coastal fishers livelihood in peril sea surface temperature (sst) and tropical cyclones in...

8/21/2019 Coastal Fishers Livelihood in Peril Sea Surface Temperature (SST) and Tropical Cyclones in Bangladesh-2012

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SAYEDUR RAHMAN CHOWDHURYM SHAHADAT HOSSAINMD SHAMSUDDOHAS M MUNJURUL HANNAN KHAN

COASTAL FISHERS LIVELIHOOD IN PERIL:SEA SURFACE TEMPERATURE AND TROPICAL CYCLONESIN BANGLADESH


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COASTAL FISHERS LIVELIHOOD IN PERIL:SEA SURFACE TEMPERATURE AND TROPICAL CYCLONESIN BANGLADESH

SAYEDUR RAHMAN CHOWDHURYM SHAHADAT HOSSAINMD SHAMSUDDOHAS M MUNJURUL HANNAN KHAN


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ii| Coastal Fishers Livelihood in Peril

Authors:

Sayedur Rahman Chowdhury is a Professor at the Institute of Marine Sciences and Fisheries of the

University of Chittagong, Bangladesh. He is involved in teaching and research in oceanography, coastal

geomorphology, and coastal environmental changes.

M Shahadat Hossainis an Associate Professor at the Institute of Marine Sciences and Fisheries of the

University of Chittagong, Bangladesh. He is involved in teaching and research in coastal zonemanagement, climate change challenges, fisheries management, community livelihood, and coastal

resilience modeling.

Md Shamsuddoha is the Chief Executive of Center for Participatory Research and Development-

CPRD, a research based non-government organization in Bangladesh. He is involved in climate change

negotiation of the UNFCCC, also involved in the implementation of research projects on climate change

and disaster risk reduction in home and abroad.

S M Munjurul Hannan Khanis a Deputy Secretary of the Ministry of Environment and Forests of the

Government of Bangladesh. He is involved in climate change negotiation of the UNFCCC as well as AR5

process of IPCC. Also, he is involved with a number of global and national research projects on climatechange, biodiversity conservation and environmental management.

Field data collectors:

Data organizer :Subrata Sarker

Citation :

Chowdhury,S.R., M.S.Hossain, Md. Shamsuddoha and S.M.M.H.Khan (2012). Coastal Fishers

Livelihood in Peril: Sea Surface Temperature and Tropical Cyclones in Bangladesh, CPRD, Dhaka,

Bangladesh. 54 pp.

Muhammed Forruq Rahman

Muhammed Atikul Haque

M. Ziaur Rahaman

Subrata Sarker

Sraboni Chowdhury

Shyamal Chandra Basak

Md Royhanur Islam

Amirul Haque

Debashish Sarker

Ahsanul Haque

Md Sakibul Islam

Khin Ma U


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Coastal Fishers Livelihood in Peril | iii

This book has been published as one of the deliverables of the project titled Linking local level climate

change vulnerability to the national level policy framework on adaptation implemented by the Center of

Participatory Research and Development-CPRD with support from Foreign and Commonwealth Office

(FCO) of the British High Commission, Dhaka.

Views expressed in this publication are those of the authors and do not necessarily reflect the views ofCPRD and the British High Commission, Dhaka.

Publisher :

Center for Participatory Research and Development-CPRD, Dhaka, Bangladesh. Published in 2012.

Copyright :

2012 Center for Participatory Research and Development-CPRD. All rights reserved.

ISBN :

978-984-33-5423-5

Cover concept and design:

Front:Cyclogenesis locations, Cyclone tracks, and the Eye of the super cyclone Sidr (2007) overlaid on SST

Climatology of Bay of Bengal;Back:Fishers going out to sea for fishing near Sonadia Island, Coxs Bazar (by

Sayedur Rahman Chowdhury)

Printers :

Helpline Resources, 46/1 Purana Paltan, Dhaka- 1000, Phone : 9571813, 9568579

Available from:Center for Participatory Research and Development-CPRD

House-138, Flat-A6, Road-3, Block A

Niketon Housing, Gulshan-1

Dhaka 1212, Bangladesh

Tel: +88-02-9860042

Mail: [email protected], [email protected]

Web:www.cprdbd.org


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iv| Coastal Fishers Livelihood in Peril

Acknowledgement

The publication titled Coastal Fishers Livelihood in Peril: Sea Surface Temperature and Tropical

Cyclones in Bangladesh is a credit to the collaboration between Center for Participatory Research

and Development (CPRD) and Institute of Marine Sciences and Fisheries (IMSF) of the University

of Chittagong and dedication of its authors, whose proficiency and commitment have enriched the

study.

We are grateful to the faculty members of IMSF and colleagues at CPRD for their support and

insight in finalizing study methodology. We particularly would like to thank Prof Dr Muhammed

Zafar, Prof Dr Md Rashed un Nabi, Prof Dr Ashraful Azam Khan, Dr Sharifuzzaman, Mohammad

Muslem Uddin - the faculty members of IMSF for their inputs and suggestions.

We would like to put on record the invaluable coordination and support of Ms Shamaila Mahbub of

the British High Commission, Dhaka all through the study and publication of this book.

We would also like to acknowledge the help, information and insights received from the

questionnaire respondents during the course of the study. We have learnt much from the respondents

of coastal fishing communities, and without their help it would not have been possible to uncover

the diverse impacts of increased meteorological hazards on their livelihood.

Finally, we would like to express our sincere thanks to Foreign and Commonwealth Office (FCO)

of the British High Commission, Dhaka for its generous support in the implementation of this study.


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Coastal Fishers Livelihood in Peril | v

Contents

Executive Summary___________________________________________________________ 1

Part 1: Sea Surface Temperature and Tropical Storms______________________________ 5

1. Introduction 2. Objectives

3. Study Area

4. Data source and data aggregation

5. Software

6. Methodology

6.1. Preprocessing

6.2. Sampling framework

6.3. Zonal aggregation

6.4. Analyses / Examination of variability

7. Results and Discussion

7.1. Seasonal SST variability 7.2. Spatial (Geographical) SST variability

7.3. Long term SST trends

7.4. Seasonal and geographical distribution of Tropical Cyclones

7.5. Tropical Cyclone Trend

7.6. Favourable Temperature for cyclogenesis

8. Limitations

9. Conclusion

9.1. Recommendations

References

Part 2: Meteorological hazards in fishers livelihoods_____________________________ 25 1. Introduction

2. Study Sites

3. Materials and Methods

4. Findings

4.1. Climatic extremes (anomalies)

4.2. Meteorological hazards depressions and cyclones

4.3. Trends of yearly average cyclone warning signals during 2000-2011

4.4. Impacts of depressions and cyclones on fishing days

4.5. Impacts of depressions and cyclones on coastal properties

4.6. Impacts of depressions and cyclones on fishing expenditures

4.7. Credit risks and increasing indebt household number

4.8. Impacts of depressions and cyclones on food security

4.9. Impacts of depressions and cyclones on women and children

4.10. Fishers migration for alternative income

4.11. Trends of fishers missing due to depressions and cyclones

4.12. Disaster impacts on livelihood

5. Conclusive remarks

References

Appendix__________________________________________________________________ 49


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List of Abbreviations

ASCII American Standard Code for Information Interchange

AR5 Assessment Report 5 (IPCC)AVHRR Advanced Very High Resolution Radiometer

BMD Bangladesh Meteorological Department

CBOs Community Based Organizations

CPP Cyclone Preparedness Program

DSST Daytime SST

DNSST Day-Night combined SST

DNSSTTS Day-Night combined SST Time Series

ENVI Environment for Visualizing Images

ENSO El-Nino Southern Oscillation

GIS Geographical Information System

HH Household Head

IBTrACS International Best Track Archive for Climate Stewarship

NCC National Coordination Committee

NCDC National Climate Data Center

NGOs Non-Governmental Organizations

NOAA National Oceanic and Atmospheric Administration

NODC National Oceanographic Data Center

NSST Nighttime SST

POES Polar Operational Environmental Satellites

RSMAS Rosenstiel School of Marine and Atmospheric

SLA Sustainable Livelihood Approach

SSF Small-Scale Fisheries

SPSS Statistical Package for Social Sciences

SSHA Sea Surface Height Anomaly

SST Sea Surface Temperature

TRMM Tropical Rainfall Monitoring Mission


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Executive Summary

Bangladesh is one of the most disaster prone countries of the world and here climatic events are

