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


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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.
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Environment, Development and Sustainability
1 3
Analyzing climate variability andits eects inSundarban
Biosphere Reserve, India: rearmation fromlocal
MehebubSahana1· SuaRehman1· RaihanAhmed1· HaroonSajjad1
Received: 13 October 2018 / Accepted: 14 March 2020
© Springer Nature B.V. 2020
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
1 Department ofGeography, Faculty ofNatural Sciences, Jamia Millia Islamia, NewDelhi, India
M.Sahana et al.
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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…
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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.
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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
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
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
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…
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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…
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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…
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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
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
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5. Households perceptions on variability
Households perception on weather and climate
variability Code
Delayed monsoon 12
Fewer rainy days 12
Shorter rainy season 12
Rainfall decreased 12
Summer longer 12
Temperature increased 12
Summer earlie
Warm winter 12
Late winter 12
Winter shorte
Frequency of flood increased123
Frequency of cyclone increased123
Soil salinity increased 12
Availability of groundwater decreased123
Availability of surface water decreased123
Overall weather changed123
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)
suffered (2)
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
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
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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
Land with tree plantation (bigha)
Agricultural land (bigha)
10 How many times your household members collect items from the forest recourses?
ItemsTrips / week Amount / trip Total / month Year
Fire wood
11 Health and treatment (what are the major diseases affected by your family members?)
Members of
Diseases 1Diseases2 Maternal
Local dr. RHCs DHCs
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12 Heath problem due to climate change
Heath problem due to
climate change
Don’t know
change (3)
Vector borne diseases
Water borne diseases
Arsenic poisoning
Infectious diseases
Child diseases
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
Mode of
Reasons of
Flood, cyclone, storm
effect your
Male Female yes/no How
15 Effective Adaptation strategies
Effective Adaptive
Very highly
Alternative crops
Cultivating HYV rice
Alternative fish varieties
Prawn cultivation
Poultry and duck rearing
Reclaim the degraded
Cultivating vegetables
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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
Within 12 hours 1
Within 1 da
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
(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
(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
(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
Financial assistance
19 Access to government programmes and policies effect their life:
Adaptive strategies optedhighly
Noteffective Negatively
Formation of SGS
(SSDC) Programme
Mobile health service
Ecological policies
Fishing, wood collection
Others (Specify)
20 What the governments provided the land for bank erosion? Yes /No.
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... The mangroves of the SBR are unique ecosystems that also play an important role in reducing cyclones and cycloneinduced storm surges, protecting against flooding, storing and sequestering carbon, and supporting local livelihoods for food, fuel, timber and building materials (Sahana et al. 2019a, b;Halder et al. 2021). The SBR is home to several species of wildlife, including the Royal Bengal Tiger, Gangetic dolphins, spotted deer, estuarine crocodiles, rhinos, many species of birds and fish, several species of reptiles, innumerable invertebrates and various types of animals (Sahana et al. 2021). The Indian Sundarban consists of 102 islands, of which 54 are habitable and scattered across the buffer and transition zone of the biosphere reserve and 48 are in equilibrium and comprise the forest of the mangrove reserve (Sahana et al. 2021). ...
... The SBR is home to several species of wildlife, including the Royal Bengal Tiger, Gangetic dolphins, spotted deer, estuarine crocodiles, rhinos, many species of birds and fish, several species of reptiles, innumerable invertebrates and various types of animals (Sahana et al. 2021). The Indian Sundarban consists of 102 islands, of which 54 are habitable and scattered across the buffer and transition zone of the biosphere reserve and 48 are in equilibrium and comprise the forest of the mangrove reserve (Sahana et al. 2021). The region has tropical humid climate with very short and dry winters between November-February. ...
... Cyclone activity was highest during the months of April to June and October to December (Girishkumar and Ravichandran 2012). Cyclones that occur between April and June affect people, crops, infrastructure, natural vegetation, communication and animals, but those that occur between October and December affect only people and agriculture (Sahana et al. 2021). In the last 40 years, the Bay of Bengal region has experienced 255 cyclones, ranging from low to severe categories. ...
