thesis - 4214

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Welcome to Thesis Presentation

Presented ByAparna Barman

Roll.: 4214; Session: 2013-14Registration No.: Ha-2156Department of Fisheries

University of Dhaka

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Vulnerability of Fisheries to Climate Change in Bangladesh: A Composite

Index Approach

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Introduction Objectives Methodology Results and discussion Conclusions

Outline

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Rational Global climate is changing

(IPCC 2014) The fisheries sector is

considered amongst the worst affected by climate change (FAO 2012)

Bangladesh has been identified as extremely vulnerable country to climate change impacts (IPCC 2007; Met Office 2011; World Bank 2013)

Figure 1: Vulnerability of Fisheries to climate change at Global scale (Source: Allison et al. 2009).

Fisheries sector of Bangladesh has been identified as the most vulnerable to climate change in the World (Allison et al. 2009)

Introduction

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What is climate change? Any Change in climate over time, whether due to natural variability

or as a result of human activity (IPCC 2007)

Vulnerability? The degree to which a system is susceptible to or unable to cope

with adverse effects of climate change (IPCC 2001)

The attribute of vulnerability is the combined effect of exposure, sensitivity and adaptive capacity, where - Exposure: the nature and degree to which a system is exposed to

significant climate variations Sensitivity: the degree to which a system is affected either adversely or

beneficially Adaptive Capacity: the ability of a system to adjust to climate change

Introduction

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Climate change impacts on fisheries

Climate Variables

• Temperature variation• Rainfall variation• Sea level rise• Land erosion• Flood• Cyclone• Storm surge etc.

Source: Daw et al. 2009

Introduction

Impacts on fisheries

• Change in yield• Change in species distribution• Increased variability of catches• Change in seasonality of

production• Damaged infrastructure• Damaged gears• Increased danger at sea• Flooding of fishing

communities etc.

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Research gap Bangladesh fisheries

about 3.69% of national GDP (22.60% agricultural GDP)Provide 60% of animal protein intake (FRSS 2015) Supports almost 17.5 million people directly and indirectly

So fisheries is an important sector and vulnerability of fisheries to climate change need to be studied

Community level vulnerability and adaptation to climate change in some parts of Bangladesh has been studied (Brouwer et al. 2007; Ullah and Rahman 2014; Islam et al. 2014; Ahmed and Diana 2015)

But district level vulnerability of 64 district of Bangladesh was not studied

Introduction

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To determine the vulnerability of culture fisheries, capture fisheries and overall fisheries to the impact of

climate variability and change

Objectives

Composite vulnerability index approach Computes vulnerability indices by aggregating data for a set

of indicators Steps –

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Selecting indicators

Standardizing indicators

Calculating sub-indices

Calculating vulnerability

Methodology

Selecting indicators The degree of direct relevance to fisheries Availability district level data

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Methodology

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices 11

Exposure

• Variation in past maximum temperature

• Variation in past minimum temperature

• Future temperature • Variation in past rainfall• Future precipitation• Past sea level change• Storm surge• Past land erosion• Cyclone

Sensitivity

• Fish production (Culture/capture/ov-erall fisheries)

• Total water area (Culture/capture/ov-erall fisheries)

Adaptive Capacity

• Less poverty• GDP• Literacy rate• Electricity coverage• Housing structure• Monthly expenditure• Road• Primary school• Secondary school• Tube well

Indicators (Capture/culture/overall fisheries)

Methodology

Data collection

Bangladesh Meteorological Department (BMD) Bangladesh Bureau of Statistics (BBS) Met Office 2011 Center for Environmental and Geographic Information Service

(CEGIS) Fisheries Resource Survey System (FRSS) World Bank 2014 Yu et al. 2011 Khondker and Mehzab 2015

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Methodology

Calculating sub-indices

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Methodology

Indicators Calculations

Temperature• Standard deviation of 30 days of a month• Mean value of standard deviation of the months of

all years (varies from 1975-2014)

Rainfall• Standard deviation of 30 days of a month.• Mean value of standard deviation of the months of

all years (varies from 1975-2014)

Production • Standard deviation of production from the year 2003-2015

For rest of the indicators single value were collected and usedValues of the indicators were composited to create the values of

exposure, sensitivity and adaptive capacity

Calculating vulnerability:The values of exposure, sensitivity and adaptive capacity were

combined to create vulnerability

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Methodology

Where, V = VulnerabilityE = Exposure S = SensitivityAC = Adaptive Capacity

The final vulnerability values depends equally on all three components (i.e. exposure, sensitivity and adaptive capacity)

