Analyzing climate variability and its effects in Sundarban Biosphere Reserve, India: reaffirmation from local communities

Abstract

Climate variability and continual occurrence of cyclones, flood and storm surge have greater implications on fragile ecosystem of Indian Sundarban Biosphere Reserve (SBR). Centurial meteorological data were used to assess the climate variability in the Reserve. Multivariate principal component analysis was performed to identify the consistency among the conditioning parameters of climate. Cyclones, severe cyclone, storm surge height, salinity intrusion, pH and surface water temperature were assessed to figure out the overall consequences of climate variability. Field investigation from 570 households from coastal and inland blocks (administrative division of the district) was carried out to reaffirm the variability in climatic conditions and its environmental, economic and social consequences in SBR. Study revealed wide spatiotemporal variation in temperature and rainfall. Rise in sea level, flood and high storm surge height and salinity intrusion were inducing vulnerability in the coastal blocks. Communities in Gosaba, Kultali, Kakdwip, Sagar, Patharpratima and Namkhana blocks largely showed high level of agreement for climate variability. Hence, the study calls for effective adaptation and mitigation strategies.

Full text

Vol.:(0123456789)
Environment, Development and Sustainability
https://doi.org/10.1007/s10668-020-00682-5
1 3
Analyzing climate variability andits eects inSundarban
Biosphere Reserve, India: rearmation fromlocal
communities
MehebubSahana1· SuaRehman1· RaihanAhmed1· HaroonSajjad1
Received: 13 October 2018 / Accepted: 14 March 2020
© Springer Nature B.V. 2020
Abstract
Climate variability and continual occurrence of cyclones, flood and storm surge have
greater implications on fragile ecosystem of Indian Sundarban Biosphere Reserve (SBR).
Centurial meteorological data were used to assess the climate variability in the Reserve.
Multivariate principal component analysis was performed to identify the consistency
among the conditioning parameters of climate. Cyclones, severe cyclone, storm surge
height, salinity intrusion, pH and surface water temperature were assessed to figure out
the overall consequences of climate variability. Field investigation from 570 households
from coastal and inland blocks (administrative division of the district) was carried out to
reaffirm the variability in climatic conditions and its environmental, economic and social
consequences in SBR. Study revealed wide spatiotemporal variation in temperature and
rainfall. Rise in sea level, flood and high storm surge height and salinity intrusion were
inducing vulnerability in the coastal blocks. Communities in Gosaba, Kultali, Kakdwip,
Sagar, Patharpratima and Namkhana blocks largely showed high level of agreement for
climate variability. Hence, the study calls for effective adaptation and mitigation strategies.
Keywords Climate variability· Centurial meteorological data· Multivariate PCA·
Affirmation· Sundarban Biosphere Reserve
1 Introduction
From the early nineteenth century to late twentieth century, the issue of climate change
has been appealing scholars globally (Vlassopoulos 2012) and subsequently emerging
as public agenda and a policy issue (Seacrest etal. 2000; Moser 2010). Consequently,
it invited the attention of researchers and climate variability and change gained magnet-
ism in climate change studies for the last few decades. This realization has encouraged
to ascertain the extent of climate variability at varied spatial scales, i.e., regional to local
level (Katz and Brown 1992; New etal. 2000; Giorgi 2006; Giorgi et al. 2009; Hansen
* Haroon Sajjad
haroon.geog@gmail.com
1 Department ofGeography, Faculty ofNatural Sciences, Jamia Millia Islamia, NewDelhi, India

M.Sahana et al.
1 3
and Indeje 2004). Climate variability refers to yearly and seasonal fluctuations in climatic
conditions of a region due to several factors. Pacific Decadal Oscillation (PDO), La Niña
and El Niño events, volcanic eruption, etc., are the evidences of such variability (Dinse
2009). In many situations, it becomes difficult to analyze climate variability due to inher-
ent complexity of the variables and their interaction (Meehl etal. 2000). There is a still
some mechanism to understand the nature of climate variables and to draw meaningful
conclusions to arrive at some kind of climate pattern. It is not beyond recognition that
climate variability and change have severely affected natural system, altered precipitation,
created abnormal weather events as heat and cold waves, caused ocean salinity and raised
sea level. The climate extremes include two broad groups: one being consisted of climate
variables which can easily be analyzed and other constituted of more complicated phe-
nomena as floods, droughts, cyclones, storms, etc. The main point of debate is whether
these extreme events are of natural origin or result of anthropogenic modifications to envi-
ronment (Easterling etal. 2000).
Intergovernmental panel on climate change (IPCC) in its fifth assessment report pro-
jected global 1m sea level toward the end of twentieth century higher than the last two
millennia (Mengel etal. 2016). The report also mentioned that there would be substantial
changes in environment globally by 2100 including ocean acidification, sea level rise to
59cm, heavy precipitation and rise in air temperature to 6.4°C. Low lying coastal islands,
especially big deltas of Asia, are more vulnerable and likely to experience increasing fre-
quent storm surge events, pervasive floods, coastal erosion and disastrous cyclones. The
impact of climate change-induced hazards has already been seen worldwide registering
deaths of nearly 1 million people during 1991–2005 (ISDR 2008). Various scholars have
assessed increasing frequency of climate-induced hazards and extremity of weather events
(Trenberth and Owen 1999; Frich etal. 2002; Dastagir 2015). Asia has been the most vul-
nerable continent to climate-induced hazards and their impacts followed by North and
South America, Africa, Europe and Australia. Disasters of hydrological and geophysical
origin mostly affect the African countries and cause wide-scale vulnerability (Guha-Sapir
etal. 2012). The impact of climate change is distinctly observed in Asian countries includ-
ing India (Hijioka etal. 2014). India occupies sixth rank among various countries facing
extreme weather events (Kreft etal. 2014). Deltaic ecosystems in India are particularly
more vulnerable to climate variability and anthropogenic activities affecting biotic and abi-
otic communities (Das etal. 2004).
An increased impact of climate change on agricultural productivity, food security, water
balance and biodiversity has been gaining attention of scientific community globally (Jain
etal. 2013). Till date, few attempts were made to assess climate variability in the deltaic
ecosystems (Solomon etal. 2007; Nash and Grab 2010; Neal and Phillips 2011; Ahmed
etal. 2017). Availability of timely climate data and its understanding of records are pre-
requisite for examining the extent of climate variability in a region. The knowledge of
spatial and temporal evidences of climate variability has been instrumental in identifying
the drivers of variability and examining implications on society (Oguntunde etal. 2012).
Several scholars have attempted to assess the climate variability using meteorological vari-
ables (Mooley and Parthasarathy 1984; Khaki etal. 2018; Kumar and Jain 2010). However,
reaffirmation of ground reality is generally not attempted. New etal. (2000) emphasized
that high-resolution gridded data are worthwhile for assessing spatial and temporal climate
variability, producing climatic models and ascertaining climate change scenarios. Previ-
ous and current scenarios of climate variability exist in researches, but their future predic-
tion is somehow missing (Mu and Ziolkowska 2018). The authors analyzed the relationship
between climate variability (1976–2005) and rising food demands in order to project the

