Storm Surges and Coastal Erosion in Bangladesh - State of the System, Climate Change Impacts and 'Low Regret' Adaptation Measures

Storm surges and coastal erosion in Bangladesh - State of the system, climate change impacts and 'low regret' adaptation measures

By:

Mohammad Mahtab Hossain

Master Thesis

Master of Water Resources and Environmental Management

Leibniz Universität Hannover

Franzius-Institute of Hydraulic, Waterways and Coastal Engineering, Faculty of Civil Engineering and Geodetic Science

Advisor: Dipl.-Ing. Knut Kraemer

Examiners: Prof. Dr.-Ing. habil. T. Schlurmann Dr.-Ing. N. Goseberg

Submission date:

13.09.2012

Prof. Dr. Torsten Schlurmann Hannover, Managing Director & Chair 15 March 2012 Franzius-Institute for Hydraulic, Waterways and Coastal Engineering Leibniz Universität Hannover Nienburger Str. 4,

30167 Hannover

GERMANY

Master thesis description for Mr. Mahtab Hussein Storm surges and coastal erosion in Bangladesh - State of the system, climate change impacts and 'low regret' adaptation measures

The effects of global environmental change, including coastal flooding stem- ming from storm surges as well as reduced rainfall in drylands and water scarcity, have detrimental effects on countries and megacities in the costal regions worldwide. Among these, Bangladesh with its capital Dhaka is today widely recognised to be one of the regions most vulnerable to climate change and its triggered associated impacts.

Natural hazards that come from increased rainfall, rising sea levels, and tropical cyclones are expected to increase as climate changes, each seriously affecting agriculture, water & food security, human health and shelter. It is believed that in the coming decades the rising sea level alone in parallel with more severe and more frequent storm surges and stronger coastal erosion will create more than 20 million people to migrate within Bangladesh itself (Black et al., 2011). Moreover, Bangladesh’s natural water resources are to a large part contaminated with arsenic contaminants because of the high arsenic contents in the soil. Up to 77 million people are exposed to toxic Nienburger Str. 4 30167 Hannover, Germany arsenic from drinking water (Reich, 2011). Ph. +49 (0)511 762-19021 Given that background, the current MSc thesis should collect indicators as Fax +49 (0)511 762-4002 well as assess and critically discuss the present and likely future state of the coastal system and establish strategies as well as solutions in regard to [email protected] www.fi.uni-hannover.de storm surges and coastal erosion effects in Bangladesh.

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Master thesis description for Mr. Mahtab Hussein

Storm surges and coastal erosion in Bangladesh - State of the system climate change impacts and 'low regret' adaptation measures

In order to conduct a holistic overview of the state of the system, possible climate change impacts and possible 'low regret' adaptation measures with special emphasis on storm surges and coastal erosion in Bangladesh, the thesis should encompass and take into consideration the following aspects:

. Description of the country Bangladesh in regard to the theme of the thesis, i.e. geography and climate, rough overview of economy and demographic structure.

. In-depth review of governmental structure including an institutional mapping (mandate, experiences, capacities, etc.) of the most relevant institutions and governmental bodies, research institutes and universities in Bangladesh related to Disaster Risk Reduction (DRR) and the Hyogo Framework for Action (HFA) in straight accordance to Djalante et al. (2012) carried out recently for Indonesia. Where are the missing links and what needs to be organized or tackled additionally?

. Disaster history and experiences: When and what has been affected in the country and statistics of losses? What have been the lessons learned from these experiences? How and what experiences did federal government and local governments take action on creating “goog governance” structures in relation to climate change effects? What are the synergies in regard of the preparation and strategies to global change?

. Summary of (joint) research projects and international development initiatives in Bangladesh or in particular in Dhaka, what has been in focus and to which degree the results have been implemented into preparedness or adaptation programmes concerning DRR measures.

. Anticipated (direct) climate change impacts (Karim and Mimura, 2008; Madsen and Jakobsen, 2004), effects of SLR related to exposure and vulnerability of the people and assets. What elements are at risk?

. Anticipated (indirect) climate change related impacts concerning storm surges, and in consequences local sea states and wave action regarding

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Master thesis description for Mr. Mahtab Hussein

Storm surges and coastal erosion in Bangladesh - State of the system climate change impacts and 'low regret' adaptation measures

coastal erosion (now and then). Set-up and calibration of coastal see wave atlas by means of phase-averaging model (SWAN) in order to inte- grate current sea states and future projections of wave action to derive a trustworthy data base for the coastline and estuaries of Bangladesh.

. Tentative adaptation measures in relation to recent SREX report and possible solutions encompassing so-called "low-regret" adaptation measures (technically, politically and socially) recently defined within the IPCC- Special Report Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX)

From the work flow listed above, main scientific emphasis might be put on the part considering the coastal see wave atlas and is expected to account for about one third of the given working time of six months of the thesis. For completing this particular task apart from the other more literature review work, computational power as well as versions of SWAN, MATLAB and Ar- cGis will be made available for the student under supervision of the depicted examiners and advisor.

Three printed versions of the thesis have to be delivered along with the digital thesis and a well-arranged work data archive. The data archive has to contain all raw data, all used computational and MATLAB routines, simulation input files of all presented simulation runs together with the MATLAB post-processing routines and plots.

The arranging of the routines for later work and the documentation of the work flow is part of the work and will thus be taken into account for the grad- ing. After the thesis is delivered, it will be presented in a talk with following discussion of 30 minutes to the examiners and advisor.

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Master thesis description for Mr. Mahtab Hussein

Storm surges and coastal erosion in Bangladesh - State of the system climate change impacts and 'low regret' adaptation measures

Literature

Black et al., Migration as adaptation, NATURE, VOL 478, 2011, p. 449

Djalante, R., Thomalla, F., Sinapoy, M.S., Carnegie, M., Building resilience to natural hazards in Indonesia: progress and challenges in implementing the

Hyogo Framework for Action, Natural Hazards, 2012, pp. 1-25.

Karim, M.F., Mimura, N., Impacts of climate change and sea-level rise on cyclonic storm surge floods in Bangladesh, Global Environmental Change,

2008, Vol. 18 (3), pp. 490-500.

Madsen, H., Jakobsen, F., Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal, Coastal Engineering, 2004, Vol. 51 (4), pp.

277-296.

Murty, T.S., Flather, R.A., Henry, R.F., The storm surge problem in the Bay of Bengal, Progress in Oceanography, 1986, Vol. 16 (4), pp. 195-233.

Reich, S., Conflicting studies fuel arsenic debate, NATURE, VOL 478, 2011, p. 437

IPCC-SREX, Managing the Risks of Extreme Events and Disasters to Ad- vance Climate Change Adaptation, Summary for policy makers, 2011 http://ipcc-wg2.gov/SREX/

Date of issue: 15th March 2012 Closing date: 14th September 2012

1. Examiner 2. Examiner

Prof. Dr.-Ing. habil. T. Schlurmann Dr.-Ing. N. Goseberg

Advisor

Dipl.-Ing. Knut Kraemer

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Master thesis description for Mr. Mahtab Hussein

Storm surges and coastal erosion in Bangladesh - State of the system climate change impacts and 'low regret' adaptation measures

Seite 5/5 ACKNOWLEDGEMENT This thesis work has been done according to the requirement of the Master of Science degree of Water Resources and Environmental Management (WATENV), Faculty of Civil Engineering at Leibniz University Hannover, Germany. First of all, I give thanks to almighty Allah (God) who has given me the ability to complete the tasks. After that, I would like to express my sincere gratitude to my advisor, Dipl.-Ing. Knut Kraemer and examiners Dr.-Ing. N. Goseberg and Prof. Dr.-Ing. habil. T. Schlurmann for their guidance, valuable suggestions, and insightful comments on my work. Special thanks to Dipl.-Ing. Nils Kerpen, who provided me an electronic key to work at the Franzius CIP-Pool at any time. I would like to express my appreciation to Bangladesh Meteorological Department (BMD) and Bangladesh Water Development Board (BWDB) for their help with data provision which was very vital for the completion of the required tasks. I am grateful to World Meteorological Organization (WMO) for providing financial support and for giving me the opportunity to participate in the WATENV course. I wish to extend my sincere gratitude to my dearest friend Lojek Oliver, who generously made an effort to translate my abstract to German and Ellen Bonna who helped to check my grammatical errors.

Last but not least, I would like to express my thanks to my family, wife, children, relatives, friends and my parents for their everlasting support and patience. Thank you all, I am sincerely grateful. Mohammad Mahtab Hossain Leibniz University Hannover, Germany September 2012

ABSTRACT Bangladesh is vulnerable to several natural disasters. Tropical cyclones from the Bay of Bengal accompanied by storm surges are one of the major disasters in Bangladesh. For many years, coastal erosion has been becoming a regular natural phenomenon in Bangladesh. This study is mainly focused on the storm surges and coastal erosion hazard in Bangladesh with their adaptation measures considering the impact of current and future states of climate. Data has been collected from different internet sources and Bangladesh Meteorological Department (BMD) to model the coastal erosion by SWAN (Simulating of Waves Nearshore). SWAN is a widely used third generation wave model; however this study is the first for Bangladesh. The study concluded that, although Bangladesh has seriously addressed the Disaster Risk Reduction (DRR) and climate change issue there is still some commitment and capacities required to achieve DRR due to lack of resources and research work. Modeling by SWAN shows that the rate of erosion along the coast of Bangladesh increases with the increasing wind speed. The study also shows that the rate of erosion in 2030 and 2050 will be increased due to sea level rise but it will not be increased significantly. However, new areas in the coast will be inundated and affected by erosion. Key Words: Tropical Cyclones, Disaster, Storm Surges, Bay of Bengal, Adaptation, SWAN, Coastal Erosion.

ZUSAMMENFASSUNG Bangladesch wird durch diverse Umweltkatastrophen bedroht. Tropische Zyklone aus der Bucht von Bengalen begleitet durch Sturmfluten stellen mit eine der schlimmsten Katastrophen dar. Küstenerosion ist seit vielen Jahren ein Phänomen mit dem Küstenstaaten wie Bangladesch zu kämpfen haben. Diese Arbeit behandelt maßgeblich die Sturmfluten sowie die daraus resultierende Erosionsgefahr für die Küste in Bangladesch unter Einbeziehung vorhandener Schutzmaßnahmen unter derzeit vorherrschenden, sowie möglichen zukünftigen Klimaeinflüssen. Die Studie stützt sich maßgeblich auf eine Literaturrecherche. Daten wurden zum einen von verschiedenen Internetquellen sowie dem Bangladesh Meteorological Department (BMD) zusammengetragen, um Küstenerosion mit der Software SWAN (Simulating Waves Near Shore) zu modellieren. SWAN, ein Wellenmodell der dritten Generation, ist ein weit verbreitetes Programm das bereits zur Simulation von Seegangsverhältnissen in vielen komplexen Feld Studien auf der gesamten Welt eingesetzt wurde. Die Simulation für die Küste von Bangladesch die in dieser Studie durchgeführt wurde, stellt jedoch eine Primäre dar. Die Untersuchungen ergaben, dass Bangladesch sowohl Maßnahmen zur Katastrophenminderung umgesetzt hat als auch den Klimawandel ernst nimmt. Dennoch bestehen nach wie vor ein gewisses Restpotential zur Katastrophenminderung, welches jedoch aufgrund mangelnder Ressourcen nicht voll ausgeschöpft werden kann. Die Simulation mit SWAN zeigte einen Zusammenhang zwischen steigender Küstenerosion und zunehmenden Windgeschwindigkeiten auf. Des Weiteren erlaubt die Simulation eine Aussage über die zukünftige Entwicklung der Erosion zu tätigen. Demnach werden die Erosionsraten im Jahr 2030 sowie 2050 entlang der Küste aufgrund steigender Meeresspiegel nicht signifikant ansteigen. Allerdings deuten die Ergebnisse darauf hin, dass neue Gebiete im Inland überflutet werden und von Erosion betroffen sein könnten.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS…………………………………………………………...... i

ABSTRACT...... ii

ZUSAMMENFASSUNG...... iii

TABLE OF CONTENTS………………………………………………………………… iv

LIST OF TABLES ………………………………………………………………………. ix

LIST OF FIGURES……………………………………………………………………... x

LIST OF APPENDICES………………………………………………………………... xii

ABBREVIATIONS & ACRONYMS…………………………………………………... xiii

CHAPTER 1: INTRODUCTION…………………………………………… 1

1.1 Bangladesh ……………………….………………………………………….. 1

1.1.1 General Background……………………………………………………. 1

1.1.2 Geography and Climate of Bangladesh………………………………….. 1

1.1.3 Demographic, Economic, Social and Cultural Characteristics of Bangladesh………………………………………………………………………. 3

1.1.4 Governance Style of Bangladesh………………………………………... 4

1.2 Natural Hazards in Bangladesh………..………………………………….. 5

1.2.1 Cyclones and Storm Surges……………………………………………... 5

1.2.2 Floods………………………………………………………………….... 6

1.2.3 River Bank Erosion………………………………………………………. 6

1.2.4 Coastal Erosion ………………………………………………………….. 6

1.2.5 Earthquakes ………………………………………………………...... 6

1.2.6 Droughts ………………………….…………………………………….... 7

1.2.7 Tornados …………………………………………………………………. 7

1.2.8 Arsenic Contamination………………………………………………….. 7

1.2.9 Salinity Intrusion ………………………………………………………... 7

1.3 Climate Change and Sea Level Rise in Bangladesh...... 8

1.4 Objectives of the study work…………...... 9

1.5 Outline of the Report…………………..……...... 9 CHAPTER 2: PHYSICAL PHENOMENA AND DISASTER RISK REDUCTION ………………………….…………………….……………………. 11

2.1 Introduction ………………………………………………………………….. 11

2.2 Cyclone and Storm Surges ………………………………………………... 11

2.2.1 Introducing cyclones and storm surges...... 11

2.2.2 Classification of Cyclones …………………..………………………….. 12

2.3 Waves in Coastal Areas ……………...... 13

2.3.1 Introduction …………………………………………………………….. 13

2.3.2 Wind Generation in Coastal Areas……………………………………... 14

2.3.3 White-Capping………………………………………………………….. 14

2.3.4 Bottom Friction…………………………………………………………... 15

2.3.5 Depth-Induced (Surf) Breaking………………………………………….. 17

2.4 Terminology on Disaster Risk Reduction...... 18

2.5 Hyogo Framework for Action (HFA) 2005-2015………………………. 20 CHAPTER 3: CLIMATE CHANGE IMPACTS, DISASTER HISTORY (STORM SURGES) AND EXPERIENCES IN BANGLADESH …………………………………………………………………. 22

3.1 Introduction ………………………………………………………………….. 22

3.2 Experiences from the Past Disasters (Storm Surges)…………….…... 22

3.3 Climate Change Impacts in Bangladesh ……………….……………….. 26

3.3.1 Climate Change Observed in Bangladesh ………..…………………….. 26

3.3.2 Frequency and Intensity of Cyclone in Future in Bangladesh …………. 28

3.3.3 Intensity of Impacts on different sectors due to Climate Change …..…... 28

3.3.4 Actions in relation to climate change effects in Bangladesh ………….... 29

3.4 Bangladesh’s Exposure and Vulnerability to Natural Hazards ……... 31 v

3.4.1 Exposure in Bangladesh and Elements are at Risk …………………….. 31

3.5.2 Vulnerability to Hazard Risks ………………………………………….. 32 CHAPTER 4: IMPLEMENTATION OF DISASTER RISK REDUCTION PROGRAMMES - HYOGO FRAMEWORK FOR ACTION IN BANGLADESH ...... 34

4.1 Disaster Management System in Bangladesh ……………………….. 34

4.2 Institutional Mapping for Disaster Risk Reduction in Bangladesh ... 35

4.2.1 Institutional Linkages ……………………………………………….….. 35

4.2.2 Missing Links ……………………………………………………….….. 38

4.3 National progress on the implementation of the Hyogo Framework for Action...... 38

4.3.1 Implementation of HFA Priorities for Action in Bangladesh ………….. 38

4.3.2 Discussions and Recommendations on the Implementation of HFA in Bangladesh ………………………………………………………………….….. 43

4.4 Development Projects related to DRR in Bangladesh ………….…….. 46

4.4.1 Key Donor Engagements ……………………………………………….. 46

4.4.2 Situation of the Current Research ……………………………………….. 46

4.4.3 Development Projects Related to DRR in Bangladesh ………………….. 47 CHAPTER 5: MODEL SET-UP, CALIBRATION AND ANALYSIS OF EROSION ALONG BANGLADESH’S COAST ………………. 50

5.1 Introduction ………………………………………………………………….. 50

5.2 Available Data ………………………………………………………………. 50

5.2.1 Bathymetry ……………………….…………………………………….. 50

5.2.2 Tide and Current ………………………………………………….…….. 51

5.2.3 Water Level …………………………………………………………….. 51

5.2.4 Wind ……………………………………………………………………. 51

5.2.5 Waves ……………………………………..……………………………. 52

5.3 SWAN Model ………….………………………………………………….... 52 vi

5.3.1 Co-ordinate System in SWAN ……………………………………….... 53

5.3.2 Grid System in SWAN ……………………………………………….... 53

5.3.3 Boundary Conditions in SWAN ……………………………………….. 55

5.4 Overall Model Set-up …….……………………………………………….. 55

5.5 Sensitivity Analysis and Model Calibration …………..……………….. 56

5.5.1 Sensitivity Analysis…………………………………………………..…. 56

5.5.2 Model Calibration …………………….………………………….…….. 58

5.6 Model Application to calculate the Erosion along Bangladesh’s Coast ………………………………………………………………………………….. 59

5.6.1 Erosion at the Current Sea States ……………………………………….. 62

5.6.1.1 Discussion on the Erosion Scenarios for the Current Sea States……...... 62

5.6.1.2 Causes of Erosion in Coastal Waters……………………………………. 65

5.6.1.3 Analysis of erosions at different cross sections along the coast of Bangladesh …………………………………………………………..…………. 66

5.6.2 Comparison of Erosion Considering Climate Change …………………. 68

5.6.2.1 Comparison of Erosion at Current Sea State regarding Climate Change… 68

5.6.2.2 Change in rate of Erosion due to Climate Change ………………………. 70

5.6.2.3 Effects of SLR on Erosion ………………………………………………. 71 CHAPTER 6: ADAPTATION MEASURES FOR EXTREME EVENTS MANAGEMENT …………………………………………………. 72

6.1 Adaptation and Management for Changing Climate …………………. 72

6.2 Low Regret Adaptation in Bangladesh ………………………………….. 73

6.3 Costs of Adaptation Measures to Tropical Cyclones and Storm Surges …………………………………………………………………………..…….. 76 CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS…………………………………………….……….. 78

7.1 Conclusions ……………………………………………………………….… 78

7.2 Recommendations ……………………………………………………….… 79 vii

REFERENCES ……………………………………………..……………..…….. 81

APPENDICES ……..……………………………………………………….……. 86

LIST OF FILES IN CD…………………………………………………….……. 105

DECLARATION…..………………………………………………………….…. 106

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LIST OF TABLES Table 1.1: The population statistics for Bangladesh according to final census report (BBS, 2011)………………………………………………………………………..……..…………….. 3

Table 1.2: Economic status of Bangladesh (BTI, 2012)……………………………..………….. 4

Table 1.3: The inundation scenarios in Bangladesh due to sea level rise (Ali, 1996)…………... 9

Table 2.1: Classification of cyclones in South Asian Sub-Continent (RRCAP, 2001) ………... 12

Table 2.2: Classification of cyclonic disturbances presently in use by Bangladesh (WMO, 2010)...... 13

Table 2.3: The relative importance of the various processes in sea waters (Holthuijsen, 2007)

………………………………………………………………………..…………………….…. 13

Table 3.1: Trend of SLR along the coast of Bangladesh (Singh, 2001) …………………….… 27

Table 3.2: Impact of climate change on various sectors (MoEF, 2005) ………………………. 28

Table 3.3: Typical scenarios in coastal zone (BBS, 2011) ..…………………………..………. 33

Table 4.1: Some development projects that have been taken recently for disaster Management and climate change adaptation (AKP, 2010)…………………………………..……..…………….. 47

Table 4.2: Donor engagements and plans for medium to long-term (Year- 2022) disaster risk mitigation in Bangladesh (ISDR, 2009a) ………………………………………….……….. 48

Table 5.1: Season wise maximum daily wind speeds along Bangladesh’s coast during 2001-2011

………………………………………………………………………………………………..... 51

Table 5.2: Recommended discretizations for spectral grid in SWAN…………………..….….. 55

Table 5.3: The default settings in SWAN that have been used in this project…………………. 56

Table 5.4: Two boundary conditions for sensitivity analyses…………………………………... 57

Table 5.5: The formulas and other required constant values that were used in SWAN………... 60

Table 6.1: Adaptation cost to cyclone and storm surges by 2050 in Bangladesh (WB, 2010c)…. 76

LIST OF FIGURES Figure 1.1: Three coastal regions in Bangladesh…………………………..……..…………….. 2

Figure 1.2: Map of Bangladesh with some areas prone to a specific natural hazard..………….. 8

Figure 2.1: Storm surge (wunderground.com)…………………………………………………... 12

Figure 2.2: Transferring of wind energy into JONSWAP spectrum in deep and shallow water,

( 3.5 m, and = 20 m/s) (Holthuijsen, 2007)……………………………... 14

Figure 2.3: White-capping source term, in JONSWAP spectrum, in deep and shallow water,

( =3.5 m and (Holthuijsen, 2007)...... 15

Figure 2.4: The bottom friction dissipation influenced on JONSWAP spectrum, ( =3.5 m and (Holthuijsen, 2007) ……………………..………………….…………….…. 17

Figure 2.5: The influence of surf-breaking on JONSWAP spectrum, ( =3.5 m and (Holthuijsen, 2007)………………………………………………………………………….… 18

Figure 3.1: Monthly distribution of recorded storm surges (Cyclones) in Bangladesh during the period of 1584 to 2009 ……………………………………………………………………….………. 23

Figure 3.2: Season wise distribution of cyclones that hit Bangladesh in year: 1584 - 2009…... 23

Figure 3.3: Frequency of storm surges in Bangladesh in 10 year periods: 1890-2009 …….….. 24