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Coastal areas play an important role in the global food and economic system, but are vulnerable to a number of coastal hazards such as cyclones and storm surges. The Sundarban Biosphere Reserve (SBR) is located on the east coast of India and has a rich diversity of aquatic and terrestrial flora and fauna. The region is frequently affected by coastal hazards such as cyclones and storm surges. The objective of this study is to investigate the impact of Super Cyclone Amphan on land use land cover (LULC) in SBR. For this purpose, the land use land cover (LULC) map of the study area before and after the cyclone was first constructed using four machine learning algorithms: Support Vector Machine (SVM), Spectral Angle Mapper and Maximum Likelihood Classifier. In addition, the accuracy of these methods was evaluated using the confusion matrix. The result shows that the SVM basis provides better accuracy than the other methods. After evaluating the accuracy, the detection of changes in the LULC was analysed. The result shows that forest cover in the region decreased significantly (about 38.8%) due to super cyclone Amphan. In addition, agricultural land, swamps, sandbanks and beaches increased by 49.51%, 39.57%, 17.40% and 6.93% respectively. The findings of this study can be used by local and state disaster management authorities to prepare the effective disaster management plans for the region.
... Studies analysing the changing pattern of cyclones in the region concluded that, though the frequency has not changed much over time, the intensity has increased severely (Lambert et al., 2008;Danda et al. 2011). However, Chakraborty (2015 acknowledged that vulnerability to severe cyclones have increased since the last century while Sahana et al. (2020) stated that the incidence of tropical cyclones in the area intensified during the last decade. India Meteorological Department (IMD) analysed a dataset of last one hundred and twenty years related to high intensity cyclone frequencies in the northern Bay of Bengal, concluding that there has been a rise of 26% in their frequency and trying to link it with increasing sea surface temperature (Singh 2007). ...
... Although various authors (Ali et al. 2020;Sahana et al. 2019Sahana et al. , 2020 incorporated relief and slope variables in identifying priority blocks, but in case of Sundarban the mean elevation and slope are very low: ~2 m above MSL and 0.0049 rad, respectively (Payo et al. 2016). Thus, relief considerations do not affect cyclone vulnerability, as the whole area is susceptible to cyclones and thus was not included here as one of the main criteria. ...
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Sundarban is a designated world heritage with world’s largest contiguous mangrove forest cover, shared by India and Bangladesh. The Indian part is divided into two morphological units: reclaimed section and non-reclaimed mangrove forests. Here alarming changes are reported in light of erratic monsoons, increasing sea levels, salinity, frequent cyclones and coastal flooding, vanishing islands, and socio-economic crisis; leading to growing numbers of ecological refugees. It is one of the most cyclone prone areas in the world. Though there are piecemeal efforts in tackling the menace of cyclone, a holistic cyclone preparedness plan with bottom-up approach is still missing. This lacuna was glaringly highlighted during cyclone Amphan on May 20, 2020, when the state was reeling under the COVID-19 pandemic. To minimize the loopholes in the existing cyclone mitigation practices, the paper tries to prepare a process prototype by involving (a) Occurrence-Resistance-Resilience Analysis and (b) Preparedness-Risk Reduction-Capacity Assessment: encompassing physical and socio-economic parameters. For the first part, vulnerable zone identification through data mining of cyclone occurrence and identification of eroding and stable sections; analysing land use change; studying hydrographic and hydrologic condition affecting the embankment stability; and scrutinising the socio-economic condition and infrastructural amenities present needs to be conducted. The second part entails devising effective management strategies through the following: upgradation of disaster warning system; optimising the location of cyclone shelters and relief distribution centres; efficient relief distribution; and viability checking of insurance cover. This proactive approach reflecting the ground reality would be beneficial for formulating an effective Cyclone Preparedness Plan with a participatory approach.
... In order to protect the soil cover effectively, it is crucial to uphold a dense vegetation cover. The Indian part of the delta is currently facing some significant concerns due to anthropogenic and natural causes like climate change (Thakur and Dey 2018;Sahana et al. 2020). Numerous natural processes actively alter the geomorphological features of this delta under the impact of rivers and tides (Chakrabarti 1995;Adarsa et al. 2012;Appeaning Addo 2015). ...