Analyzing climate variability andits effects inSundarban…
1 3
future potentiality. Hu and Bates (2018) examined the impact of climate variability and
global sea level rise on the coastal inhabitants. They suggested that reduced sea level in
Philippines and western parts of Australia is insignificant, while external forces are causing
variability at a greater extent.
Climate change has posed potent impacts on the biota and social livelihood of Indian
Sundarban Biosphere Reserve. Frequent coastal disasters in response to climate variability
and change have significantly impacted the future prospects of the Sundarban. The impacts
can be largely seen on social structure of the Reserve (Roy and Guha 2017). The present
study aims to present a rigorous evaluation of climate variability, changes in water qual-
ity (salinity and pH), surface water temperature and frequency of extreme events, namely
cyclones, severe cyclones and storm surge, in Sundarban Biosphere Reserve, India. This
study makes a comprehensive assessment to analyze the major causative factors for induc-
ing climate variability and identifying its extent through affirmation of local communities
on climate change in the study area. Variability in air temperature, rainfall pattern, poten-
tial evapotranspiration and frequency of wet days was analyzed using 115-year meteoro-
logical data. No systematic scientific attempt was made to examine the extent of climate
variability and change in the active ecological Reserve. Hence, this study exhibits a cred-
ible and modest attempt to address variability and contemplate field-based observations on
climate change. The study will further ameliorate the understanding on climate variability,
persistent coastal disasters and the agreement of locals on climate change and variability.
Comprehensive assessment presented here will help in formulating coherent policy frame-
work and identifying the priority areas.
2 Study area
The Indian Sundarban Biosphere Reserve (SBR) owes its significance due to magnifi-
cent mangrove forest and luxuriant biodiversity. The Biosphere is located between 21° 32′
to 22° 40′ north latitudes and 88° 05′ to 89° 51′ east longitudes (Fig. 1). This Reserve
is spread over two districts of West Bengal, namely North and South 24 Parganas, and
consisting of 19 community development blocks (administrative divisions of the district).
This Reserve contains the contiguous stretch of mangrove on the planet and offers habi-
tat for a number of vegetation types and wildlife. Royal Bengal Tiger, Ganges dolphins,
spotted deer, estuarine crocodiles, rhinoceros, bird species, fish species, several types of
reptiles, innumerable invertebrates and various forms of beasts are found in the Reserve.
Vivid topographic features such as tidal creeks and waterways, mudflats, embankments
and sandbars can clearly be seen in the study area. Sundarban covers an area of about ten
thousand square kilometers of which nearly 38% lies in Indian territory. It relishes tropi-
cal climate with plenteous rainfall during monsoon. The Reserve receives 15 to 20cm of
average annual rainfall. Extremity of temperature is common during summer, whereas tem-
perature drops down to 9.2°C during winter. More than 4.5 million people reside in the
Reserve and depend on forest resources and agriculture for their sustenance. Dried shrubs
and bushes are used as fertilizers and fuel by the local communities.
It is remarkable to note that the climate variability in SBR is higher than global
average. The deltaic Sundarban ecosystem is under constant risk due to sea level rise,
frequent cyclones, storm surge, flood and coastal erosion. The Sundarban has experi-
enced an increase in the average air temperature by 0.50°C over the past 100years. If

M.Sahana et al.
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the present rate of increase continues, the average air temperature will rise by 1°C by
2050 (Hazra etal. 2002). The maximum increase in rate of rainfall was found during
post-monsoon (4.42mm/year) followed by monsoon (3.84mm/year) and pre-monsoon
(0.98 mm/year). Heavy rainfall during Kharif (rainy season crop) season and delayed
monsoon are common phenomena in SBR (Mandal etal. 2013, 2015). Such changes in
climatic conditions in Indian Sundarban need to be addressed to reduce the extent of
vulnerability among coastal inhabitants.
Fig. 1 Location map of the study area: a Sundarban in India, b SBR covering North and South 24 Parganas
districts and c community development blocks in Sundarban Biosphere Reserve