considered an integral part of the social fabric. More than 3.5 million coastal peoples livelihood

directly or indirectly depend on fishing and related activities under extremely difficult conditions,

and their economic hardship is most likely to be aggravated by climate change and its manifestationsin various means. This study undertakes to examine the probable linkage of the changing regimes in

Sea Surface Temperature, Tropical Cyclones and related climatic hazards with the declining

livelihood of the coastal fishers. Chapters of this book are organized into two sections, the first

documenting the Sea Surface Temperature and Tropical Cyclones in the Bay of Bengal based on

analysis of available climatic data between 1985 and 2009; and the second documents fishers own

experience and perceptions of the changing climate based on analysis of survey data from ten

coastal areas of Bangladesh.

1. Sea Surface Temperature

Monthly Daytime and Night time Sea Surface Temperature (SST) over the Bay of Bengal from1985 to 2009 have been analysed to find out (a) seasonal variations, (b) geographical distribution,

and (c) long term trend (i.e. rise or fall) of temperature. While the first two kinds of variations are

mainly linked to local weather and climate, and annual climatic cycles associated with monsoons;

the third is expected to be coupled with global and regional Climate Change including Global

Warming. Observed long-term often non-cyclic and irreversible climatic variabilities are linked to

changing patterns of precipitation and drought, intensification of tropical cyclones and climatic

disturbances, which are anticipated to be bringing about permanent change in fishing effort and

livelihood of the fishing communities all along Bangladesh coast. This part of the technical chapter

aims at identifying important components of temperature variability over the Bay of Bengal.

Seasonal variability:

SST over the Bay of Bengal has distinct bimodal seasonal cycles with two warm and two cool

seasons in a year, one cycle being more prominent than the other. During the Winter (January) SST

remains low and reaches it maximum during the Summer (April-May) followed by a secondary low

in Monsoon (July-August) and secondary high in around October before it starts to cool down again.

There are, however, differences in the pattern at different. Seasonal range of monthly mean

temperature is relatively small at southern Bay of Bengal and large at the northern Bay.

Coastal Fishers Livelihood in Peril | 1


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Geographical variability:

Northern Bay of Bengal close to Bangladesh coast is generally cooler than the southern Bay of

Bengal, particularly in winter. Strong zonal temperature gradient develops in the cooler months

during the Northeast monsoon. Such strong zonal gradient becomes apparent from November to

March. Zonal temperature gradient weakens or disappears during the summer and wet months.

Long Term variability:

SST is found to increase everywhere in the Bay of Bengal. Night time SST is found to increase

at a much faster rate than the Daytime SST. Night SST is considered more reliable indicator of

temperature than daytime SST. It has increased by 0.30-0.48C since 1985 at rates between 0.0126

and 0.0203 per year. Daytime SST also shows increase trends everywhere, ranging from 0.20 to

0.46C during the period, annual rates ranging from 0.0086 to 0.0191.

Like the seasonal variations, long term trend also exhibits zonal variations. Both Day and Night

SST are increasing faster in the mid latitudes of the bay. This zone is generally not the breeding

ground of Bay of Bengal cyclones, but most cyclones have to travel through this area before making

landfalls.

If the trends found in this study are the true long term trends and the temperature increase

continues in the future at similar rates we would expect a rise of Night time SST in the Bay of

Bengal from 0.5 to 0.8C and daytime SST from 0.35 to 0.72C by 2050.

2. Tropical Cyclones

Tropical storms in the Bay of Bengal including cyclones and depressions during the period

1985-2009 have been studies to understand their frequency, seasonal and geographical distribution

of origin, landfall distribution and long term trend. April-May and October-November are the two

main seasons producing storms of cyclonic strength. Those cyclones generally originate in the

southern Bay of Bengal and the Andaman Sea, and most of them make landfalls in Bangladesh and

Myanmar. During the southwest monsoon depressions and storms form at the northern bay, but they

typically falls at the Orissa coast of India.

Cyclone frequency is found to be increasing during the study period. During this period Bay of

Bengal has produced on average 5.48 storms per year or once every 9.49 weeks. With an increasing

rate above, we may experience a frequency of 7.94 storms per year or once every 6.54 weeks by

2050.

Favourable SST for cyclone formation is also studied. Results indicate that increasing SST in

cooler months and in cooler regions at the bay can be interpreted as widening cyclone season, and

larger cyclone breeding grounds.

Practical consequences of the warming sea:

Tropical cyclones or the so called atmospheric heat engines gather heat energy from the warm

sea water and reinforce their momentum by gaining more heat and moisture as they travel through

warm areas of the sea. Therefore, we experience two cyclone seasons in Bangladesh one in

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April-May and the other in October-November, when the sea surface temperature remains

relatively high. The cyclone seasons in the Bay of Bengal are likely to widen further as the cooler

months become warmer. Moreover, as the usually cooler high latitude zones get warmer, cyclones

will get larger replenishment area for gaining heat energy, thus increasing the risk of cyclones at the

coast.

The rapidly warming middle Bay of Bengal can appear to be a source of danger as storms

traveling across this zone will have access to more heat energy and moisture to remain strong, or to

become even stronger. This zone can even become a potential breeding ground for cyclones and

depressions.

However less obvious it might be, more difficult weather conditions for small scale fishers arise

from the so called rough seas characterised by windy condition and wavy sea surface, or high

seas. Much of the wind and as a result waves are direct consequences of atmospheric convection

cells of varying magnitudes which in turn are driven by temperature difference between two places

at sea, or between the sea and the land. Changing regime of temperature at sea is likely to bring

about changes in the local weather and wind-wave system which may pose additional hazard forartisanal fishers; nevertheless, the nature of change cannot be predicted without modelling those

processes.

3. Meteorological hazards in fishers livelihoods

Primary data were collected through the survey of 500 fishers household from 10 coastal

locations. The questionnaire addresses family data, crafts and gears with fishing durations, catch

rates, market prices, trends of climatic changes with effects on fisheries and fishers livelihood. Data

analysis reveals that increased windstorm, wave height and current velocity are the major climatic

hazards in the Bay of Bengal in recent years.

Most of the fishers agree on the increase of recent meteorological hazards i.e. depressions and

cyclones in the fishing zone of the Bay of Bengal that increases fishing expenditures. Coastal fishers

enhances their religious activities when they realize a cyclone is imminent with danger signals and

adopt wait- and-see approach before leaving their homes for a cyclone shelter. Tropical cyclones

and tidal surges damage houses, boats, fish landing jetties, roads and other physical assets that make

the fishers workless. Fishing is the only livelihood option for coastal fishers; economic

diversification with other income generating activities does not mean anything to them. The big

question is how the fishers can sustain in warmer water with lot of disasters and without fishing

initiatives.