V = [ ( E + S ) – AC ]

Standardizing Indices Resulting values of exposure, sensitivity, adaptive capacity and vulnerability

were standardized Rescaled in a range 0 to 1

Methodology > Composite index > Selecting indicators > Indicators > Data collection > Calculating sub-indices > Calculating vulnerability > Standardizing indices

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Quartiles Categories

First quartile Low

Second quartile Moderate

Third quartile High

Fourth quartile Very high

Methodology

GIS software (ArcMap 10.3) used to map vulnerability in district level

Indexsi = Where

Indexsi = Normalized value Si = Actual valueSmax = The maximum value Smin = The minimum value

Categorized based on quartiles

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Districts Exposure Districts Exposure Districts ExposureDhaka 0.59 Kushtia 0.64 Naogaon 0.64Faridpur 0.55 Magura 0.61 Natore 0.64Gazipur 0.59 Meherpur 0.62 Pabna 0.64Gopalganj 0.5 Narail 0.61 Rajshahi 0.64Jamalpur 0.55 Satkhira 0.6 Sirajganj 0.59Kishoreganj 0.6 Barguna 0.51 Bandarban 0Madaripur 0.5 Barisal 0.54 Brahmanbaria 0.49Manikganj 0.59 Bhola 0.71 Chandpur 0.72Munshiganj 0.59 Jhalokati 0.48 Chittagong 0.59Mymensingh 0.6 Patuakhali 0.63 Comilla 0.49Narayanganj 0.59 Pirojpur 0.38 Cox's Bazar 0.43Narsingdi 0.59 Dinajpur 0.98 Feni 0.96Netrakona 0.6 Gaibandha 0.93 Khagrachhari 0.66Rajbari 0.55 Kurigram 0.98 Lakshmipur 0.88Shariatpur 0.78 Lalmonirhat 0.98 Noakhali 0.97Sherpur 0.6 Nilphamari 1 Rangamati 0.33Tangail 0.58 Panchagarh 1 Habiganj 0.7Bagerhat 0.41 Rangpur 0.98 Maulvibazar 0.7Chuadanga 0.62 Thakurgaon 0.98 Sunamganj 0.74Jessore 0.61 Bogra 0.55 Sylhet 0.74

Jhenaidah 0.61 Chapai Nawabganj 0.64Khulna 0.91 Joypurhat 0.55

Exposure value of culture fisheriesResults and Discussion

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Districts Sensitivity Districts Sensitivity Districts SensitivityDhaka 0.47 Kushtia 0.45 Naogaon 0.46Faridpur 0.5 Magura 0.45 Natore 0.46Gazipur 0.46 Meherpur 0.44 Pabna 0.49Gopalganj 0.47 Narail 0.44 Rajshahi 0.47Jamalpur 0.48 Satkhira 1 Sirajganj 0.44Kishoreganj 0.46 Barguna 0.46 Bandarban 0.45Madaripur 0.51 Barisal 0.67 Brahmanbaria 0.47Manikganj 0.47 Bhola 0.51 Chandpur 0.47Munshiganj 0.49 Jhalokati 0.46 Chittagong 0.56Mymensingh 0 Patuakhali 0.54 Comilla 0.59Narayanganj 0.48 Pirojpur 0.61 Cox's Bazar 0.82Narsingdi 0.57 Dinajpur 0.59 Feni 0.45Netrakona 0.47 Gaibandha 0.44 Khagrachhari 0.46Rajbari 0.46 Kurigram 0.45 Lakshmipur 0.46Shariatpur 0.47 Lalmonirhat 0.47 Noakhali 0.45Sherpur 0.45 Nilphamari 0.45 Rangamati 0.44Tangail 0.45 Panchagarh 0.47 Habiganj 0.41Bagerhat 0.6 Rangpur 0.46 Maulvibazar 0.42Chuadanga 0.45 Thakurgaon 0.47 Sunamganj 0.47Jessore 0.45 Bogra 0.47 Sylhet 0.41

Jhenaidah 0.45 Chapai Nawabganj 0.49Khulna 0.6 Joypurhat 0.45

Sensitivity values of culture fisheriesResults and Discussion

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Districts Adaptive capacity Districts Adaptive capacity Districts Adaptive capacityDhaka 1 Kushtia 0.34 Naogaon 0.33Faridpur 0.24 Magura 0.18 Natore 0.21Gazipur 0.59 Meherpur 0.19 Pabna 0.38Gopalganj 0.27 Narail 0.16 Rajshahi 0.45Jamalpur 0.31 Satkhira 0.32 Sirajganj 0.19