Analyzing climate variability andits effects inSundarban…
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3 Database andmethodology
The meteorological data of average annual mean, minimum and maximum temperature,
average annual rainfall, wet-day frequency, average annual cloud coverage and potential
evapotranspiration (PET) collected from Indian Meteorological Department (IMD) were
used to assess climate variability during 1901–2015. The meteorological data from IMD
have been used by various scholars for analyzing Indian summer monsoon (Ramesh and
Goswami 2007), variability in rainfall (Rahman etal. 2009), rainfall prediction (Pai etal.
2014; Wang etal. 2015) and variability in rainfall in Gangetic West Bengal (Ghosh 2018).
IMD has been providing accurate data for meteorological variables of India over many
years (Rajeevan et al. 2006). Sea level rise data for four stations, namely Sagar Island,
Gangra, Haldia and Diamond Harbour, maintained by permanent service for mean sea
level (PSMSL) were obtained to determine the rate of sea level rise from 1937 to 2012
(75years). Permanent service for mean sea level (PSMSL) has been conducting sea level
analysis globally since 1933. These data have been used by for analyzing sea level rise
trends in Indian and Bangladesh Sundarban (Pramanik et al. 2015; Kusche et al. 2016;
Brammer 2014; Nandy and Bandyopadhyay 2011). Historical cyclones and storm surge
data were collected from the Indian Metrological Department (IMD) for calculating
extreme surge height and return period during 1891 to 2010. Pre-monsoon and post-mon-
soon surface water quality data (pH, salinity and temperature) recorded at Sagar Island
and Canning stations during 1980 to 2015 were obtained from the Department of Marine
Science, University of Calcutta. These data have also been used by Mitra etal. (2009a) and
Banerjee (2013) for analyzing the surface water quality in Indian Sundarban. The impact
of climate variability on socioeconomic conditions of the people was examined through
field work using structured questionnaire. A total of 570 households were sampled pur-
posively to verify the results. The methodology adopted for sampling of households con-
sisted of three stages. In the first stage, two villages (one from adjacent to coastline and one
from inland) from 19 blocks of the study area were selected. In this way, 38 villages were
selected randomly from the study area. In the second stage, selection of households from
villages was made. From each of the village, five households each from high-, medium-
and low-income category were chosen. In this way, 15 households were selected from each
village. Thus, a total of 570 households were sampled from the whole study area. The head
of the household belonged to above 40years of age. The level of agreement regarding con-
ditioning factors was estimated in different blocks of the study area.
Annual and seasonal trend of meteorological variables was examined to ascertain the
crucial component of variability (Fig.2).
The multivariate principle component analysis (PCA) was performed to understand the
seasonal variation and more influencing weather elements in the SBR. Rate of sea level
change and the trend in sea level rise were assessed through scatter plots for the four sta-
tions situated along the Reserve. Storm surge height for 120-year return period was deter-
mined through probabilistic analysis (Mahendra etal. 2010). The height of storm surge was
calculated as:
where T(z) refers to the return period and 1/Q(z) is the average time at which sea level was
higher than z.
Trend and rate of change of surface water pH, salinity and temperature were assessed
using linear regression.
T(z)=1∕Q(z)

M.Sahana et al.
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4 Results anddiscussion
Average temperature variation for North and South 24 Parganas districts showed a posi-
tive linear trend. An increasing rate of 0.005°C and 0.004°C was observed in North and
South 24 Parganas districts, respectively, during 1901–2015. It is clearly evident that the
oscillation of average annual air temperature has changed continuously (Fig.3a, b). The
finding is in line with IPCC report (2014) which also stated that an increase in tempera-
ture has occurred over the twentieth century in large parts of Asia. May and June months
experienced an increase in maximum temperature in North 24 Parganas, while April, May,
June and July were warmer months in South 24 Parganas during indicated years (Fig.3c,
d). North 24 Parganas recorded maximum temperature in the month of May (i.e., 29.5°C),
whereas it was lowest in the month of January (i.e., 19.4°C). Maximum and minimum
temperatures were also recorded in the month of May (24.9°C) and January (16.6°C) in
South 24 Parganas, but these were lower as compared to North 24 Parganas. Low tempera-
ture gradient in South 24 Parganas is due to its vicinity to coast. This finding is in line with
the study of Debnath (2013). He has also reported that not much change in temperature
pattern was observed in South 24 Parganas. Season-wise temperature variability analysis
revealed an increase in summer temperature in both North 24 Parganas (0.007°C) and
South 24 Parganas (0.021°C). Danda (2010) reported an increase in both land and sea
temperatures in the Sundarban Biosphere Reserve and warned that if the trend continued,
it would rise up to 1°C by 2050. Mandal etal. (2013) found warming of islands and their
diurnal variation with variability in temperature. The average annual rainfall has increased
in SBR. The early and delayed monsoon and heavy winter rainfall occurred frequently
during 1901–2015. An increase in the average annual rainfall at the rate of 0.063mm in
North 24 Parganas and 0.273mm in South 24 Parganas was observed (Fig.4a, b). Spatial
Fig. 2 Methodological framework of the study

Analyzing climate variability andits effects inSundarban…
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variation in rainfall is mainly due to varied topography and direct influence of southwest
monsoon in South 24 Parganas (Nandargi and Barman 2018). The overall rainfall during
monsoon has increased at the rate of 0.045mm/years in the coastal Bay of Bengal. Rain-
fall has shown a declining trend during winter (January–February) and slightly increas-
ing trend during pre-monsoon rainfall (March–May) in both the districts. An increasing
trend of rainfall in monsoon season (June–September) in North 24 Parganas (0.041mm/
year) and South 24 Parganas (0.561mm/year) was observed during 1991–2015. South 24
Parganas district enjoys high rainfall due to its location near coast. IPCC (2014) in its fifth
assessment report highlighted increasing warming and rainfall variability across the South
Asia.
The annual wet days in Sundarban increase with increasing number of heavy rainfall
days (> 10mm). An increasing trend of frequency of wet days in North 24 Parganas
(0.002days/year) and South 24 Parganas (0.001days/year) districts was observed dur-
ing 1990–2015 due to increase in rainy days (Fig.5a, b). Guhathakurta etal. (2011) have
Fig. 3 Trend in temperature: average annual temperature in North 24 Parganas (a) and in South 24 Parganas
(b), average monthly, mean maximum and mean minimum temperature in North 24 Parganas (c) and in
South 24 Parganas (d)
Fig. 4 Trend in average annual rainfall in North 24 Parganas (a) and South 24 Parganas (b)