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1. Introduction

Heat condition of the Ocean in the form of Sea Surface Temperature (SST) is one of the most

important variables used in Climate Change monitoring programs and is often related to other

variables such as sea level change, hurricane intensity, etc. (Vinogradova 2009). Despite being very

important in global and regional climate, Indian Ocean is the least studied Ocean of all and the Bay

of Bengal is even less understood. Being a maritime state Bangladesh and its climate is dominated

by the Indian Ocean monsoon and maritime climate over the Bay of Bengal. Atmospheric

depressions and cyclones, moisture and cloud from the ocean, and heat exchange between ocean and

the atmosphere have profound impacts on almost every aspects of Bangladesh from agriculture to

health, transportation to economy, literature to peoples livelihood to name a few horizons.

Tropical cyclones have a particularly important role in the countrys economy and lives. Bay of

Bengal is a potentially energetic region for the development of cyclonic storms; about 7% of the

global cyclonic storms are formed in this region (Gray 1968). Though many external and internal

factors (e.g., low-level relative vorticity, vertical wind shear, SST, mixed level depth, tropospheric

stability, mid-tropospheric humidity) are considered favourable for cyclonic storms (McPhaden et

al. 2009a), in particular for those forming in the Bay of Bengal (Yokoi and Takayabu 2010; Yu and

McPhaden 2011), SST is considered one of the major drivers of cyclogenesis (Krishna and Rao

2010) or of storm intensification (DeMaria and Kaplan 1994; Whitney and Hobgood 1997). SST is

discussed in the context of global warming related enhancement of cyclonic activity (Webster et al.

2005; Michaels et al. 2006; Emanuel 2007; Elsner et al. 2008) and their destructive power (Emanuel2005) however difficult, uncertain, and conflicting the results might be (Walsh 2004; Landsea et al.

2006; Knutson et al. 2010). Higher SST results in increased water vapour in the lower tropospeher

and thus fuels the convection (Krishna and Rao 2010). It is anticipated that in the process of global

warming cyclone activities may further intensify (Krishna 2009), thereby causing havoc to coastal

communities. Three elements associated with a cyclone are identified to cause destruction, namely,

(1) heavy and prolonged rain, (2) storm surge, and (3) very strong winds (Kotal et al. 2008). Under

the continued increase in green house gas emissions and global warming studying and

understanding SST and its trend are therefore critical for disaster preparedness and hazard evasion.

1CHAPTER

Sea Surface Temperature (SST)

and Tropical Cyclones



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2. Objectives

The central objective of this study is to examine the long term variability of Sea Surface

Temperature (SST) over the Bay of Bengal using satellite based SST record as a vital indicator of

changing climate in the maritime regime. It is anticipated that with changing climate and warming

sea, coastal waters are increasingly becoming or are likely to become hostile for small country

fishing boats used by the artisanal fishing communities. Distribution and frequency of tropical

cyclones including depressions are also intended to be studied to understand how coastal fishers

communities are likely to be affected by changes in the climate resulting in the so called rough

seas. Among other objectives is to examine the geographical and seasonal variations of

temperature.

3. Study Area

For studying the distribution and temporal variations of SST in the Bay of Bengal the area

bounded between 6 and 22 North latitudes, and between 80 and 95 East longitudes was selected

(Figure 1). Only the Bay of Bengal basin area is taken for observations and the Andaman Sea isexcluded. The bay is effectively divided into 16 one-degree latitude zones for further analysis. For

studying tropical storms the Andaman Sea in included as part of the Bay of Bengal because cyclones

generated in both of these basins create equally hazardous situations for coastal areas in surrounding

states.

4. Data source and data aggregation

Radiometric measurements by the Advanced Very High Resolution Radiometer (AVHRR)

sensor onboard Polar Operational Environmental Satellites (POES) namely, NOAA-7, 9, 11, 14, 16,

17 and 18 of the National Oceanic and Atmospheric Administration (NOAA), USA were used to

create SST fields using Pathfinder algorithm developed by University of Miami's Rosenstiel Schoolof Marine and Atmospheric Science (RSMAS) and the NOAA National Oceanographic Data Center

(NODC). Details of this dataset can be found in Kilpatrick et al. 2001. These SST products are

called Pathfinder SST. The latest major version Pathfinder 5 is a reanalysis of the AVHRR data

fields, and is distributed in global oceanic coverages at continuous 4x4 km2 cells. Daily, 8-day,

monthly, and annual average fields are distributed for the period 1985-2009. For this study Daytime

and Night time Monthly average fields for 300 consecutive months (25 years) from January, 1985

to December, 2009 were acquired from NOAAs data repository (ftp://ftp.nodc.noaa.gov). It has

been demonstrated that at least 17 years of temperature data is required for identifying slowly

evolving warming signals in lower atmospheric temperature record (Santer et al. 2011). It is

assumed that similar time scale would also be adequate to identify signal in sea surface temperature

which is more consistent in its variations than its atmospheric counterpart.

In addition to Pathfinder data, HadiSST from UKs Met Office Hadley Centre for Climate

Prediction and Research was also acquired for the Bay of Bengal on a space averaged monthly mean

basis for a longer period (1870-2011). This data is used to examine long term trends in SST.

HadISST compares well with other published analyses, capturing trends in global, hemispheric, and

regional SST (Rayner et al. 2003).



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Figure 1.Map showing the study area of the Bay of Bengal, and 263 randomly selected sampling points; bars onthe right and bottom axes show the relative distribution of points in each degree interval in Latitude and Longitude

respectively; the star at the centre shows the geographic centre of the points distribution, and the light coloured

ellipse is the area within 1 standard deviation from the centre. Also shown is a 1 degree grid over the study area

Several tiers of data aggregation were carried out to produce various derivative datasets for

subsequent analyses. First, Daytime and Night time SST (DSST and NSST) were averaged to create

Day-Night combined SST (DNSST) for 300 months during the period. Locations were discarded

where data was missing in either DSST or NSST dataset resulting in missing value in DNSST.

DNSSTym=(DSSTym+NSSTym)/2; where y=19852009, m=JanDec

Second, SST climatology datasets were created separately for Day, Night and Day-nightcombined (DSSTClim, NSSTClim and DNSSTClim). For each month, daytime climatology

(DSSTClim) was created by averaging 25 DSST of that month from the 25 year period. The

procedure was similarly carried out for NSST and DNSST for 12 months, such that-

DSSTClimm= Sum(DSSTm)/Count of valid data (max=25),

NSSTClimm= Sum(NSSTm)/Count of valid data (max=25), and

DNSSTClimm= Sum(DNSSTm)/Count of valid data (max=25)



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Third, corresponding climatology dataset was deducted from each DSST, NSST and DNSST

field to produce monthly daytime, night time, and combined SST anomaly (deviations from

seasonal normal) fields, such that-

DSSTAnomym= DSSTym DSSTClimm

NSSTAnomym= NSSTym- NSSTClimm

DNSSTAnomym= DNSSTym- DNSSTClimm

Finally, daytime, night time and day-night combined SST time series (DSSTTS, NSSTTS,

DNSSTTS) were created by combining all DSST, NSST, and DNSST datasets such that each time

series dataset contains 300 months monthly mean temperature record in sequence. Similarly,

daytime, night time and day-night combined SST Anomaly time series (DSSTAnomTS,

NSSTAnomTS, DNSSTAnomTS) were created by combining all DSSTAnom, NSSTAnom, and

DNSSTAnom datasets such that each time series dataset contains 300 months monthly mean

temperature anomaly (deviations from normal) record in sequence.

