113.9 m to 831.4 m during the 40-year period from 1978 to 2018, respectively. This indicates that total migration of shoreline towards the land area of study village during last 40 years was 1590.5 m with an alarming annual rate of change of 39.76 m yr⁻¹, which is very remarkably high and alarming situation of erosion and may lead to future disaster for the coastal village. Mahapatra et al. (2014) analyzed the shoreline changes along the south Gujarat coast using Remote-Sensing Satellite data from 1972, 1990, 2001, and 2011 in USGS Digital Shoreline Analysis System (DSAS). The satellite data were used to delineate high-water line (HTL) based on various geomorphology and land-use and land-cover features. The Linear Regression Method (LRR) was used to calculate shoreline change rate and the coastal areas were classified into high erosion, low erosion, stable, low accretion, and high accretion coast. The results indicated that about 69.31% of the South Gujarat coast is eroding, about 18.40% of coast is stable and remaining 12.28% of the coast is accreting in nature and the strong tidal currents accompanied by wave action and reduced the sediment load of the river were the major causes of the coastal erosion (Mahapatra et al., 2014). Misra and Balaji (2015) analyzed the shoreline change dynamics along the coastal districts of South Gujarat multi-temporal remote-sensing satellite data from 1990 to 2014. The coastal zone along the three districts of Surat, Navsari, and Valsad in South Gujarat is reported to be facing serious environmental challenges, especially due to anthropogenic impacts. This study evaluates the decadal changes in historical shoreline changes, using satellite images of Landsat TM, ETM, and OLI. The analysis of shoreline changes in these districts revealed significant variations in the form of shoreline erosion. The extents of the shoreline accreting or eroding for the periods 1990–2001, 2001–2014 and 1990–2014 were analyzed and the results indicated that, of the entire length of 117 km, about 65% of the shoreline is observed to be eroding over the period of 24 years.

1.4 Climate projections related to SST and implications to coastal areas

Climatic projections are essential to frame resilient strategies towards futuristic impacts of climate changes on fish species and habitat. The variations of climatic variables such as SST, Sea Surface Salinity (SSS), Sea Level Rise (SLR), Precipitation (Pr), and pH were projected along the Indian Ocean to obtain Climate projections for 2030, 2050, and 2080. The projections showed no significant changes in the pattern of precipitation. Changes in sea-level rise and sea surface salinity reduce water quality, spawning and seed availability, increased disease incidence and damage to freshwater aquaculture system by salinization of groundwater. The results show that variation in SST and pH has a potential impact on marine fisheries while SSS, SLR, and precipitation affect the aquaculture systems (Akhiljith et al., 2019). Climate change is causing the global SST to rise. The studies carried out by (Varela et al., 2022), indicated that the warming rates are lower in coastal areas affected by the upwelling that buffers the SST warming. The influence of the Canary upwelling system on coastal SST throughout the 21st century was analyzed taking advantage of the high spatial resolution of the Global Climate Models (GCMs) from the CMIP6 project to capture upwelling features.

1.5 Objectives

The major objective of this study is to monitor the shoreline changes along the coastal districts in Gujarat using multi-temporal Landsat digital data over the period of 42 years from 1978 to 2020. The detailed objectives of this study are:

1. To analyze the impact of SST and Sunspot activity on shoreline change along the coastal districts of Gujarat State.

2. To analyze the impact of SST and CO₂ on the coastline change.

3. To study the Sunspot activity and its impact on SST and the coastline change.

1.6 Study area

The study area comprises of 16 coastal districts in Gujarat State extending from the Valsad to the coast of Kachchh in the north. Gujarat is located between latitudes 20°10′ N and 24°50′ N and longitudes 68°40′ E and 74°40′ E. Gujarat has the longest coastline among the coastal states of India (about 1617 kilometres, or nearly one-third of the country’s total coastline length), as well as two gulfs, Gulf of Kachchh and Gulf of Khambhat. The map of the study area covering the coastal districts of Gujarat State is given in Figure 1.

Figure 1. Location map of coastal districts in Gujarat state.