Shoreline detection and estimation of changes is a well-established concept in the field of coastal zone management. Recent technological advancements in the form of machine learning (ML) have transformed shoreline detection methodologies. However, due to the constantly changing nature of coasts, identification of the boundary between land and ocean has become an intricate process. In this particular investigation, long-term changes in shoreline along the lower segment of the Indian Sundarbans Delta (ISD) are estimated by employing Landsat sensor’s optical imageries spanning 28 years from 1995 to 2023. A fully automated approach involving support vector machine (SVM) classification algorithm with a 99.5% accuracy and zero-crossing edge detection algorithm for shoreline extraction from optical imagery has been proposed. The implementation stage of shoreline extraction utilizes Google Earth Engine’s cloud platform. In contrast, subsequent analysis to calculate shoreline changes conforms to the Digital Shoreline Analysis System (DSAS v.5), an extension of ArcGIS Desktop’s functionality. This research article examines the long-term shoreline changes in the Indian Sundarban Delta. The study uses three statistical measures: end point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). EPR analysis shows significant erosion on Kanak Island and Bhangaduni Island up to 88.21 m. LRR statistics reveal negative trends on the eastern side. NSM analysis highlights maximum accretion in the northwestern part of the delta. This study offers valuable insights into dynamic coastal processes in the area.
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Agriculture in the Indian Sundarbans is mainly dominated by kharif rice-based cropping systems. The cropping intensity of this region largely depends on growing cycle of kharif rice. The present study aimed to determine the spectral pattern of kharif rice and estimate the rice grown area in the Gosaba CD block of Indian Sundarbans in 2017, 2018, and 2019 years using multi-temporal Sentinel-1 Synthetic Aperture Radar data. To estimate the rice grown area, a simple binary classification was carried out using the carefully selected threshold values of co (VV) and cross-polarized (VH) backscatter coefficients (\(\sigma^\circ \)) obtained during the growth cycle of rice. The results revealed that the \(\sigma^\circ \) obtained at the transplanting stage was lower. \(\sigma^\circ \) gradually increased with the advancement of the crop. The cross- polarized backscatter values were higher than the co-polarized backscatter values as the vertical canopy structure attenuated co-polarized signals. The Radar vegetation Index (RVI) showed a bell-shaped profile attaining its peak during the middle of October which coincided with the grain filling stage of the crop. About 41, 40, and 45% of the total study area were covered by kharif-rice in 2017, 2018, and 2019, respectively. The advancement of the rice crop over the study area was clearly shown by the spatial variation of the RVI in the rice growing areas and this would help to implement necessary management actions in the rice fields as well as to decide the sowing site and time for the winter crops for cropping intensification.
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Flood is always a source of social lamentation, huge infrastructural losses and disruption to economic activities in Bhagirathi Sub-basin in India. Climate variability and increasing flood incidents have created a dilemma for social, economic and environmental conditions of the affected communities. These implications necessitate assessing overall flood vulnerability to minimize their short and long-term impacts. This study presents a comprehensive analysis of composite vulnerability among the flood affected communities in Bhagirathi Sub-basin. Data for analyzing composite flood vulnerability were derived from an in-depth survey of 432 households selected through stratified random sampling method in the Sub-basin. Domains of vulnerability such as quality of life, social & economic status, health impacts, ecological implications, losses and adaptation were examined. A total of 95 indicators of these domains were considered to prepare composite vulnerability index of the selected villages. Relationship between vulnerability and households’ characteristics was ascertained using cross tabulation and multinomial logistic regression. Analysis of composite vulnerability index (CVI) revealed very high vulnerability in Nutanhat, Bakkhali, Jhara, Gopalpur, Jayarampur, Titiha, Uchildaha and Mohanpur villages. High vulnerability was observed in Banagram, Mayapur, Amravati, Gobindapur, Raichak Boltala, Talim Nagar Minakhan and Majhirmana villages while Kalna Municipality was found under moderate vulnerability. High losses, ecological & health implications and low socioeconomic conditions of the households aggravated very high to moderate vulnerability in these villages. Gender, income and land possession were found strongly associated with high vulnerability while flood insurance, farming purposes and changes in rainfall pattern were identified inducing moderate vulnerability. CVI analysis assisted in identifying the priority villages for effective policy implications. The study calls for policy implications for lessening the impact of flood in the Sub-basin.