Figure 3.4: Different type of disturbances that hit Bangladesh in the period: 1890-2009……... 25

Figure 3.5: Number of death due to super cyclonic storms that hit Bangladesh recently……... 25

Figure 3.6: Financial damages due to super cyclonic storms that hit Bangladesh recently …... 26

Figure 3.7: Bangladesh’s exposure and vulnerability to natural hazards (a) frequency of occurrence; (b) number of people died; (c) number of people affected; (d) vulnerability to cyclone hazard (Data from ISDR, 2009a; MoWCA, 2010) ………………………………………………………..... 31

Figure 3.8: Area exposed to the Bay of Bengal in Bangladesh (Appendix 3.2) ……………... 32

Figure 3.9: Comparions of population (a) density for whole country with coastal area only and (b) male to female ratio for whole country with coastal area only (BBS, 2011) …………….…... 33

Figure 4.1: Disaster management system in Bangladesh……………….…..……………..….. 35

Figure 4.2: Institutional (key governmental) map to reduce the risk of disaster in Bangladesh………………………………………………………….………………………..... 37

Figure 5.1: A graphical representation of bathymetry that is used in SWAN model…………... 50

Figure 5.2: Wind stations that were considered to calculate the rate of erosion and different channels along the coast of Bangladesh………………………………………………………………...... 52

Figure 5.3: Area, points, and buoys that were used in SWAN……………………………...….. 57

Figure 5.4: Comparison of SWAN outputs with forecasted data (a) at point-1; (b) at point-2 for Hs, (c) at point-1; (d) at point-2 for Tp, (e) at point-1; (f) at point-2 for wave direction……………..... 59

Figure 5.5: Cross sections that were considered for comparison and analysis of erosion ……... 61

Figure 5.6: Bottom level (a) along cross section A-A and B-B; (b) along cross section C-C…... 61

Figure 5.7: Comparison of the rate of erosion using different bottom friction model along cross section (a) A-A; (b) B-B …………………………………………...…………………………………..... 62

Figure 5.8: Erosion scenarios along the coast of Bangladesh at high tides for (a) 5 m/s western wind; (b) 5 m/s southern wind; (c) 10 m/s western wind; (d) 10 m/s southern wind; (e) 15 m/s southern wind; (f) 20 m/s southern wind; (g) 30 m/s southern wind …………………………………….……... 64

Figure 5.9: Wave orbital velocity with and without bottom friction along A-A (a) for 5 m/s wind; (b) for 30 m/s wind…………………………………………………………………….……….…... 65

Figure 5.10: Erosion at current state due to different wind, at high tides along (a) A-A; (b) B-B; (c) C- C; at Low tides along (d) A-A; (e) B-B; (f) C-C………………………………………………... 67

Figure 5.11: Comparison of the rate of erosion at current state and, in 2030 along (a) A-A; (b) B-B; (c) C-C; in 2050 along (d) A-A; (e) B-B; (f) C-C………………………………………….……… 69

Figure 5.12: Change in erosion due to 30 m/s wind considering SLR along (a) A-A; (b) B-B; (c) C- C………………………………………………………………………………………………... 70

Figure 5.13: Simplified model of landward coastal retreat under SLR (modified from UNEP, 2010)…………………………………………………………………………………….….….. 71

Figure 6.1: The approaches to adapt and manage for climate change (IPCC, 2012)…….….….. 72

Figure 6.2: Cyclone and Flood information flows in Bangladesh (modified from UNEP, 2010)……………………………………………………………………………………...….….. 74

Figure 6.3: Closure dam under construction at Jamuna river, Bangladesh (UNEP, 2010)…………………………………………………………………………………….………. 75

Figure 6.4: Plantation of vetiver along polder (Islam, 2003)……………………………..….….. 76

LIST OF APPENDICES

Appendix 3.1: Natural disasters (Cyclones/Storm Surges) in Bangladesh (Khan, 2012; SDC, 2010; RRCAP, 2001; Karim and Mimura, 2008; Murty et al., 1986; Ali, 1999; Choudhury et al., 1997; Shamsuddoha, 2008; BMD; Banglapedia; DMB)…………………………..……..…………….. 86

Appendix 3.2: Districts and Upazilas of Bangladesh’s coastal zone (MoEF, 2007)..……….….. 90

Appendix 3.3: Detailed damages by selected cyclones that hit Bangladesh recently (MoWCA, 2010; DMB)…………………………………………………………………………………………... 91

Appendix 3.4A: Population census in Bangladesh (BBS, 2011) ………………………….…... 92

Appendix 3.4B: Population census in Bangladesh (BBS, 2011)...... 93

Appendix 3.5: Population and household scenarios in the coastal area of Bangladesh (BBS, 2011)

………………………………………………………………………..…………………….…. 94

Appendix 3.6: Population and households vulnerable to the natural hazards (BBS, 2011)….… 95

Appendix 5.1: Tide levels that have been considered in SWAN model…………….…………. 96

Appendix 5.2: Number of days of wind blowing from a direction along the coast of Bangladesh for the period 2001-2011 (BMD) ..……………………………………………………………..………. 98

Appendix 5.3: The results of sensitivity analysis for different condition by using two boundary conditions (Table 5.4)………………………………..……………………..……..…………….. 99

Appendix 5.4: The data that is considered for the model calibration and comparison of the results at point- 1 & 2 …………………………………………………………………………….……….. 100

Appendix 5.5: SWAN calibration results and forecasting data at point- 1& 2 for the period 08.06.12 06:00 to 15.06.12 18:00………………………………………………………………………..... 101

Appendix 5.6: The data which is used for model application at current satate…………..….….. 101

Appendix 5.7: Significant wave height and wave period for different wind speeds and durations…………..………………………………………………………………………...….. 102

Appendix 5.8: A typical command file for SWAN computation………………………..….….. 103

Appendix 5.9: Critical bed shear of soil along the coast of Bangladesh (Barua et al., 1994)….. 104

Appendix 5.10: Data has been used for the future projections along the coast of Bangladesh…. 104

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ABBREVIATIONS & ACRONYMS

ADB Asian Development Bank AFD Armed Forces Division

BADC Bangladesh Agricultural Development Corporation BAU Bangladesh Agricultural University BBS Bangladesh Bureau of Statistics BCAS Bangladesh Centre for Advanced Studies BCCRF Bangladesh Climate Change Resilience Fund BCCSAP Bangladesh Climate Change Strategy and Action Plan BCS Bangladesh Civil Service BIDS Bangladesh Institute of Development Studies BIWTA Bangladesh Inland Water Transport Authority BIWTC Bangladesh Inland Water Transport Corporation BMD Bangladesh Meteorological Department BRAC Bangladesh Rural Advancement Committee BRRI Bangladesh Rice Research Institute BTRC Bangladesh Telecommunication Regulatory Commission BTV Bangladesh Television BUET Bangladesh University of Engineering and Technology BWDB Bangladesh Water Development Board

CARE Co-operative for Assistance and Relief Everywhere CC Climate Change CBA Community Based Adaptation CCA Climate Change Adaptation CCC Climate Change Cell CCDMC City Corporation Disaster Management Committee CCF Climate Change Fund CDM Comprehensive Disaster Management CDMP Comprehensive Disaster Management Programme CEGIS Center for Environmental and Geographic Information Services CIDA Canadian International Development Agency COP Conference of Parties of UNFCCC CPP Cyclone Preparedness Programme CPPIB Cyclone Preparedness Program Implementation Board CRA Community Risk Assessment CSDDWS Committee for Speedy Dissemination of Disaster Related Warning/ Signals

DAE Department of Agriculture Extension DANIDA Danish International Development Agency DC Deputy Commissioner

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DFID Department for International Development DG Director General DGoF Directorate General of Food DM Disaster Management DMA Disaster Management Act DMB Disaster Management Bureau DMC Disaster Management Committee DMIC Disaster Management Information Centre DMRD Disaster Management and Relief Division DMTATF Disaster Management Training and Public Awareness Building Task Force DNA Damage and Need Assessment DoE Department of Environment DoH Directorate of Health DoRR Directorate of Relief and Rehabilitation DPHE Department of Public Health Engineering DRR Disaster Risk Reduction/Directorate of Relief and Rehabilitation DU Dhaka University

EC European Commission ECNEC Executive Committee of the National Economic Council EGPP Employment Generation Programme for the Poorest EIA Environment Impact Assessment EOC Emergency Operation Centre EPAC Earthquake Preparedness and Awareness Committee ERD Economic Relations Division EU European Union

FFW Food for Work FFWC Flood Forecasting and Warning Centre FPOCG Focal Point Operation Coordination Group of Disaster Management FSCD Fire Service and Civil Defense

GFDRR Global Facility for Disaster Reduction Recovery GoB Government of Bangladesh GPWM Guidelines for Participatory Water Management GSB Geological Survey of Bangladesh

HFA Hyogo Framework for Action

ICDDR,B International Centre for Diarrhoeal Disease Research, Bangladesh ICTs Information and Communication Technologies IDB Islamic Development Bank IMDMCC Inter-Ministerial Disaster Management Co-ordination Committee

xiv

INGO International Non-Government Organization IPCC Inter-governmental Panel on Climate Change IUCN International Union for Conservation of Nature IWM Institute of Water Modeling IWRM Integrated Water Resource Management

JBIC Japan Bank for International Cooperation JICA Japan International Cooperation Agency

LACC Livelihood Adaptation to Climate Change LDC Least Developed Country LGD Local Government Division LGED Local Government Engineering Department LGI Local Government Institution

MDG Millennium Development Goal MoA Ministry of Agriculture MoD Ministry of Defence MoEd Ministry of Education MoEF Ministry of Environment and Forests MoFA Ministry of Foreign Affairs MoFDM Ministry of Food and Disaster Management MoF&P Ministry of Finance and Planning MoHA Ministry of Home Affairs MoHFW Ministry of Health and Family Welfare MoH&PW Ministry of Housing and Public Works MoI Ministry of Information MoLG&RD Ministry of Local Government, Rural Development and Cooperatives MoPME Ministry of Primary and Mass Education MoSh Ministry of Shipping MoS&T Ministry of Science and Information and Communication Technology MoWR Ministry of Water Resources MSL Mean Sea Level

NAPA National Adaptation Programme of Action NBR National Board of Revenue NDMAC National Disaster Management Advisory Committee NDMC National Disaster Management Council NEC National Economic Council NFI Non-food items NGO Non-Government Organization NLUP National Land-Use Policy NPDM National Plan for Disaster Management xv

NPDRR National Platform for Disaster Risk Reduction

OPEC Organization of the Petroleum Exporting Countries

PDMC Pourashava Disaster Management Committee PRSP Poverty Reduction Strategy Paper PWD Public Works Department PMO Prime Minister’s Office PSTU Patuakhali Science and Technology University

RB Bangladesh Betar RF Rainfall Station RRI River Research Institute RVCC Reducing Vulnerability to Climate Change project

SAARC South Asian Association for Regional Cooperation SIDA Swedish International Development Authority SLR Sea Level Rise SOD Standing Orders on Disasters SPARRSO Space Research and Remote Sensing Organization SST Sea Surface Temperature

TBM Tidal Basin Management TR Test Relief

UDMC Union Disaster Management Committee UzDMC Upazila Disaster Management Committee UK United Kingdom UNDP United Nations Development Programme UNFCCC United Nations Framework Convention on Climate Change UN/ISDR United Nations International Strategy for Disaster Reduction UP Union Parishad UzP Upazila Parishad

VGF Vulnerable Group Feeding

WB The World Bank WL Water Level Gauge WMO World Meteorological Organization

Glossary Adivasi indigenous people Char low-lying river island xvi

Parishad elected council for a local government (e.g. Union, Upazila, etc.) Pourashava urban local government meant for ‘Municipality’ Union lowest tier of local government in Bangladesh comprised of a number of Wards Upazila lowest administrative unit comprising of a number of Unions

xvii

CHAPTER 1: INTRODUCTION

1.1 Bangladesh 1.1.1 General Background Bangladesh is recognized worldwide as one of the most vulnerable countries to natural disasters and to the impacts of global warming and climate change (SDC, 2010; DOE, 2007). Almost every year, Bangladesh experiences one or more disasters- such as tropical cyclones, storm surges, coastal erosion, floods, and droughts- resulting in massive loss of life and property and hampering the development activities (Ali, 1999). “In 2004, the United Nations Development Programme (UNDP) ranked Bangladesh the number one nation at risk for tropical cyclones and number six for floods” (Luxbacher and Uddin, 2011). Rapid global warming has caused fundamental changes to Bangladesh’s climate and as a result millions are suffering (DOE, 2007). It is therefore necessary to understand its vulnerability in terms of population and sectors at risk and its potential for adaptation to climate change (DOE, 2006). Climate change is not only altering the disaster risk through increased weather related risks, sea-level rise (SLR) and temperature and rainfall variability, but also through increases in societal vulnerabilities from stresses on water availability, agriculture and ecosystems (MoFDM, 2009). In this context, one of the key issues in Bangladesh is to reduce the disaster risk. For this purpose, more comprehensive and systematic efforts at the international, national and local levels are important to take into account (Djalante et al., 2012). It was proved that disaster should be managed holistically from prevention, mitigation through to rehabilitation and reconstruction. Although global reduction of greenhouse gas emission (i.e. mitigation) is a must to overcome the challenge in the long-run, adaptation is a short-term but essential measure to tackle the climate change impact locally. Therefore, disaster risk reduction and climate change mitigation and adaptation provide a common area of concern: reducing the vulnerability of communities and achieving sustainable livelihood development (MoFDM, 2009).

1.1.2 Geography and Climate of Bangladesh Bangladesh is a low-lying deltaic country in South Asia, which is formed by the Ganges, the Brahmaputra and the Meghna rivers (DMB, 2010). Bangladesh is a developing country of low deltaic plain located between 20°34ʹ to 26°38ʹ North latitude and 88°01ʹ to 92°42ʹ East longitude. The country occupies an area of 147,570 sq. km. (BBS, 2011). Its maximum extension is about 440 km in E-W direction whereas 760 km in N-S direction (Hoque, 2006). Bangladesh is located at the interface of two quite different settings. To the north of the country lie the Himalayas foot plain and the Khasi-Jainta hills, and to the south are the Bay of Bengal and the Indian Ocean. Those different settings control, modify, and regulate the climate of the country (Ali, 1996). Geologically it is a part of the Bengal Basin, which is built up by sediments washed down from the highlands on three sides of it. It is bordered on the west, north and east by India, on the southeast by Myanmar (Karim and Mimura, 2006). The total length of the land border of Bangladesh is about 4,246 km, of which 93.9% is shared with India and the rest with Myanmar (Hoque, 2006). There are 57 cross-boundary rivers, of

Chapter 1 which 54 are shared with India whereas other three rivers with Myanmar and Bangladesh is the common lower riparian zone of all these trans-boundary rivers (Chowdhury, 2010). There are more than 310 rivers and tributaries which have made this country a land of rivers (DMB, 2010). The coastal area represents an area of 47,201 km2, which is about 32% of Bangladesh’s total geographical area. In terms of administrative consideration, 19 districts out of 64 are considered as coastal districts (BBS, 2011; MoEF, 2007). About 10% of the country is 1 m above the mean sea level, and one-third is under tidal excursions (Ali, 1999). The country has a coastline of about 710 km along the Bay of Bengal (MoWR, 2005). The country covers three discrete coastal regions - western, central, and eastern coastal zones which are shown in Figure 1.1. The western part is known as the Ganges tidal plain. Average land elevation is below 1.5 m MSL. The southwestern part of the region is covered by the world’s largest mangrove forest (6017 km2), popularly known as Sundarbans. The mangrove forests act as barriers to the furiousness of tropical cyclones and storm surges. Erosion is comparatively small in this region but it suffers from salinity and tidal flooding (Karim and Mimura, 2006). The Sundarbans was declared by the UNESCO as a natural world heritage site in 1997 (Islam, 2008). The central region is the most active one, and this area suffers from continuous erosion and accretion (Karim and Mimura, 2006). The very active Meghna River estuary situates in this region. The combined flow of 3 powerful rivers – namely, the Ganges, the Brahmaputra, and the Meghna, are commonly called as the GBM river system and ranked as one of the largest river systems in the world - discharges with the name as Lower Meghna into the northeastern corner of the Bay of Bengal. This estuarial region suffers from the most disastrous effects of tropical cyclones and storm surges in the world (Ali, 1999; Karim and Mimura, 2006). The GBM river systems carry 85% of the total dry season flow passing through the coastal zone of Bangladesh (Islam, 2008). The eastern region has higher elevation and this zone is relatively stable part among other coastal regions in the country. The world longest natural beach (120 km) is situated in this region (Karim and Mimura, 2006).

89°0'0"E 90°0'0"E 91°0'0"E 92°0'0"E

23°0'0"N 23°0'0"N

Eastern Region

Char Changa 22°0'0"N 22°0'0"N

Hiron Point Central Region Cox's Bazar Western Region 21°0'0"N Bay of Bengal 21°0'0"N

89°0'0"E 90°0'0"E 91°0'0"E 92°0'0"E Figure 1.1: Three coastal regions in Bangladesh 2

Chapter 1

Bangladesh is an agro-based country (Habib, 2011). It has subtropical monsoon climates which have wide seasonal variations in rainfall, moderately warm temperatures, and high humidity (Hoque, 2006). The climate of Bangladesh can be classified under the following four seasons: The first is Winter or Northeast Monsoon (December to February): maximum temperature is 31.1°C whereas occasional minimum is 5°C with mean temperature is 18-21°C and average rainfall is about 1.5% of the total annual rainfall. The second is Summer or Pre-Monsoon (March to May): mean temperature is 23-30°C which occasionally rises 40.6°C and average rainfall is 17% of the total annual rainfall. The third is Southwest Monsoon or Monsoon (June to September): monsoon is both hot as well as humid and average rainfall is about 72.5% of the total annual rainfall. The fourth is Autumn or Post-Monsoon (October and November): short-living season, average rainfall receives is about 9% of the total annual rainfall (Habib, 2011; DOE, 2006). The mean annual rainfall is about 2300 mm whereas the average annual rainfall varies from 1,200 mm in the extreme west to over 5,000 mm in the northeast (DOE, 2006).

1.1.3 Demographic, economic, social and cultural characteristics of Bangladesh Bangladesh is a unitary, independent and sovereign republic called the People’s Republic of Bangladesh. Bangladesh became an independent country on March 26, 1971 by the liberation war against Pakistan, which ended on 16 December 1971 with the victory of Bangladesh forces and the surrender of the occupying Pakistani Army. Bangladesh was under Muslim rule for five and a half centuries and entered into British rule in 1757. At the time of the British rule, it was a part of the British Indian province of Bengal and Assam. In August 1947, it achieved independence from British rule along with the rest of India and formed a part of Pakistan known as East Pakistan until it became independent on 16 December 1971 (Dhaka, 2006). Table 1.1: The population statistics for Bangladesh according to final census report (BBS, 2011) Area (147570 km2) Male Female Population Total Average Annual Total Population Density/km2 Households Growth Rate % 144,043697 72,109796 71,933901 976 32,173630 1.37 Yearly Growth 1974 (-) 1981 (2.32) 1991 (2.01) 2001 (1.58) 2011 (1.37) Rate %

Table 1.1 shows that the total number of households is more than 32 million and population density is 976, which makes Bangladesh one of the most densely populated countries of the world. The number of male and female is about equal. Population annual growth rate shows a decreasing trend from 2.32 in 1981 to 1.37 in 2011, which is about half. About 98% of Bangladeshi are ethnic Bengali and speak Bangla. Urdu-speaking, non-Bengali Muslims of Indian origin, and various tribal groups make up the rest. Mainly in urban areas, the educated people can speak English. Most of Bangladeshis (around 88.3%) are Muslims, but Hindus represent a minority. Small numbers of Buddhists, Christians, and animists are

Chapter 1 also present in Bangladesh. Bangladesh has a long and rich historical and cultural past, which combines Dravidian, Indo-Aryan, Mongol/Mughul, Arab, Persian, Turkic, and Western European cultures (Dhaka, 2006). Table 1.2: Economic status of Bangladesh (BTI, 2012) Economic Indicators 2007 2008 2009 2010 GDP $ mn 68415.4 79554.4 89359.8 100357.0 GDP Growth % 6.4 6.2 5.7 6.1 Inflation (CPI) % 9.1 8.9 5.4 8.1 Foreign Direct Investment % of GDP 1.0 1.3 0.8 1.0 Export Growth % 13.0 7.0 0.0 0.9 Import Growth % 16.0 -2.1 -2.6 0.7 Current Account Balance $ mn 856.9 926.2 3556.1 2502.4

Gender HDI Rank Life Expectancy (68 Years) HDI (0.5) Inequality - GDP/Capita 146 of 187 (0.55) $1659 Poverty (Population living on Aid/Capita Gini Index UN Education - - less than 2 $ a day) 81.3% $7.6 31.0 Index (0.415)

Table 1.2 shows that the Gross Domestic Product (GDP) of Bangladesh is increasing and the growth rate of GDP is about 6% which is lower than the South Asian GDP growth rate (WB, 2010a). The inflation rate is relatively higher in comparison with the developed countries but similar to other South Asian countries (WB, 2010a). Export and Import growth rates are showing a decreasing trend. The Human Development Index (HDI) is a complex statistic, which is used to rank countries by standard of living. HDI of Bangladesh is 0.5 which includes the country as one of the low human development countries and ranked 146 out of 187 countries (UNDP, 2011). About 81.3% of populations, whose income is less than 2 USD per person per day among whom about 34% live with less than 1 USD per person per day (SDC, 2010). Therefore, it is clear that a large number of populations in Bangladesh are living below the poverty level which indicates the severity of poverty or vulnerability in Bangladesh.