North Indian Ocean Tropical Cyclone data (1877-2010) was retrieved from International BestTrack Archive for Climate Stewardship (IBTrACS) of the National Climatic Data Center (NCDC),

NOAA which stores the best estimates of storm position and intensity at 6-hourly intervals on a

global scale, known as best-track data (Knapp et al. 2010). The data is available in the form of

track points for each recorded cyclone.

5. Software

Different software is used for preparing, pre-processing, analysing and presenting data and

analytical results. Several small computer programs are written in Matlab to batch process the

acquired SST datasets for subsequent image processing in ENVI, and GIS analyses in ArcGIS.

Statistical analyses are done in S-Plus. Microsoft Excel is used for various data aggregation andconversions.

6. Methodology

6.1 Preprocessing:

Bay of Bengal subsets from the Global oceanic SST coverages which were created for all

datasets and data aggregations as described in para 5, were carried out in Matlab environment using

scripts written for this purpose. Resulting datasets were saved on disk as band sequential binary/raw

files of floating point numbers and geographic reference information were written in separate header

files.

Indian Ocean tropical cyclone track points in ASCII file format was converted into GIS data

layer using Latitude and Longitude values in the record. Track lines GIS data layer is created from

the track points layer. Cyclons initiation locations are extracted from the points layer using the time

record to create a Cyclogenesis data layer. A Bay of Bengal subset is created from the Cyclogenesis

layer and is filtered further to include only the cyclones that occurred during the study period

(1985-2009). Track points and track lines are filtered to include only those records which

correspond to the filtered Cyclogenesis points.



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6.2 Sampling framework:

Acquired SST products and their processed derivatives are raster data and are very useful for

visual representations in the form of surfaces and maps. However, it becomes necessary to extract

data at discrete point locations for statistical analyses. Extracting data at each cell location (every

4x4 km2) for Day and Night for 300 months would result in very large tabular datasets perhaps

without additional statistical advantage, and would be cumbersome to manage in most statistical

software. Therefore, 263 random point locations within the stipulated study area are selected for

statistical analysis and plotting. These sampling locations were generated by ArcGIS using random

latitude and longitude values. Spatial distribution characteristics of the sampling points are

examined and are summarized in table 1, and shown in figure 1.

SST and SST anomalies for 300 months were gathered from the datasets at 263 point locations,

subsequently plotted and analysed for detection of seasonality and trend. SST climatology values

were also gathered for these sampling points for analysis of zonal seasonal patterns.

Table 1.Average Nearest Neighbour Summary of the sampling points

6.3 Zonal aggregation:

SST, SST anomaly and SST Climatology data was further aggregated based on the latitude of the

sampling point locations. Summary and aggregate statistics were thus generated for 16 one-degree

latitude zones of the Bay of Bengal.

6.4 Analyses/Examination of variability:

Seasonality in SST was examined using seasonal plots and associated values. Geographical

variability was examined using color maps, isotherms (figure 2) and data plots (figure 3). Long term

trend was examined using trend-line plots (figure 5) and Ordinary Least Square linear regressionco-efficients (table 3).

Cyclogenesis points and cyclone track lines are plotted monthwise on maps and associated data

is analyzed to examine the geographical and seasonal distribution of tropical cyclones. Annual

cyclone frequencies are plotted and modelled as a simple linear regression line as described by

Rydn 2011 to determine the long term trend in frequency, if any.

Number of points (N) :

Observed Mean Distance :

Expected Mean Distance :

Nearest Neighbour Ratio :

Z Score :

p-value :

Description of spatial distribution :

263

0.484372 degree

0.449936 degree

1.076535 degree

2.374493

0.017573 (Sig. at 95%)

Slightly Scattered, nearly random



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Average SST during the month of the formation of each cyclone is extracted from the day-night

combined SST time series data using the month and location of corresponding cyclogenesis

location. Henceforth, this temperature is described as the favourable temperature for

cyclongenesis.

Figure 2.Day-Night Combined SST Climatology (DNSSTClim) over Bay of Bengal in different months during

1985-2009, characterised by (a) well defined SST gradient in winter months (NorthEast monsoon?) (Nov-Mar); (b)gradient weakens or disappears in monsoon months

7. Results and Discussion

7.1 Seasonal SST variability:

Monthly mean SST climatology averaged over the period 1985-2009 shows strong seasonal

fluctuations in SST, particularly in the northern Bay of Bengal. Strong temperature gradient

develops over the Bay during the cooler months from November to March and the gradient weakens

or disappears during the southwest monsoon (figure 2).

Various physical factors including oceanic circulation, wind, river discharge (Sengupta and

Ravichandran 2001), nearshore upwelling or absence thereof, precipitation-evaporation, El

Nino-Southern Oscillation (Nagura and Konda 2007), Indian Ocean Dipole phenomenon (Rao et al.

2002) govern the distribution of temperature over the bay. For example, during the southwest

monsoon, forcing by inflow from BoB rivers increases SST by 0.5-1C along the northeast coast of

India. This is because coastal Kelvin waves driven by the Ganges-Brahmaputra River inflow

suppress coastal upwelling along that coast (Han et al. 2001).



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SST over the Bay of Bengal has distinct bimodal seasonal cycles with two warm and two cool

seasons in a year, one cycle being more prominent than the other (figure 3 & 4). Other authors

(Lakshmi et al. 2009; Murty et al. 1998) reported similar seasonal cycles. During the Winter

(January) SST remains low and reaches it maximum during the Summer (April-May) followed by a

secondary low in Monsoon (July-August) and secondary high in around October before it starts to

cool down again. There are, however, differences in this pattern at different latitudes (North toSouth) as regards (a) the size (amplitude) of the total annual variation, and (b) the timing of the

peaks and lows.

Seasonal range of monthly mean temperature is small at southern latitudes (about 2C at 6-7N)

and gradually increases towards the North attaining a large range of about 6C at 21-22N (figure

4). Also noticeable is the fact that higher latitude zones (near the coast) attain their peak temperature

in Summer about a month or more later than the lower latitudes (i.e., lagging behind); but warms up

about a month or so before high latitude zones in Autumn.

A summary of seasonal SST fluctuations over the study period in BoB is shown in table 2.


20

22

24

26

28

30

32

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Months

SST(C)

Mean

Max.

Min.

Mean+1

Mean-1

LEGEND

Figure 3.

SST Climatology

(DNSSTClim) distribution

in different months in the

Bay of Bengal based on

all available 4 km x 4 kmcells (outliers removed)

Figure 4.

Seasonal cycle of SST (DNSSTClim)

in different Latitude zones over the

Bay of Bengal, characterized by (1)

nearly semi-annual cycles, (2) higher

variation in high latitudes (6C at

21-22N) to lower variation in low

latitudes (2C at 6-7N); (3)

peaking lags in spring/summer at

higher latitudes and in autumn at low

latitudes 22

23

24

25

26

27

28

29

30

31


HigherLatitudes

LowerLatitudes

Months

SST(C)

6-7

9-10

20-21

19-20

18-19

17-1816-17

12-1314-15

21-22


19/63

7.2 Spatial (Geographical) SST variability

Northern Bay of Bengal close to Bangladesh coast is generally cooler than the southern Bay of

Bengal, particularly in winter. Strong zonal temperature gradient develops in the cooler months

during the Northeast monsoon (figure 2 & 4). Such strong zonal gradient becomes apparent from

November to March (23-24C at 21-22N; >28C at 6-7N). Zonal temperature gradient weakens or

disappears during the summer and wet months of southwest monsoon.