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Sundarbans is the largest mangrove forest of the world and shared between India with a 40% of landmass and remaining with Bangladesh. The ecosystem of Sundarbans is dynamic and in the developmental stage by formation of new islands through soil erosion and sedimentation. It is a low-lying area with an average elevation below high tide line. Traditional method of rice-based farming system is common in low lying coastal land of Sundarbans. Climate change, rising of sea level and frequent cyclones have been changing the crop production and cropping pattern in the Sundarbans delta. The communities of Gosaba, Kultali, Kakdwip, Sagar, Patharpratima and Namkhana blocks of Sundarbanss have been affected by climate change and soil salinity. The agricultural production system is totally hampered after the strike of cyclonic storm 'Aila' in 2009, and super cyclone 'Amphan' in 2020 due to high salinity and pH condition of soil. Human migration is very common in Sundarbans delta because of extreme poverty and at least one family member is working in other states of India in 75% of families. Crop cultivation is very challenging due to high salinity and changing climate in Sundarbans. Use of salt tolerant variety, land shaping, use of organic manure, rain water harvesting are the key elements to manage the saline soil of Sundarbans delta. HIgHlIgHtS m Ecological vulnerability and soil salinity are major problems of agriculture in Indian Sundarbans. m All possible options including agronomic interventions as described are to be adopted for agricultural sustainability of Sundarbans.
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The Sundarbans is a unique and highly productive ecosystem in the intertidal zone. This mangrove ecosystem is the aggregation of plants, animals, and microorganisms that are acclimatized to the unsteady, fluctuating environment of the tropical seashore zone. This mangrove ecosystem is a highly valued ecosystem in terms of ecology, environment, and economics. The mangrove ecosystem of the Sundarbans is a World Heritage Site and a unique wetland in terms of its biodiversity and ecology. As the largest coastal wetland in the world, Sundarbans covers an area of 1,000,000 hectares of land and water, of which 60% is situated in Bangladesh and the remaining 40% in India. This area experiences annual rainfall of 1600-1800 mm and severe cyclonic storms. The huge amount of sediment carried by the rivers helps in the improvement of soil quality. The biodiversity includes 350 species of vascular plants, 250 species of fish, 300 species of birds, 250 species of insects, 70 species of mammals, 7 species of amphibians, 49 species of reptiles, and numerous species of plankton and microbes. Sundarbans is the exclusive habitat of many rare and endangered animals (Batagur baska, Pelochelys bibroni and Chelonia mydas) and plants. It is the main habitat of the Royal Bengal tiger (Panthera tigris). The biodiversity is threatened for several reasons, like deforestation, erosion, pollution, overexploitation of fish, floral, and faunal components. Climate change is also a big problem. The paper discusses the necessity of conservation strategies, which are needed for the protection of biodiversity in the Sundarbans.
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The Sundarban is the world’s largest contiguous mangrove forest and stores around 26.62 Tg of blue carbon. The present study reviewed the factors causing a decline in its blue carbon content and poses a challenge in enhancing the carbon stock of this region. This review emphasized that recurrent tropical cyclones, soil erosion, freshwater scarcity, reduced sediment load into the delta, nutrient deficiency, salt-stress-induced changes in species composition, mangrove clearing, and anthropogenic pollution are the fundamental drivers which can potentially reduce the total blue carbon stock of this region. The southern end of the Ganges–Brahmaputra–Meghna Delta that shelters this forest has stopped its natural progradation due to inadequate sediment flow from the upper reaches. Growing population pressure from the north of the Sundarban Biosphere Reserve and severe erosion in the southern end accentuated by regional sea-level rise has left minimal options to enhance the blue carbon stock by extending the forest premises. This study collated the scholarly observations of the past decades from this region, indicating a carbon sequestration potential deterioration. By collecting the existing knowledge base, this review indicated the aspects that require immediate attention to stop this ecosystem’s draining of the valuable carbon sequestered and, at the same time, enhance the carbon stock, if possible. This review provided some key recommendations that can help sustain the blue carbon stock of the Indian Sundarban. This review stressed that characterizing the spatial variability of blue carbon with more sampling points, catering to the damaged trees after tropical cyclones, estuarine rejuvenation in the upper reaches, maintaining species diversity through afforestation programs, arresting coastal erosion through increasing sediment flow, and combating marine pollution have become urgent needs of the hour. The observations synthesized in this study can be helpful for academics, policy managers, and decision makers willing to uphold the sustainability of the blue carbon stock of this crucial ecosystem.