1.1.4 Governance Style of Bangladesh The President in Bangladesh, who is the head of state but holds a largely ceremonial post because the president has limited administrative power whereas the real power is held by the Prime Minister, who is the head of the government. The President is elected by the legislature (Parliament) every five years. The President appoints the legislative, executive and the judiciary. The President also appoints the Prime Minister who must be a Member of Parliament (MP) and whom the President thinks commands the confidence of the majority of

Chapter 1 other Members of Parliaments. The cabinet is formed of ministers selected by the Prime Minister but appointed by the President. At least 90% of the ministers must be MPs whereas the other 10% can be non-MP experts, who are called "technocrats" but the rule is that technocrats are not otherwise disqualified from being elected MPs. The President can dissolve Parliament upon the written request of the Prime Minister any time. The Parliament is unicameral, which is formed by 300 elected MPs by the people of Bangladesh by vote. Extra 45 seats are reserved for women and to be distributed among political parties in proportion to their numerical strength in the Parliament (Dhaka, 2006). Bangladesh's judiciary is a civil court system and it is still based on the British model. The highest court of appeal is the Appellate Bench of the Supreme Court. On the local government level, the country is separated into divisions, districts (Zila), sub-districts (Upazila), unions, and villages. Local officials are elected at the union level and they are called Chairman. There is no election at the village level but members are selected by government. All larger administrative units are conducted by the members of the civil service (Dhaka, 2006).

1.2 Natural Hazards in Bangladesh Bangladesh is exposed to a multitude of natural hazards with highly varying occurrence, season and extent of effects.

1.2.1 Cyclones and Storm Surges Tropical cyclones accompanied by storm surges from the Bay of Bengal are one of the major disasters in Bangladesh. The country is one of the worst victims of all kind of cyclonic casualties in the world (SDC, 2010). Damage to life and property due to cyclonic storms is enormous. In the coastal regions, the damage is mainly due to induced storm surges, particularly over the low elevation coastal margins. This is why; the coastal zone of Bangladesh could be termed a geographical "death trap" due to its extreme vulnerability to cyclones and storm surges (Shamsuddoha and Chowdhury, 2007). The massive loss of life by cyclone is due to the high density of population in this area, people living in poverty within poorly constructed houses, the inadequate number of cyclone shelters, and the extremely low- lying land of the coastal zone (Ahmed, 1999). A UNDP report (titled ‘Reducing Risk of Natural Disasters: A Development Challenge’) mentions that among the Asian countries Bangladesh is most highly prone to cyclonic disaster. The report also states that cyclone caused the death of 250 thousand people worldwide, of whom 60% were in Bangladesh during 1980 to 2000 (Shamsuddoha and Chowdhury, 2007). Although cyclones and floods have occurred in Bangladesh over the centuries, the damage is increasing due to growing population and infrastructure development in the coastal zone (Ahmed, 1999). Cyclones pose multiple threats from severe winds, storm surges, and heavy rainfall that cause in both surface and river flooding. Cyclones associated with tidal waves caused massive loss of lives and property. Therefore, cyclonic storms have always been a major concern to coastal plains and offshore islands of Bangladesh (Shamsuddoha and Chowdhury, 2007).

Chapter 1

1.2.2 Floods Floods are annual phenomena in Bangladesh. Normally the most severe floods occur during the months of July and August (DMB, 2010). Regular river floods (during monsoon season) affect 20% of the country which may increase up to 67% in extreme years like the 1998 flood. The floods of 1988, 1998 and 2004 were simply disastrous (SDC, 2010). There are four types of flood in Bangladesh (DMB, 2010):  Monsoon floods along major rivers during the monsoon rains (June-September).  Flash floods caused by overflowing of hilly rivers of eastern and northern Bangladesh (Normally during April-May and September-November).  Rain floods caused by drainage congestion during heavy rains.  Coastal floods caused by storm surges.

1.2.3 River Bank Erosion River morphology in Bangladesh is highly dynamic. The main rivers are braided, and form islands (chars) between the braiding channels. Many of these chars are highly unstable, "move with the flow" and are extremely sensitive to changes in the river morphology (SDC, 2010). Losses by river erosion happen slowly and gradually. Although losses due to river erosion are slow and gradual, they are more destructive and far-reaching than other sudden and devastating calamities. River erosion effects are long-term (DMB, 2010). According to the Bangladesh Water Development Board about 1,200 km of river banks are actively erodible (SDC, 2010).

1.2.4 Coastal Erosion The natural shape of Bangladesh coastal and marine areas are controlled by dynamic processes such as tides, wave actions, strong winds and sea level variations. Over the last two centuries, huge changes have taken place due to continuous land erosion and accretion along the coastline. This process is the most severe in the Meghna estuary (MoEF, 2007). The people in the coastal area are increasing and they are the worst victims. Studies explain that major erosion occurs along the wider channels (Meghna estuary). Most of the erosion of the Bay of Bengal front was due to storm surges and continuous wave actions (Ahmed, 1999). The area of Sandwip Island, for example, was 1,080 sq km in 1780, but now it has been reduced to only 238 km2 and in Hatiya, erosion is taking place at the rate of 400 meter/year (Ahmed, 1999). Hatiya (Upazila, Noakhali District) has reduced from 1000 km2 to only 21 km2 over 350 years whereas Swandip ( Upazila, Chittagong District) has lost 180 km2 in the last 100 years. Kutubdia (Upazila, Cox's Bazar District) has reduced from 250 km2 to only 60 km2 during the period 1880 to 1980 by the process of strong tidal actions and cyclonic effects. Bhola (District) Island has been squeezed from 6400 km2 to 3400 km2 since 1960. In each year the GMB river system carries 6 million cusecs of water with 2179 million metric tons of sediment which causes water logging and flooding in the monsoon period and is responsible for the accretion process in this area (Shamsuddoha and Chowdhury, 2007).

1.2.5 Earthquakes Bangladesh and the north-eastern Indian states are one of the seismically active regions of the 6

Chapter 1 world, and have experienced numerous large earthquakes during the past 200 years (DMB, 2010). During 1869-1930, five earthquakes with magnitude M≥7 have hit parts of Bangladesh, out of which two had their epicenters inside Bangladesh. Although no major event occurred during the last decades, seismicity is still high for Bangladesh. Bangladesh University of Engineering and Technology (BUET) prepared a new seismic zoning map and recognized that 43% of the areas of Bangladesh are rated high risk, 41% moderate whereas 16% at low risk (SDC, 2010).

1.2.6 Droughts Droughts mainly occur in the western parts of Bangladesh (Rajshahi and Rangpur Division) and in the Chittagong Hill tracts area (SDC, 2010). Bangladesh is at high risk from droughts. During the period 1949 to 1991, Bangladesh faced droughts 24 times (DMB, 2010). In recent years, the frequency and intensity of drought has been increasing continuously and affects the agricultural production, mainly rice (SDC, 2010).

1.2.7 Tornados Tornados (It is called Kalbaishakhi in Bangladesh) are mainly occurring in two transitional periods (Pre-monsoon and Post-monsoon). They are suddenly formed and of brief duration and are extremely localized in nature. Therefore, it is very difficult to locate Tornados or forecast their occurrence with the available techniques at present. They may cause also a lot of havocs and destructions (SDC, 2010). Since independence in 1971, Bangladesh has experienced at least eight major tornados, killed on an average more than 100 people in each event and caused severe damage in their narrow paths (SDC, 2010).

1.2.8 Arsenic Contamination Arsenic contamination is growing in Bangladesh and at present, it is considered to be a dangerous environmental threat as well as a serious health risk (contaminating drinking water). It is defined as a public health emergency in Bangladesh. Although there are geological reasons (arsenic complexes present in soils), the excessive extraction of water for irrigation and domestic water supply have accelerated the problem (SDC, 2010). Ground water in 61 out of 64 districts in Bangladesh is contaminated with arsenic. According to a study conducted by the British Geological Survey and DPHE, arsenic concentrations in the country range from less than 0.25 mg/l to more than 1600 mg/l (DMB, 2010).

1.2.9 Salinity Intrusion Saline water intrusion is mostly seasonal in Bangladesh. During winter the saline front starts to penetrate into inland, and the affected areas rise sharply from 10% in the monsoon to over 40% in the dry season. It is observed that dry water flow (Upstream) trend has declined. Therefore, sea flow (saline water) is moving far inside the country causing in contamination both in surface and ground waters (DMB, 2010). It is measured that saline water intrusion has increased which will be intensified with the sea level rise. It is highly seasonal and affects crop productivity (SDC, 2010).

Chapter 1

88°0'0"E 89°0'0"E 90°0'0"E 91°0'0"E 92°0'0"E 93°0'0"E

N 26°0'0"N 26°0'0"N

25°0'0"N 25°0'0"N

24°0'0"N 24°0'0"N

23°0'0"N 23°0'0"N

22°0'0"N 22°0'0"N

Bay of Bengal 21°0'0"N 21°0'0"N Coastal Districts

Flash Flood Prone Area Drought Prone Area 20°0'0"N Flood Prone Area 20°0'0"N

88°0'0"E 89°0'0"E 90°0'0"E 91°0'0"E 92°0'0"E 93°0'0"E Figure 1.2: Map of Bangladesh with some areas prone to a specific natural hazard

1.3 Climate Change and Sea Level Rise in Bangladesh Although the impacts of global warming and climate change are over the world, this problem is very high for Bangladesh because of the population is chronically exposed and vulnerable to a range of natural hazards. Climatic hazards, including extremes like floods, cyclones, tornado, storm surges, tidal bores, etc are not new but climate variability, change and extremes in Bangladesh due to the effects of global warming have already been evidenced and may intensify the problems (DOE, 2007). Bangladesh is a low-laying deltaic country which will face the serious consequences due to sea level rise including permanent inundation of huge land masses along the coastline. There is a clear evidence of changing climate in

Chapter 1

Bangladesh which is resulting in changes in the precipitation, increasing annual mean temperature and sea level rise (Shamsuddoha and Chowdhury, 2007). It is projected that Bangladesh will be affected by sea level rise (SLR) in future which will be caused by a large coastal areas inundation (SDC, 2010). Table 1.3: The inundation scenarios in Bangladesh due to sea level rise (Ali, 1996) Sea Level Rise (m) Inundation (km2) % of total area (Bangladesh) 1.0 14,000 10.0 1.5 22,320 15.5

Table 1.3 shows the severity of SLR in Bangladesh in future. Bangladesh is a densely populated county. If it’s 10% or 15.5% area goes under water in future due to sea level rise, millions of people will migrate to inner area of Bangladesh and the country will face acute problems.

1.4 Objectives of the Study Work The study will focus on the disaster history and experience and the implementation of the Disaster Risk Reduction Progammes with mentioning of the relevant institutions in Bangladesh. The study also will assess and critically discuss the present and likely future state of the coastal system (wave action regarding coastal erosion) and focus on the adaptation measures with special emphasis on storm surges and coastal erosion. As a summary, the investigations, as the aims of the project are perused in this thesis are listed below:  To introduce Bangladesh in regard to geography, climate, economy, demographic structure, governance style along with vulnerability to natural hazards, sea level rise and climate change.  To collect the Disaster (Cyclone) history in Bangladesh and explain the lessons gathered by the experiences due to cyclones that hit Bangladesh.  To develop an institutional map with most of the relevant institutions and governmental bodies, research institutes and universities in Bangladesh related to Disaster Risk Reduction.  To calculate the rate of erosion along the coast of Bangladesh due to wave actions over the years.  To investigate the impact of climate change regarding coastal erosion in Bangladesh.  To mention the adaptation measures regarding SREX report to manage the Extreme Events and Disasters due to climate change in Bangladesh.

1.5 Outline of the Report The present report is arranged as follows:

 Chapter 1 contains the introduction to introduce Bangladesh.  Chapter 2 contains the physical phenomena and disaster risk reduction terminology.  Chapter 3 collects the past recorded disaster histories (storm surges) and analyzes to gather the lessons. 9

Chapter 1

 Disaster risk reduction system and an institutional map for disaster risk reduction in Bangladesh are presented in Chapter 4. Achievements of Bangladesh in implementing Hyogo Framework for Action are summarized and also discussed here. Few development projects for disaster risk reduction and climate change adaptation in Bangladesh are also mentioned here.  Chapter 5 contains the modeling part with the help of SWAN model to analyze the rate of erosion along the coast of Bangladesh at current and future climate projections.  Chapter 6 presents the low regret adaptation measures in Bangladesh to manage the impacts of climate change in relation to SREX report.  Finally conclusion and recommendation will be provided in chapter 7.

CHAPTER 2: PHYSICAL PHENOMENA AND DISASTER RISK REDUCTION 2.1 Introduction The coast of Bangladesh is a vulnerable zone prone to natural disasters like cyclone, storm surge, flood, erosion, etc. and it is also a zone of opportunities due to presence of many economic activities like coastal fisheries and shrimp, forest, salt and minerals, harbors, airports, tourism complexes, etc. (MoWR, 2005). Cyclonic storms have always been a major concern to coastal plains and offshore islands of Bangladesh and they also slow down the pace of social and economic developments in this region (MoWR, 2005). It is forecast that climate change will increase the frequency and severity of tropical cyclones in Bangladesh (Luxbacher and Uddin, 2011). River erosion and loss of coastal habitable and cultivable land is a severe national problem and another major natural hazard in Bangladesh. Although erosion does not cause loss of lives, it leads to huge economic losses, lessens people’s assets and making them unable to set up roots (Shamsuddoha and Chowdhury, 2007). “DRR (Disaster Risk Reduction) is the development and application of policies and practices that minimize risks to vulnerabilities and disasters” (MoFDM, 2009). Therefore, to reduce the vulnerability and disaster risk to natural hazard, DRR Programmes e.g. Hyogo Framework for Action (HFA) should be implemented. To predict the coastal erosion problem, a numerical model is necessary to simulate the wave actions along the coast of Bangladesh. Several aspects should be understood to simulate the wave. Additionally, scales, conditions, and data availabilities have to be determined to approach the subject. In another words, the information to be obtained must be known beforehand. To choose a suitable simulation method, some wave processes or parameters become more noticeable than the others which depend on that particular case. Waves in coastal waters have to be understood clearly to explain the erosion phenomenon. In general, the coastline erosion results in serious social and economic consequences. Thus, forecasting the coastline change in order to carry out the possible solutions to mitigate the erosion is essential for this area. For this purpose, information on wave conditions in the area of interest is required. To estimate the wave conditions in coastal areas, a numerical wave model can be used. In the present study, a wave model (SWAN) has been developed to simulate and predict the nearshore wave action along the coast of Bangladesh.

2.2 Cyclone and Storm Surges 2.2.1 Introducing cyclones and storm surges Typhoons are tropical revolving storms. They are called ‘Cyclones’ in English, when they occur in the area of Indian Ocean. Oscillations of the water level in a coastal or inland, resulting from atmospheric forces in the weather system are known as storm surges. Its period may vary in a range from a few minutes to a few days. Storm surges are developed by two principal factors: pressure drop and wind stress. Therefore, a storm surge is partly caused by

Chapter 2 pressure differences within a cyclonic storm and partly by high winds acting directly on the water (Khan, 2012). Cyclones are formed in the ocean in two characteristic belts in the tropical regions, north of latitude 10°N and south of 10°S. When the cyclone progresses closer to the coast at shallow water (where the water depth decreases), a surge is generated. This generated surge is higher if the continental shelf is longer as well as shallower and the wind is stronger. If the surge wave coincides with a high tide, the (total) height is further increased which is more dangerous. At land, the cyclone rapidly dies. The northern part of the Bay of Bengal (the coast of Bangladesh) is particularly vulnerable to storm surges and coastal flooding, which is developed by tropical cyclonic activity (Madsen and Jakobsen, 2004). Figure 2.1 shows a detailed picture of the storm surges. The height of storm surge alone is 15 ft. If this storm surge hits at normal high tide which is 2 ft here, then storm surge coincides with high tide and forms a total height 17 ft which is more dangerous. If the same storm surge hits at low tide then the total height must be less than 15 ft which is less hazardous in comparison to first one.

Figure 2.1: Storm surge (wunderground.com)

2.2.2 Classification of Cyclones Cyclones have been classified in different areas mainly on the basis of wind speed. Some time, pressure drops also have been considered. Table 2.1: Classification of cyclones in South Asian Sub-Continent (RRCAP, 2001) Depression Winds up to 62 km/h Cyclonic Storm Winds from 63-87 km/h Severe Cyclonic Storm Winds from 88-118 km/h Severe Cyclonic Storm of Hurricane Intensity Winds above 118 km/h

Cyclones have been classified in Table 2.1 on the basis of their intensity of wind speeds. In South Asian Sub-Continent, mainly these four types of classification have been used.

Chapter 2

Table 2.2: Classification of cyclonic disturbances presently in use by Bangladesh (WMO, 2010) Type of Disturbance Corresponding Wind Speed Low pressure area Less than 17 knots (less than 31 km/h) Well marked low 17- 21 knots (31-40 km/h) Depression 22- 27 knots (41-51 km/h) Deep Depression 28- 33 knots (52-61 km/h) Cyclonic Storm 34 -47 knots (62-88 km/h) Severe Cyclonic Storm 48- 63 knots (89-117 km/h) Severe Cyclonic Storm with a Core of Hurricane 64 – 119 knots (118-221 km/h) Wind Super Cyclonic Storm 120 knots and above (222 km/h or more)

Table 2.2 shows the classification of cyclonic disturbances that are used by Bangladesh for national purposes. These classifications are also based on the intensity of wind speeds. After classification, the warnings are issued by BMD in four stages for the government officials as per Standing Orders for Disasters (SOD) in Bangladesh. Warnings are provided to ports and other relevant communities and disseminated it to the stakeholders (WMO, 2010). In this thesis paper, the classification that is used by Bangladesh for national purposes has been taken into account to classify the disturbances (Cyclones) that hit Bangladesh.

2.3 Waves in Coastal Areas 2.3.1 Introduction Evolution of waves is affected by many processes. All physical processes are not equally important for oceanic and coastal waters. There is a relative importance of various processes. Table 2.3: The relative importance of the various processes in sea waters (Holthuijsen, 2007) Oceanic waters Coastal waters Process Shelf seas Nearshore Harbour Wind generation ●●● ●●● ● ○ Quadruplet wave-wave interaction ●●● ●●● ● ○ White capping ●●● ●●● ● ○ Bottom friction ○ ●● ●● ○ Current refraction/energy bunching ○/● ● ●● ○ Bottom refraction/shoaling ○ ●● ●●● ●● Breaking (depth-induced; surf) ○ ● ●●● ○ Triad wave-wave interaction ○ ○ ●● ● Reflection ○ ○ ●/●● ●●● Diffraction ○ ○ ● ●●● ●●●=dominant, ●●= Significant but not dominant, ●= of minor importance, ○= negligible.

From the table 2.3, it is clear that the process of generation, wave-wave interaction and white- capping are more important in oceanic waters than they are in shallow (near shore) waters but 13

Chapter 2 bottom friction and current refraction are more important phenomena in shallow waters than they are in deep waters. Shoaling and wave breaking are especially important in coastal waters for the sediment transport whereas reflection and diffraction are important at harbor. In coastal waters, the propagation of waves is influenced by a limited (shallow) water depth and changing wave amplitude (shoaling, refraction and diffraction). Shallow water also influences the generation, nonlinear wave-wave interaction and dissipation. Therefore, to model the waves in coastal waters, one needs to take into account more processes than in oceanic waters (Holthuijsen, 2007).

2.3.2 Wind Generation in Coastal Areas The formulations and procedures for generating the waves by wind are quite similar in deep waters and in shallow waters. The important parameter for the generation of waves is the ratio of wind speed over the phase speed of the waves. When waves propagate from deep to shallow waters, the phase velocity decreases, thus the ratio of wind speed over the phase speed of the waves increases consequently, enhancing the transfer of energy to the waves. In other words, wind generates higher energy into the spectrum in finite depth (shallow waters) than it does in infinite depth or oceanic waters (Holthuijsen, 2007). Figure 2.2 depicts that transferring of wind energy into JONSWAP spectrum at shallow waters (10 m water depth here) is higher than that in the deep waters for the same wind input but the peak energy develops at the same frequency both at deep and shallow waters.

Figure 2.2: Transferring of wind energy into JONSWAP spectrum in deep and shallow water,

( 3.5 m, and = 20 m/s) (Holthuijsen, 2007)

2.3.3 White-Capping Wave breaking in deep water is called white-capping, which is a very complicated phenomenon and a dissipater of energy in JONSWAP spectrum. It involves highly nonlinear hydrodynamics. Wave breaking itself in general is a poorly understood phenomenon. There is no generally accepted and precise definition of wave breaking. Quantitative measurements are also very difficult to carry out. When waves move from deep waters to coastal waters, shoaling tends to raise their steepness, thus white-capping tends to become more effective in coastal waters (Holthuijsen, 2007).

Chapter 2

Figure 2.3 shows the white capping phenomenon which is an energy dissipater. The energy loss due to white capping at shallow waters (10 m water depth here) is higher in comparison with the energy loss at deep waters. As white capping is an energy dissipater, its spectrum shows negative direction or opposite direction to the JONSWAP spectrum.

Figure 2.3: White-capping source term, in JONSWAP spectrum, in deep and shallow water, ( =3.5 m and (Holthuijsen, 2007)

2.3.4 Bottom Friction Bottom friction is a very important term for energy dissipation in spectrum. It is a dominant mechanism for bottom dissipation for continental shelf seas with a sandy seabed. A transfer of energy and momentum depend on the wave field itself and on characteristics of the bottom. There are three models to describe the bottom friction. Collin develops the first model. The time-averaged energy-dissipation rate at the bottom ̅̅̅ ̅ ̅ (per unit bottom surface area) can be expressed as

̅̅̅ ̅ ̅ ̅ ̅ ̅ ̅̅ ̅ ̅̅ ̅ ̅̅ ̅ ̅̅ ̅ ̅̅ ̅ (2.1)

Where and are the magnitude of the (time-varying) shear stress and particle velocity respectively. Collin (1972) described the shear stress as follows

(2.2) where is the density of water and is a bottom friction (or drag) coefficient, thus the energy-dissipation rate becomes

̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ (2.3) For random waves Collins (1972) expressed the formula: ̅̅̅̅̅ (2.4)

Chapter 2

Where, is the root-mean-square orbital velocity at the bottom. By replacing

with [ ] ( and estimating from the wave spectrum, the ( formula (2.4) becomes:

( [ ] ( (2.5) ( With

⁄

[∫ ∫ [ ] ( ] (2.6) (

Or, in terms of variance density (divide by

( [ ] ( (2.7) ( Madsen et al., 1988; Weber, 1989, 1991a, 1991b develop the second model. They formulated the dissipative character of the turbulent boundary layer with the basic parameter such as grain size of the sand. The results of their model can be also expressed as (2.7). The only difference is that they estimate for the bottom-friction coefficient in different way. The parameter which is used to determine the friction (for sandy bottom) is ̃ , which is known as normalized bottom roughness. It can be calculated as:

̃ = (2.8)

Where is a bottom roughness length and is the root-mean-square amplitude. There is another parameter called the Shields parameter ( ), it represents the capacity of the wave to set the bottom in motion (Tolman, 1995).