7.3 Long term SST trends

Variabilites of varying time scales from several years to decades are generally found in sea

surface temperature records; these are called long term oscillations. El-Nino Southern Oscillation

(ENSO) in the Pacific or similar events are often attributed to such inter-annual/decadal variations

in temperature. However, this technical study examines the 25 year SST record for identifying a

liner trend during this period.

Sea surface temperature is found to increase across the board in all 16 one-degree latitudes zones

over the Bay of Bengal studied (table 3). Day time and Night time SST are separately examined for

trends (figure 5). Night time SST is found to increase at a much faster rate than the Daytime SST

(figure 6). Night SST is for several reasons considered more reliable indicator of temperature than

daytime SST. It has increased by 0.30-0.48 Celsius since 1985 at rates between 0.0126 and0.0203 per year. Daytime SST is also found to have registered increase everywhere, ranging from

0.20 to 0.46 Celsius during the period, annual rates ranging from 0.0086 to 0.0191.

Like the seasonal variations, long term trend also exhibits zonal variations (figure 6). Both Day

and Night SST are increasing faster in a zone between 15N and 19N latitudes. This zone is

generally not the breeding ground for the deadliest cyclones in Bangadeshs history, but in recent

years at least one deadly cyclone, Aila in 2008, was formed here. Further warming in this zone may

translate into storms of similar strengths more often than was in the past.


Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Minimum

21.82

22.87

24.51

26.67

25.76

21.92

25.07

22.76

24.93

26.49

26.62

23.73

Maximum

28.79

29.04

29.70

31.10

30.92

30.92

30.83

29.96

30.03

30.33

29.58

29.02

Mean

26.93

27.27

28.43

29.68

29.72

29.13

28.49

28.25

28.69

29.12

28.61

27.57

Median

27.09

27.41

28.54

29.94

29.74

29.14

28.48

28.22

28.68

29.20

28.71

27.74

St.Dev.

1.03

0.97

0.76

0.60

0.28

0.36

0.35

0.35

0.40

0.37

0.33

0.78

Table 2.Basic statistics of the day-night combined SST climatology over the Bay of Bengal in different months

during 1985-2009 (after outlier removal)


20/63

Any changes in the seasonal patterns have also been investigated by analysing SST trends in

individual months. Results indicate that at the low and mid-latitude zones early summer temperature

is dropping while the late summer temperature is rising quickly. In other months and at other latitude

zones SST is consistently rising at a rate of about 0.02C per year (table 4; figure 7). Present

investigation cannot, however, ascertain the cause and effect relationship behind such exceptional

rates of change in SST in only a couple of months. Nevertheless, the implications of theseexceptional can prove to be very significant as regards shifting or widening of cyclone seasons.

Latitude Zones (N)

21-22

20-21

19-20

18-19

17-18

16-17

15-16

14-15

13-14

12-13

11-12

10-11

09-10

08-09

07-08

06-07

Day

0.0167

0.0130

0.0169

0.0164

0.0191

0.0150

0.0133

0.0120

0.0101

0.0086

0.0108

0.0110

0.0115

0.0138

0.0138

0.0167

Night

0.0176

0.0177

0.0182

0.0203

0.0180

0.0195

0.0182

0.0153

0.0144

0.0142

0.0143

0.0164

0.0126

0.0140

0.0135

0.0158

Day-Night Combined

0.0148

0.0163

0.0183

0.0187

0.0192

0.0170

0.0158

0.0140

0.0123

0.0117

0.0136

0.0143

0.0130

0.0151

0.0153

0.0185

Table 3.Trend of SST (anomalies) in degree Celsius per year in different latitudes of the Bay of Bengal during

1985-2009 (all positive values signify rising trends everywhere, and all models statistically significant at >99%)

Day-Night Combined rate is not the arithmetic average of the Day and Night time rates; it is independently

estimated from the Day-Night Combined dataset. Due to missing data in Day and Night datasets in mutually exclusive

locations, the resulting aggregated combined time series itself is not a simple average of Day and Night time series.

High Anomali es Smooth line Trend l ine

Low Anomali es Smooth line Trend l ine

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

DaySSTanomalies

Years

High Anomalies Smooth l ine Trend l ine

Low Anomalies Smooth l ine Trend l ine

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

NightS

STanomalies

Years

Figure 5.Monthly mean Daytime SST anomalies (left) and Night time SST anomalies (right) from January, 1985

to December, 2009 in high and low latitudes of the Bay of Bengal; 12-months running mean smooth-lines and OLS

linear trend-lines show increasing trendsCoastal Fishers Livelihood in Peril | 13


21/63

0.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

SSTAnomaliesTrend(CperYear)

Latitude

22 21 20 19 18 17 16 15 14 13 12 11 10 09 08 07 06N

Mean + 1 s.d.

Mean - 1 s.d.

Mean Rate

Day Night CombinedFigure 6.

Trends of Daytime, Night time, and

Day-Night Combined SST Anomalies

during 1985-2009 at 1 degree intervals of

Latitude from 6-22N (inconsistent

occurrence of missing values in source

datasets prevent the Combined trends

from being the arithmetic averages of

Day and Night Trends) (2) Night

Temperature increasing faster in most of

the Bay Bengal except at low latitudes

Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

High

0.0140

0.0177

0.0167

0.0185

0.0271

0.0110

0.0160

0.0116

0.0100

0.0206

0.0260

0.0224

Mid

0.0197

0.0256

0.0172

0.0097

-0.0168

0.0167

0.0123

0.0245

0.0089

0.0139

0.0208

0.0293

Low

0.0163

0.0196

0.0195

0.0003

-0.0257

0.0156

0.0146

0.0360

0.0222

0.0142

0.0157

0.0260

Table 4.Trend of SST (anomalies) in degree Celsius per year in different months during 1985-2009

Figure 7.

Trend of Night time Monthly SSTAnomalies during 1985-2009 at

different latitude zones (May SST

falling while August SST rising fast at

mid and low latitudes, while other

monthly SSTs are rising at rates

between 0.005 and 0.027 degrees per

year)

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04


HIGH MID LOW

Mean Rate

Mean + 1 s.d.

Mean - 1 s.d.

SSTAnomaliesTrend(C

peryear)

Months



22/63

It is difficult to tell whether the trends found in this study are the true long term trends and not

due to inter-decadal oscillations. It is even more difficult to tell whether the rate of increase will

remain the same, cycle through decades, decelerate or accelerate. Any forecast of future climatic

conditions require complex modeling taking into account all possible climatic variables and

processes, and is therefore left outside the scope of this study. However, if the trends found in this

study are the true long term trends and the temperature increase continues in the future at similarrates we would expect a rise of Night time SST in the Bay of Bengal from 0.5 to 0.8 Celsius and

daytime SST from 0.35 to 0.72 Celsius by 2050.

Long term (1870-2011) space averaged SST data over Bay of Bengal (6-22N and 80-95E) was

also examined (table 5; figure 8). Statistically significant rising trends are found in all months SST

over this period at rates ranging from 0.00302 to 0.00481C per year with the 12 month average of

0.00401C. Rates found for 1985-2009 therefore indicate 4-5 fold faster increase in temperature in

recent years.

Though Belkin (2009) categorized the Bay of Bengal as one of the rapidly warming Large Marine

ecosystems with temperature increasing at 0.04C/year, the current rates in this study seem to be about

half that rate. Source of such large disagreement cannot be, however, ascertained; it may be arising

from differences in source datasets, model building, and/or geographic and time scale. Hurrel et al.