1.6.1 Characteristics of Gujarat coastline

The state of Gujarat has the longest coast line in India, which right from Jakhau in the Kachchh District to Umargam in the Valsad district. There are two prominent indentations in this stretch, namely, the Gulf of Kachchh and the Gulf of Khambhat, which has rich coastal resources and ecosystems such as mangroves, coral reefs, salt marshes, sand dunes, and estuaries. Based on the distinct variation in the land-form categories, the Gujarat coast has been broadly classified into five regions: (1) The Rann of Kachchh, (2) Gulf of Kachchh, (3) The Saurashtra Coast, (4) Gulf of Khambhat, and (5) The South Gujarat Coast. The coast lines of the Gulf of Khambhat and Kachchh together form about 65% of the coast line of the state. The shore line of the Gulf of Kachchh has extensive mudflats and mangroves. The sandy beach of the gulf is made up of fine sand and broken shells and a high salinity occurs along the shore line. The shore line of Saurashtra has a less indented sandy beach and forms a continuous linear strip from Dwarika to Diu. There are numerous mudflats and marsh vegetation along the coast, and it shows variations like sandy beaches, marshy land, mudflats; beaches are absent at places. About 549 villages are inhabited along the coastal area with a total population of more than one million. There are 13 coastal districts and 35 coastal talukas or blocks touching the sea water (Patel Ajay et al., 2014).

2. Materials and methods

2.1 Multi-temporal satellite datasets

In this study, Landsat-II Multi-Spectral Scanner (MSS), Landsat-IV Thematic Mapper (TM), and Landsat-VIII Operational Land Imager (OLI) sensors were downloaded from the website https://earthexplorer.usgs.gov/ for the years 1978, 1998, and 2020 covering coastal districts in Gujarat state. The details of Landsat digital data analyzed for this study are given in Table 1.

Table 1. Details of Landsat multispectral data acquired for 1978, 1998, and 2020.

Sr. No.	Satellite/Sensor	Spatial Resolution (m)	Path /Row	Acquisition Date
1	Landsat- II	60	159/045	27-Dec-1978
2	Landsat- II	60	159/046	27-Dec-1978
3	Landsat- II	60	160/045	28-Dec-1978
4	Landsat- II	60	160/046	28-Dec-1978
5	Landsat- II	60	161/045	29-Dec-1978
6	Landsat- II	60	162/045	30-Dec-1978
7	Landsat- II	60	162/044	30-Dec-1978
8	Landsat- II	60	163/043	16-Nov-1978
1998
9	Landsat- V	30	151/045	01-Dec-1998
10	Landsat- V	30	151/044	01-Dec-1998
11	Landsat- V	30	150/045	26-Dec-1998
12	Landsat- V	30	150/044	26-Dec-1998
13	Landsat- V	30	149/046	03-Dec-1998
14	Landsat- V	30	149/045	03-Dec-1998
15	Landsat- V	30	148/046	28-Dec-1998
16	Landsat- V	30	148/045	28-Dec-1998
2020
17	Landsat- VIII/ OLI	30	148/045	24-Dec-2020
18	Landsat- VIII/ OLI	30	148/046	24-Dec-2020
19	Landsat- VIII/ OLI	30	149/044	29-Nov-2020
20	Landsat- VIII/ OLI	30	149/045	29-Nov-2020
21	Landsat- VIII/ OLI	30	149/046	31-Dec-2020
22	Landsat- VIII/ OLI	30	150/044	22-Dec-2020
23	Landsat- VIII/ OLI	30	150/045	22-Dec-2020
24	Landsat- VIII/ OLI	30	151/045	27 November 2020

The methodology flow-chart of shoreline and erosion monitoring is given in Figure 2.

Figure 2. Methodology flow-chart of shoreline and erosion monitoring in Gujarat state.

2.2 Remote-sensing satellite data analysis

The multispectral and multi-temporal Landsat data of the study area were analyzed using the following steps:

The TM and OLI Landsat data utilized in this investigation are orthorectified products. Landsat images were georeferenced using the study area’s base maps. Landsat satellite images of 1978, 1998 and 2020 were interpreted to map and monitor the coastline changes that took place over the last 42 years along the coastal districts of Gujarat State.

2.3 Field data collection

The field data related to coastline based on HTL and status of erosion was recorded in each coastal district. The sample points for field data collection in each district were selected based on interpretation of Landsat data of 1978 and 2020. The areas where maximum change in coastline and erosion has occurred were identified and field data such as wave height, coastline extent based on HTL, slope, and erosion extent were recorded. At selected places, GPS measurements of eroded areas and coastline were recorded for accurately locating these areas on the Landsat Satellite images. Some of the field photographs of coastal areas in different districts are given in Figure 3.

Figure 3. Field photographs of coastal areas in different districts in Gujarat state.