(2.9)

( )

Where and are the densities of sand and water, respectively, is a representative grain diameter and is the coefficient for skin friction. Hasselmann et al. (1973; JONSWAP) develops the third model, which can be also expressed as (2.7) and who estimates for the bottom-friction coefficient, in different way and who characterized their observations of swell dissipation with

= /(g (2.10)

2 -3 2 -3 And = 0.038 m S . For fully developed wind- sea condition, = 0.067 m S (Holthuijsen, 2007). Figure 2.4 depicts the loss of energy in a JONSWAP spectrum at shallow waters (10 m water depth here) due to bottom friction which is also a dissipater. In deep water, the wave action does not reach the bottom. As a result, there is no loss of energy due to bottom friction at deep waters. Bottom friction is very important to explain the erosion at coastal waters. Bottom friction can be calculated by using any of those mentioned three models by SWAN.

Chapter 2

Figure 2.4: The bottom friction dissipation influenced on JONSWAP spectrum, ( =3.5 m and (Holthuijsen, 2007)

2.3.5 Depth-Induced (Surf) Breaking The energy of waves dissipates strongly due to wave breaking. This phenomenon in oceanic water is known as white-capping, whereas in shallow water additional to white-capping; depth-induced (surf) breaking is one of the most important energy dissipating processes. The average energy loss in a single breaking wave (per unit time, per unit horizontal bottom area) was studied by Battjes and Janssen (1978); they formulated the dissipation in a bore (a hydraulic jump) as:

(2.11)

Where is a tunable coefficient, is the inverse of the (zero crossing) wave period

and is the height of the breaking wave. In terms of variance, the above equation can be expressed as

̅̅̅̅̅̅ ̅ (2.12)

̅ Where in the mean zero-crossing frequency of the breaking waves, is the fraction of breaking waves.

is estimated statistically by Rayleigh distribution as

( ) (2.13)

Where is the root-mean-square wave height √ , and is the zeroth-order moment of the wave spectrum. The maximum wave height is generally expressed

( ̅ (2.14) 17

Chapter 2

Where, the value of the breaking index may depend on the wave steepness and bottom slope (Holthuijsen, 2007). Figure 2.5 shows the wave breaking due to limited depth in coastal waters. If there is no depth induce breaking phenomenon, the wave height increased infinitely. But in practically wave breaks due to limited depth which is another energy dissipater in JONSWAP spectrum.

Figure 2.5: The influence of surf-breaking on JONSWAP spectrum, ( =3.5 m and (Holthuijsen, 2007)

2.4 Terminology on Disaster Risk Reduction Disaster An understanding of the term ‘disaster’ is very important for Disaster risk management. ISDR (2009b) defines Disaster as a serious disturbance to a community or society which causes widespread losses and impacts to human, material, economic or the environmental such that it exceeds the society’s to depend on their own resources. Sheehan and Hewitt (1969) define Disaster with quantity of losses- as any event which causes at least 100 human deaths or 100 human injuries or 1 million USD economic damages. The severity of a disaster may vary place to place, community to community. For example, if a cyclone causes serious disturbance or human deaths/injuries or serious economic damages to a society then that cyclone is a disaster for that society.

Disaster Risk Reduction ISDR (2009b) defines disaster risk reduction as a systematic approach to analyze and manage risk factors of disaster. This approach includes reducing exposure to hazards, lessened vulnerability of people and property, management of land and the environment and enhanced preparedness for adverse events. A typical example of such systematic approach is the Hyogo Framework for Action (HFA).

Chapter 2

Mitigation ISDR (2009b) defines Mitigation as the strategies and actions to reduce the adverse impacts of hazards. On the other hand, U.N. ISDR (2002) defines Mitigation as those structural and non- structural measures which can reduce the adverse impact of hazards and environmental degradation. Examples of mitigation measures are the strategies to reduce the green house gas emissions.

Adaptation Adaptation is defined by the IPCC as the process of adjusting to actual or expected climate to reduce harm or utilize beneficial opportunities (IPCC, 2001). There are four adaptation options. These are no-regret, low-regret, win-win, and flexible.

Low-regrets adaption measures These adaptation measures are those measures that can be beneficial under the current climate as well as a range of future climate conditions (IPCC, 2012). For example, early warning systems, ecosystem management and restoration, etc. are the potential low-regret measures.

Hazard A Hazard is a situation that can be harmful for human and livelihoods or a cause for economic or environmental damages (ISDR, 2009b). Harriss et al. (1978) defines Hazards as the threats to human life and well-being, goods, and the environment. A cyclone is a hazard since it can cause harm to human and livelihood.

Vulnerability The situation of a society or asset which makes it prone to be adversely affected by a hazard (ISDR, 2009b). Puente (1999) defines the propensity that may incur loss as Vulnerability. Vulnerability is measured indirectly on the basis of poverty, construction type, etc.

Risk ISDR (2009b) defines Risk as the combination of the probability of an event with its adverse effects. Lerbinger (1997) defines Risk as the probability that death, injury, illness, property damage, and other undesirable consequences stems from a hazard. For example, a high voltage power supply means there is hazard. If a person uses that power supply without any precaution, he is at risk. But if he uses the same power line with sufficient precaution then he is not at risk or is less at risk.

Exposure Darlington and Lambert (2001) mentioned that, Exposure refers to the number of people, structures and activities that could be adversely affected by hazards. For example, two cities are affected by same hazard and 10% of house and 2% of people of both cities affected. But city A has a population 1 million whereas city B has a population 5 millions. So, city B has higher exposure in compare to city A to that hazard.

Chapter 2

Coping Capacity ISDR (2009b) mentioned that, coping capacity is the capability of people, organizations and systems to tackle an adverse situation by using their own skills and resources. Therefore, the higher the coping capacity of a society, the lesser at risk they are.

Resilience ISDR (2009b) defines Resilience as the ability of a society or a system to absorb, resist and recover efficiently from the adverse effects of a hazard but essential basic structures and functions will be preserved and restored. Resilience includes the coping capacity plus the capability to completely recover as prior to an event.

2.5 Hyogo Framework for Action (HFA) 2005-2015 The World Conference on Disaster Reduction was held from 18 to 22 January 2005 in Kobe, Hyogo, Japan, and adopted the present Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters (here after referred to as the “Framework for Action”). The Conference presented a strategic and systematic approach to reducing vulnerabilities and risks to hazards for building the resilience of nations and communities to disasters. Three strategic goals are recommended in the conference. The first one is integration of disaster risk into sustainable development policies, planning and programming at all levels effectively and focus on disaster prevention, mitigation, preparedness and vulnerability reduction. The second is strengthening of institutions, mechanisms and capacities at all levels to building resilience to hazards. The third is integration of risk reduction approaches into the design and implementation of emergency preparedness, response and recovery programmes (DMB, 2011; Djalante et al., 2012; ISDR, 2005). To achieve those three goals, five Priorities for Action have been suggested. The first priority action is: ensure that disaster risk reduction is a national and a local priority with a strong institutional basis for implementation. There are four indicators for the first priority action: (1) The presence of policy and legal framework for DRR, (2) Availability of resources to implement DRR plans and activities, (3) Community participation and decentralization and (4) The functioning of a national multi sectoral platform for DRR. The second priority action is: identify, assess and monitor disaster risks and enhance early warning. There are four indicators for the second priority action: (1) National and local risk assessments and vulnerability information, (2) Data monitoring, archiving and disseminating system, (3) Presence of early warning systems for all major hazards and (4) National, local, regional/trans boundary risk assessments. The third priority action is: use knowledge, innovation and education to build a culture of safety and resilience at all levels. There are four indicators for the third priority action: (1) Availability of information on disasters to stakeholders, (2) School curricula, education material and relevant trainings on DRR, (3) Research on multi- risk assessments and cost benefit analysis and (4) Countrywide public awareness strategy. The fourth priority action is: reduce the underlying risk factors. There are six indicators for the fourth priority action: (1) Integration of DRR with development plans and policies, (2) Social 20

Chapter 2 development policies and plans to reduce people’s vulnerability, (3) Economic plans and policies to reduce the economic vulnerability, (4) Planning and management of human settlements considering DRR, (5) DRR into post disaster recovery and rehabilitation processes and (6) Disaster risk impact assessments of major development projects. The fifth priority action is: strengthen disaster preparedness for effective response at all levels. There are four indicators for the fifth priority action: (1) Policy and capacities for disaster risk management, (2) Disaster preparedness plans and contingency plans at all administrative levels, (3) Financial reserves and contingency mechanisms and (4) Relevant information exchanging procedure (DMB, 2011; Djalante et al., 2012; ISDR, 2005). There are five levels of Progress to score an achievement. The first score is 1, which indicates a minor progress with few signs of forward action in plans or policy. 1 is the minimum score for an achievement. The second score is 2, which indicates some progress, but without systematic policy and/or institutional commitment. The third score is 3, which indicates that an institutional commitment is attained, but achievements are neither comprehensive nor substantial. The fourth score is 4, which means substantial achievement attained but with recognized limitations in capacities and resources. The last and fifth score is 5, which means comprehensive achievement with sustained commitment and capacities at all levels. 5 is the highest score for an achievement (Djalante et al., 2012). Therefore, the degree of progress against all 22 key activities or core indicators is defined on a scale of 1 (lowest) to 5 (highest). These values are then averaged to assess the progress for each HFA priority. The scores of all five HFA Priorities are averaged again to obtain a single score for each country. Higher the score means better the achievement (Djalante et al., 2012).

CHAPTER 3: CLIMATE CHANGE IMPACTS, DISASTER HISTORY (STORM SURGES) AND EXPERIENCES IN BANGLADESH 3.1 Introduction Bangladesh has been identified as one of the most vulnerable countries to climate change by the international community. This high vulnerability is due to a number of hydro-geological and socio-economic factors such as geographical location, topography, extreme climate variability, high population density and poverty incidence and high dependence on agriculture (DOE, 2006). Bay of Bengal is particularly vulnerable to storm surges and coastal flooding, which is developed by tropical cyclonic activity (Madsen and Jakobsen, 2004).

3.2 Experiences from the Past Disasters (Storm Surges) Bangladesh experienced 157 (recorded) cyclones (wind speed>61 km/h) and cyclone induced storm surges which caused about two million deaths during 1584-2009 (Appendix 3.1). There were also lots of depressions (about 68 depressions in Bangladesh during 1877-1995 (Ali, 1999)) that have not been considered here. There is seasonal and monthly variation of cyclone hitting in Bangladesh. Although cyclones are destructive, their severities are not the same. The cyclones in 1970, 1991 and 2007 were the most catastrophic for Bangladesh. There was massive economic loss and thousands of deaths during these years. Figure 3.1 shows the monthly distribution of cyclones and storm surges that hit Bangladesh. Monthly distribution shows that the cyclones that hit Bangladesh are not the same over the year. The maximum number of cyclones occurs in May. The number of cyclones in April, October and November are also relatively high and statistics show that a lot of cyclones that hit Bangladesh in these four months are devastating. About 75% of the total cyclones occurring (from 1584-2009) occurs during these four months. A considerable number of cyclones also happen in March, June, September and December but these cyclones are relatively less destructive in comparison with the cyclones that occur in April, May, October, and November. About 18% of the total cyclones occurring (from 1584-2009) occurs during March, June, September and December. Few cyclones hit Bangladesh in the rest of four months (about 7% only) and in these months; the cyclones were not so destructive. So, Bangladesh is safe from cyclone hazard in February whereas January, July and August are relatively calm and quite as well.

Chapter 3

45 40

35 30 25 20 15 10

Number Cyclones of Number 5 0

Month

Figure 3.1: Monthly distribution of recorded storm surges (Cyclones) in Bangladesh during the period of 1584 to 2009

There are four seasons in Bangladesh (chapter 1). Seasonal distribution of the occurrence of cyclones show that cyclones mainly hit Bangladesh in the Pre-Monsoon (March to May) and the Post-Monsoon (October and November) seasons. More than 80% of the total cyclones in Bangladesh occur during these two seasons with the Pre-Monsoon alone contributing 48%. Thus, about half of the total cyclones occur in the Pre-Monsoon. In winter, (December to February), only 7% of the total cyclones happen whereas the Monsoon season (June to September) holds 12%. Seasonal distribution of the cyclone’s occurrences is depicted in the Figure 3.2.

Winter Post- 7% Monsoon 33%

Pre- Monsoon Monsoon 12% 48%

Figure 3.2: Season wise distribution of cyclones that hit Bangladesh in year: 1584-2009

Chapter 3

A ten year period frequency distribution of cyclones (storm surges) shows that frequency of the occurrence of cyclone since 1960 has increased with maximum cyclones occurred during 1990-1999 (Figure 3.3). However, this frequency decreased 2000-2009. Despite this observed decrease, Luxbacher and Uddin (2011) forecast that climate change will increase the frequency and severity of tropical cyclones in Bangladesh. Frequency of occurrence of cyclonic disturbances is depicted in Figure 3.3.

15 Frequency 10

Figure 3.3: Frequency of storm surges in Bangladesh in 10 year periods: 1890-2009

Figure 3.4 depicts the number of different cyclonic disturbances in Bangladesh during 1890- 2009. Among these four cyclonic disturbances (The sequence of the strength of cyclonic disturbances is Cyclonic Storm < Severe Cyclonic Storm < Severe Cyclonic Storm with Hurricane < Super Cyclonic Storm) the Super Cyclonic Storm is the strongest whereas Cyclonic Storm is the weakest due to less wind speeds (Chapter 2). The number of the occurrence of cyclonic storm is the highest and the number of the occurrence of super cyclonic storm is the lowest. That means, the stronger the cyclonic disturbances are, the less frequent they will occur and vice versa. The return period of Hurricane and Severe Cyclonic Storm are 4.25 (28 numbers in 120 years) and 3.8 (31 numbers in 120 years) years respectively and Cyclonic Storm hit Bangladesh with about 1.4 (85 numbers in 120 years) year return period whereas Super Cyclonic Storm with a surge height (surge plus tide) of about 10 m occurs in Bangladesh with a return period about 20 years (statistics since 1970, Appendix 3.1).

Chapter 3

90 80 70

60 50 40 Number 30 20 10 0 Cyclonic Storm Severe Cyclonic Severe Cyclonic Super Cyclonic Storm Storm with Storm Hurricane Type of Disturbance

Figure 3.4: Different type of disturbances that hit Bangladesh in the period: 1890-2009

Figure 3.5 shows the number of death due to recent occurring super cyclonic storm in Bangladesh. Here three super cyclonic storms have been taken for comparisons which are at similar strength. About 500,000 people died due to super cyclone in 1970 but about 150,000 died due to super cyclone in 1991 which is less than one thirds of the previous one. In 2007, the number of deaths due to super cyclone was only about 3,500 which indicate that the number of deaths decreased tremendously although population was about double in 2007 compare with that in 1970. This improvement is due to the implementation of a lot of disaster risk reduction projects and adaptation measures during this period in Bangladesh e.g. there were no significant early warning systems in Bangladesh in 1970 whereas Bangladesh has significantly developed early warning and dissemination systems in 2007.

600000

500000

400000

300000

200000 Number Death of Number

100000

0 Year: 1970 Year: 1991 Year: 2007

Figure 3.5: Number of death due to super cyclonic storms that hit Bangladesh recently

Chapter 3

Figure 3.6 shows the economic damages due to three similar strength super cyclones that hit Bangladesh in the year 1970, 1991 and 2007. Although all of these three cyclones had similar strength (similar wind speeds), economic damages were not the same. In 1970, the economic damages due to super cyclone were very low but increased dramatically in 1991. The economic damages further increased in 2007. This is due to infra-structural development such as Schools, Hospitals, Bridges, Culverts, Roads etc. and the improvement of people’s livelihood conditions in Bangladesh. Thus, the increasing economic development in Bangladesh results in increasing economic damages by cyclones (disasters). Increasing exposure of people and economic assets has been the main influence of long-term increase in economic damages due to natural disasters (IPCC, 2012), which is already proved in Bangladesh.

4000

3500

3000

2500

2000

1500

1000

500

0 Year: 1970 Year: 1991 Year: 2007 Wind Speed in Km/h Damage in Million USD

Figure 3.6: Financial damages due to super cyclonic storms that hit Bangladesh recently

3.3 Climate Change Impacts in Bangladesh 3.3.1 Climate Change Observed in Bangladesh Impacts of climate change have already been recorded in Bangladesh in the form of temperature extremes, irregular or excessive rainfall and increased number of extreme floods, cyclones, droughts, salinity intrusion into the country. Bangladesh recorded 5°C (in the three northern districts) in January 2007 which is the lowest temperature in 38 years. More than 100,000 people were affected by that cold weather and over 130 people died due to cold-related diseases. Crop production was also affected. An extremely high temperature (42.08°C) was recorded in Jessore on 27 April 2009 which was the highest in 14 years. ICDDR,B served a number of patients in that time which they never experienced since 45 years (DMB, 2010). Habib (2011) showed an increasing trend of annual maximum and minimum temperature during last 60 years (1950-2010). The annual mean temperature increased at the rate of 0.0037° C/year during 1961 to 1990 but from 1961 to

Chapter 3

2000, the increased rate was 0.0072° C which is about double and an indicator of increasing warmth in Bangladesh (Shamsuddoha and Chowdhury, 2007). Heavy rainfall occurred in Dhaka city on 14 August 2004 (341 mm) and 333 mm on 27 July 2009 in 24 hours whereas 290 mm in six hours, a record six-hour rainfall for the capital in 60 years resulted in serious drainage congestion. A total of 425 mm rainfall on 11 June 2007 within 24 hours in Chittagong resulted in a landslide and killed at least 124 people. It also caused destruction to houses, roads and embankments, as well as electricity, gas lines and communication facilities. The rainfall was the heaviest previous last 25 years (Habib, 2011). On the other hand, in 2009 there was 21% less rain during the monsoon period (June-August) and the northern districts suffered from drought. Droughts were reported even in the coastal zone. Habib (2011) analyzed a positive trend of average rainfall during last 60 years (1950- 2010). He also showed that the frequency of heavy rainfall has considerable increasing trend during pre-monsoon (+0.00258/year) and during monsoon (+0.0053/year). An increased number of severe floods hit Bangladesh in the last decade. Recurring floods occurred in year 2002, 2003, 2004, and twice in 2007 (July-August and September). The number of flash floods in the hilly terrain of eastern and north eastern part of Bangladesh has also been increasing. Additionally, the numbers of cyclones that hit Bangladesh and storm surges are increasing. For example, Super Cyclonic Storm Sidr hit Bangladesh on 15 November 2007, Cyclone Nargis on 2 May 2008 hit Myanmar (near the Bangladesh’s coast), Cyclone Rashmi occurred on 26 October 2008, and Cyclone Aila hit Bangladesh on 25 May 2009. The number of days with cautionary Signal No. 3 or more increased substantially, which resulted in a reduced number of fishing days for coastal fishers (DMB, 2010). SLR along the coast of Bangladesh is a critical variable that may amplify the vulnerability of the people who live there. Singh (2001) carried out a study on relative sea level rise in Bangladesh. He used 22 years record of tidal data for the period 1977-1998 pertaining to the three stations on the Bangladesh coast. This data was obtained by Bangladesh Inland Water Transport Authority (BIWTA). He showed rising trend of sea level along the coast of Bangladesh for three different regions. This is shown in the table below: Table 3.1: Trend of SLR along the coast of Bangladesh (Singh, 2001) Station Name Region Latitude (N) Longitude (E) Trend (mm/year) Hiron Point Western 21°48′ 89°28′ 4.0 Char Changa Central 22°08′ 91°06′ 6.0 Cox’s Bazar Eastern 21°26′ 91°59′ 7.8

There are three regions along the coast of Bangladesh (chapter 1). Singh (2001) analyzed the trend of SLR along the coast of Bangladesh for three different regions separately (Table 3.1). The result shows an increasing trend of SLR along the coast of Bangladesh for all three regions but the rate of SLR is not same for all regions. The rate of SLR along the eastern region is the highest whereas for the western region, is the lowest. By considering the average

Chapter 3

SLR of all three regions for future projections, the result shows about 12 cm SLR by year 2030, about 30 cm SLR by year 2050 and about 60 cm SLR by year 2100. The SAARC Meteorological Research Centre (SMRC) also analyzed sea level changes of 22 years data and showed 18 cm SLR by 2030, 30 cm SLR by 2050 and 60 cm SLR by 2100 (Mohal et al., 2006).

3.3.2 Frequency and Intensity of Cyclone in Future in Bangladesh One of the necessities, but not sufficient condition for the formation of tropical cyclone is that the sea surface temperature should have a minimum temperature of about 26°-27° C. The relationship between sea surface temperature and cyclone formation has been well established that almost all tropical cyclones form in warm water (Ali, 1999). Ali (1996) analyzed the cyclone frequency in the Bay of Bengal for 1881-1990. He analyzed with ten-year plots of cyclones, and one plot was made for all types of cyclones: depressions, cyclonic storms, and severe cyclonic storms. The result showed no increasing or decreasing tendency in cyclone numbers between 1881 and 1990. Although 27° C SST is necessary to develop a cyclone but it may not remain constant in future for the Bay of Bengal due to climate change. Global warming may lead to increased moisture convergence and latent heat release in the Bay of Bengal that may ultimately increase the number and duration of tropical cyclones in a warmer atmosphere (Choudhury et al., 1997). Although there is no clear idea whether global warming and sea level rise will have any effect on cyclone frequency, there are speculations that cyclone intensity might be affected. If temperature of the sea surface increases 2°C or 4°C then the maximum wind speed will increase 10% and 22% respectively, using the threshold temperature of 27°C (Ali, 1996). The maximum wind speed of the 29 April 1991 cyclone was 225 km/h. Ali (1996) calculated that if the same cyclone occurred with sea surface temperatures 2°C and 4°C higher, the wind speed would have been 248 km/h and 275 km/h respectively.