(1999) documented the inherent problems in several SST datasets; and measured differences amongdifferent datasets where they found disagreements in several aspects of the time series data. However,

since then many of the datasets have undergone substantial improvements in reanalysis and algorithms

(Kilpatrick et al. 2001; Rayner et al. 2003), and one can only expect better estimates in recent years.

Interpretation of trends based on short periods like the current time series may sometimes be

mislead by the presence of Long Term Persistence in the data which requires much longer time

series and specific analytical treatments thereof. Moreover, even when trends are found statistically

significant they may or may not bear real-term significance unless the findings are reconciled

against the physical system in question. Cohn and Lins (2005) put that in context as follows:

Figure 8.

Long term monthly (grey) and

annual mean (red) SST based on

HadISST data (1870-2011), overall

mean (horizontal straight line) and

long term trend (sloping straight

line) also shown

25.5

26.0

26.5

27.0

27.5

28.0

28.5

29.0

29.5

30.0

30.5

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

MonthlyMeanSST(C)

Years

Dec-Feb

Apr-Jun

Jul-Oct

Mar,Nov

Jan

30.2

Feb

29.6

Mar

30.4

Apr

35.9

May

36.8

Jun

42.0

Jul

45.6

Aug

47.6

Sep

47.6

Oct

48.1

Nov

45.3

Dec

42.7

Mean

40.1

Table 5.Long term (1870-2011) monthly and annual HadISST Trend (x104) over Bay of Bengal



23/63

Hydroclimatological time series often exhibit trends. While trend magnitude can be determined

with little ambiguity, the corresponding statistical significance, sometimes cited to bolster scientific

and political argument, is less certain because significance depends critically on the null hypothesis

which in turn reflects subjective notions about what one expects to see. From a practical

standpoint, however, it may be preferable to acknowledge that the concept of statistical significance

is meaningless when discussing poorly understood systems.

7.4 Seasonal and geographical distribution of Tropical Cyclones

Though not as intense as the Western Pacific, Northern Indian Ocean particularly the Bay of

Bengal is a basin of strong tropical cyclone activities. Each year 5-10 tropical storms originate in the

Bay of Bengal and the Andaman Sea, and every year a few of them make landfalls in eastern Indian,

western Myanmar and Bangladesh coasts (Alam et al. 2003). Some of them become super cyclones

(Category 5) and batter the coastal regions surrounding the Bay resulting in immense damage to

lives and properties. Bangladesh is particularly vulnerable to tropical storms and associated storm

surges, and was hit by devastating super cyclones many times in the past.

In this study the geographical and seasonal distribution of storms and depressions are visualized using

Geographic Information Systems, and is shown in figure 9 & 10. The general pattern of weaker southwest

monsoon cyclones forming at the northern bay and making landfalls across north-eastern Indian coast; and

stronger pre- and post-monsoon cyclones forming at the middle and southern bay and making landfalls

across Bangladesh, Myanmar and south-eastern Indian coasts is easily discernible without further analyses.

Frequency histograms reveal the seasonal occurrence of cyclones in different months and forming at

different latitudes (figure 10). February appears to be the safest month - no storm was formed in this

month during the 25 year period. April-May and October-November are the two seasons of enhanced

storm activities. The most damaging cyclones were brewed during either of these two seasons. Zonal

distribution (figure 10, left) reveals that cyclones and depressions are formed in all latitudes over the bay.

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!! !!

!!

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IndiaMyanmar

Sri Lanka

Bangladesh

Indonesia

Two

Giri

Ward

Aila

Sidr

Mala

1991Laila

Bijli

Nisha

Akash

Rashmi

Nargis

Fanoos

Khai-Muk

!! Jan

!! Mar

!! Apr

!! May

!! Jun

!! Jul

!! Aug

!! Sep

!! Oct

!! Nov

!! Dec

IndiaMyanmar

Sri Lanka

Bangladesh

Indonesia

Sidr

Bijli

1991

Mala

Laila

Two

Fanoos

Giri

Ail

a Ak

as

h

Rashmi

Nargis

Khai-Muk

Ward

Nisha

Figure 9. Geographic and monthly distribution of the origins of major tropical cyclones (left) and

their tracks (right) in the Bay of Bengal (1985-2009) characterized by concentration of monsoon storms

in the northern bay



24/63

Landfall direction from the location of cyclones origin is analysed, and summarised in a rose

diagram (figure 11). It is visible that the southwest monsoon cyclones generally make landfalls

across Orissa coast, India; winter cyclones make landfalls across Madras and Vishakhapatnam

coasts, India; and the rest which are generally the stronger storms can hit anywhere around the bay

from India to Bangladesh to Myanmar.

7.5 Tropical Cyclone Trend

Tropical cyclones during the last 25 year period have registered an increase in annual frequency by

0.0492 cyclones per year (figure 12). During this period Bay of Bengal has produced on average 5.48

storms per year (figure 10, right) or once every 9.49 weeks. With an increasing rate above, we may

experience a frequency of 7.35 storms per year or once every 7.08 weeks by 2050.

0

2

4

6

8

10

12

14

22 21 2 0 19 18 17 16 1 5 14 1 3 12 11 10 9 8 7 6N

Latitude Zones

NumberofCyclones

Months

Mean

Mean

0

5

10

15

20

25

30

35

Jan Fe b M ar A pr May Jun Jul A ug Se p O ct Nov Dec

Figure 10.Number of occurrence in different latitudes (left), and annual frequency and trend (right)

of tropical cyclones in the Bay of Bengal during 1985-2009

Figure 11.

Relative frequency of storm landfalls indifferent directions in 5 bimonthly

periods during 1985-2009 (total 95

storms), roses are placed at the

approximate mean geographic centres of

cyclogenesis locations (placement

slightly adjusted to avoid overlaps),

length and direction of the petals

indicate the relative frequency and

direction of landfall location from the

location of cyclogenesis; radii of the

circles show approximate mean

direction; storms dissipated in the sea

are not shown

Jun-Jul

Apr-May

Aug-Sep

Oct-Nov

Dec-Jan

IndiaMyanmar

Sri Lanka

Bangladesh

Indonesia



25/63

Sing et al. (2001) has found that cyclogenesis has been doubled during a 122 year period,

particularly during November and May, the peak seasons of severe cyclones; and opined that the

coastal states including Bangladesh are becoming more prone to severe cyclones during these

months. They also noted that there has been a 17%-25% increase in the intensification of storms

during November from disturbance to cyclone, and from cyclone to severe cyclones. Krishna

(2009) in a recent study of the northern Indian Ocean cyclones found that the intensification of

tropical cyclones to severe cyclonic strengths is becoming highly likely than was thought before.

Stronger warming resulting in weakening of vertical wind shear may lead to development of severe

cyclones even in summer monsoon months, when normally weak cyclones and depressions are

formed due to ventilation in the troposphere (Yang et al. 2011). Khan et al. (2010) reported an

increase in severe and very severe cyclonic storms with increasing SST in the northern Bay ofBengal (10.5-21.5N; 80.5-97.5E). In contrast to the general trend, however, cyclones are reported

by some to be in decline during the southwest monsoon (Mandke and Bhide 2003; Jadhav and

Munot 2009), a season important for northeast Indian coast.

7.6 Favourable Temperature for cyclogenesis

An attempt was made to examine prevailing SST at the time of the genesis of the storms which

may be described as favourable temperature for cyclogenesis. SST at the location of origin of 97

storms could be extracted from the day-night combined SST time series data (DNSSTTS); for the

rest of the storms there was no SST value at the location of storms origin in that month due to

sustained cloud cover. Storms are found to initiate at prevailing temperature between 27.53C and30.68C, with an average of 29.07C. Basic statistics of favourable temperature (day-night

combined SST) for cyclogenesis in different months are given in table 3 and figure 13.