2.4 Sea surface temperature data

The SST data covering Gulf of Kachchh, Khambhat, and Saurashtra coast along coastal areas of Gujarat State were collected for the years from 1860 to 2020 from https://climexp.knmi.nl/getindices.cg

2.5 Annual sunspot activity data

The solar activity is mainly comprised of the sunspots, which are strong magnetic fields on the Sun. Sunspot number rise from minimum to maximum and then fall back to minimum in an about 11 years, widely known as 11-year sunspot cycle. The Annual mean Sunspot number data were collected from the website of National Centres for Environmental Information: https://www.ngdc.noaa.gov/stp/solar for the years from 1960 to 2020 (Burud et al., 2021 and Chaudhari et al., 2021).

3 Results and discussion

3.1 Coastline mapping using Landsat data of 1978, 1998, and 2020 in coastal districts

The shoreline changes have been measured by various researchers using Remote-Sensing and GIS data along the Indian coast. The coastal erosion and accompanying shoreline change in Chandipur, Balasore district, Orissa, in relation to sea surface height anomaly was analyzed using multi-temporal satellite imagery during the period 1990 to 2010 by Mukhopadhyay et al., 2011. A study was carried out using the Remote-Sensing satellite data from 2001 to 2011 by Mahapatra et al., 2013 In the coastal areas of South Gujarat, it was determined that coastal erosion is a significant problem. The results indicated that approximately 83.06% of the South Gujarat coast is eroding, 10.15% is stable, and 6.78% is accreting; the length of the eroding coast is greater than that of the stable and accreting coasts.

In this study, the major shoreline indicator namely HTL was considered for delineation of coastline using multi-temporal Landsat Satellite data. The HTL defines the point on the ground when the spring tide high water line reaches. The Landsat Satellite data of 1978, 1998, and 2020 have been interpreted to demarcate HTL in each coastal district based on various geomorphological and land-use/land-cover types. The delineated HTL using Landsat data have been considered as coastline in various coastal districts. The extents of the coastline erosion or accretion for the periods of 1978, 1998, and 2020 were delineated, and the variations in coastline erosion over different time periods were monitored. The coastline delineated using Landsat data of December 1978 and 1998 covering Coastal Districts in Gujarat state are given in Figure 4 and coastline delineated using Landsat data of of December 2020 is given in Figure 5.

Figure 4. Coastline delineated using Landsat data of December 1978, and 1998 covering coastal districts in Gujarat State.

Figure 5. Coastline delineated using Landsat data of December 2020 covering coastal districts in Gujarat state.

3.2 Monitoring changes in coastline from 1978 to 2020 in coastal districts

The coastline changes along the coastal districts in Gujarat state have been studied using multi-temporal Landsat data and GIS for delineation of areas of different erosion classes. Many coastal districts along Gujarat coast are vulnerable to various degree of erosion due to various natural as well as anthropogenic reasons and existing land use/land cover (LULC). Therefore, LULC, geomorphology, slope, and HTL in each coastal district were considered for delineation of coastline and erosion categories. The changes in coastline during last 42 years from 1978 to 2020 were delineated and erosion/accretion processes were monitored. The mapping of coastline in different districts indicated that the maximum changes were observed in Kachchh, Valsad, Bharuch, and Vadodara districts. The detailed coastline changes delineated in these four districts are given in Figure 6. The Gulf of Kachchh has extensive mudflats with a large area under mangroves. The coast is also showing a large number of developmental activities with construction of ports, harbors, jetties, pipe lines, industries, salt pans etc. There are few seasonal small rivers draining into the Gulf of Kachchh. The Gulf of Khambhat, in general, shows shoreline dynamics around river mouths or estuaries and extensive mudflats indicating large inter-tidal zone along the coastline. The Saurashtra coast is in general rocky and wave dominating while, on the other hand, the South Gujarat coast shows major erosion all along from Dandi in Surat district to Umargam in Valsad district. Employing the decadal coastline delineation using multi-temporal and multispectral Landsat data from 1978, 1988, 1998, 2008, and 2018 years covering the Gulf of Khambhat regionKrunal et al., (2021) reported the decadal shoreline change increased from 113.9 m to 831.4 m during the 40 years interval from 1978 to 2018. They estimated that this shoreline change was 1590.5 (± 60) m with an alarming rate of change of 39.76 m yr⁻¹.

Figure 6. Detailed coastline changes delineated in Kachchh, Valsad, Bharuch and Vadodara districts.