3.3.3 Intensity of Impacts on different sectors due to Climate Change Bangladesh already experiences the effects of climate change. However, the impacts of climate change on different sectors are not the same. Some sectors faced acute problems by some physical processes due to climate change. Table 3.2: Impact of climate change on various sectors (MoEF, 2005) Physical Vulnerability Contex

Sea Level Rise Flood Cyclone Erosion Sectoral Extreme and Coastal Salinity Drought River Flash and Vulnerability Temperature Storm Inundation Intrusion Flood Flood Accretion Context Surges Crop +++ ++ +++ +++ + ++ +++ - Agriculture ++ + + ++ ++ + + - Fisheries ++ ++ +++ - - + +++ - Livestock + ++ - - ++ + + +++ Infrastructure ++ +++ ++ - ++ + + - Industries

Chapter 3

++ +++ +++ - ++ - + - Biodiversity +++ + +++ - ++ - ++ - Health Human ------+++ +++ Settlement ++ + - - + - + - Energy Note: +++ refers to high, ++ refers to moderate, and + refers to low level of relationship

Table 3.2 shows the impact of climate change on different sectors in Bangladesh clearly. Agriculture sector will face the great challenge in future due to climate change. Extreme temperature, sea level rise are the physical processes that will affect all of the sectors except human settlement. Drought is only important for crop agriculture and fisheries whereas erosion and accretion only affect the infrastructure and human settlement sectors. Energy sector will be mainly affected by extreme temperature. Biodiversity will be highly affected by sea level rise and cyclone and storm surges will affect all of the sectors.

3.3.4 Actions in relation to climate change effects in Bangladesh Government of Bangladesh has already developed (BCCSAP) “Bangladesh Climate Change Strategy and Action Plan 2009” to build the capacity and resilience of the country to meet the challenge of climate change. Government of Bangladesh also developed (NAPA) “The national Adaptation Programme of Action” in 2005 to provide a response and to address the urgent and immediate needs of adaptation and priority programmes (MoEF, 2009). Bangladesh has seriously addressed the implementation of both actions (BCCSAP and NAPA) by which good governance to manage climate change effects will be attained. BCCSAP is a 10 year programme (2009-2018). The first phase (2009-2013) is ongoing which is based on six major pillars and the BCCSAP lists 44 programmes under the six major pillars. The first pillar is ensuring the food security, social protection and health. To achieve this objective, 9 programmes have been recommended. These 9 programmes are building the institutional capacity of research centres and researchers, building coping system to different agro-climatic regions, adaption against drought, in fisheries, livestock, and health sectors, ensuring water supply and sanitation, protecting livelihood for ecologically vulnerable areas and vulnerable socio-economic groups. The second pillar is further strengthening further the country’s comprehensive disaster management capacity. To achieve this objective, 4 programmes have been recommended. These 4 programmes are improving early warning and dissemination system for flood forecasting, cyclone and storm surges, awareness rising and risk management (insurance). The third pillar is infrastructure development to cope with the impacts of climate change. To achieve this objective, the implementation of 8 programmes has been recommended. These 8 programmes are repair and maintenance of flood embankments, cyclone shelters, polders, improvement of urban drainage, adaptation against flood, cyclone and storm surges, controlling river bank erosion and dredging. The fourth pillar is improving research and knowledge management to predict the impact of climate change on different sectors. To achieve this objective, 7 programmes have been recommended. These 7 programmes are establishing a research centre, developing climate change model, monitoring and modeling SLR, monitoring of ecosystem and biodiversity, indentifying macro and

Chapter 3 sectoral economic impacts, monitoring and supporting the migrated population, and monitoring tourism related issues in Bangladesh. The fifth pillar is integrating mitigation and low carbon emissions for development. To achieve this objective, 10 programmes have been recommended. These 10 programmes are improving energy efficiency, managing gas exploration and reservoir, developing coal based power stations, utilizing renewable energy, lowering methane emission, managing urban waste, afforesting and reforesting, intruding energy saving devices, developing energy and water efficiency, and improving in energy consumption. The last and sixth pillar is focusing on capacity building and institutional strengthening. To achieve this objective, 6 programmes have been recommended. These 6 programmes are revising of sectoral policies, mainstreaming climate change, strengthening human resources capacity, strengthening gender consideration, strengthening institutional capacity, and incorporating climate change in the media (MoEF, 2009). The ministry of Environment and Forest is the key ministry to address all climate change related work including international negotiation. There is a committee called National Environment Committee to address all environmental related strategy. There is another committee, National Steering Committee formed by all relevant ministries and civil society representative to develop and overseeing the implementation of national climate change action. NDMC, MoFDM, DMD are also involve with MoEF to work with together. The BMD, SPARRSO, under the MoD, the FFWC, BWDB, under the MoWR are also the key institutions in this field (MoEF, 2009). Although Bangladesh emits a little green house gas but it is also focused in BCCSAP to further reduce the green house gas emissions. Bangladesh seriously started to address the climate change issue after the COP meeting which was held in 2007 in Bali. Bangladesh has already submitted papers to United Nations Framework Convention on Climate Change (UNFCCC), which is an initial national communication (MoEF, 2009). By considering all of the aspects mentioned above, it is clear that Bangladesh has already developed strategies to make the country more resilient to climate change. Bangladesh also implements some CBA programmes. This is a part of good governance. Disaster risk reduction and climate change adaptation influences decentralization and community participation which support good governance. But there is still a lot setback with accountability and transparency to implement the programmes. Corruption is a problem like other South Asian countries. Although Bangladesh has managed to continue peace and political stability, make slow but steady progress in civilizing corruption perceptions, and strengthen public financial management in recent years (WB, 2010b). The current government’s Digital Bangladesh by 2021 vision suggests mainstreaming ICTs as a pro-poor tool to eliminate poverty, ensure good governance and social equity through quality education, healthcare and law enforcement for all, and prepare the people for climate change (PMO, 2010).

Chapter 3

3.4 Bangladesh’s Exposure and Vulnerability to Natural Hazards 3.4.1 Exposure in Bangladesh and Elements are at Risk Cyclones and floods have occupied the greatest risk to Bangladesh (ISDR, 2009a). Cyclone is one of the hazards that Bangladesh suffers most frequently and most of the people die due to cyclone hazard (Figure 3.7(a) and 3.7(b)). Figure 3.7(a) shows that the number of occurrences of cyclone hazard is 137 which is the highest in comparison with other hazards that occurred during 1907-2004. Figure 3.7(b) depicts that the maximum number of people died in Bangladesh due to cyclone hazard. So, it is clear that Bangladesh is exposed to cyclone hazard and Bangladesh remains one of the worst sufferers from cyclonic casualties in the world. Figure 3.7(c) shows that floods in Bangladesh affect a greater number of populations in comparison with any other natural hazards. Millions of acres crops and millions of houses and livestock were washed out and affected by cyclones and storm surges hazard during 1970- 2009 (Figure 3.7(d)). Institutions, bridges, culverts, roads and embankments were also directly affected by cyclones and coastal erosions (Appendix 3.3).

(a) Frequency of Occurence of Major (b) Number of People Died in Major Natural Disasters (1907-2004) Natural Disasters (1907-2004)

Hazard Hazard Exposure Vulnerability Cyclone (137) Drought (5) Cyclone (614,112) Drought (18) Earthquake (6) Flood (64) Earthquake (34) Flood (50,310)

(c) Number (000,000) of People Affected (d) Vulnerability to Cyclone Hazard in by Major Natural Disasters (1907- 2004) Million (1970-2009) 37

12 10 Hazard 4 Vulnerability Cyclone (638) Drought (250) Crops No. of No. of No. of Affected in Affected People Livestock Earthquake (0) Flood (3697) Acre House Affected Died Figure 3.7: Bangladesh’s exposure and vulnerability to natural hazards (a) frequency of occurrence; (b) number of people died; (c) number of people affected; (d) vulnerability to cyclone hazard (Data from ISDR, 2009a; MoWCA, 2010)

Figure 3.8 shows the area of Bangladesh which is directly exposed to coast to cyclone and erosion hazard. There are 19 districts (147 upazilas) out of 64 districts which are called coastal districts in Bangladesh and 48 upazilas in 12 districts (out of 19 coastal districts) are directly exposed to the sea and or lower estuaries. These areas are known as the exposed coast and the remaining 99 upazilas of the coastal districts are termed interior coast.

Chapter 3

Bay of Bengal Area Exposed to the Coast in Bangladesh

Figure 3.8: Area exposed to the Bay of Bengal in Bangladesh (Appendix 3.2)

Cyclone 1991 hit Bangladesh and caused about 150,000 people’s death. Mohal et al. (2006) calculated that if the same cyclone occurs with sea level rise (32 cm), then the inundated delta area would increase from 42% to 51.2%. Again, due to the climate change, if SST increases 2°C then the maximum wind speed will increase 10% (Ali, 1996). Therefore, if cyclone 1991 hit Bangladesh with 10% increased wind speed along with 32 cm SLR, then it would increase the surge height by 1.2-1.7 m near Kutubdia-Cox.s Bazar, eastern coast of Bangladesh (Mohal et al., 2006).

3.4.2 Vulnerability to Hazard Risks The people who live in the exposed coast are considered as vulnerable partly or fully to surge flooding. More than 35 million (now more than 38.5 million (BBS, 2011)) people lived in the coastal zone of Bangladesh who were exposed to cyclones, storm surges, rough seas, salinity intrusion and permanent inundation due to sea level rising. Over 3 million people who lived in an area of 4,200 km2 in 72 offshore islands were extremely vulnerable. The main source of income of around 0.5 million households is fishing in the Bay of Bengal. Working days were lost due to rough weather in the Bay (DMB, 2010). Population density in coastal area is 816 whereas the density for the whole Bangladesh is 976 which is higher compare to coastal zone (Figure 3.9(a)). One of the reasons for this density scenario is people’s migration from the coastal area to inner parts. Figure 3.9(b) shows that the number of female is higher than the number of male in the coastal area. This may be due to travelling of men for job around the country for life sustenance against the poverty in the coastal zone. But, a significant number of transitory people come to the coastal areas during the fishing period from the inner parts of the country. These fishermen are one of the most vulnerable groups in the coastal zone (Karim and Mimura, 2008).

Chapter 3

(a) Population Density per sq. km (b) Ratio of Male to Female 976 M*100/F

100.2 816 97.6

Bangladesh Coastal Bangladesh Coastal

Figure 3.9: Comparions of population (a) density for whole country with coastal area only and (b) male to female ratio for whole country with coastal area only (BBS, 2011)

Disasters adversely affect all aspects of children’s daily life because children have the right to get clean water, sanitation, food, health and education which is seriously hampered due to disasters. Increase of disaster’s frequency and intensity weakens people’s resilience and increases poverty as a result it affects the children, other dependent and vulnerable groups. Under these circumstances, infants, young children, and pregnant and lactating women (PLW) are vulnerable to malnutrition and micronutrient deficiencies. For their dependent and risk prone positions, women and children are particularly prone to any form of vulnerability. From the analysis of the damage and loss assessment of different disasters, it is clear that children are more vulnerable to every disaster. Climate change or particularly SLR will intensify the problems or alter the problems to new social dimensions (MoWCA, 2010). Table 3.3: Typical scenarios in coastal zone (BBS, 2011) Child <15 years 35.6% Total Household 100% Old 65+ 5.1% Household Vulnerable 72.6% Total Vulnerable or Dependent 40.7% Disable 1.5%

Typical coastal scenarios show that 35.6% of coastal populations are children and 5.1% is old (Table 3.3). Thus, at least 40.7% people are vulnerable or dependent. 1.5% of coastal population is disabled which includes speech, vision, hearing, and physical, mental, autism disability. Scenarios also show that 72.6% houses are vulnerable to cyclone hazard due to unstable construction by earth or other unstable materials. Detailed data is presented in Appendix 3.4A, 3.4B, 3.5, and 3.6.

CHAPTER 4: IMPLEMENTATION OF DISASTER RISK REDUCTION PROGRAMMES - HYOGO FRAMEWORK FOR ACTION IN BANGLADESH 4.1 Disaster Management System in Bangladesh Disaster management system in Bangladesh is divided into two parts. The first is the disaster management regulative framework which provides the legislative basis and a detailed institutional framework for disaster risk reduction. The second is the necessary actions for disaster management at national and sub-national level which are guided and described in the regulative framework (SDC, 2010). Disaster management act provides legal basis for the protection of life and property and creates mandatory obligations and responsibilities on different ministries, committees and appointments. Disaster management plans, guidelines for government at all levels and standing orders on disaster have been formulated under disaster management act (SDC, 2010). The national disaster management plan provides the overall guidelines for the different sectors and the disaster management committees at all levels (national and local level such as district, upazila, union) to develop and implement specific plans for their respective areas. Few hazard specific management plans are also developed, such as flood management plan, cyclone and storm surge management plan, tsunami management plan, earthquake management plan, etc. Guidelines for the government at all levels are formulated to assist ministries, NGOs, disaster management committees and civil society in implementing disaster risk management. MoFDM issued the standing orders on disaster in January 1997 (revised, August 2008) to guide and monitor activities related to disaster management in Bangladesh. Different national and sub-national (local level) committees have been developed by this standing order on disaster (SDC, 2010; MoFDM, 2009; DMB, 2010). National Disaster Management Council (NDMC) headed by the Honorable Prime Minister and Inter-Ministerial Disaster Management Co-ordination Committee (IMDMCC) headed by the Minister in charge of MoFDM coordinate and ensure disaster management activities at national level. National Disaster Management Advisory Committee (NDMAC) headed by an experienced/skilled person having been nominated by the Prime Minister advises NDMC at crisis situations. National Platform for Disaster Risk Reduction (NPDRR) and Earthquake Preparedness and Awareness Committee (EPAC) coordinate and facilitate the relevant stakeholders. Cyclone Preparedness Program Implementation Board (CPPIB) reviews the preparedness activities in the face of initial stage of an impending cyclone. Focal Point Operation Coordination Group of Disaster Management (FPOCG), NGO Coordination Committee on Disaster Management (NGOCC), Disaster Management Training and Public Awareness Building Task Force (DMTATF), and Committee for Speedy Dissemination of Disaster Related Warning/ Signals (CSDDWS) headed by DG, DMB coordinate the disaster related training, public awareness and NGOs activities and ensure the speedy dissemination of warning among the people. Sub-national committees (DDMC, UzDMC, UDMC, PDMC, and CCDMC) review and implement the disaster management activities within its own

Chapter 4 jurisdiction and maintain continuous coordination with DMB (SDC, 2010; MoFDM, 2009; DMB, 2010). The entire disaster management system in Bangladesh is shown in Figure 4.1.

Disaster Management Act

Guidelines for Disaster Standing Orders Government at Management on Disaster all levels Plans

Sectoral Hazard Specific MoFDM Development Plans Corporate Plan NDMAC NDMC IMDMCC Plans

Cyclone NPDRR Management Agency Plan MoFDM Plan & EPAC

Flood Local Level Management Plans DRR DMB DGoF CPPIB Paln

Earthquake Management CSDDWS DMTATF FPOCG NGOCC Plan

Tsunami Management DDMC Plan

Others UzDMC CCDMC PDMC

UDMC

Figure 4.1: Disaster management system in Bangladesh

4.2 Institutional Mapping for Disaster Risk Reduction in Bangladesh 4.2.1 Institutional Linkages Government of Bangladesh has seriously addressed the issue of disaster risk reduction. Although all ministry, divisions, departments and autonomous bodies have general roles and responsibilities to reduce the risk of disaster, there are some key ministries and departments who are primarily involved in this issue. Cooperation and coordination (links) among different ministries and departments are mandatory to ensure the disaster risk reduction effectively (MoFDM, 2009; DMB, 2010). DMB created in 1992 under the Ministry of Relief at that time (renamed as Ministry of Disaster Management and Relief which is merged with Ministry of Food in 2002 and currently called MoFDM). MoFDM is the key ministry for coordinating national disaster management efforts across all agencies. DMB is the focal point for the Hyogo Framework for Action (HFA) and it advises the government on all matters relating to disaster management. Three agencies named DMB, DRR, DGoF are under the MoFDM. MoFDM is linked with

Chapter 4 most of the ministries and departments related to disaster risk reduction over the country (Choudhury, 2008). A disaster management regulative framework is strongly recommended by HFA. MoFDM is responsible to develop a legal policy and planning framework with the connection of MoEstablishment/Molaw. MoEd and MoPME are linked with MoFDM to ensure progressive learning and capacity building through training and primary, secondary and tertiary level education about DRR. MoF&P is linked with mainly MoEF, MoA, MoFDM whereas MoEF is linked with MoFDM, MoWR and Universities to ensure the mainstreaming of disaster risk reduction. MoFDM (DMB) works with MoEstablishment/MoLaw, MoF&P, MoLG&RD and MoHA to strengthen institutional mechanisms. MoS&T, MoWR, MoFDM, University (BUET) and Research Institutions work together to update hazard maps. MoLG&RD, MoHA, AFD, MoH&PW, MoS&T, MoD, MoEd, Universities help MoFDM (DMB) to conduct earthquake and tsunami vulnerability assessment. BUET helps MoH&PW as collaboration to update and ensure compliance of the Bangladesh National Building Code. MoFDM, MoWR and MoLG&RD work together to strengthen national capacity for erosion prediction and monitoring and utilize the erosion prediction information at local level. HFA suggested that early warning systems have to be placed for all major hazards, with outreach to communities. Cyclones, floods and droughts are the main hazards in Bangladesh. BMD under MoD is the authorized Government organization for all meteorological activities in the country e.g. to observe different meteorological parameters and to provide weather forecasts for public, farmers, mariners and aviators on routine basis. BMD is also authorized for awareness campaign and warning for cyclone and tsunami. BMD provides the earthquake information as well. BWDB is responsible to construct and maintain all major surface water development projects like major polders, embankments, sluice gates and Flood Control, Drainage and Irrigation projects (FCDI) with command area more than 1000 hectares. BWDB constituted in 1959. FFWC is under BWDB which is authorized to forecast the flood over the country except coastal area. BWDB is also responsible to collect all hydrological data over the country. The DAE under MoA is responsible to provide efficient and effective needs based extension services to all categories of farmer to promote sustainable agricultural and socio- economic development over the country. DAE is also responsible for drought warning. MoSh (BIWTA, BIWTC) receive time to time weather information from BMD to ensure the security of their ships, signals, lighthouse and buoys, jetties and ferries. MoI receives information about cyclone, flood, drought, etc. from BMD, FFWC, DAE and disseminate through RB, BTV, BTRC to the public. CPP volunteers (66,000) disseminate cyclone warnings to the population at risk and help them to evacuate to cyclone shelters or other safe areas. AFD, MoS&T and MoHA help MoFDM, MoWR (FFWC) and MoD (BMD) for technical and technological capacity building to strengthen emergency response system. MoHF (DoH) trains MoFDM volunteers about oral saline, first aid and preventative medicine. DoH also undertakes awareness and education campaigns about health care, including public health, hygiene, sanitation and safe drinking water. MoFA establishs and maintains contact with Donor/foreign government especially at emergency period and also maintains liaison with MoFDM. MoLand develops a sector wise risk mitigation and preparedness strategy plan with

Chapter 4

MoLG&RD, MoWR, MoA. DPHE helps local government to ensure supply of safe and arsenic free drinking water. Local government institutions are connected to MoFDM and MoLG&RD to reduce the risk of disaster within their own jurisdiction (MoFDM, 2009; DMB, 2011; DMB, 2010; SDC, 2010; FFWC, 2010). All of those links that are presented above are depicted in Figure 4.2.

MoI MoWR Research MoA BR, Organization BWDB CEGIS, IWM DAE BTRC, FFWC BCAS, BIDS BADC BTV SPARRSO MoLand

MoF&P MoSh MoD MoS&T BMD BIWTC MoEF BIWTA DoE Universities PMO BUET, DU, AFD MoLG&RD BAU, PSTU LGED

DPHE MoH&PW MoFDM RAJUK, CDA DMB KDA, RDA DRR Local

CPP Level MoEd DGoF

MoPME MoHA BFS&C D MoHFW UN, DFID, DoH MoEstablish- JICA, WB, UNDP and Link with MoFDM ment/MoLaw Link with Others MoFA Others Secondary Connection Donors

Figure 4.2: Institutional (key governmental) map to reduce the risk of disaster in Bangladesh 37

Chapter 4

4.2.2 Missing Links Although Government of Bangladesh has made considerable progress in implementing the issue of disaster management to reduce the risk of disaster there are still few missing links and gaps in Bangladesh. Links of ministries or departments with universities is relatively less. There are 31 public, 51 private and 2 international universities in Bangladesh (UGC, 2009). But links show that few ministries and departments are connected with only 4 of those universities namely Bangladesh University of Engineering and Technology (BUET), University of Dhaka (DU), Bangladesh Agricultural University (BAU), and Patuakhali Science and Technology University (PSTU). This is clearly insufficient. This is mainly due to lack of research works and research funding. Few ministries and departments are linked with the local level that is not also sufficient. Local level organizations are not well connected to universities and research institutions. Research organizations are not also well linked with universities and those research organizations are situated in Dhaka only instead of all over the country.