Figure 12.

Annual frequency and trend

of tropical cyclones in the

Bay of Bengal during

1985-2009

b= 0.0492

0

2

4

6

8

10

12

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

CycloneFrequency

Years


26/63

Table 6.Favourable temperature (day-night combined) for cyclogenesis in different months in the Bay of Bengal

during 1985-2009

Yu and McPhaden (2011) found favourable SST and SSHA in the path of super cyclone Nargis

which followed the path and made landfall in Myanmar. Khan et al. (2010) found 27C as the

favourable temperature for the formation of severe and very severe cyclonic storms, and reported

their decline above 29C. Their findings compare well with the lower bound of the current study, but

other conclusions cannot be compared because they did not report monthly ranges of favourable

temperature.


Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Overall/Total

Minimum

28.58

-

29.78

28.95

28.65

28.05

27.53

27.90

28.43

27.90

27.53

27.60

27.53

Maximum

28.58

-

29.78

30.19

30.41

30.68

29.18

29.48

30.08

29.96

29.78

29.25

30.68

Average

28.58

-

29.78

29.63

29.45

29.72

28.40

28.76

29.34

29.04

28.79

28.30

29.07

Std.Dev.

-

-

-

0.47

0.60

0.72

0.68

0.60

0.59

0.50

0.62

0.55

0.70

With SST

1

0

1

5

11

12

4

6

6

28

14

9

97

All

1

0

1

5

13

14

6

9

6

28

18

9

110

SST in the month of Cyclogenesis (C) Number of Storms

Figure 13.

Favourable temperature

for cyclogenesis in different

months in the Bay of Bengal

during 1985-2009

Mean

Mean+1

Mean-1

Climatology

Favourable SST

Months

26

27

28

29

30

31

NightSST


Median

25%

75%

Min

Max

Outliers

LEGEND


27/63

8. Limitations

Satellite observation of SST are based on infrared radiation leaving only a thin film of the sea

surface no thicker than a millimetre, which renders it extremely difficult to validate the result with

ground truth data, and it is for this reason why the SST may deviate to some extent from the temperature

of the water column bulk. Hurrel et al. (1999) documented the inherent problems in several SST

datasets; and measured differences among different datasets where they found disagreements in several

aspects of the time series data. Murty et al. (1998) suggested the necessity of improved algorithm for

SST computation particularly under cloud cover. Bhat et al. (2004) has suggested improved performance

of SST derived from Tropical Rainfall Monitoring Mission (TRMM) microwave imager data, but

TRMMs temporal coverage is still not adequate to conduct a long term study.

9. Conclusion

Tropical cyclones or the so called atmospheric heat engines gather heat energy from the warm sea

water and reinforce their momentum by gaining more heat and moisture as they travel through warm

areas of the sea (Terry 2007). Therefore, we experience two cyclone seasons in Bangladesh one inApril-May and the other in October-November, when the sea surface temperature remains relatively

high. The cyclone seasons in the Bay of Bengal are likely to widen further as the cooler months too

become warmer. Moreover, as the usually cooler high latitude zones get warmer, cyclones will get larger

replenishment area for gaining heat energy, thus increasing the risk of cyclones at the coast. It has been

demonstrated that strong SST front on her path helped the deadly super cyclone Nargis retain her full

strength and she was guided by the SST front (Yu and McPhaden 2011) until making landfall in

Myanmar in 2008 leaving a hundred thousand dead.

The rapidly warming zone between 15 and 19N latitudes can appear to be a source of danger as

storms traveling across this zone will have access to more heat energy and moisture to remain strong, or

to become even stronger. This zone can even become a potential breeding ground for strong cyclones. Itcan be noted here that the devastating storm Aila (2009) which battered a large part of Bangladesh coast

was originated in this zone (figure 8). Probable linkage of increased SST with livelihood of coastal

fishers communities is shown schematically in figure 14.


Figure 14.

Probable linkage of increased

SST with livelihood of coastal

fishers communities

Warming ofCooler Months

Extended

Cyclone Seasons

Warming ofCooler Regions

MoreWater Vapour

More

Cyclones

MoreStormy Days

StrongerCyclones

FewerSea-going Days

DECLINING LIVELIHOODUNCERTAINTY

Effect on Fish Biology/Migration/Availability?

Increase in Sea Surface Temperature

New CycloneForming Regions


28/63

However less obvious it might be, more difficult weather conditions for small scale fishers arise

from the so called rough seas characterised by windy condition and wavy sea surface, or high seas.

Much of the wind and as a result waves are direct consequences of atmospheric convection cells of

varying magnitudes which in turn are driven by temperature gradient between two places at sea, or

between the sea and the land. Changing regime of temperature at sea is likely to bring about changes inthe local weather and wind/wave system which may pose additional hazard for artisanal fishers;

nevertheless, the nature of change cannot be predicted without modelling those processes.

9.1 Recommendations

Several important aspects of distribution and variability in Sea Surface Temperature (SST) have

been examined and documented in this study. But it cannot be considered a comprehensive account

unless some very relevant questions are answered and variability in SST is explained in terms of

ocean-atmosphere interactions within a broader framework of Indian Ocean maritime climatology.

Bay of Bengal unlike most tropical oceans is dominated by enormous discharge of fresh water fromthe rivers; therefore salinity plays a dominant role in determining Sea Surface Height Anomaly (SSHA).

Cyclone heat potential is generally computed from SSHA which is not entirely governed by SST in the

Bay of Bengal (Yu and McPhaden 2011). It is therefore suggested to be not relying entirely on SST and

be cautious when interpreting results based on SST alone. Mandke and Bhide (2003) have noted that

despite the persistence of favourable SST cyclone activity had dropped during a period they studied

because other atmospheric parameters remained unfavourable. Any future assessment of cyclonic

potential and its trend over Bay of Bengal should take into account other oceanic-atmospheric

parameters.



29/63

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1. Introduction

The worlds fisheries provide more than 2.6 billion people with at least 20% of their average

annual per capita protein intake (FAO 2007). As the planets climate changes so too will

populations, species and ecosystems, with profound consequences for fisheries change (Edwards et

al. 2002). Most of the worlds fishers live in developing countries and work in small-scale fisheries

(SSF; those that work from shore or from small boats in coastal and inland waters; Allison and Ellis

2001). These fisheries make important but poorly quantified contributions to national and regional

economies, and to the food security and development of many millions of people (UNDP 2005).

Bangladesh is one of the most disaster prone countries of the world and here climate change events

are considered as social fabrics. Almost every year, the country experiences disasters such as tropicalcyclones, tidal surges, coastal erosion, floods, and droughts. The Ganges-Brahmaputra-Meghna River

basin is one of the most populous river basins in the world. The rivers originate in the Himalayas,

flows generally southeast wards for about 2500 km, eventually emptying into the Bay of Bengal

through Bangladesh. Flood and drought in the Ganges-Brahmaputra-Meghna may affect, directly or

indirectly, the fate of nearly one-sixth of the population of the world.

More than 3.5 million coastal peoples livelihoods directly or indirectly depend on fisheries and

related activities. The coastal fishers are poor; however their economic hardship is most likely to be

aggravated under climate change. It is recognized that sea surface temperature (SST) in the Bay of

Bengal shows an increasing trend in all the seasons. The increasing SST fulfils one of the major

preconditions of the formation of an increased number of depressions and low pressure systems inthe Bay of Bengal. Increasing numbers of low pressure system means that for an increasing number

of days per annum the sea will be rough along with high tides along the shore a change in the

coastal environment which will hinder traditional fishing activities in the open sea. In simple terms,

poor fishers will have lesser number of active days, lesser amount of catch per annum and perhaps

lesser income (in terms of both income opportunities and lesser catch). Those of whom would try to

minimize the apparent loss by defying warnings related to rough sea episodes and taking chances,

they might have to risk their lives frequently.