3.3 Coastline change monitoring and erosion assessment in coastal districts

The coastline in different coastal districts was delineated using Landsat data of December 1978, December 1998, and December 2020 which are given in Figures 6 and 7. For monitoring extent of coastline erosion/accretion, the coastlines of 1978, 1998, and 2020 were superimposed on Landsat data of December 2020 and presented in Figure 7. The composite map showing extent of coastline erosion/accretion of the coastline during 1978, 1998, and 2020 is given in Figure 8. The Coastal slopes are along with coastal geomorphology, this is the most important aspect to consider when assessing the effects of sea-level rise and change in coastline at any given coastal area. The slope values in different coastal areas were estimated using the contour maps generated by analysis of DEM data. The larger contour spacing indicated the lower slope values, and it was observed that these areas were getting submerged during high tide. These areas are more prone to further erosion process resulting in the risk of inundation of the coastal villages. During field data collection at various coastal districts which revealed that in some of the areas, more than 1 km have been eroded during the last four decades resulting in serious threat to some of the villages getting inundated if the process of erosion continues.

Figure 7. Changes in coastline delineated using Landsat data during last 42 years from 1978 to 2020 covering coastal districts.

Figure 8. Map indicating changes in coastline delineated using Landsat data during last 42 years from 1978 to 2020 covering coastal districts.

3.4 Coastline erosion assessment

The coastal areas in different districts have various landforms and very rich in biodiversity. However, each given region is facing environmental degradation due to coastal erosion, increasing salinity ingress, salt water intrusion in the coastal aquifers etc. These areas have become vulnerable to erosion due to varied coastal processes and anthropogenic pressures. Among 16 coastal districts, 10 districts are reported to be suffering from erosion consisting of 45.8% of the total coastline of Gujarat state under high, medium, and low erosion category. Kachchh district has the maximum coast under high erosion category followed by Jamnagar, Bharuch, and Valsad (Gujarat Ecology Commission, 2018). The coastal vulnerability is assessed based on the processes of accretion and erosion. In the current investigation, coastal vulnerability to erosion is measured by length of eroded areas along the coastal zones in different districts based on HTL delineation on Landsat Satellite data of 1978, 1998, and 2020. The measured length of erosion in each coastal district was assessed and if the coastline change rate is less than 300 m, it is considered as low, between 100 and 500 meters; it is considered as a moderate erosion and more than 1 km, it is considered as high erosion.

The coastline changes in coastal districts were estimated for each of the bi-decade i.e., from 1978 to 1998, 1998 to 2020 and a total during 42 years from 1978 to 2020. The estimated changes in length of coastline, which is classified into three-erosion categories, namely, high, moderate, and low in different districts, are given in Table 2. The significant changes in coastline and associated variations in the form of shoreline erosion were observed in different districts. The estimated total length of erosion in different districts in each category during the period of 42 years from 1978 to 2020 is given in Table 3.

Table 2. Measured length of erosion (km) using Landsat data in different districts from 1978 to 2020.

	Measured Length of Erosion (km)
District	High (> 1 km)			Moderate (300 m to 1 km)			Low (< 300 meters)
	1978–1998	1998–2020	Total	1978–1998	1998–2020	Total	1978–1998	1998–2020	Total
Kutch	32.1	30.2	62.3	78	37.6	115.6	70	62.7	132.7
Jamnagar	7	5.9	12.9	7	6.5	13.5	30	34.5	64.5
Junagadh	0	0	0	0	0	0	49	51	100
Amreli	0	0	0	0	0	0	10	11	21
Bhavnagar	0	0	0	0	0	0	26	33.8	59.8
Ahmedabad	0	0	0	0	0	2.8	5	6.3	11.3
Bharuch	2.9	3.3	6.2	14	7	21	20	7.5	27.5
Surat	0	0	0	0	0	0	5	6.2	11.2
Valsad	1.3	1.8	3.1	0	0	0	19	21.9	40.9

Table 3. Total estimated length of erosion (km) under different categories from 1978 to 2020.