Kishoreganj 0.27 Barguna 0.2 Bandarban 0Madaripur 0.13 Barisal 0.58 Brahmanbaria 0.29

Manikganj 0.13 Bhola 0.18 Chandpur 0.39

Munshiganj 0.32 Jhalokati 0.28 Chittagong 1Mymensingh 0.51 Patuakhali 0.26 Comilla 0.81

Narayanganj 0.53 Pirojpur 0.28 Cox's Bazar 0.17Narsingdi 0.33 Dinajpur 0.35 Feni 0.42

Netrakona 0.13 Gaibandha 0.26 Khagrachhari 0.09

Rajbari 0.12 Kurigram 0.17 Lakshmipur 0.23

Shariatpur 0.13 Lalmonirhat 0.02 Noakhali 0.43

Sherpur 0.12 Nilphamari 0.08 Rangamati 0.13

Tangail 0.4 Panchagarh 0.04 Habiganj 0.13

Bagerhat 0.36 Rangpur 0.33 Maulvibazar 0.23

Chuadanga 0.18 Thakurgaon 0.12 Sunamganj 0.06Jessore 0.55 Bogra 0.35 Sylhet 0.49

Jhenaidah 0.36 Chapai Nawabganj 0.14Khulna 0.65 Joypurhat 0.22

Adaptive capacity values of culture, capture and overall fisheriesResults and Discussion

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Vulnerability of Culture Fisheries

Results and Discussion

Very high High Moderate Low05

101520253035

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74

Culture fisheries vulnerability

North Bengal districts are very highly vulnerable because of very high exposure and low adaptive capacity

Shahid and Behrawan (2008) found northern and northwestern districts highly exposed to drought

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Districts Exposure Districts Exposure Districts ExposureDhaka 0.36 Kushtia 0.59 Naogaon 0.39Faridpur 0.54 Magura 0.37 Natore 0.39Gazipur 0.36 Meherpur 0.37 Pabna 0.59Gopalganj 0.31 Narail 0.37 Rajshahi 0.59Jamalpur 0.54 Satkhira 0.57 Sirajganj 0.57

Kishoreganj 0.37 Barguna 0.51 Bandarban 0

Madaripur 0.31 Barisal 0.74 Brahmanbaria 0.3

Manikganj 0.36 Bhola 0.84 Chandpur 0.84

Munshiganj 0.56 Jhalokati 0.5 Chittagong 0.57

Mymensingh 0.37 Patuakhali 0.79 Comilla 0.3

Narayanganj 0.36 Pirojpur 0.43 Cox's Bazar 0.46Narsingdi 0.36 Dinajpur 0.6 Feni 0.79

Netrakona 0.37 Gaibandha 0.77 Khagrachhari 0.4

Rajbari 0.54 Kurigram 0.8 Lakshmipur 0.94

Shariatpur 0.88 Lalmonirhat 0.6 Noakhali 1

Sherpur 0.37 Nilphamari 0.61 Rangamati 0.2

Tangail 0.56 Panchagarh 0.61 Habiganj 0.42

Bagerhat 0.45 Rangpur 0.6 Maulvibazar 0.42

Chuadanga 0.37 Thakurgaon 0.6 Sunamganj 0.45Jessore 0.37 Bogra 0.54 Sylhet 0.45

Jhenaidah 0.37 Chapai Nawabganj 0.59Khulna 0.76 Joypurhat 0.34

Exposure values of capture fisheries Results and Discussion

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Districts Sensitivity Districts Sensitivity Districts SensitivityDhaka 0.19 Kushtia 0.59 Naogaon 0.23Faridpur 0.7 Magura 0.54 Natore 0.46Gazipur 0.22 Meherpur 0.51 Pabna 0.58Gopalganj 0.5 Narail 0.51 Rajshahi 0.36Jamalpur 0.68 Satkhira 1 Sirajganj 0.58

Kishoreganj 0.53 Barguna 0.45 Bandarban 0.39

Madaripur 0.51 Barisal 0.58 Brahmanbaria 0.42

Manikganj 0.6 Bhola 0.83 Chandpur 0.29

Munshiganj 0.44 Jhalokati 0.48 Chittagong 0

Mymensingh 0.65 Patuakhali 0.92 Comilla 0.16

Narayanganj 0.24 Pirojpur 0.54 Cox's Bazar 0.4Narsingdi 0.46 Dinajpur 0.41 Feni 0.24