2 CHAPTERMeteorological hazards

in fishers livelihoods


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This study applied the Sustainable Livelihood Approach (SLA) in an effort to understand

fishing community resilience with the level of dependency upon the available assets. SLA provides

a way of thinking about livelihoods of poor people in the context of vulnerability (DFID 1999). The

application of SLA in the form of climate change adaptation helps researchers and practitioners

identify pressing constraints and positive strengths of climate resilient livelihoods in coastal areas

with overlaps between micro and macro links. According to the SLA model developed by DFID(1999), the framework compromises three components: livelihood assets (natural, financial, social,

human and physical), vulnerability context (vulnerability analysis) and structure and process

(institutional analysis) (figure 1). SLA has seldom been applied to field situations especially in the

field of fisheries (Hossain et al. 2012; Iwasaki et al. 2009; Hossain et al. 2007; Allison and

Horemans 2006; Allison and Ellis 2001).

Vulnerability

Vulnerability is typically defined as a combination of the extrinsic exposure of groups or

individuals or ecological systems to a hazard, such as climate change, their intrinsic

sensitivity to the hazard, and their lack of capacity to modify exposure to, absorb, and

recover from losses stemming from the hazard, and to exploit new opportunities that arise in

the process of adaptation (Adger et al. 2005; Brooks et al. 2005; Smit and Wandel 2006).

Vulnerability to climate change depends upon three key elements: exposure (E) tophysical effects of climate change, the degree of intrinsic sensitivity of the natural resource

system or dependence of the national economy upon social and economic returns from that

sector (S), and the extent to which adaptive capacity (AC) enables these potential impacts to

be offset (Adger 2000; IPCC 2001).There are no objective, independently derived measures

of exposure, sensitivity, or adaptive capacity, and so their relevance and interpretation

depend on the scale of analysis, the particular sector under consideration and data

availability (Turner et al. 2003; Sullivan and Meigh 2007).


Human

Soc

ial

Phys

ical

Financial

Natural

Laws

Policies

Culture

Institutions

PROCESSES

Figure 1.Sustainable livelihoods framework (DFID, 1999)

VULNERABILITY

CONTEXT

SHOCKS

TRENDS

SEASONALITY

LIVELIHOOD ASSETSTRANSFORMINGSTRUCTURES &

PROCESSES

STRUCTURES

Levels ofgovernment

Privatesector

LIVELIHOOD

STRATEGIES

Inorderto

achieve

LIVELIHOODOUTCOMES

More income

Increasedwell-being

Reducedvulnerability

Improved foodsecurity

More sustainableuse of NR base

Influence

& access


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Adaptation

Adaptation has been defined as the adjustments in ecological, social, and economic

systems in response to actual or expected climatic stimuli and their effects or impacts, which

moderates harm or exploits beneficial opportunities (IPCC 2007). The conceptual

framework (Figure 2) for this report reflects the literature on adaptation and addresses the

following questions raised by Smit et al. (2000), Smit and Wandel (2006): (i) Adaptation to

what? (ii) Who or what is adapting? (iii) What adaptation strategies are available? A fourth

question has been added in this investigation: How good is the adaptation strategy?

In answering the first question, Adaptation to what?, our case study concerns adaptation

to depressions and cyclones. With regard to Who is adapting?, we examine actors at the

community level - male and female fishers. In the coastal areas, mens traditional roles and

knowledge in fishery resource management and food security are considered crucial

determinants of the household food security status. Gender is therefore an important factor

in assessing adaptation options in coastal Bangladesh, where men are responsible for

fishing, landing, transporting and trading, while women are responsible for drying, saltingand coking. Since men play a dominant role in individual capture fisheries production in

Bangladesh, men are likely to bear the primary responsibility for adaptation during

depressions and cyclones - including finding alternative ways to feed their family.


Figure 2.

Conceptual framework showing climate related

hazards that are adapted to, groups that adapt,

adaptation strategies and evaluation criteria of

adaptation strategies (modified from Smit et al. 2000)

Adaptation to what?

Climate related hazards

Tropical cyclone

Tidal surges

Depressions

Who adapts?

Male /Female

Fisher

Farmer

Trader

How good is the

adaptation strategy?Evaluation of strategies

Positive score

Negative score

No impact

What adaptation strategies are available?

Cyclone Depression

Fishing boat, net

Fish landing jetty

Aquaculture pond

Cropland

Saltpan

Post-harvestprocess

Fishing boat,net

Aquaculture pond

Saltpan


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2. Study Sites

Ten coastal and island Upazilaswere selected for fishers survey in the study. Four sites

were from the near shore islands namely Hatiya, Sanwdip, Kutubdia and Maheshkhali. The

remaining six sites included the exposed coastal areas of Ramgati, Sitakinda, Chittagong

city, Anwara, Coxs Bazar and Teknaf. Location of the survey sites are shown in figure 3.

3. Materials and Methods

The sampling and data collection methodology for the fishers household survey is

shown in figure 4. From each site one fishers village was selected, where 80-90% of thepopulation involved fully in fishing and fishing related activities. In general, people in these

villages are landless and a majority live in khasland (government owned land). The study

sample consisted of 50 household heads in each site for a total sample size of 500 household

(HH) heads in 10 sites. The household heads were selected from each village following a

stratified random sampling procedure.


Figure 3.Geographical location of the study area with data collection sites


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Both primary and secondary data were used in the study. The primary data were taken from

fishers households through the survey of household head. The sample households were interviewed

using open and close-ended questions. The first part of questionnaire included personal and family

level data, types of fishing crafts and gears with fishing duration and catch as well as market price

and fishers share to that price. The second part of the questionnaire addressed trends of climatic

changes with effects on marine environment, fisheries and fishers livelihood. The additional partreferring attachment for each questions addressed fishers specific comment and perception on

climate change. Fishers climate change vulnerability scoring/ranking has described in table 1.

A detailed questionnaire was prepared for the fishers household survey and was field tested.

Five team of data collectors (two members in each team) were formed and trained. Thequestionnaire is shown in Appendix.

Data collection took place from 25th February to 15th March 2012.

Databases were developed inMS Exceland SPSS softwarefor entering and storing data, which

were then checked and crosschecked before analysis.


Figure 4.

Sample selection methodology of

the fishers household survey

along the coast of Bangladesh

-

-

-

Scores

1

2

3

4

5

6

Class

No impact/ change

Minimum

Moderate

Maximum

Massive

Unknown

Description

The interviewee feel no change/impact during the professional period (20-30 years)

The interviewee experienced least or negligible changes/impacts during the

professional period (20-30 years)

The interviewee experienced obvious changes/impacts during the professional period

(20-30 years)

The interviewee experienced extensive changes/impacts during the professional

period (20-30 years)

The interviewee confirm unbearable wide changes/impacts during the professional

period (20-30 years)

The interviewee dont know the answer of the question

Table 1.Fishers climate change vulnerability scoring/ranking in the Bay of Bengal coast of Bangladesh

Selected sites

Fishers villageFish landing

centerFish trading

center

Random survey

of 50 HH heads

Data collection

methods

Ques

coastal fishers livelihood in peril sea surface temperature (sst) and tropical cyclones in...

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