M.Sahana et al.
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also noticed an increase in the number of rainy days in Gangetic West Bengal over dec-
ades. No increasing and decreasing trend in PET was observed in both the districts over
the study period (Fig.6a, b). However, there were marked variations in PET in the study
area. The coefficient of determination of PET was 0.037 for North 24 Parganas and 0.011
for South 24 Parganas district. The best fit line for average annual PET revealed more
variation in the North 24 Parganas district. North 24 Parganas has less dense network
of water bodies as compared to South 24 Parganas. Also, there is marked variation in
topography and vegetation in these two districts which leads to variation in the potential
evapotranspiration.
Multivariate analysis of influencing factors revealed that average monthly tempera-
ture, cloud coverage, PET, rainfall, vapor pressure and wet-day frequency are signifi-
cantly correlated to each other. Average monthly temperature, maximum temperature,
minimum temperature and vapors pressure were positively correlated during autumn
season. The average annual temperature was negatively correlated with rainfall, ETc
and wet-day frequency during summer season. However, rainfall, ETc and wet-day
frequency were positively related with each other during the same season. A signifi-
cant positive correlation of average annual rainfall was found with the average monthly
temperature, maximum monthly temperature and minimum monthly temperature dur-
ing rainy and winter seasons in the study area. Three stages of principal component
analysis (PCA1, PCA2 and PCA3) were applied to explain the consistency among vari-
ous weather components during four seasons over the study period (Tables 1 and 2).
Average monthly temperature, ETc and temperature range during summer season; aver-
age monthly temperature, mean monthly temperature, vapor pressure and temperature
range during winter season; average monthly temperature, minimum monthly tempera-
ture, vapor pressure during autumn and PET and average monthly rainfall during rainy
Fig. 5 Trend in average annual wet-day frequency in North 24 Parganas (a) and South 24 Parganas (b)
Fig. 6 Trend in average annual variability of potential evapotranspiration in North 24 Parganas (a) and
South 24 Parganas (b)

Analyzing climate variability andits effects inSundarban…
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Table 1 Component matrix of climatic variables of North 24 Parganas district in SBR (extraction method: PCA)
a Components extracted
Component matrixaAutumn Rainy season Summer Winter
C 1 C 2 C 3 C 1 C 2 C 3 C 1 C 2 C 3 C 1 C 2 C 3
Average monthly temp 0.44 0.85 0.13 0.67 − 0.49 0.36 0.89 0.40 0.08 0.48 0.78 0.27
Cloud coverage 0.44 − 0.61 − 0.15 − 0.46 − 0.08 − 0.75 − 0.57 − 0.20 − 0.57 0.54 − 0.58 − 0.03
ETc − 0.84 0.32 0.26 0.77 0.39 − 0.42 0.75 0.58 0.14 − 0.72 0.47 0.39
Max monthly temp − 0.07 0.93 0.28 0.92 − 0.13 − 0.11 0.96 − 0.13 0.21 0.32 0.85 − 0.16
Min monthly temp 0.76 0.64 0.02 0.19 − 0.75 − 0.28 0.67 − 0.70 0.21 0.88 0.42 − 0.04
PET − 0.94 0.14 0.22 0.86 0.45 0.11 0.54 0.79 0.11 − 0.46 − 0.09 0.55
Rainfall 0.48 − 0.40 0.73 0.35 0.47 0.61 − 0.68 − 0.03 0.65 0.31 − 0.49 0.66
Temperature range − 0.94 0.13 0.24 0.79 0.56 − 0.10 0.40 0.87 0.11 0.89 0.26 0.25
Vapor pressure 0.68 0.64 0.00 0.28 − 0.62 0.48 0.66 − 0.54 − 0.02 0.75 0.32 0.24
Wet-day frequency 0.52 − 0.47 0.65 − 0.44 0.53 0.13 − 0.75 0.06 0.53 0.47 − 0.56 0.53

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Table 2 Component matrix of climatic variables of South 24 Parganas district in SBR (extraction method: PCA)
a Components extracted
Component matrixaAutumn Rainy season Summer Winter
C 1 C 2 C 3 C 1 C 2 C 3 C 1 C 2 C 3 C 1 C 2 C 3
Average monthly temp 0.34 0.93 0.03 0.69 − 0.56 0.39 0.78 0.54 0.06 0.12 0.97 0.12
Cloud Coverage 0.65 − 0.41 − 0.18 − 0.46 0.20 − 0.08 − 0.45 − 0.14 − 0.62 − 0.65 − 0.27 0.16
ETc − 0.85 0.31 0.22 0.77 0.56 − 0.03 0.82 0.46 − 0.12 0.90 0.14 0.30
Max monthly temp − 0.23 0.94 0.16 0.93 − 0.10 0.29 0.37 0.09 0.68 0.64 0.69 0.29
Min monthly temp 0.70 0.70 0.08 0.19 − 0.87 0.42 0.53 − 0.80 0.10 0.35 0.92 − 0.02
PET − 0.94 0.22 0.23 0.83 0.53 0.03 0.70 0.68 − 0.04 0.93 − 0.10 0.21
Rainfall 0.52 − 0.27 0.76 0.39 0.54 0.70 − 0.80 0.15 0.21 − 0.53 − 0.16 0.75
Temperature range − 0.96 0.03 0.21 0.78 0.61 − 0.09 0.62 0.75 0.08 0.88 0.34 0.28
Vapor pressure 0.60 0.68 − 0.06 0.34 − 0.52 0.03 0.58 − 0.61 − 0.07 − 0.39 0.83 0.02
Wet-day frequency 0.66 − 0.12 0.67 0.42 0.53 0.67 − 0.82 0.11 0.30 − 0.69 − 0.14 0.59

Analyzing climate variability andits effects inSundarban…
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season showed consistency in North 24 Parganas district. Average monthly temperature,
maximum monthly and temperature range during summer season; average monthly tem-
perature, mean monthly temperature and temperature range during winter season; aver-
age monthly temperature, minimum monthly temperature, during autumn and PET and
average monthly rainfall and wet-day frequency during rainy season showed consist-
ency in South 24 Parganas district. The analysis revealed that average monthly tempera-
ture and rainfall have the maximum influence on climate variability in the study area.
This variability can be explained due to dry season during November–April and a wet
monsoonal period (June–September) in the study area. The rainfall intensity increases
from mid-March to July due to thunderstorm during pre-monsoon season and later due
to influence of southwest monsoon. The rainfall again decreases sharply in October
(Nandargi and Barman 2018).
Climate change-induced sea level rise, salinization of water bodies and deterioration
of mangrove health have significantly affected the coastal ecosystem of the Sundarban
(Mahadevia and Vikas 2012). Singh etal. (2014) highlighted that sensitive and dynamic
environmental conditions of coastal areas making it among the most vulnerable ecosys-
tem in the world. The mean sea level is subjected to average local land benchmark or the
fluctuations caused by waves and tides. The rate of sea level change was assessed by data
collected from four coastal stations, namely Sagar Island, Gangra, Haldia and Diamond
Harbour (Fig.7). Wide variations were found in sea level rise at all the stations. The rate
of sea level change was calculated as − 2.9 mm/year at Sagar Island, ± 0.93 mm/year at
Gangra, ± 2.84 mm/year at Haldia and ± 4.1 mm/year at Diamond Harbour (Fig. 7a–d).
Pramanik (2015) observed 0.30-m change in sea level in Haldia and Diamond Harbour
stations during 1970–2014. Two stations experienced an increase in the rate of sea level,
and Diamond Harbour experienced an increased rate higher than the global mean sea
level change rate (3.1mm/year). The main causes of variation in sea level rise are tropical
Fig. 7 Trend in sea level rise at four stations: a Sagar Island, b Haldia, c Gangra and d Diamond Harbour
for indicated years