4.3 National progress on the implementation of the Hyogo Framework for Action 4.3.1 Implementation of HFA Priorities for Action in Bangladesh Bangladesh’s government has started to seriously address the subject of disaster management following the Hyogo Framework for Action 2005-2015 (HFA) to which Bangladesh is one the signatory south Asian countries. The achievements and setbacks of Bangladesh from 2009 to 2011 in the implementation of the five priorities of HFA are presented below: The first priority action is to ensure that disaster risk reduction is a national and a local priority with a strong institutional basis for implementation. A regulative framework for disaster management includes the relevant legislative, policy and institutional framework which are important to create mandatory obligations and responsibilities on ministries, committees and appointments (DMB, 2010). There are four indicators for the first priority action: (1) The presence of policy and legal framework for DRR, (2) Availability of resources to implement DRR plans and activities, (3) Community participation and decentralization and (4) The functioning of a national multi sectoral platform for DRR (ISDR, 2005). Bangladesh achieved a score of 4 out of 5 for the first priority action (DMB, 2011; Djalante et al., 2012). This means that achievement is not comprehensive but substantial and there is still a level of commitment and capacity for achieving DRR. The indicator 1 encompasses the presence of policy and legal framework for DRR at all levels (at national and local). This study finds that draft of National Disaster Management Policy has been made and a final draft of the National Disaster Management Act has been submitted which is under approval process. National Disaster Management Plan (2010-2015) has been approved in April 2010 and revised standing orders on disaster (SOD) have also been approved. A number of sectoral plans e.g. agriculture, water management, education, livestock, fisheries, water and sanitation, health, and small cottage industries have been taken into consideration by DMRD. There is also the National Renewable Energy

Chapter 4

Policy. There is some hazard specific plans e.g. cyclone, flood, tsunami, earthquake, etc. There is a poverty reduction strategy paper (PRSP-II) in Bangladesh. National Education Policy 2010 has been approved (DMB, 2011; DMB, 2010). The indicator 2 encompasses the availability of resources to implement DRR plans and activities. This study finds that about 4.5% of national budget was allocated as DRR budget. Hundred million USD per year was allocated in the year 2009-2010 and 2010-2011 as climate change fund. As hazard proofing sectoral development investments 1.5 billion USD was allocated. Hundred million taka for Capacity Building in Disaster Management and 110 million USD as the Bangladesh Climate Change Resilience Fund (BCCRF) were allocated. For irrigation and removal of water from water-logging areas 42.5 million USD was allocated. Agriculture Insurance Scheme’ worth 1.07 billion USD was provided for the small and medium farmers. Budget was allocated to construct 20 new cyclone shelters. For vulnerability reduction, 127 million USD to support old age people, 14.5 million USD to support insolvent disabled persons, 4.2 million USD to support lactating mothers of low income working group, 47 million USD to support widow, divorced, and distressed women, 10 million USD to support of street children and orphans, 4.7 million USD as endowment fund for Disabled Service and Assistance Centers, 818 million USD as Food Security programmes and 142 million USD as Employment Generation Programme were provided (DMB, 2011). The indicator 3 is community participation and decentralization through the delegation of authority and resources to local levels. Desk study shows that donors, international organizations and civil society have actively involved in Bangladesh with many aspects of DRR. Local governments have legal responsibility for DRR. In SOD, it is mentioned that the local authority shall arrange preparedness for emergency steps to meet the disaster and to mitigate distress without waiting for any help from the centre. There are also budget allocations for the local government. INGOs, local NGOs and local level Union Disaster Management Committee (UDMC) members have already implemented about 60,000 risk reduction small scale interventions. Multi disciplinary training were held on Comprehensive Disaster Management (CDM) where 800 UDMCs, 100 journalists, 150 university teachers, 150 trainers working for public and private training institutes, academies and resource centers participated. A large number of civil society members were also trained. With the support from development partners and World Bank, initiatives to strengthen the local government system (Upazila and Union level) have been taken (MoFDM, 2009; DMB, 2011). The indicator 4 is the functioning of a national multi sectoral platform for DRR. My investigation identified a multi-sectoral National Platform for Disaster Risk Reduction (NPDRR) under the leadership of DMRD Secretary in Bangladesh. NPDRR is formed by 4 civil society members, 12 different sectoral organizations member and 2 women’s organizations member. NDMAC is also a multi-sectoral platform for DRR. SOD suggested developing a multi-level decentralized mechanism of Councils and Committees from the national to grassroots levels. There are 12 national level committees and also committees at the local level (MoFDM, 2009; DMB, 2011). The second priority action is to identify, assess and monitor disaster risks and enhance early warning.

Chapter 4

Early warning systems, in particular for extreme events e.g. cyclones, floods (that may be predicted only few hours before) is very important for DRR (UNDP, 2005). There are four indicators for the second priority action: (1) National and local risk assessments and vulnerability information, (2) Data monitoring, archiving and disseminating system, (3) Presence of early warning systems for all major hazards and (4) National, local, regional/trans boundary risk assessments (ISDR, 2005). Bangladesh achieved a score of 3.5 out of 5 for the second priority action (DMB, 2011; Djalante et al., 2012). This means that achievement is not substantial and there is still some commitment and capacity for achieving DRR. The indicator 1 is national and local risk assessments based on available hazard and vulnerability information and include those risk assessments for key sectors. Literature review shows that there are national risk assessment methods and tools for flood and cyclone in Bangladesh. In revised SOD, 12 guidelines are present for risk assessment. DMRD under MoFDM has already developed detailed risk assessment mapping for earthquake and tsunami for three major cities, Dhaka, Chittagong and Sylhet and also planned to develop it for new eight cities. By using participatory tools, GoB and various humanitarian actors assess the local level risk assessment in most high-risk areas. Drought prone areas and cyclone prone areas have already been identified. Recently river bank erosion prediction model has been developed. There is also progress in assessing disaster and climate risk in agriculture sector. Risk assessment of schools, hospitals and cyclone shelters has still not been done. However, initiatives have been taken (DMB, 2011). The indicator 2 is data monitoring, archiving and disseminating system in place. This study finds that there is a disaster loss database and disaster losses are systematically reported, monitored and analyzed. There is a Disaster Management Information Centre (DMIC) at Disaster Management and Relief Bhaban which is connected to local level offices to centralize all of the hazard and disaster information. CDMP is supporting early warning system for flash flood and key location specific flood warning and CPP to expand their work in five new upazilas in west coast. BRAC has established 5 micro- climatic weather stations to support BMD. Poverty map is updating to use it for risk assessment at pre-crisis prriod. Limited progress has been done to develop a detailed vulnerability map for different specific hazard (DMB, 2011). The indicator 3 is presence of early warning systems for all major hazards with outreach to communities. Literature review shows that there are early warning systems in Bangladesh for major hazards. BMD is responsible for early warning for Cyclone. BMD is also responsible for Tsunami early warning in collaboration with Intergovernmental Oceanographic Commission (IOC). FFWC under BWDB is responsible for early warning for Flood. DAE under MoA is responsible for early warning for Drought. The Community Based Flood Information System (CFIS) is an innovative initiative to disseminate flood forecasting messages to the local communities through mobile phones. Two mobile phone companies, Grameenphone (private) and Teletalk have recently started to disseminate instant early warning messages to their subscribers in two districts, Shirajgonj (flood prone) and Cox’s Bazar (cyclone prone) and planned to expand it 14 coastal districts which is organized by DMB (DMB, 2011; SDC, 2010). The indicator 4 is national and local risk assessments will consider regional/trans boundary risks assessments to ensure a regional cooperation. Institutional arrangements exist between FFWC and India (Central Water Commission) to deliver upstream hydro meteorological data. At the time of 40

Chapter 4 planning, trans-boundary issues have been considered in Bangladesh. There are arrangements between Bangladesh and India to share the information regarding avian influenza (FFWC, 2010; DMB, 2011). The third priority action is to use knowledge, innovation and education to build a culture of safety and resilience at all levels. Disasters can be dramatically reduced by informing and motivating people towards a culture of disaster prevention and resilience, which requires proper data collection, compilation and dissemination of relevant knowledge and information on hazards, vulnerabilities (DMB, 2010). There are four indicators for the third priority action: (1) Availability of information on disasters to stakeholders, (2) School curricula, education material and relevant trainings on DRR, (3) Research on multi-risk assessments and cost benefit analysis and (4) Countrywide public awareness strategy (ISDR, 2005). Bangladesh achieved a score of 3.25 out of 5 for the third priority action (DMB, 2011; Djalante et al., 2012). This means that achievement is not substantial and there is still some commitment and capacity for achieving DRR. The indicator 1 is availability and accessibility of information on disasters to stakeholders at all levels. A desk study shows that there is a network of experts named Bangladesh Disaster Management Education Research and Training (BDMERT) in Bangladesh which is actively working. Key government ministries, research institutions and civil society organizations also have their own websites. Disaster Management Information Centre (DMIC) of DMB also provides information services on disaster to country wide stakeholders. The early warning information (especially flood and cyclone) is available through email and websites and DMB, BMD, CPP and FFWC have been contributing significantly in dissemination of early warning and disaster messages to stakeholders. BTRC, RB, BTV, print and electronic media have also involved in disaster information sharing for community preparedness (DMB, 2011). The indicator 2 is involvement of DRR concept in School curricula, education material and relevant trainings. This study finds that DRR concept is already included in the national educational curriculum in Bangladesh in Primary, Secondary, University levels and also as professional DRR education programmes. Few public and private universities recently introduce Degree programme at tertiary level. In 1997, initiatives have been taken to introduce of DRR programme in various training institutions, universities, research institutions and public services training centres. The draft Disaster Management Act also suggested an establishment of an independent institute for DM training and research. MoEd and MoPME decided to develop a large number of school-cum-flood shelters in flood prone region. Although DRR concept is included in the educational system there is a lack of trained teachers to attain the desired outcomes (DMB, 2011). The indicator 3 is research methods and tools for multi-risk assessments and cost benefit analysis are developed and strengthened. This study finds that DRR is included in the national scientific application and research agenda. Risk assessment mechanism is already being practiced by different development organizations in their respective working areas e.g. for earthquake and tsunami risk assessment. A guideline is already developed for constructing disaster resilient educational institutes. The economic costs and benefits of DRR have not been studied yet. DMRD has already decided to establish a Library to help for the research work (DMB, 2011). The indicator 4 is countrywide public

Chapter 4 awareness strategy to stimulate a culture of disaster resilience. There are public education campaigns on DRR for risk prone communities in Bangladesh. DMB has introduced an Annual Media Award to encourage media personnel in disaster related reporting. National debate has been telecasted each year on disaster issues. Bangladesh Television has introduced a regular programme since April 2008 on DRR and Media has introduced a number of discussions, talk shows on disaster issues. The development of public awareness is a challenge due to societal heterogeneity e.g. different class, gender, age, sex, caste, religion, ethnic minority, old age population. Education has to be done on different levels for better cooperation of the respective societal groups or classes. Bangladesh is one of the countries in the world with the largest NGO communities. These NGOs help government of Bangladesh to create countrywide public awareness on disaster (DMB, 2011; SDC, 2010). The fourth priority action is to reduce the underlying risk factors. Reducing the underlying risk factors need to be integrated into different sector development planning and programmes as well as in post-disaster situations (DMB, 2010). There are six indicators for the fourth priority action: (1) Integration of DRR with development plans and policies, (2) Social development policies and plans to reduce people’s vulnerability, (3) Economic plans and policies to reduce the economic vulnerability, (4) Planning and management of human settlements considering DRR, (5) DRR into post disaster recovery and rehabilitation processes and (6) Disaster risk impact assessments of major development projects (ISDR, 2005). Bangladesh achieved a score of 3.17 out of 5 for the fourth priority action (DMB, 2011; Djalante et al., 2012). This means that achievement is not substantial and there is still some commitment and capacity for achieving DRR. The indicator 1 is integration of DRR with development plans and policies. This study shows that there is a mechanism to protect regulatory ecosystem service. There is integrated planning e.g. ICZM. Bangladesh has prepared NAPA and BCCSAP. There are Climate Change Fund (CCF) and Climate Change Cell (CCC) in Bangladesh. There are some climate change adaptation projects but payment for ecosystem services has not been implemented yet (DMB, 2011). The indicator 2 is implantation of social development policies and plans to reduce people’s vulnerability. It was observed that there are some plans and policies to increase the resilience of risk prone people. There are some facility e.g. Vulnerable Group Feeding (VGF), Food for Work (FFW), Test Relief (TR) and Gratuitous Relief (GR) to reduce and support the poor people in Bangladesh. There is currently no provision for crop and property or micro insurance in Bangladesh. Experience shows that the programmes to reduce the vulnerability are still insufficient (DMB, 2011; SDC, 2010). The indicator 3 is existence of economic plans and policies to reduce the economic vulnerability. It was also observed that GoB is implementing coastal and wetland biodiversity project in partnership with the community and civil society at four ecologically critical areas and there are some projects which are incorporating DRR (DMB, 2011). The indicator 4 is planning and management of human settlements considering DRR. There is little enforcement for the National Building Code. Currently National Building Code is updating. National Land Zoning and National Land Use Planning are preparing by MoLand. Building code is a very challenging issue to implement over the country (DMB, 2011). The indicator 5 is incorporation of DRR into post disaster recovery and rehabilitation processes.

Chapter 4

Investigation shows that post disaster recovery programmes are explicitly incorporate for DRR in Bangladesh. NGOs incorporated DRR in post-disaster response and recovery projects. This tool is new for Bangladesh. Therefore, it will take time to adjust with these new methodologies (DMB, 2011). The indicator 6 is disaster risk impact assessments of major development projects. The Environmental Impact Assessment (EIA) and Disaster Risk Assessment are now mandatory for any large project in Bangladesh (DMB, 2011). The fifth priority action is to strengthen disaster preparedness for effective response at all levels. If authorities, individuals and communities in hazard-prone areas are well prepared to combat disaster, this preparation will reduce the disaster impacts and losses dramatically (DMB, 2010). There are four indicators for the fifth priority action: (1) Policy and capacities for disaster risk management, (2) Disaster preparedness plans and contingency plans at all administrative levels, (3) Financial reserves and contingency mechanisms and (4) Relevant information exchanging procedure (ISDR, 2005). Bangladesh achieved a score of 3.75 out of 5 for the fifth priority action (DMB, 2011; Djalante et al., 2012). This means that achievement is not substantial and there is still some commitment and capacity for achieving DRR. The indicator 1 is the existence of policy and capacities for disaster risk management. Investigation shows that there are policies and progremmes for school and hospitals for emergency preparedness. There are guidelines to build schools and hospitals resilient to disaster but lack of capacity makes it difficult to implement in the field level (DMB, 2011). The indicator 2 is the existence of disaster preparedness plans and contingency plans at all administrative levels. There are plans to face a major disaster in Bangladesh. About 66,000 volunteers are prepared over the country to deal with a major disaster. 30,000 members were taken part into the training on ‘Comprehensive Disaster Management’. GoB has recently purchased some rescue equipments. An Emergency Operation Centre (EOC) is developed under DMRD. Due to lack of resources, training and rehearsals cannot be continued over the year (DMB, 2011). The indicator 3 is presence of financial reserves and contingency mechanisms for effective response. There are national contingency funds but no catastrophe insurance facilities in Bangladesh. GoB has allotted 42 million USD to face climate risk in Bangladesh. There is a national relief fund (contingency) to address a quick response to a disaster in Bangladesh up to local level and discussion is ongoing to develop a National Disaster Response and Recovery Fund (DRF). Experience shows that contingency fund is sufficient to face a medium-scale disaster but additional support is required for major disaster (DMB, 2011; DMB, 2010). The indicator 4 is existence of relevant information exchanging procedure. There are methods and procedures to assess the damage, loss and requirement to tackle the situation at the time of disaster in Bangladesh. DMB already has a cell named Damage and Need Assessment (DNA) and another multi-hazard Risk Vulnerability Assessment Modeling and Mapping (MRVA) cell is going to be established (DMB, 2011).

4.3.2 Discussions and Recommendations on the Implementation of HFA in Bangladesh A critical discussion Bangladesh’s progress in implementing the HFA to build the community safe and more resilient to disaster is provided here. Folke et al. (2003) proposed four

Chapter 4 important factors to develop resilience: (1) Learning from crises to live with change and uncertainty, (2) Nurturing ecological and social diversity for reorganization and renewal, (3) Expanding and combining different types of knowledge for learning and problem-solving, and (4) Creating opportunities for self-organization to deal with cross-scale dynamics to gain social-ecological sustainability; including the strengthening of the local institutions. Learning from crises to live with change and uncertainty: HFA Priority Action 5 includes measures to strengthen disaster preparedness at all level to provide an effective response to disaster. Bangladesh achieved a score 3.75 here which means institutional commitment is attained but there is still a gap. Lack of resources is a problem necessary for consideration by the Government. Nurturing ecological and social diversity for reorganization and renewal: Diversity is a part of resilience which provides a system to continue in the face of change (Folke et al., 2003). Hence the participation and collaboration of different sectors and institutions is important for better coordination and achievement of the priorities. Additionally, this will help to reduce the underlying risk factor (HFA Priority Action 4) which is an important issue. There is some institutional coordination but a lot of setbacks with implementation in Bangladesh. Due to this, it achieved the lowest score 3.17 here. So, Bangladesh needs to focus on this issue. Multi sectoral platform can support the development of sustainable policies to reduce the risk of disaster (HFA Priority Action 1). Substantial achievement has been gained by Bangladesh here (a score of 4 was attained in this priority action). Expanding and combining different types of knowledge for learning and problem-solving: Knowledge about hazards and physical, social, economic and environmental vulnerabilities to disaster is very important to reduce the risk of disaster and disaster can be dramatically reduced by informing and motivating people through knowledge about disasters (ISDR, 2005). Bangladesh achieved a score 3.5 in implementing HFA Priority Action 2 (identify, assess and monitor disaster risks and enhance early warning) and further improvement is ongoing under BCCSAP programmes whereas Bangladesh achieved a score 3.25 in implementing HFA Priority Action 3 (use knowledge, innovation and education to build a culture of safety and resilience at all levels). So, Bangladesh needs to further emphasis to fill the gap to expand their knowledge for solving the problems. Creating opportunities for self-organization to deal with cross-scale dynamics to gain social- ecological sustainability: Resilience may be a precondition for adaptive capacity which includes learning and resources management rule as experience gathered (Folke et al., 2003). HFA Priority Action 1 (ensure that disaster risk reduction is a national and a local priority with a strong institutional basis for implementation) can provide a legal basis for disaster risk reduction. Although Bangladesh achieved a substantial score 4 to implement HFA Priority Action 1 there is still a gap because Disaster Management Act is still in a final draft which needs to be accepted by parliament for field level implementation. IFRCRCS (2008) mentioned five characteristics which a community can be identified as safe and resilient to disaster. The first is if the community can assess and monitor risks and are protected from the disaster risks to minimize losses and damages when a disaster strikes. 44

Chapter 4

Bangladesh achieved a score 3.5 in implementing HFA Priority Action 2 (identify, assess and monitor disaster risks and enhance early warning). Bangladesh has the capability to assess and monitor the risk but there is still a gap in the early warning and dissemination systems. This is why; Bangladesh is implementing programmes to further improve early warning and dissemination system for flood forecasting, cyclone and storm surges under BCCSAP. The second characteristic is if they can sustain their basic community functions and structures despite the impact of disasters. Bangladesh achieved a score 3.75 in implementing HFA Priority Action 5 (strengthen disaster preparedness for effective response at all levels) and a score 3.25 in implementing HFA Priority Action 3 (use knowledge, innovation and education to build a culture of safety and resilience at all levels). That means Bangladesh has an institutional commitment and knowledge for effective response to disaster but there is still gap due to lack of sufficient resources which must be focused on. Bangladesh has to focus on gaining additional knowledge through research work for facing future challenges. The third characteristic is if the community can be reconstructed after a disaster and work towards reducing the vulnerability in future. Bangladesh achieved a score 3.75 in implementing HFA Priority Action 5 and a score 3.17 in implementing HFA Priority Action 4 (reduce the underlying risk factors). Although Bangladesh has preparation to respond to disaster there are still risk factors which need addressing. The fourth characteristic is if they clearly understand developing safety and resilience as a long-term process which needs a continuous commitment to tackle the effects of climate change in future and to adapt the future problems and challenges. Bangladesh achieved a score 3.25 in implementing HFA Priority Action 3 and a score 3.17 in implementing HFA Priority Action 4. Bangladesh understands that time is needed to achieve resilience. So, Bangladesh has focused on knowledge gathering and reducing the risk factors which is a lengthy process. The last characteristic is whether the community understands the meaning of safety and disaster resilience in such a way that it will provide a greater opportunity to meet development goals. Bangladesh achieved a score 4 in implementing HFA Priority Action 1 (ensure that disaster risk reduction is a national and a local priority with a strong institutional basis for implementation). Bangladesh has already developed policy, plan for disaster risk reduction and gained a substantial achievement. Bangladesh achieved a score 3.53 out of 5 in implementing Hyogo Framework for Action which is higher than the world average 3.0. The score achieved by Bangladesh is also higher than some South Asian countries e.g. Nepal, Bhutan, etc. (Djalante et al., 2012). But there is still some commitment and capacity for achieving DRR in Bangladesh. So, my first recommendation is to focus on reducing the underlying risk factors. Participation and collaboration of different sectors and institutions need to be ensured to reduce the risks. Enforcement of rules and regulations need to be implemented at all levels. My second recommendation is to focus on achieving knowledge to understand and solve future problems. Research work will help to understand future problems and to develop the sustainable way to solve the problems. My third recommendation is to update the early warning systems and to enhance proper dissemination systems. Mobile companies, media, local authorities, and NGOs should work together to develop a sustainable dissemination systems. My fourth recommendation is to improve the institutional capacity and capability. Continuous training

Chapter 4 for governmental officials and other related stakeholders should be provided. My fifth recommendation is to ensure sufficient budgetary allocation to enforce DRR initiatives. Government should focus to develop cooperative international relationship to find necessary support for DRR.

4.4 Development Projects related to DRR in Bangladesh 4. 4.1 Key Donor Engagements The national disaster management institutes have collaborative linkages with a host of technical and scientific organizations, like the Flood Forecasting and Warning Centers (FFWCs) under BWDB, Bangladesh Meteorological Department (BMD), Center for Environmental and Geographical Information Services (CEGIS), Institute for Water Modeling (IWM), and the Space Research and Remote Sensing Organization (SPARRSO). GoB and other donors are providing the financial support to them for further development. A number of international financing institutions such as WB, UNDP, JICA, ADB, IDB, DFID, NGOs etc are also involved in financing and supporting disaster management and risk mitigation interventions in Bangladesh. The Disaster Emergency Response Group (DER) is a forum for information sharing, together with government representatives, donor agencies and the NGO community. DANIDA, SIDA, CIDA, Saudi Arabia and other Arab countries are also involved in financing in Bangladesh for different DRR and climate change adaptation programmes. The Arab countries especially, and private donors are involved for the construction of multi- purpose disaster shelters (ISDR, 2009a; SDC, 2010).