	Measured Length of Erosion (km)
District	High	Moderate	Low	Total
Kutch	62.3	115.6	132.7	310.6
Jamnagar	12.9	13.5	64.5	90.9
Junagadh	0	0	100	100
Amreli	0	0	21	21.0
Bhavnagar	0	0	59.8	59.8
Ahmedabad	0	0	11.3	11.3
Bharuch	6.2	21.0	27.5	54.7
Surat	0	0	11.2	11.2
Valsad	3.1	0	40.9	44
Total	84.5	150.1	468.9	703.5

The results presented in Table 2 indicate that the highest coastal erosion is observed in Kutch district followed by Jamnagar and Bharuch districts under all the three erosion categories of high, moderate, and low. Only low erosion category was observed in four districts, namely, Junagadh, Amreli, Bhavnagar, and Ahmedabad, which can mainly be attributed to different LULC, geomorphology and flat to gentle slope terrains along the coastal areas. The total estimated erosion as well as erosion status in terms of different erosion categories like high, medium, and low in different study districts is presented in Figure 9.

Figure 9. Estimated coastal erosion under different categories during last 42 years from 1978 to 2020.

Gujarat has the longest coastline (24% of Indian sea coast) of 1,600 km out of which 703.6 km is subjected to erosion, which is almost 45.1% of the total Gujarat coastline during the period of 42 years from 1978 to 2020 is observed to be under different classes of erosion. This situation of erosion is very alarming along these coastal districts. The similar results were published by the (Gujarat Ecology Commission, 2018), indicating among 16 coastal districts, the 10 districts are reported to be suffering from erosion consisting of 45.8% of the total coastline of Gujarat state under high, medium, and low erosion category. Kutch district has the maximum coast under high erosion category followed by Jamnagar, Bharuch, and Valsad.

3.5 District-level coastal erosion change monitoring

The district-level changes in the coastal erosion during 1978 to 1998 and 1998 to 2020 were computed to monitor and identify the districts with severity of erosion process. The changes with respect to first two decades (1978 to 1998) were computed during the second two decades from 1998 to 2020 period. The district-level changes in Length of Erosion (km) and percent changes in different districts from 1978 to 1998 and 1998 to 2020 time period are given in Table 4.

Table 4. District-level changes in length of erosion (km) and percent changes in different districts from 1978 to 2020.

	Measured Length of Erosion (km)
District	1978–1998	1998–2020	% Change
Kutch	180.1	130.5	72.5
Jamnagar	44.0	110.3	250.7
Junagadh	49.0	51.0	104.1
Amreli	10.0	11.0	110.0
Bhavnagar	26.0	33.8	13.0
Ahmedabad	5.0	6.3	16.0
Bharuch	36.9	17.8	48.2
Surat	5.0	6.2	124.0
Valsad	20.3	23.7	116.8

It was observed that Jamnagar district has highest erosion almost 2.5 times during the period of 1998 to 2020 as compared to during 1978 to 1998. This was followed by Surat (1.24 times) and Valsad district (1.17 times). The changes in erosion status in other districts during 1998 to 2020 time period were marginal indicating that there is not much change in the erosion status as compared to 1978 to 1998 time period.

3.6 Measurement of sea surface temperature (SST) and sea-level rise

The SST data covering Gulf of Kachchh, Gulf of Khambhat, and Saurashtra coast along the coastal areas of Gujarat State were collected from KNMI website and analyzed to study the trends of SST over the period of 160 years from 1860 to 2020. The normalized SST data covering Gulf of Kachchh, Khambhat, and Saurashtra coast from 1860 to 2020 are plotted in Figure 10. The analysis indicates that SST shows the gradual increasing trend in all the three coastal areas in Gujarat state. Saurashtra coast shows highest SST values and Gulf of Khambhat shows lowest SST values. However, Gulf of Khambhat shows the highest increase of SST by 1.5°C, followed by Saurashtra coast by 1.0°C and followed by Gulf of Kachchh by 0.75°C over the period of 160 years.

Figure 10. Normalized SST data covering Gujarat coast from 1860 to 2020.

3.7 Sea surface temperature (SST) in relation to CO₂ emission

The rising trend in SST is a result of the rising CO₂ content in the atmosphere. The data of SST and CO₂ from 1960 to 2020 collected from the website of World Meteorological Organization have been analyzed to study the relationship between them. The results of regression analysis are plotted in Figure 11(a,b). It is observed that the CO₂ showed a gradual increase of 0.2 to 0.8 ppm during 1960 to 2000, while during the last two decades, from 2001 to 2020, the increase was exponentially high from 0.8 to 2.0. Incidentally, a similar trend was also observed in the SST dataset. This enables us to conjecture that with increase in CO₂ concentration the SST also increases. However, the steep rise in both the parameters, SST and CO_2, has been observed since 2000. The regression analysis of SST and CO₂ showed very good correlation with correlation coefficient of ~0.86 indicating that CO₂ is likely source for increase in SST.