M.Sahana et al.
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cyclone, sand encroachment, sedimentation and tidal ingression. Gentle slope, lower eleva-
tion and very high tidal amplitude are also attributed to sea level rise at Diamond Har-
bour and surrounding areas of Hugli estuary (Pramanik etal. 2015). Only Sagar Island has
experienced decline in sea level due to the large deposition of sediments brought by River
Ganga.
An increase in the frequency and intensity of cyclones in Sundarban Biosphere
Reserve is due to an increase in sea surface temperature. For example, in case of
cyclone Aila, low-level circulation was developed over the southern Bay of Bengal.
The shear pattern at a time of cyclogenesis was developed with maximum convection.
Tropical cyclones of different intensities (63–120km/h) are a regular and recurring phe-
nomenon in the SBR during July and September almost every year. On the basis of the
intensity and frequency of occurrence of the tropical cyclones in the Bay of Bengal,
these can be classified into two types: cyclonic storms and severe cyclones. The histori-
cal records of the occurrence of cyclones in the study area revealed that the cyclones
which occurred in 1976 and the “Aila” cyclone which occurred in 2009 were the most
destructive ones during 1891–2010. Historical data of 120 years were used for ana-
lyzing cyclonic storms, severe cyclones and storm surge height in the study area. The
results revealed that the SBR has registered a 26% increase in tropical cyclones dur-
ing the indicated years. Temperature trend has been found intensifying the severity of
cyclones from March till monsoon. Nor’wester (Kal baisakhi) thunderstorms are devel-
oped during this period. This type of thunderstorms is formed due to convergence of
two contrasting air masses: one being dry coming from northwest and another moist
maritime coming from southeast (Chatterjee et al. 2015). An increase in temperature
and its consequent cyclone intensity are linked to storm surge height and storm surge.
The result of Machineni etal. (2019) also supported this finding. It has been observed
that cyclones in Bangladesh Sundarban were less frequent but more intense during
1992–2000 (Hazra etal. 2002). The meandering rivers and complex network of tidal
waters have raised the sensitivity of the SBR to cyclone and storm surge. It has also
been found that inundation level of 7m could inundate 90% area under settlements,
beaches and swamps (Sahana and Sajjad 2019). The frequency of tropical cyclones
has further increased during the last 10 years. The number of tropical cyclones mak-
ing landfall on the different blocks of SBR and their return periods are shown in Fig.8.
Gosaba, Kultali, Patharpratima, Namkhana and Sagar were found to be the most severe
cyclone-affected blocks in SBR with less than 10-year return period. The storm surge
Fig. 8 a Frequency of cyclones, b frequency of severe cyclones and c storm surge height for indicated years

Analyzing climate variability andits effects inSundarban…
1 3
during the cyclone Aila (2009) reached inland up to 120km. The blocks situated in the
northern part of the SBR are less affected by the severe cyclones. Chakraborty (2015)
identified that Sundarban has become more vulnerable to cyclones since the last cen-
tury. In coastal Bangladesh, more than 70 major cyclonic events are experienced in last
two centuries (Akber etal. 2018). Myanmar, Sri Lanka, Bangladesh and India are vul-
nerable nations to storm surge-induced flood, sea level rise and cyclones (World Bank
2018). The maximum surge height over 120-year return period was found to be 15.6m
in the study area. The surge height was recorded to be the maximum during the severe
cyclones. Sagar, Gosaba, Namkhana, Patharpratima and Kultali blocks have recorded
more than 12m surge height (Fig.8c). The blocks situated in the northern part of the
Reserve have recorded less than 4m surge height during last 120years (Fig.8).
Sea surface temperature-induced cyclonic storm surge has compounding effect on
salinity intrusion along the coast of Sundarban Biosphere Reserve. Haider etal. (1991)
reported such events usually occur in late May and early November due to cyclonic
activities in Bengal delta. Cyclones are developed involving a complex process wherein
sea surface temperature reaches 27°C (Agrawala etal. 2003). Surface water salinity data
during 1980–2015 of Sagar Island and Canning station were used to assess the trend of
surface water salinity in SBR. The Sagar Island showed a significant declining rate of
salinity for both the pre-monsoon (0.076 psu) and post-monsoon (0.050 psu) seasons
(Fig.9a, b), while the Canning station showed increasing trend in the surface water salin-
ity for pre-monsoon (0.21psu) and post-monsoon (0.28psu) seasons during 1980–2015.
Geographical location of these two stations largely influences the varying rate of salinity.
Sagar is located in the western part of the Reserve where Hooghly and Muriganga Rivers
receive snow melt water from Himalayas due to receding of Gangotri Glacier over last
three decades (Hasnain 2002). An increasing trend in salinity (0.02psu/decade) in Can-
ning station was observed (Fig.9b). Mitra et al. (2009b) identified decrease in salinity
in the western part and increase in salinity in the eastern part of the Reserve. Increas-
ing water salinity is directly attributed to climate change in Indian Sundarban Biosphere
(Banerjee 2013). Trivedi etal. (2016) also indicated the same trend in salinity in west-
ern and eastern parts of the Reserve during 1984–2013. They further added that man-
groves provide effective barrier to surface salinity, while increasing salinity was attrib-
uted to anthropogenic activities and waste disposal. Mangroves also protect the coastal
ecosystem from the impact of storm tidal surge (Raha 2014). Solid waste disposal and
siltation in the city of Kolkata and the connected estuaries and channels contributed to
high salinity at this station. Intrusion of saline water further increases rate of increase
in salinity. This station records higher salinity than the average increase rate of salinity
in the Indian Ocean (IPCC 2007). The spatial distribution of surface water salinity is
shown in Fig.9. The amount of carbon dioxide and heat energy received by ocean brings
Fig. 9 Trend of salinity in a Sagar and b Canning