This chapter offers real-world practical examples of current socio-ecological concerns and transformations related to climate change. This chapter, divided into subsections, represents empirical evidence for the ideas in this book and is especially beneficial to graduate, postgraduate, and research students and researchers for providing new perspectives on how adverse consequences of climate change are impacted and linked to integrated socio-ecological support systems, as well as helping them to understand the psycho-social and socio-physical aspects of climate change/variability.
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Long-term spatial and temporal trends of rainfall at monthly, seasonal, and annual scales have been studied for 12 meteorological stations of the Gangetic West Bengal located in Eastern India during 1901–2002 using 102 years of rainfall data. The non-parametric Mann-Kendall test and Sen’s slope estimator were used to detect trends and their slope. The changes are calculated in percentage over the time period. The results highlight a marked increase in post-monsoon (33.87%), the overall increase in annual (2.61%), a considerable decrease in winter (14.83%) as well as pre-monsoon (4.03%), and an inconsequential increase in monsoonal (1.21%) rainfall. In the annual and monsoonal series, the trend is positive in the southern half but negative in the northern counterpart. A considerable decrease in rainfall during June and August at most stations signifies that monsoon is losing in the early monsoonal months with an occurrence of mid-season dry spells. The increase in rainfall during September (13.80%) and October (34.38%) reveals that the monsoon is shifting toward these late monsoon and post-monsoon months, respectively. Both the decrease of rainfall in June (early monsoonal month), as well as an increase in rainfall in September (late monsoonal month) and October (start of post-monsoonal month), suggest that the monsoon is being delayed on its onset and withdrawal. In the Rarh region, monsoon rainfall is reducing whereas post-monsoon rainfall is increasing. In the Deltaic region, both monsoon and post-monsoon rainfalls are increasing. Such altering patterns of rainfall call for reviewing the agricultural practices and water use in this region.
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Storm protection service of mangrove is often undervalued. This paper empirically assessed the damage avoided by the Sundarbans mangrove forest in 15 villages of southwest coastal Bangladesh, considering the super cyclone Sidr as reference point. The extent of damage of the cyclone was estimated for three situations: (1) villages protected by mangrove and within a polder, (2) villages not protected by mangrove but within a polder, and (3) villages not protected by mangrove and not within a polder. A questionnaire survey was conducted on 300 households selected from these villages by stratified random sampling. Significant differences in all the considered damage assessment attributes were obtained comparing the villages protected by mangrove with those not protected by mangrove. Quantifiable monetary loss associated with the cyclone was TK 69,726 (US$ 1025) per household in the villages sheltered by mangrove, which was about half compared to the villages not in the shadow of mangrove. Loss incurred per household was highest in villages that are not protected by mangrove and polder. Although local people highly valued the storm protection service of the Sundarbans mangrove forest, it is necessary to be emphasized in the disaster management policy of Bangladesh.
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Observational evidence points to a warming global climate accompanied by rising sea levels which impose significant impacts on island and coastal communities. Studies suggest that internal climate processes can modulate projected future sea level rise (SLR) regionally. It is not clear whether this modulation depends on the future climate pathways. Here, by analyzing two sets of ensemble simulations from a climate model, we investigate the potential reduction of SLR, as a result of steric and dynamic oceanographic affects alone, achieved by following a lower emission scenario instead of business-as-usual one over the twenty-first century and how it may be modulated regionally by internal climate variability. Results show almost no statistically significant difference in steric and dynamic SLR on both global and regional scales in the near-term between the two scenarios, but statistically significant SLR reduction for the global mean and many regions later in the century (2061-2080). However, there are regions where the reduction is insignificant, such as the Philippines and west of Australia, that are associated with ocean dynamics and intensified internal variability due to external forcing.