4.4.2 Situation of the Current Research There is a considerable overlap between disaster risk reduction and climate change adaptation (SDC, 2010). Bangladesh is an agro-based country (Habib, 2011). This is why; research work is mainly focused on Agriculture or there is few disaster risk reduction and climate change adaptation integrated research works. But the reality is there is no broadly accepted research agenda existing in Bangladesh (IIED, 2009). Recently Bangladesh completed few of research works related to climate change adaptation along with DRR. National Adaptation Programme of Action (NAPA) was developed in 2005 after which BCCSAP was also developed in 2009. CARE-Bangladesh along with BCAS, and Bangladesh Rice Research Institute (BRRI), has completed a project namely Reducing Vulnerability to Climate Change (RVCC) to observe the vulnerabilities of the poorest to extreme weather events. Adaptation research mainly focuses on local level responses to climate change, agricultural impacts and responses to crop adaptations, the health impacts of floods, droughts and disasters. Comprehensive Disaster Management Programme (CDMP) was started in 2003, which was a strategic, institutional and programming approach to provide long-term support for risks reduction. The second phase of this project is running and will continue until the end of 2014. A lot of research has also been carried out to know how the climate change affects different sectors like land, water, food, health, nutrition, etc. IUCN, Action Aid and Practical Action are the three international organizations who are working for community-based adaptation to climate change (AKP, 2010). IIED (2009) includes few 46

Chapter 4 research priorities e.g. modeling of future climate scenarios to understand the trend of land and water which may be affected in future, vulnerability, impact, risk assessments and sectoral cost-benefit analysis to know the impacts of climate change on human, to develop infra-structure development standards.

4.4.3 Development Projects Related to DRR in Bangladesh There is some development and research projects that are ongoing or expected in the future for DRR and to adapt the future climate change in Bangladesh are presenting below.

Table 4.1: Some development projects that have been taken recently for disaster Management and climate change adaptation (AKP, 2010)

Project Period Funding Activities Agency National Adaptation Programme 2005 UNDP The project was implemented by Ministry of of Action to Climate Change Environment and Forests to cover the area of agriculture, water, forestry, fisheries, livestock, health, infrastructure, industry, communication and socio-economic aspects to identify the required action. Climate Change and Disaster 2006- DFID Screening of DFID –Bangladesh Portfolio. Risk 2007 Climate Change Cell 2004- DFID To support the Ministry of Environment and 2009 Forests to establish the Climate Change Cell (CCC). Current support focuses on adaptation that includes work on modeling, research, cross- ministerial coordination and inputs to community risk assessment processes. Chars Livelihoods Programme 2004- DFID A programme working in Jamuna chars on a 2010 range of livelihoods support activities. Structured consultation on a 2007- DFID To develop a climate change strategy by the Climate Change Strategy and 2008 Department of Environment/CCC. Action Plan for Government of Bangladesh Economic Empowerment of the 2008 - DFID Challenge fund for NGOs targeting the extreme Poorest Challenge Fund 2015 poor – to help them lift themselves out of poverty. Community based Adaptation to 2007- UNDP To reduce vulnerability of coastal communities Climate Change through Coastal 2010 to impacts of climate change by increasing Afforestation. resilience. Community-Based Adaptation 2007- UNDP Interventions are in line with national priorities (CBA) Programme under CDMP 2009 with respect to vulnerability and/or adaptive (Comprehensive Disaster capacity development of local communities. Management Programme). Climate Management Plan for the 2008 DANIDA Assist GoB partners to conduct a climate Agricultural Sector screening and develop a climate management plan for the Agricultural Sector. EC Support to NAPA 2008- EC- To implement one or more of the priority implementation 2012 Bangladesh projects identified under NAPA Comprehensive Disaster 2009- EC and To implement climate change related Management (CDMP-II) 2014 DFID components.

Maximum projects that are mentioned above are already implemented and few of them are still ongoing financed by different donor agencies (Table 4.1). NAPA is an important project to identify the immediate necessary actions to adapt the climate. Recently climate change cell

Chapter 4 is established under the MoEF and BCCSAP is completed in 2009. There are some CBA projects which are important for good governance and disaster risk reduction. CDMP (Comprehensive Disaster Management project) is for long-term disaster risk reduction and capacity building project. There are few projects that are already implemented to reduce the vulnerability. Community based Adaptation to Climate Change through Coastal Afforestation is a cross-sectoral measure by which forestation, preservation of environment and barrier against cyclone will be provided. So, few of mentioned projects are research projects and others are the adaptation projects. All of the projects mentioned above (Table 4.1) are to reduce the climate risk and thus all are based on HFA Priority Action 4.

Table 4.2: Donor engagements and plans for medium to long-term (Year- 2022) disaster risk mitigation in Bangladesh (ISDR, 2009a)

Strategy Planned Activities Probable Development Partners 1) Risk (i) Detailed, National Level Multi- Hazard Risk and Vulnerability WB/GFDRR, UNDP, Identification Assessment & Modeling. Others and (ii) Supporting Community Risk Assessments up to Union Levels. UNDP, DFID, CDMP Assessment 2) (i) Disaster Forecasting and Warning systems. JICA, EC, CDMP WB,ADB, JICA/JBIC, Strengthening (ii) Construction of New, and Rehabilitation of Existing, Disaster IDB, Kuwait, Saudi, and Shelters. and OPEC Funds Enhancing (iii) Strengthening and institutionalizing disaster preparedness. UNDP, DFID , CDMP Emergency (iv) Strengthening Local Communication and Sustained Public WB, CDMP, IFRC Preparedness Awareness and Sensitization Campaigns. (i) Establishing an Institute for Disaster Management Training. UNDP, DFID ,CDMP 3) (ii) Professionalizing the Present Disaster Management Institutions. UNDP, CDMP Institutional (iii) Building the Capacity of DMB for Damage, Loss and Needs WB, ADB, UNDP, Assessments CDMP Capacity (iv) Mainstreaming disaster risk reduction and mitigation process. UNDP, CDMP Building WB, ADB, UNDP, (v) Fostering Public-Private Partnership Forums at National level. CDMP WB, ADB, Dutch (i) River Bank Protection Improvement Program. Govt. WB, ADB, Dutch (ii) Coastal Embankment Improvement Program. Govt. 4) Risk WB, ADB, (iii) Upgrading the Standards for roads construction. Mitigation JICA/JBIC, Others Investments (iv) Aforestation of Coastal Belt. WB, ADB, Others WB, ADB, Dutch (v) Sundarbans restoration and improvement programme. Govt., Others WB, ADB, Dutch (vi) Gorai River Restoration Program. Govt., Others (i) Capacity building and Strengthening the Climate Change Cell DFID , UNDP, CDMP (CCC) within DoE. (ii) Developing climate change and climate variability scenario and DFID , UNDP, CDMP 5) Climate prediction models. Change Risk (iii) Conducting research to strengthen knowledge on climate DFID , UNDP, CDMP Mitigation change and climate variability impacts. And Others and (iv) Identifying climate change adaptation options through action DFID , UNDP, CDMP Adaptation research. (v) Incorporating climate change and climate variability impact DFID , UNDP, information in DRR programs and strategies. CDMP, WB, ADB, JBIC/JICA, Others 48

Chapter 4

DFID , UNDP, (vi) Designing and Implementing capacity building programs to CDMP, understand the climate change impacts. Others GOB , IFIs, UN, 6) (i) Establishment of Disaster Response Fund Introducing Bilateral Donors Catastrophe (ii) Catastrophe Risk Financing of Rare Events GOB , WB, GFDRR, Risk ADB Financing 7) Support to the Disaster Partial implement of Hyogo Framework for Action 1, 2, 3, 4, 5 GFDRR Management Programme

Table 4.2 shows some long-term projects to reduce the risk of disaster in Bangladesh. Few of them are already ongoing and rest is proposed for the future. Project 3(v) is based on HFA Priority Action 1. Project 1(i), 1(ii) and 2(i) are based on HFA Priority Action 2. Project 2(iv), 3(i), 3(ii) and 3(iii) are based on HFA Priority Action 3. Project 2(ii), 3(iv), 4(i), 4(ii), 4(iii), 4(iv), 4(v), 4(vi), 5(i), 5(ii), 5(iii), 5(iv), 5(v), and 5(vi) are based on HFA Priority Action 4. Project 2(iii), 6(i), and 6(ii) are based on HFA Priority Action 5. Project 7 is already ongoing and at the end of 2012, it will be completed which is based on HFA different sub-priority wise. All of these projects are planned to complete by year 2022 to make Bangladesh resilient to disasters.

CHAPTER 5: MODEL SET-UP, CALIBRATION AND ANALYSIS OF EROSION ALONG BANGLADESH’S COAST 5.1 Introduction In the coastal regions of Bangladesh, there is continual erosion and accretion due to inland fresh water flows, tides, tidal surges, and high winds. Most of the frontal erosion of the Bay of Bengal was due to storm surges and continuous wave actions. An overall seaward extension of the delta was observed due to presence of net accretion at certain places on the Bay side (Ahmed, 1999). The SWAN (Simulating Waves Nearshore) model has been used in this thesis to investigate the erosion problem along the coast of Bangladesh. Scenarios of erosion problems due to climate change (Sea level rise) in future also will be investigated with the help of SWAN.

5.2 Available Data The coastal zone of Bangladesh is characterized by a low elevation, a lot of small and large river mouths, scattered islands (known as chars) of different sizes and strong hydro- morphodynamics. The Meghna Estuary, one of the largest estuaries on the earth, is situated at the central part of the coastline and plays a vital role on the coastal hydraulics of the upper Bay of Bengal. The eastern coastline is north-south aligned and relatively higher in elevation. Due to sedimentation and erosion induced by tidal flow and river discharge, the location and geometry of channels along the coast of Bangladesh strongly changes even within a few years (Ahmed, 1998; Azam et al., 2004). The following subsections will discuss all the available data needed to investigate the erosion problem along Bangladesh’s coast with the help of SWAN model.

5.2.1 Bathymetry The data for bathymetry was obtained from NOAA, National Geophysical Data Center in spherical co-ordinates. It has then been converted into SWAN structural grid format by using MATLAB. Figure 5.1 shows the bottom level that is considered in SWAN.

Figure 5.1: A graphical representation of bathymetry that is used in SWAN model 50

Chapter 5

5.2.2 Tide and Current Tides in Bangladesh coast originate from the Indian Ocean. After that, it enters into the Bay of Bengal through the two submarine canyons, the ‘Swatch of No Ground’ and the ‘Burma Trench’ and thus arrives very near to the 10 fathom contour line at Hiron point and Cox’s Bazar respectively around the same time. There are two most dominant principal constituents are M2 and S2 whose natural periods of oscillations are 12 hours 25 minutes and 12 hours respectively. Due to extensive shallowness of the North-Eastern Bay (Bangladesh’s Coast), the tidal range and friction distortions concurrently increased by the rise to partial reflections (Mondal, 2001). Tidal waves are affected at least by four main factors causing amplification and deformation of the waves when they approach the coastal belt and coastal islands of Bangladesh. These are: Coriolis acceleration, the width of the transitional continental shelf, the coastal geometry, and the frictional effects due to fresh water flow and bottom topography. Tidal velocity was measured during pre-monsoon and post-monsoon season at different channel along the coast of Bangladesh. Result shows that the maximum velocity at Lower Meghna river is 1.14 m/s, the velocity at Shahbazpur Channel varies 1 3.2 m/s, the velocity at Hatia Channel varies 1 m/s, the velocity at Sandwip Channel varies 1 1 0 m/s (Ahmed, 1998). Locations of different Channels are depicted in Figure 5.2.

5.2.3 Water Level Water level (Tide level) data has been downloaded from the web site for Cox’s Bazar (Figure 5.2) tidal station. Water level for different time in June, 2012 has been taken into account for the sensitivity analysis and model calibration whereas maximum high tide and low tide in May, 2012 have been chosen for the model application. Tidal level data is given in appendix 5.1.

5.2.4 Wind Bangladesh Meteorological Department (BMD) is the authorized Governmental organization for all meteorological activities in the country. Wind data has been taken from BMD for the period from 2001 to 2011. Figure 5.2 depicts four wind stations that have been used for wind calculations. Forecasted wind data is also downloaded from Bangladesh Marine weather website which is used for the model sensitivity analysis and model calibration. Seasonal maximum wind speeds is calculated and presented in the Table 5.1. Table 5.1: Season wise maximum daily wind speeds along Bangladesh’s coast during 2001-2011 Winter Summer Monsoon Autumn

Maximum wind speed in ( ) 7.72 29.32 15.02 11.52

Table 5.1 shows the maximum daily mean wind speeds in different seasons along the coast of Bangladesh. BMD presents wind data as daily mean speed and a daily mean direction for a wind station. Wind data is collected from BMD for the period 2001-2011 and are processed season wise. Table 5.1 shows that the maximum daily mean wind speed is in summer whereas the minimum daily mean wind speed is in winter. The maximum daily wind speed is about 30

Chapter 5

m/s in summer. This is why; the calculation of the rate of erosion has been done up to 30 m/s wind speed. Season wise number of days of wind blowing from different directions along the coast of Bangladesh for the period 2001-2011 is given in (Appendix 5.2).

B C

Hatiya

Mongla Khepupara Wind Station Bay of Bengal A Sandwip Channel Cox's Bazar B Shahbazpur Channel C Hatiya Channel D Lower Meghna River

Figure 5.2: Wind stations that were considered to calculate the rate of erosion and different channels along the coast of Bangladesh

5.2.5 Waves Wave data is not available along the coast of Bangladesh. However, there are few websites that provide forecasted wave and wind data. Such data was downloaded and used for this study. Nearshore forecasted wave and wind data was downloaded daily for the period from 5th June, 2012 to 14th June, 2012. This data is based on Global Wave Watch III model. After that the data was processed and used in SWAN. Offshore wave data was downloaded from NOAA Wave watch III, web site. Other required data are also downloaded from website.

5.3 SWAN Model In SWAN the basic equation that is used to describe the waves is the action balance equation;

(5.1)

Formula 5.1 represents the action balance equation where N ( , ; x, y, t ) is the action density as a function of intrinsic frequency , direction , horizontal co-ordinates x and y and time t. The first term on the left-hand side denotes the local rate of change of action density in time. The second and third terms represent the propagation of action in geographical space

(with propagation velocities ). The fourth term denotes shifting of the relative frequency due to variations in depth and current (with propagation velocity in ). The fifth term represents depth-induced and current-induced refraction (with propagation velocity At the right hand side, the term S [=S ( , ; x, y, t ) ] is a source

Chapter 5

term; which represents the effects of generation, dissipation, and non-linear wave-wave interactions (Ris et al., 1999). The basic equation can be expressed in spherical coordinates:

(5.2) with longitude, λ and latitude, .

5.3.1 Co-ordinate System in SWAN In order to perform the wave computation model, it is necessary to have clear idea of the basic co-ordinate system that is applied in a numerical model. In SWAN, two co-ordinate systems must be selected to set up the model.

The first co-ordinate system is for geographical locations. All geographical locations must be defined in the so-called problem co-ordinate system according to the two following co- ordinate systems in SWAN:  CARTESIAN: All locations and distances are in meters. Co-ordinate is given with respect to x and y axes chosen arbitrarily by the user.  SPHERICAL: All co-ordinates of locations and geographical grid sizes are given in degrees, x is longitude x=0 means Greenwich meridian and x>0 is the East of meridian; y is latitude with y>0 means the Northern hemisphere. Input and output grids have to be oriented with their x-axis to the East, mesh sizes are in degrees. All other distances are in m. The second co-ordinate system is for the directions of winds and waves. There are two options for the convention of the directions of winds and waves in SWAN, they are:  The CARTESIAN convention: The direction where waves are going to or where the wind is blowing to that means the direction to where vector points, measured counter clockwise from the positive x-axis of this system in degrees.  The NAUTICAL convention: The direction where waves are coming from or where the wind is blowing from, measured clockwise from geographic North.

5.3.2 Grid System in SWAN The grid system is used in SWAN model may be either curvilinear or rectangular grid. Three grids must be defined in SWAN computations are mentioned below.

Input grid Input grid is a grid on which the bathymetry, current, water level, friction coefficients and wind field are defined. Input grids may be different from each other, both in dimension and orientation. The spatial resolution of the input grid depends on the accuracy of the spatial details required. Users should choose the spatial resolutions for those input grids in such way that the relevant spatial details are properly resolved and special care is needed in case with extremely complex coastal area and estuary. However, it should be noted that higher the

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resolution, higher the accuracy of the results will be, but at the same time, it needs more time and computer space.

Computational grid Computational grid is a grid on which model solves action balance equation. In SWAN, users can define the orientation (direction), the dimension and the resolution of computational grid, which include the geographical and spectral grids. These two grid systems can be defined independently from each other. Geographical grid: Geographical grid describes the orientation, dimension and the resolution of the area in which wave computation are to be performed. Three types of grid can be used: a regular rectangular grid ( x=constant, =constant), an irregular rectangular grid ( x=variable, =variable) and a curvilinear grid. If higher grid resolution is locally required, grid nesting is optionally available in the SWAN model. By this nesting option, the computations are performed on a coarse grid for a higher area and subsequently on a finer grid for a smaller area. The boundary conditions for the finer grid are obtained from the coarse grid. The x, y resolution and the orientation of the computational grid is defined by the user. In case of spherical coordinates regular grids must always oriented E-W, N-S. The spatial resolution of the computational grid should be selected in such a way that it is sufficient to solve relevant details of the applied wave field. To get the better results, the resolution of the computational grid and the input grid could be used approximately equal, by this way the error due to interpolation between grids could be minimized. In principle the input grid should cover a larger area than the computational grid both in space and time. If the computational grid exceeds the dimensions of an input, the region outside the input grid, SWAN assumes that the particular parameter is identical to the value closer to the boundary. In addition to the computational grid in geographical space, SWAN also calculates also wave propagation in spectral space. So, for each geographical grid the spectral grid has to be mentioned as explained below. Spectral grid: The computational spectral grid needs to be provided, which consists of the frequency space and directional space. Frequency space: frequency space is simply defined as a minimum and maximum frequency and the frequency resolution that is proportional to the frequency itself (common is =0.1f), where f is the frequency. Directional space: In directional space, usually the directional range is the full 360° unless when waves travel within a limited directional range, which is convenient to reduce the computer time and/or space. The directional resolution is determined by the number of discrete directions provided by the user. Table 5.3 contains the recommended guide lines to choose the discretization in SWAN for application in coastal areas.

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Table 5.2: Recommended discretizations for spectral grid in SWAN Directional resolution for wind sea conditions Directional resolution for swell sea conditions Frequency range 0.04 f Spatial resolution

Table 5.2 shows the guidelines for choosing spectral grid in SWAN. Table 5.3 presents all required values that have been used in SWAN for this thesis.

Output grid SWAN can provide outputs on spatial grids that are independent from input grids and computational grids. An output grid must be specified by the user. It must be kept in mind that the information on an output grid is obtained from the computational grid by bi-linear interpolation. Therefore if possible, it is wise to keep three grid systems identical to avoid the interpolation error.

5.3.3 Boundary Conditions in SWAN It is essential to mention the boundary conditions both in the geographical and spectral space to facilitate the integration process of the action balance equation. Boundary conditions in the geographical space: The boundaries of the computational grid in SWAN are either land or water. In case of land there is no problem. The land does not generate waves and in SWAN it absorbs all incoming wave energy. But in the case of water, boundary is a problem. If no wave conditions are known along such a boundary, SWAN then assumes that no waves enter the area and that waves can leave the area freely. This assumption is obviously wrong if incorporated in the model. If there are available observations, they can be used as input at the boundary. Boundary conditions in spectral space: In frequency space the boundaries are fully absorbing at the lowest and highest discrete frequency so that wave energy can freely propagate across these boundaries. If the full circle is used then no boundary conditions are required. But for the reason of economy, it is also possible to provide directional sectors instead of a full circle.

5.4 Overall Model Set-up In this assignment, calculations have been carried out with the latest version SWAN 40.85. The standard settings were applied here to select the different processes in all computations as pre SWAN implementation manual guidelines (SWAN team, 2011). The processes that have been used in this project are tabulated below:

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Table 5.3: The default settings in SWAN that have been used in this project Process Explanation Generation Mode GEN3 1) This is strongly recommended by the manual. 2) Employing the quadruplet wave-wave interaction. 3) Using three different theories of Komen et al., 1984, Janssen, 1991 and Hasselmann et al., 1985 to define the Whitecapping and Quadruplets processes whereas 1st and 2nd generations have used only Holthuijsen and De Boer, 1988. Physical process Whitecapping Komen et al., 1984. Default coefficients. Quadruplets Default coefficients. Depth induced Battjes and Janssen, 1978. Default coefficients. wave breaking Bottom friction (Hasselmann et al., 1973, JONSWAP). Default value. Triads Trfac= 0.10 cutfr= 2.20 urcrit=0.02 urslim=0.01. Set Constant water level, RHO= 1025 and NAUT convention. Stationary/ Stationary 2D 2D mode is more realistic than 1D mode. Due to lack of available nonstationary mode data, only stationary mode is used here. mode Coordinates Spherical The area is large enough to use spherical coordinates. Computational Regular 83°E to 95°E and 18°N to 23°N,1 minute resolution. Grid Circle fmin=0.05, fmax=1.00, mdc=36, msc=31. Bathymetry Structural Mesh 1 minute resolution for whole domain. Wind condition Uniform wind condition in computational grid. Current effect Absence of Although the effect of current near the estuary is significant at least in current effect Monsoon but this study is made without current due to lack of available data. Boundary The shape of JONSWAP spectrum. Default value. Because the result of condition spectra JONSWAP over fetches that are most relevant to the Engineer (Holthuijsen, 2007). Accuracy Standard accuracy Drel=2%, Dhoval=0.02m, Dtoval=0.02s, Npnts=98.5%, Nmax or command criterion mxitst=15 iterations. Output Block & Point Mat file, 2 points (91.25, 21.00) & (88.75, 21.00) to check the model result for sensitivity analysis and model calibration.