Netrakona 0.37 Gaibandha 0.61 Khagrachhari 0.38

Rajbari 0.54 Kurigram 0.78 Lakshmipur 0.12

Shariatpur 0.58 Lalmonirhat 0.58 Noakhali 0.36

Sherpur 0.42 Nilphamari 0.62 Rangamati 0.5

Tangail 0.75 Panchagarh 0.39 Habiganj 0.29

Bagerhat 0.85 Rangpur 0.64 Maulvibazar 0.32

Chuadanga 0.58 Thakurgaon 0.28 Sunamganj 0.09Jessore 0.56 Bogra 0.34 Sylhet 0.63Jhenaidah 0.67 Chapai Nawabganj 0.57Khulna 0.92 Joypurhat 0.32

Sensitivity values of capture fisheries Results and Discussion

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Vulnerability of Capture Fisheries

Results and Discussion

Very high High Moderate Low05

1015202530354045

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39

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Capture fisheries vulnerability

Bhola district has the highest vulnerability.

Dhaka district has the lowest vulnerability.

Hasan et al. (2011) also found Bhola district vulnerable to climate change and affected by different disasters

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Districts Sensitivity Districts Sensitivity Districts SensitivityDhaka 0.43 Kushtia 0.46 Naogaon 0.44Faridpur 0.48 Magura 0.49 Natore 0.47Gazipur 0.45 Meherpur 0.49 Pabna 0.45Gopalganj 0.46 Narail 0.48 Rajshahi 0.44Jamalpur 0.48 Satkhira 0.39 Sirajganj 0.45

Kishoreganj 0.49 Barguna 0.47 Bandarban 0.48

Madaripur 0.48 Barisal 0.46 Brahmanbaria 0.47

Manikganj 0.48 Bhola 0.44 Chandpur 0.42

Munshiganj 0.46 Jhalokati 0.48 Chittagong 0.42

Mymensingh 0 Patuakhali 0.46 Comilla 0.32

Narayanganj 0.44 Pirojpur 0.47 Cox's Bazar 0.46Narsingdi 0.47 Dinajpur 0.48 Feni 0.44

Netrakona 0.48 Gaibandha 0.46 Khagrachhari 0.49

Rajbari 0.48 Kurigram 0.49 Lakshmipur 0.44

Shariatpur 0.48 Lalmonirhat 0.48 Noakhali 0.38

Sherpur 0.47 Nilphamari 0.49 Rangamati 1Tangail 0.46 Panchagarh 0.48 Habiganj 0.46

Bagerhat 0.38 Rangpur 0.48 Maulvibazar 0.51

Chuadanga 0.49 Thakurgaon 0.49 Sunamganj 0.64Jessore 0.31 Bogra 0.42 Sylhet 0.51Jhenaidah 0.5 Chapai Nawabganj 0.49Khulna 0.46 Joypurhat 0.46

Sensitivity values of overall fisheries Results and Discussion

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Vulnerability of Fisheries

North Bengal and coastal districts are very highly vulnerable

Results and Discussion

Shariatpur district has the highest vulnerability

Dhaka district has the lowest vulnerability

Very high High Moderate Low05

10152025303540

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95

Overall fisheries vulnerability

Islam et al. (2014) found fishing communities of Barguna district higher livelihood vulnerability

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Vulnerability of different districts vary according to their exposure, sensitivity and adaptive capacity.

Because of different level of exposure – the highest sensitivity does not always lead to the highest

vulnerabilitythe highest adaptive capacity does not always results lowest

vulnerability Vulnerability of a certain district is highly context-dependent A large number of factors influence vulnerability of a district

Conclusions > Implications > Future Research

Conclusions

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Implications Allow the policymakers to easily identify the most vulnerable

districts. Take necessary steps to decrease vulnerability and increase

adaptive capacity. Can easily understand where to spend the funding for climate

change in the context of fisheries sector. Finally the very highly vulnerable districts can learn from the low

vulnerable districts to reduce vulnerability.

Conclusions > Implications > Future Research

Conclusions

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Future Research The reasons of very high or low vulnerability at field level. Fish habitat level vulnerability to climate change. Species level vulnerability to climate change. Vulnerability of Bay of Bengal fisheries.

Conclusions > Implications > Future Research

Conclusions

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Thank you