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The Sundarban Mangrove Forest (SMF) is an intricate ecosystem containing the most varied and profuse natural resources of Bangladesh. This study presents empirical research, based on primary and secondary data, regarding the social-ecological system (SES), social-ecological dynamics, different stakeholders and relevant management policies of small-scale or artisanal fisheries such as the SMF; showing how, despite extensive diversification, the livelihood activities of the artisanal fishers in the SMF all depend on the forest itself. Regardless of this critical importance of mangroves, however, deforestation continues due to immature death of mangroves, illegal logging, increased salinity, natural disasters and significant household consumption of mangrove wood by local people. As the mangroves are destroyed fish stocks, and other fishery resources are reduced, leading to moves of desperation among those whose livelihood has traditionally been fishing. The present study also considers several risks and shock factors in the fishers' livelihood: attacks by wild animals (especially tigers) and local bandits, illness, natural disasters, river bank erosion, and the cost of paying off corrupt officials. The artisanal fishers of the SMF have adopted different strategies for coping with these problems: developing partnerships, violating the fisheries management laws and regulations, migrating, placing greater responsibility on women, and bartering fishing knowledge and information. This study shows how the social component (human), the ecological component (mangrove resources) and the interphase aspects (local ecological knowledge, stakeholder's interest, and money lenders or middle man roles) of the SMF as an SES are linked in mutual interaction. It furthermore considers how the social-ecological dynamics of the SMF have negative impacts on artisanal fishermen's livelihoods. Hence there is an urgency to update existing policies and management issues for the sustainable utilization of the SMF resources, eventually contributing to the improvement of the artisanal fishers' livelihoods.
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Abstract Introduction Rangeland ecosystems provide multiple ecosystem services, including feed resources for wild and domestic herbivores in semi-arid areas. However, under the ever increasing environmental changes, the impact of rainfall variability on the productivity and vegetation dynamics of rangelands are the great challenges that pastoral community are facing today. As a result, the potentials of most rangelands in semi-arid ecosystems affect the livestockproduction. Therefore, we studied the interconnections between the long-term rainfall variation and the rangeland Water Requirement and Satisfaction Index (WRSI) in Mieso, Jigjiga, and Shinile districts under pastoral conditions of Ethiopia. Methods The base period rainfall data (1984–2015) was obtained from the National Meteorological Agency of Ethiopia, whereas the future rainfall trend was predicted using MarkSim software (Representative Concentration Pathways 4.5 GHG concentration trajectory). Mann-Kendall’s statistical tests, coefficient of variation, LEAP software (version 2.61), and Minitab Software (version 15) were used to assess the relationship between rangeland WRSI and long-term rainfall variability. Results The result indicated that mean annual rainfall anomaly had strong positive correlation with rangeland WRSI in Mieso (P
The depth-integrated hydrodynamic models have wide application in coastal management. These models have been extensively used in modelling storm surges and related inundation, coastal engineering, tidal forecasting and vulnerability assessments over continental selves across the world. However, there is a general paucity in storm surge modelling studies where an integration of distributed land use information with depth-integrated hydrodynamic models had been tested. The landuse plays a vital role as far as the overland flow is concerned as it determines the flow velocity and travel time vis-à-vis the extent and depth. Therefore, it is essential to test the storm surge inundation models with inclusion of distributed land use land cover. In order to understand the impact of land use land cover inclusion in the storm surge model, a severe cyclone AILA is considered in this study for the assessment over the Bay of Bengal of North Indian Ocean region. Four decades of land use information are fed in the model to test the impact in storm surge inundation extent and depth. The results suggest a significant change in water depth and inundation extent when distributed land use information is included in the simulation. Five landuse scenarios were tested in the model to examine which land use class has the maximum and minimum reduction potential for storm surge inundation. The results provide valuable information for land use based coastal flood management as well as will be useful in enhancing the skill of storm surge forecast.