M.Sahana et al.
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significant changes in water chemistry and reduces pH and increases dissolved carbon
dioxide. This change is resulted in ocean acidification phenomenon (Godbold and Calosi
2013). pH was analyzed to ascertain specific response to climate variability and to exam-
ine its impact on water quality in the study area. Surface water pH of Sagar Island and
Canning stations has shown decreasing trend during the last 30years. The pH range in
SBR (8.25–8.33) was found higher than the global average (8.179) for both the stations.
Rate of decrease in surface water pH was higher in Sagar Island than Canning (2.11).
Overall decrease in pH is due to anthropogenic sources. The Sagar Island experienced
continuous decrease in pH, while Canning station has shown slight increase in pH during
2005–2015 (Fig.10a, b). The decrease in pH in Sagar Island could directly link with the
waste material in the Hooghly industrial area.
4.1 Surface water temperature
The surface water temperature of Sagar and Canning stations in Sundarban Biosphere
Reserve has shown significant rising trend for both pre-monsoon and post-monsoon sea-
sons. The rate of increase in surface water temperature for Sagar Island was determined
as 0.048 °C/year during pre-monsoon and 0.055 °C/year during post-monsoon seasons
(Fig.11a). An increase in surface water temperature during pre-monsoon (0.04°C/year)
and post-monsoon (0.49°C/year) at Canning station was found due to dissolved sediments
(Fig. 11b). The rate of increase in average surface water temperature (0.40 °C/year) in
Fig. 10 Trend of pH in a Sagar and b Canning
Fig. 11 Trend of surface water temperature in a Sagar and b Canning

Analyzing climate variability andits effects inSundarban…
1 3
SBR was found much higher than the world average (0.006 °C/year). The finding is in
accordance with Mitra etal. (2009a). It generally increased as the distance from the coast
increased. Mangrove areas recorded less surface water temperature than the non-mangrove
areas. The global warming has direct bearing on increasing trend of sea surface tempera-
ture during pre- and post-monsoon periods. An increase of 0.54°C per decadal rise in aver-
age surface water temperature was registered between 1990 and 2014 (Mitra etal. 2009b;
Sahana etal. 2016).
4.2 Households’ armation onclimate variability andits eects
Perception of local people on climate variability, climate change and their effects on
ecological and socioeconomic system is very essential to identify the different vulner-
able groups (Adger 2006; Kibue etal. 2016). Climate variability and extreme weather
events were affirmed from the respondents in the SBR (“Appendix” section, Figs.12
and 13). The perception of the respondents who were above 40years of age and have
been living in the locality since the last 20years was considered. Most of the respond-
ents (77%) disclosed that the temperature has increased, and 71% respondents reported
about winter months of being relatively warmer. Sarkar and Padaria (2016) also indi-
cated that people affirm rise in temperature due to climate change in the coastal ecosys-
tem of West Bengal. Delay in monsoon was reported by 68% respondents, while 67%
Fig. 12 Reaffirmation of climate change and variability from respondents

M.Sahana et al.
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showed concern about the decrease in overall rainfall. Decrease in rainfall in Sundarban
in Bangladesh was also reported by Masum (2012). When asked about the changes in
length of the season, 64% respondents reported of the early start of summer. Results
revealed that about 85% of the respondents agreed that there had been overall change in
the climatic conditions in the study area. Nearly 79% respondents agreed that the fre-
quency of cyclones has increased. These respondents belonged to coastal blocks which
experienced high storm surge height. Most of the respondents (81%) agreed that the
Fig. 13 Spatial pattern of responses on climate change and variability in SBR

Analyzing climate variability andits effects inSundarban…
1 3
frequency of floods has increased in the study area. Nearly 72% of the respondents were
of the opinion that salinity intrusion has affected their economic activities. Cyclones,
floods and salinity intrusion have largely affected agriculture, honey cultivation and
livestock rearing of the respondents in the study area. Hajra etal. (2017) identified
that erosion, salinity and tidal surges are responsible for the land losses in the Reserve.
Similarly, Mozumder etal. (2018) concluded that salinity, cyclones and bank erosion
have largely affected the livelihood and migration in the Bangladesh Sundarban. Nearly
20% households in Sagar, 18% in Patharpratima and 17% in Namkhana were severely
affected due to losses of agricultural land caused by cyclones. The increase due to flood
events has caused maximum losses to fishing, livestock and agricultural land in Nam-
khana, Sagar, Kultali and Basanti blocks. Nearly 85% agricultural land and 15% land
with tree plantation were affected by the severe climatic events. Around 72% respond-
ents reported that soil salinity has been escalated over the time which has severely
impacted 21% sampled households in the study area. Shamsuddoha and Chowdhury
(2007) also revealed that climate change-induced salinity has reduced the crop produc-
tion. Consumption of saline water has led to death of livestock in Namkhana (14.5%),
Gosaba (10.8%), Patharpratima (10.1%) and Hingalganj (9.9%) blocks of the SBR dur-
ing last 10years. Nearly 60% respondents disclosed their preferences to migrate owing
to extreme weather events. Abdullah etal. (2016) reported that after Aila, nearly 60%
people were displaced from their shelters. The respondents from Namkhana, Sagar and
Patharpratima coastal blocks were severely affected due to damages of their houses
during the cyclonic events. An overwhelming majority of the respondents reported of
prevalence of waterborne and vector-borne diseases due to climate variability in the
study area. Similarly, Bhunia and Ghosh (2011) also indicated that outbreak of cholera
largely impacted the cyclone-affected people in the Reserve. No credible agreement was
found among the respondents on decrease in surface water availability, while there was
disagreement on the decrease in ground water availability. The analysis revealed that
the respondents from adjacent coastal blocks are highly affirmed for all indicators of
climate change and variability. However, the respondents belonging to Haroa, Canning
I, Hasnabad and Minakhan blocks reported less severity of extreme climate events as
these blocks were located away from the coast.
4.3 Policy implications
Climate variability and change have been found as the major cause for disturbing the
socioeconomic environment of the coastal communities in the Indian Sundarban Bio-
sphere Reserve. In this scenario, coherent adaptation may help in lessening the impact
of climate variability. Moreover, the capability of adjustment can be increased by pro-
viding efficacious assistance from government and stakeholders. Seawalls, barriers
for storm surge, dune augmentation and marshy land provide a natural buffer against
sea level rise and storm action. Development of advance transportation may pro-
vide effective connectivity during disasters for movement of people to a safer place.