Figure 11. (a) Relationship between SST and CO₂ data. (b) Regression analysis of SST and CO2 data.

3.8 Exploring relationship between SST and sunspot activity

The other possibility to enhance the SST is influence of the total solar irradiance, which, however, is dependent upon solar activity. The solar activity is mainly comprised of sunspots. There, we show the normalized SST data and annual mean sunspot number from 1960 to 2020 in Figure 12. This figure indicates that the peak of SST follows the peak of annual mean sunspot number until 2000. However, later during the decades of 2010 and beyond the annual mean sunspot activity was declined but the SST shows continuous increase. This indicates that the increase in SST depends more on CO2 emission relative to sunspot activity. The CO2 is a greenhouse gas and keeps the atmosphere warm enough to enhance the SST. Further, in accordance to (Lockwood et al., 2011; Hathaway, 2016); a drop in global temperatures was expected. Thus, if there were dependence on solar activity, then SST should have also gone down. On the other hand, according to the many studies, including Burud et al., 2021 and Chaudhari et al., 2021, a 200-year cycle is expected starting from mid of this century and thereby the global temperature will reach to grand minima and the Earth may have again observed the “Little Ice Age,” similar to observed in mid-17th century.

Figure 12. SST and annual mean sunspot activity data from 1960 to 2020.

4 Discussions

This study indicates that the shoreline changes along the Gujarat coast over the period of 42 years (1978–2020) were monitored using the multi-temporal Landsat data and estimated the district-level rates of change in coastal erosion. While the Kachchh district showed the highest coastal erosion however, the rate of change in coastal erosion was highest in Jamnagar district as compared to other districts over period of last four decades. The report on shoreline changes along the Indian Coasts indicated that overall about 34% of coastline is under varying degree of coastal erosion (Kankara et al., 2018). This report on the shoreline change assessment along the Gujarat coast during the period from 1996 to 2016 indicated that 43% of coast is stable while 31% is eroding.

5 Conclusions

This study was carried out to monitor the coastline change in 16 coastal districts of Gujarat state employing an integrated approach of Remote-Sensing and GIS over the period of 42 years from 1978 to 2020. The variation of SST in relation to CO₂ emission and the sunspot activity with respect to coastline change was also analyzed. The extents of the coastline erosion or accretion at two decades interval for the periods from 1978 to 1998 and 1998 to 2020 were delineated and the variations in coastline erosion over different time periods were monitored. The estimated changes in length of the coastline erosion in different districts were classified into three categories: high, moderate, and low. The results of this study indicated that out of 1,600 km of total coastline about 723.6 km is subjected to erosion, which is almost 45.9% of the total Gujarat coastline during the period of 42 years (1978–2020).

The spatio-temporal variations in erosion in different coastal districts provide critical information and of great significance on integrated coastal zone management in Gujarat. In this study, the SST data covering Gulf of Kachchh, Khambhat, and Saurashtra coast were considered to explore the trends of SST over the period of 160 years from 1860 to 2020. The results indicated that SST shows the gradual increasing trend in all the three coastal areas in Gujarat State. The Saurashtra coast shows the highest SST values and Gulf of Khambhat shows the lowest SST values. However, Gulf of Khambhat shows the highest increase of SST by 1.50°C, followed by Saurashtra coast by 1.0°C and followed by Gulf of Kachchh by 0.75°C during the period of 160 years. The impact of sunspots activity on SST was also studied and the results indicated that the SST varies in simultaneous to sunspot number. However, recent increase in SST in spite of decreasing sunspot number is found to be associated with increase in CO₂ emission, which may be either from substantial increase in industries or increasing flux of galactic cosmic rays (GCR).

The multi-temporal Remote-Sensing satellite data are very useful and a significant component in the monitoring shoreline changes for integrated coastal management and taking control measures for protecting the coastal villages. The results of this study on shoreline change along with monitoring erosion and SST enhancement may be of concern to the Government of Gujarat for sustainable development of the coastal areas. It will be helpful to adopt better land-use planning for protection of shoreline from further erosion and also to consider Integrated Coastal Zone management policies to protect coastal villages from erosion.

Acknowledgments

The authors express their sincere thanks to Shri TejPal Singh, DG, BISAG-N, MEITY, Government of India, Gandhinagar-382007, for his keen interest and encouragement to carry out this study. The Landsat data downloaded from earth explorer website, the SST data and Annual Sunspots collected from IPCC etc. are thankfully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the author(s).