Extreme weather events have been affecting local environmental sustainability. Previous literature in this field evaluates environmental sustainability based mainly on past and current resource consumption and availability, however, knowledge about the status and potential changes of environmental sustainability under future climate extremes is missing. This paper proposes an integrated approach (combining the Ecological Footprint Analysis with econometric regressions) to predict future environmental sustainability under different climate scenarios. Based on the case study of the U.S. Rio Grande Basin, the results show that this region has been sustainable in 1982–2012, although sustainability levels have been declining over time. In addition, projections for the future show that the entire region will most likely move away from sustainability by the end of this century under the high emission scenario (e.g., RCP8.5). These findings are relevant for sustainable resource management and allocation of local environmental resources as well as for decision-making support regarding climate risk adaptation and mitigation strategies.
Assessment of vulnerability to storm surge flood is not only significant for the survival of the exposed communities but also for adaptation. Defining vulnerability as the function of exposure, sensitivity and resilience capacity, we propose a composite vulnerability index (CVI) to assess village- level vulnerability to storm surge flood in the Sundarban Biosphere Reserve (SBR), India. The composite vulnerability index based analysis revealed that of the total villages (1063) located in SBR; nearly half of the villages are highly and very highly vulnerable with least capacity to cope with any kind of extreme event. The villages located in the southern parts and adjacent to coast were found more vulnerable to storm surge while those located at higher elevation in the central part showed low vulnerability. Some villages in northern part of the region were under high and very high vulnerability due to presence of low lying and waterlogged wetlands. The findings of the study may have implications for developing resilience capacity in response to storm surge flooding. Thus, the methodology can help the local policymakers in integrating local multi-hazards knowledge and in making information actionable for mitigation and adaptation of storm surge flood hazard.
With the construction of the largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) along the Blue Nile, the Nile is back in the news. This, combined with Bujagali Dam on the White Nile are expected to bring ramification to the downstream countries. A comprehensive analysis of the Nile's waters (surface, soil moisture and groundwater) is, therefore, essential to inform its management. Owing to its shear size, however, obtaining in-situ data from “boots on the ground” is practically impossible, paving way to the use of satellite remotely sensed and models' products. The present study employs multi-mission satellites and surface models' products to provide, for the first time, a comprehensive analysis of the changes in Nile's stored waters' compartments; surface, soil moisture and groundwater, and their association to climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) over the period 1992–2016. In this regard, remotely sensed altimetry data from TOPEX/Poseidon (T/P), Jason-1, and Jason-2 satellites along with the Gravity Recovery And Climate Experiment (GRACE) mission, and the Tropical Rainfall Measuring Mission Project (TRMM) rainfall products are applied to analyze the compartmental changes over the Nile River Basin (NRB). This is achieved through the creation of 62 virtual gauge stations distributed throughout the Nile River that generate water levels, which are used to compute surface water storage changes. Using GRACE total water storage (TWS), soil moisture data from multi-models based on the Triple Collocation Analysis (TCA) method, and altimetry derived surface water storage, Nile basin's groundwater variations are estimated. The impacts of climate variability on the compartmental changes are examined using TRMM precipitation and large-scale ocean-atmosphere ENSO and IOD indices. The results indicate a strong correlation between the river level variations and precipitation changes in the central part of the basin (0.77 on average) in comparison to the northern (0.64 on average) and southern parts (0.72 on average). Larger water storages and rainfall variations are observed in the Upper Nile in contrast to the Lower Nile. A negative groundwater trend is also found over the Lower Nile, which could be attributed to a significantly lower amount of rainfall in the last decade and extensive irrigation over the region.
Sundarbans is the largest mangrove forest in the world and a UNESCO World Heritage site. This area is populated by some of the world’s poorest people characterized by low levels of socio-economic indicators. However, it is one of the richest areas in the world in terms of natural resources and biodiversity. Climate change is evident here and is one of the important drivers of migration, food insecurity and poverty in this area. The basic objective of our study is to assess the socio-economic impact of climate change and its implications for availability of natural resources, and thereby to understand the adaptation needs of the people. Climate change not only impacts agricultural productivity but also the occupational structure. The decline in food security and the lack of other developmental choices in the face of climate variability are a serious threat to the economic viability of population. We have used stratified sampling techniques for data collection at household level based on pre-designed questionnaires and focus group discussion. We have tried to analyze vulnerability based on LIFE framework and log-linear regression model, and suggest some adaptation strategies to reduce vulnerability.