M.Sahana et al.
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Implementation of mitigation measures may have prolonged influence in reducing vul-
nerability to climate change and vulnerability. The analysis has a number of far-reach-
ing implications for the formulation of appropriate policies related to ecological, social
and economic conditions in SBR. The first policy suggested is toward construction of
earthen embankments in the high and very high vulnerable coastline to protect vil-
lages from flood and salinity intrusion. The second policy should be formulated toward
improvement in early warning system, establishment of flood and cyclone centers for
accommodating people at the time of extreme events. The third policy should be made
toward disaster management training. Community participation and their coordination
with non-governmental organizations (NGOs), international association and local gov-
ernmental body may reduce the vulnerability to a larger extent. Timely relief and reha-
bilitation strategies can significantly contribute to quick recovery from disaster shocks.
Holistic approach involving the process of hazard identification, community prepared-
ness, mitigation and response may perhaps make a difference.
5 Conclusion
Present study has analyzed the climate variability and its impact on the coastal com-
munities of the Sundarban. Climatic variables and parameters of surface water qual-
ity, i.e., pH, salinity and surface water temperature, were chosen to ascertain the cli-
mate variability and its impact on the socioeconomic environment of the study area.
Analysis revealed increasing trend in the average annual temperature in SBR, while
low temperature gradient was observed in the South 24 Parganas district due to its
vicinity to the coast. Rainfall has also shown increasing trend with monthly variation
clearly indicating the impact of climate variability. Multivariate analysis also revealed
consistency among the climate variables where temperature, cloud coverage, crop
evapotranspiration, rainfall, vapor pressure, wet-day frequency and temperature range
were found the major climatic controls in SBR. Changes in climatic conditions and
increased erosion were found to be the cause of rising sea level at Haldia, Diamond
Harbour and Gangra stations. The frequency of tropical cyclones has increased by
26% increase in SBR during last 120 years. Coastal blocks, namely Sagar, Gosaba,
Namkhana, Patharpratima and Kultali, recorded more than 12m surge height. Salinity
intrusion has been found as a major threat to the mangroves which is also attributed
to the action of storm surge in the coastal blocks. Anthropogenic activities have sig-
nificantly contributed in decreasing surface water pH concentration in Sagar Island.
Surface water temperature has shown a significant rising trend for both pre-monsoon
and post-monsoon seasons. Mangrove areas have less surface water temperature than
the non-mangrove areas. Communities living in Gosaba, Kultali, Kakdwip, Sagar,
Patharpratima and Namkhana coastal blocks disclosed the severity of extreme events
and changing pattern of climate variables, while the communities living in the blocks
located in northern part of the Reserve, namely Haroa, Canning I, Hasnabad, and
Minakhan, reported less impact of climate variability and extreme events. Climate
change and variability have deleterious effect on socioeconomic and ecological con-
ditions of the Reserve. The study calls for policy implications to safeguard the living
conditions of coastal communities in SBR.

Analyzing climate variability andits effects inSundarban…
1 3
Acknowledgements The authors would like to thank anonymous reviewers for their constructive comments
and suggestions for improving the quality of the manuscript.
Appendix: Questionnaire
Analyzing climate variability and its effects in Sundarban Biosphere Reserve, India:
Reaffirmation from local communities
District: Block: Village Name:
Block Name: Community Name:
Questionnaire ID: Date of Interview:
Religion……………………………………Social group…………………………………….
Name of respondent: ……………………………………Age: _______ Sex: _____
Marital Status: Married/ Unmarried/Widowed/separated/Remarried
1. Attitude questions
a. Year since you are living lived in this area?
b. Year since you have owned a property in this area?
c. Sources of drinking water(types):
d. Sources of drinking water: within premises/outside the house
e. Electricity connection in your house? a. Yes b. No.
f. If no specify the other sources:
2. Households information:
Name AgeSex EducationOccupation
3. Land tenure and land holding size:
Sl. No Land tenure system Tick where appropriate
1Land less
2cultivator
3 lease- in
4Cultivator and lease
4. Source of family incomes:
Source Income (Rs) Source Income (Rs)
Agriculture Labour
Fishing/Shrimp Culture Business
Honeycollector Tourism
Forest woods and others migrated worker
Government jobOthers

M.Sahana et al.
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5. Households perceptions on variability
Households perception on weather and climate
variability Code
Delayed monsoon 12
34
5
Fewer rainy days 12
34
5
Shorter rainy season 12
34
5
Rainfall decreased 12
34
5
Summer longer 12
34
5
Temperature increased 12
34
5
Summer earlie
r1
23
45
Warm winter 12
34
5
Late winter 12
34
5
Winter shorte
r1
23
45
Frequency of flood increased123
45
Frequency of cyclone increased123
45
Soil salinity increased 12
34
5
Availability of groundwater decreased123
45
Availability of surface water decreased123
45
Overall weather changed123
45
1= don’t know, 2= Disagree, 3= uncertain, 4= Agree, 5= strongly agree
6. Households experience of suffering from natural disaster or climate change effects?
Problems Severely
suffered (3)
Moderately
suffered (2)
Least
suffered
(1)
No
suffered (0)
Decrease of mangrove3 210
Bank erosion/soil erosion 3210
Inundation of low land 3210
Change in river flow 3210
Land degradation 3210
salinity intrusion 3210
Flood hazards 3210
Cyclone 3210
Stalinization of Agricultural land 3210
Crop production decreased 3210
Honey production decreased3 210
livestock disease increased 3210
fish production decreased 3210
7. Did climate change effect the livelihood and economy?
Livelihood effect Flood Salinity Cyclone Sea level rise Erosion
Agriculture
Livestock
Fishing
Shrimp Culture
Honey production
1=yes, 0=No
8. Types of House
Types of House Hut Muddy Semi cementedFully CementedGrass/Bamboo Others
Click yes or no
1= Yes, 0 = No

Analyzing climate variability andits effects inSundarban…
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8.1 Do you have toiled in your houses?
8.2 Do you have kitchen in your houses?
8.3 Do you have bathroom in your houses?
8.4 How many times your home affected by cyclone in last 10 years? (……..)
8.5 How many times your home affected by flood in last 10 years? (……..)
8.6 Have you, in the last 10 years, experienced any form of flood damage (including to your
home, garden or vehicle)?
Code Damaged (10yr)CycloneFlood
0No damage
1Less damage
2Suffer damage
3Severe damage
4Irreversible damage
9Wealth and Assets (Please indicate the following assets and year was purchased )
Items Number Death/destroy due to flood, cyclone
Bicycle
Motorcycle
Radio
boats
Goats
Cows
Land with tree plantation (bigha)
Poultry
Agricultural land (bigha)
Others
10 How many times your household members collect items from the forest recourses?
ItemsTrips / week Amount / trip Total / month Year
Fire wood
Honey
Grazing
Fishing
11 Health and treatment (what are the major diseases affected by your family members?)
Members of
family
Diseases 1Diseases2 Maternal
Diseases
hospitalize
d
Local dr. RHCs DHCs

M.Sahana et al.
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12 Heath problem due to climate change
Heath problem due to
climate change
Don’t know
(1)
Decreased
(2)
No
change (3)
Uncertain
(4)
Increased
(5)
Vector borne diseases
Water borne diseases
Arsenic poisoning
Infectious diseases
Child diseases
Mortality
13 Household coping mechanism for Heath issues
Coping mechanisms for health issue Yes = 1No = 0
Drink boiled water for drinking
Avoid using water from the river or pond
Use protection to avoid the diseases
Frequent checkup in near hospital
Used Vaccine and take medicine
Take advised of doctor
Go to health awareness camp
Take Ayurvedic medicine
14 Education
Education
types
Members
Locatio
n
Mode of
communicatio
n
Reasons of
dropout
Flood, cyclone, storm
effect your
education?
Male Female yes/no How
4th
8th
10th
12th
Graduate
Master
Higher
Others
15 Effective Adaptation strategies
Effective Adaptive
strategies
Very highly
effective
(4)
Highly
effective
(3)
Moderately
effective
(2)
Somewhat
effective
(1)
Not
effective
(0)
Alternative crops
Cultivating HYV rice
varieties
Alternative fish varieties
Prawn cultivation
Poultry and duck rearing
Reclaim the degraded
land
Cultivating vegetables
Non-agricultural
Migration

Analyzing climate variability andits effects inSundarban…
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Off-farm work (van,
rickshaw, driving)
Petty business
16 Early warning and Awareness
16.1 How do you obtain of information about the floods and cyclones?
No early warning (0), Radio (1), T.V (2), Newspaper (3), Panchayat (4), Neighborhood (5),
relatives (6).
16.2 How before you obtained the early warning information?
Early warning Period Code
No early warrin
g0
Within 12 hours 1
Within 1 da
y2
Before 2 days 3
Before 5 days 4
Before a week 5
16.3 Are there safe places used to protect from hazards? Yes/No
16.4 Decision after early warning?
Decision after early warning Code
Wanted to stay at own home 1
Evacuate home and shift to shelter with family 2
Go to cyclone center 3
17 Households adaptation strategies for climate change effect
17.1 Adaptation strategies for salinity inclusion
Adaptation strategies for salinity inclusio
nC
ode
(0) Do nothing
(1) Rainwater harvesting
(2) Conservation of pond water
(3) Use of pond sand filter
(4) Digging of pond
17.2 Adaptation measures implemented by fish/shrimp farmers
Changes in Fish/shrimp farming practice
sC
ode
(0) No response measures implemented
(1) Increased embankment height
(2) Digging pond inside fish farm
(3) Liming
(4) Use medicine
(5) Placing net around shrimp field
17.3 Adaptation measures for agriculture
Adaptation measures for agriculture Code
(0) No adaption option
(1) Crop diversification

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(2) Changing cropping calendar
(3) Change in cropping pattern
(4) Adopting modern faming technologies
(5) Transform agriculture land into fishery
17.4 Adaption measure for bank erosion
Adaption measure for bank/soil erosion Code
(0) No Adaptation opted
(1) Sea wall/earthen embankment
(2) Soil bunds
(3) Waterways
(4) Stone bunds
(5) Grass strips
(6) Mangrove replanting
17.5 Adaption measure for floods
Adaption measure for floo
dC
ode
(1) Storage food
(2) Migrated to safe place
(3) Migrated to other village
(4) Link with other communities
(5) Maintenance and improve the river bank
(6) Make community action team
(7) Make home with higher foundation
17.6 Adaption measure for cyclone
(1) Adaption measure for cyclone Code
(2) Sifted to cyclone centre
(3) Stay at home
(4) Migrated to other place
(5) Prepared cyclone protected home
(6) Make community action team
17.7 Household coping mechanism
Code Households Coping mechanisms Tick the right Ans
1By own savings
2By borrowing money from relatives
3By financial assistant from the Government
4By borrowing money from NGO
5By relief from the NGO
6By asking children to work
7By borrowing money from Bank
8By mortgage assets
9By stopping schooling of children
10 By reducing expenses on food and other consumption

Analyzing climate variability andits effects inSundarban…
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11 Sold assets
12 By temporarily migration to other area
17.8 Distance of the cyclone centre from your home? (…………………meter)
17.9 How much time you need to recover after cyclone Aila? (………………….days)
17.10 How much time you need to come back in normal life after Aila? (……………months)
18 Assistance from the Government and NGOs during and after the natural hazards
Item received Government/NgoRemark
Foods
Shelter/tripal
Clothes
Financial assistance
Loans
Others
19 Access to government programmes and policies effect their life:
Adaptive strategies optedhighly
effective
Moderately
effective
Noteffective Negatively
effective
MGNREGA
PDS
Formation of SGS
(SSDC) Programme
Mobile health service
Ecological policies
Fishing, wood collection
policies
Others (Specify)
20 What the governments provided the land for bank erosion? Yes /No.
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