A typical command file for SWAN computation is given in the Appendix 5.8.

5.5 Sensitivity Analysis and Model Calibration 5.5.1 Sensitivity Analysis In general, as part of the task to calibrate the model, a sensitivity analysis needs to be carried out. The results from the sensitivity analysis will be helpful to decide a set of parameters that is necessary for model calibration. Figure 5.3 shows the area that has been considered in SWAN and two points (Point-1 & 2) where the model outputs have been taken to compare the results with the forecasted data for the sensitivity analysis and model calibration. Two buoys that are considered for sensitivity analysis and model calibration are also shown in the Figure 5.3.

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83°0'0"E 85°0'0"E 87°0'0"E 89°0'0"E 91°0'0"E 93°0'0"E 95°0'0"E

24°0'0"N 24°0'0"N India Bangladesh 23°0'0"N 23°0'0"N

22°0'0"N 22°0'0"N

21°0'0"N 21°0'0"N Point- 2 Point- 1 Myanmar 20°0'0"N 20°0'0"N

19°0'0"N 19°0'0"N Buoy- 2 Buoy- 1 18°0'0"N 18°0'0"N

17°0'0"N Bay of Bengal 17°0'0"N

83°0'0"E 85°0'0"E 87°0'0"E 89°0'0"E 91°0'0"E 93°0'0"E 95°0'0"E Figure 5.3: Area, points, and buoys that were used in SWAN

There are two boundary conditions that have been used in SWAN for sensitivity analysis. Data has been presented at Table 5.4. Table 5.4: Two boundary conditions for sensitivity analyses Offshore Forecasted Data Offshore Forecasted Data Wind Condition (90.14, 18.13) Buoy-1 (87.56, 18.35) Buoy-2 Water Wind Direction Direction Date and Level Speed (Nautical Hs (m) Tp (s) Direction Hs (m) Tp (s) (Nautical Time (m) (m/s) Degree) Degree) 07.06.12 0.7 6.05 202.5 2.15 9.1 214 2.23 8.9 205 18:00 08.06.12 3.25 6.30 191.25 2.11 9.2 213 2.04 9 202 00:00

By using these two boundary conditions in SWAN, a number of parameters have been investigated to select the parameters that should be used for the model calibration. The results of the sensitivity analysis are presented in Appendix 5.3. The results of sensitivity analysis (Appendix 5.3) show similar model output results whether used Buoy-1 or Buoy-2 is used with constant boundary option. When both Buoys with variable boundary option are used at the boundary, the model result at point-1 & 2 are also look similar to the previous results. Therefore, the Buoy-1 with constant boundary option has been selected for further calculations. Model without considering the bottom friction shows relatively higher significant wave height than with friction condition. Model is fixed for 15 iterations; otherwise the accuracy level may be less than 98.5%. So, 15 iterations have been considered for further calculation. Bathymetry was used with one minute resolution for the whole area. However, it is better to use higher resolution at nearshore if this type of bathymetry is available. Due to lack of high resolution data for this study, the model output

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with and without nesting looks similar. Therefore, nesting will not be considered in other calculations. Same resolutions for all grids (Computational grid, input grid) have been considered here to avoid the interpolation errors.

5.5.2 Model Calibration Average wind speeds and wind directions of forecasted data at point- 1 & 2 have been used for model calibration. Forecasted data depicts that the significant wave height at point- 1 & 2 is similar but peak wave period at point-2 is sometimes higher than that at point-1. The forecasted data at point-1 shows that wave direction is constant over the calibration period (8th June, 2012 to 15th June, 2012) but at point-2, it is fluctuated. The data that has been used for model calibration is given in Appendix 5.4. The main objective of model calibration is to compare the model results with measured data and adjust some model parameters to coincide with the model results with the measured data. The modeled outputs of significant wave height Hs, peak wave period Tp and mean wave direction at two points are presented in Appendix 5.5. To compare the SWAN outputs with forecasted data, a graphical representation is shown in Figure 5.4. Significant wave height Hs, at point- 1 & 2 show similar trends for forecasted data and SWAN outputs for the thirty calibrations but both of them did not completely coincide (Figure 5.4(a) and 5.4(b)). For maximum the calibrated points, the forecasted significant wave height is higher than SWAN output significant wave height. The discrepancies in Peak wave period, Tp at point-1 are comparatively less with the forecasted Tp whereas at point-2, the discrepancies of peak wave period were high (Figure 5.4(c) and 5.4(d)). Although forecasted wave direction is constant over the calibration period at point-1, SWAN output is fluctuated whereas at point-2 both forecasted and calculated wave direction are fluctuated over the calibration period (Figure 5.4(e) and 5.4(f)). The SWAN outputs never match completely with forecasted data. The reasons may be:  The resolution of the bathymetry over the domain is considered same. But to get the better output, at nearshore the resolution should be higher.  At nearshore, there is no availability of measured data. Only 48 hours forecasted data was used. The forecasted data used is the output of another model. So, forecasted data may not be as accurate as measured data, hence the variability of results.  Wind over the domain is considered uniform, which is another source of error. Wind data that is used in SWAN for model calibration is also forecasted data.  Instead of measured data, the forecasted wave data at buoy-1 is used as boundary in SWAN and this forecasted buoy data are also downloaded from another website.

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4 Comparison of Hs (m) at Point- 1 4 Comparison of Hs (m) at Point- 2 3.5 3.5

3 (a) 3 (b)

2.5 2.5

2 2 Hs (m) Hs (m) 1.5 1.5 1 SWAN Forecasted 1 0.5 0.5 SWAN Forecasted 0 0 1 5 9 13 17 21 25 29 1 5 9 13 17 21 25 29 Number of observations Number of observations

12 Comparison of Tp (s) at Point- 1 16 Comparison of Tp (s) at Point- 2 10 14 12 (d)

8 (c) 10

6 8 Tp Tp (s) 4 Tp (s) 6 4 2 SWAN Forecasted 2 SWAN Forecasted 0 0 1 5 9 13 17 21 25 29 1 5 9 13 17 21 25 29

Number of observations Number of observations

250 Comparison (Deg.) at Point-1 250 Comparison of (Deg.) at Point-2 200 200 150 (e) 150 (f) 100 100 SWAN Forecasted

50 50 SWAN Forecasted Peak Peak WaveDirection PeaKWave Direction 0 0 1 5 9 13 17 21 25 29 1 5 9 13 17 21 25 29 Number of observations Number of observations Figure 5.4: Comparison of SWAN outputs with forecasted data (a) at point-1; (b) at point-2 for Hs, (c) at point-1; (d) at point-2 for Tp, (e) at point-1; (f) at point-2 for wave direction

5.6 Model Application to calculate the Erosion along Bangladesh’s Coast After calibration, the model has been applied to calculate the rate of erosion along the coast of Bangladesh. High tide and low tide water levels in May, 01 at Cox’s Bazaar have been used in SWAN (Appendix 5.1). Wind analysis results show that among required 9 wind directions; the southern wind direction is the dominant wind direction along the coast of Bangladesh in summer, monsoon, and autumn (Appendix 5.2). Additionally, western wind direction also has been used for winter to investigate the directional influence on the rate of erosion. Therefore, for the erosion investigation, both southern and western wind directions have been considered for 5 m/s and 10 m/s wind whereas for 15 m/s, 20 m/s, and 30 m/s winds, only southern direction is being selected for model application. Boundary conditions (offshore wave) have been selected with the help of forecasted data and downloaded data from another website.

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Data that is used for erosion investigation along the coast of Bangladesh is given in Appendix 5.6 and required wave data is presented in Appendix 5.7. There are few formulas used to calculate the rate of erosion. Maximum orbital velocity at bottom, can be calculated by SWAN and by using these formulas; the rate of erosion is also possible to calculate.

(5.3)

2 3 Where is the bottom shear stress N/m is the density of sea water 1,025 Kg/m ; is the wave friction factor, ranging from 0.077 to 0.30; is the maximum wave orbital velocity, which is set to in SWAN (Shi et al., 2008).

(5.4)

(5.5) Where is expressed as dry mass of material eroded per unit area per unit time Kg/m2s;

experimental/site-specific erosion constant, its value varies between 0.0002 Kg/Ns and 2 2 0.002 Kg/Ns; =Critical bed shear stress for erosion around 0.1 N/m 0.6 N/m but it should not exceed 1.0 N/m2 (Pandoe and Edge, 2008). The formulas and other related constant values that have been used to calculate the rate of erosion are tabulated below. Table 5.5: The formulas and other required constant values that were used in SWAN Formulas Range of Values Values that is used in SWAN (Average value)

(SWAN manual)

(Shi et al., 2008)

(Barua et al., 1994)

Table 5.4 shows the values that were used in SWAN. The critical bed shear stress for erosion along the coast of Bangladesh, value is less than the range (Table 5.5), because this value is calculated by physical investigation along the coast of Bangladesh (Appendix 5.9) and mentioned in the paper (Barua et al., 1994). A simplified rate of erosion was calculated here as it just shows the rate of erosion in coastal waters along the coast of Bangladesh. The calculated rate of erosion cannot explain the sediment transport which is very important to explain the morphodynamics. Morphodynamics can show the change in bottom topography and beach profile. Morphodynamics includes bathymetry, hydrodynamics, sediment transport, and Bottom-level change (Molen et al., 2004). Therefore, morphodynamics can show that eroding materials whether it will transported or not. To explain the coastal shape and profile, a morphodynamics model should be considered. The rate of erosion cannot explain all Morphodynamics processes as a result cannot show changing beach profile. The rate of erosion at different selected cross sections is compared to show the changes due to different wind speed and direction. Investigation will be done for the current sea state and at a 60

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projected future considering the climate change (sea level rise). Three selected cross sections along the coast of Bangladesh are depicted in Figure 5.5.

89°0'0"E 89°45'0"E 90°30'0"E 91°15'0"E 92°0'0"E 22°35'0"N 22°30'0"N 22°20'0"N 22°15'0"N Bangladesh C 22°5'0"N 22°0'0"N A A 21°50'0"N 21°45'0"N 21°35'0"N 21°30'0"N B B 21°20'0"N 21°15'0"N Bay of Bengal 21°5'0"N 21°0'0"N 20°50'0"N 20°45'0"N C 89°0'0"E 89°45'0"E 90°30'0"E 91°15'0"E 92°0'0"E

Figure 5.5: Cross sections that were considered for comparison and analysis of erosion

Figure 5.6 shows the bottom level along different selected cross sections along the coast of Bangladesh. Figure 5.6(a) shows that the bottom level along cross section A-A is shallower than the bottom level along the cross section B-B. Parts of cross section A-A are dry and wet but the whole cross section B-B is wet. The maximum bottom level elevation (depth) along the cross section A-A is about 13 m whereas along the cross section B-B, it is about 48 m. The bottom level elevation initially fluctuated along the cross section B-B, after that it increases gradually up to zero. Figure 5.6(b) shows the bottom level along the cross section C- C. The maximum bottom level elevation along C-C is about 57 m. The bottom level gradually increases after initial fluctuation. Bottom Level along the Coast of Bangladesh for the cross section A-A & B-B

Bottom Level along A-A Bottom Level along B-B 0

-5

-10 -15

-20

-25

-30 Bottom Level in m BottomLevel -35 -40 (a) -45

-50 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude Bottom Level along the Coast of Bangladesh for the cross section C-C 0 -5

-10

-15

-20 -25

-30

-35 Depthin m -40 -45 -50 (b) -55

-60 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 Latitude Figure 5.6: Bottom level (a) along cross section A-A and B-B; (b) along cross section C-C 61

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Bottom friction is an energy dissipater in JONSWAP spectrum. SWAN can calculate the bottom friction by using Collins, Madsen or JONSWAP expression. In this thesis, JONSWAP expression was used for bottom friction consideration. Figure 5.7(a) and 5.7(b) present the rate of erosion due to 20 m/s southern wind along A-A and B-B at high tide by considering three different bottom friction models (chapter 2). Both of the graphs show that Jonswap model gives the highest rate of erosion whereas Madsen model gives the lowest rate of erosion in comparison with the other two models (Jonswap and Collins). However, JONSWAP model was used here for bottom friction calculation which provides the highest rate of erosion. Figure 5.7 shows the rate of erosion along the coast of Bangladesh due to Collins, Madsen, and JONSWAP expression separately.

Erosion Rate at High Tide for 20 m/s wind considering different friction formulas at the cross section A-A 0.25 Erosion for Collins (a) Erosion for Jonswap

0.2 Erosion for Madsen

S 2

0.15

0.1

Erosion in Kg/m Erosion 0.05

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude

Erosion Rate at High Tide for 20 m/s wind considering different friction formulas at the cross section B-B 0.35 Erosion for Collins 0.3 (b) Erosion for Jonswap

Erosion for Madsen S

2 0.25

0.2

0.15

0.1 Erosion in Kg/m Erosion 0.05

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude Figure 5.7: Comparison of the rate of erosion using different bottom friction model along cross section (a) A-A; (b) B-B

5.6.1 Erosion at the Current Sea States 5.6.1.1 Discussion on the Erosion Scenarios for the Current Sea States Figure 5.8 depicts the erosion scenarios due to different winds in Bangladesh. Generally, the rate of erosion increases with increasing steady wind fetches. All this investigations were done at high tides. Figure 5.8(a) shows an erosion scenario for 5 m/s western wind. The rate of erosion is very low over the coastal waters in Bangladesh due to 5 m/s western wind. Erosion occurs at small regions with a maximum value of 0.55 Kg/m2s. Figure 5.8(b) shows

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an erosion scenario for 5 m/s southern wind. The erosion scenarios are similar to that of (a) and there is no significant change in erosion scenarios due to the changing wind direction. Figure 5.8(c) shows an erosion scenario for 10 m/s western wind. The scenario is still similar to that of (a) and (b). However, erosion occurs at some small regions at maximum value of 0.70 Kg/m2s. The erosion scenario did not change even with changing wind direction and occurred at similar maximum values (Figure 5.8(d)). It can therefore be concluded here that the 5 m/s and 10 m/s wind speeds have no significant erosion effects along the coast of Bangladesh despites its directional changes. For 15 m/s, 20 m/s and 30 m/s wind speeds, investigations have been done only for southern wind because in autumn, monsoon and summer mainly southern wind is dominant along the coast of Bangladesh (Appendix 5.2 and Table 5.1). With southern wind speed of 15 m/s, significant erosion takes place along the coast of Bangladesh at a maximum value of 0.80 Kg/m2s (Figure 5.8(e)). Erosion is mainly taking place along the shoreline. Figure 5.8(f) shows an erosion scenario due to 20 m/s southern wind. More areas are affected by erosion in compared to erosion at (e). The scenarios show that erosion is taken place not only along the shoreline but also some areas into the sea were also affected by erosion. The maximum value of rate of erosion due to 20 m/s southern wind is 1.60 Kg/m2s. Figure 5.8(g) shows an erosion scenario due to 30 m/s southern wind. Large areas in coastal waters are affected by erosion with a maximum value of (the rate of erosion) 1.80 Kg/m2s.

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(a) (b) (c)

(d) (e) (f)

(g)

(g) 30 m/s southern wind

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5.6.1.2 Causes of Erosion in Coastal Waters Dissipation means the loss of energy, and it is very important for the understanding the erosion phenomena in coastal waters. Dissipation in coastal waters includes white-capping, Bottom friction and Depth-induced breaking. Bottom friction is directly related to erosion and depends on the wave orbital velocity near the bottom. Due to this wave orbital velocity near bottom, shear stress at the bottom is developed. If this developed shear stress is higher than the critical shear stress of the soil, then the soil will be eroded. Therefore, the higher the wave orbital velocity nears the bottom, the higher the tendency of erosion. From the figures 5.9(a) and 5.9(b), it is clear that wave orbital velocity without bottom friction is higher or at least equal to the wave orbital velocity with bottom friction and bottom friction reduces the wave orbital velocity. In this study, critical bed shear stress for erosion used was 0.07 N/m2 (Table 5.5). By using this critical shear stress, the threshold velocity for erosion (formula in Table 5.5) can be calculated. The calculated threshold orbital velocity at bottom was 0.0269 m/s. Therefore, if the wave orbital velocity with bottom friction (that means considering bottom friction, white-capping and depth-induced breaking) is higher than 0.0269 m/s, erosion will take place and vice versa. These graphs also indicate that cross section A-A is in coastal waters thus affected by bottom friction. Figure 5.9(a) shows that the wave orbital velocity with and without bottom friction due to 5 m/s wind speed is relatively small but wave orbital velocity with bottom friction is still higher than the threshold velocity along A-A thus erosion happens. The wave orbital velocity with bottom friction is comparatively high (Figure 5.9b) for 30 m/s wind thus the rate of erosion along A-A is higher than that in 5 m/s wind speed.

Orbital velocity at High Tide for 5 m/s Southern Wind with and without bottom friction at the Cross section A-A 1.5 Cross Section A-A with bottom friction (a) Cross Section A-A without bottom friction

0.5 WaveOrbital Velocity m/s in near bottom the

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude

Orbital velocity at High Tide for 30 m/s Southern Wind with and without bottom friction at the Cross section A-A 1.5 Cross Section A-A with bottom friction (b) Cross Section A-A without bottom friction

0.5 WaveOrbital Velocity m/s in near bottom the

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude Figure 5.9: Wave orbital velocity with and without bottom friction along A-A (a) for 5 m/s wind; (b) for 30 m/s wind 65

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5.6.1.3 Analysis of erosions at different cross sections along the coast of Bangladesh Figure 5.10 shows comparative erosion scenarios at high tide and low tide along the cross sections A-A, B-B, and C-C. The trend of erosion along all cross sections is similar and this means that the higher the wind speed the higher the rate of erosion. There is fluctuation in the rate of erosion along different cross sections which is mainly due to fluctuation in water depth along that cross section (Figure 5.6 shows the bottom level along A-A, B-B, and C-C). In general, the rate of erosion along B-B is higher than that along A-A. From the longitude 89.75° E to 90.75° E, the rate of erosion along the cross section A-A is higher than that of cross section B-B; this is mainly due to the water depth. The water depth suddenly increases along B-B after 89.75° E and decreases again sharply. Thus this bottom level significantly influences the erosion in the areas along B-B. Shallow coastal areas are continuously affected by high tides and low tides but in the coastal waters where the water level is relatively higher, those regions are not significantly influenced by high tides and low tides. Figure 5.10(a) and 5.10(d) show that the rate of erosion along A-A is influenced by high tide and low tide. The maximum rate of erosion along A-A at low tides due to 30 m/s wind is about 0.2 Kg/m2s whereas at high tides, the rate is about 0.25 Kg/m2s. That means cross section A-A is shallow enough to be affected significantly by high tides and low tides. But there is no significant change in the rate of erosion along B-B due to high and low tides because along B-B, the water depth is sufficiently higher than that along A-A (Figure 5.10(b) and 5.10(e)). Along C- C, initially the rate of erosion is high after that it decreases (water depth gradually decreases along C-C) both at high tides and low tides (Figure 5.10(c) and 5.10(f)). The rate of erosion due to 30 m/s wind speed is the highest while 5 m/s and 10 m/s wind speed shows very low rate of erosion along A-A, B-B, and C-C. So, the higher the wind speed or the higher the steady wind fetch, the higher the rate of erosion and vice versa. Along parts of the cross section A-A, the rate of erosion is discontinuous because the water depths at those parts are fluctuated and whole area is not under water (Figure 5.10(a) and 5.10(d)). For 15 m/s wind speed, the rate of erosion increases sharply in comparison with 5 m/s and 10 m/s wind. That means, wind speed 15 m/s or higher is sufficient enough to influence for erosion in the coastal waters in Bangladesh. For 5 m/s and 10 m/s wind, wind direction cannot significantly influence the rate of erosion along the coast of Bangladesh.

Chapter 5

Erosion Rate at High Tide for different Wind along the Coast of Bangladesh along the cross section A-A Erosion Rate at High Tide for different Wind along the Coast of Bangladesh along the cross section B-B 0.8 0.8 5 m/s,West wind 5 m/s,West wind 5 m/s,South wind 5 m/s,South wind 0.7 (a) 10 m/s,West wind 0.7 (b) 10 m/s,West wind 10 m/s,South wind 10 m/s,South wind

0.6 15 m/s,South wind 0.6 15 m/s,South wind S

S 20 m/s,South wind 20 m/s,South wind 2 2 30 m/s,South wind 30 m/s,South wind 0.5 0.5

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude Longitude

Erosion Rate at High Tide for different Wind along the Coast of Bangladesh along the cross section C-C Erosion Rate at Low Tide for different Wind along the Coast of Bangladesh along the cross section A-A 0.8 0.8 5 m/s,West wind 5 m/s,West wind 5 m/s,South wind 5 m/s,South wind 0.7 (c) 10 m/s,West wind 0.7 (d) 10 m/s,West wind 10 m/s,South wind 10 m/s,South wind

0.6 15 m/s,South wind 0.6 15 m/s,South wind S

S 20 m/s,South wind 20 m/s,South wind 2 2 30 m/s,South wind 30 m/s,South wind 0.5 0.5

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Latitude Longitude

Erosion Rate at Low Tide for different Wind along the Coast of Bangladesh along the cross section B-B Erosion Rate at Low Tide for different Wind along the Coast of Bangladesh along the cross section C-C 0.8 0.8 5 m/s,West wind 5 m/s,West wind 5 m/s,South wind 5 m/s,South wind 0.7 (e) 10 m/s,West wind 0.7 (f) 10 m/s,West wind 10 m/s,South wind 10 m/s,South wind

0.6 15 m/s,South wind 0.6 15 m/s,South wind S

S 20 m/s,South wind 20 m/s,South wind 2 2 30 m/s,South wind 30 m/s,South wind 0.5 0.5

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 Longitude Latitude Figure 5.10: Erosion at current state due to different wind, at high tides along (a) A-A; (b) B-B; (c) C-C; at Low tides along (d) A-A; (e) B-B; (f) C-C

Chapter 5

5.6.2 Comparison of Erosion Considering Climate Change 5.6.2.1 Comparison of Erosion at Current Sea State regarding Climate Change Bangladesh has been identified as one of the most vulnerable countries to climate change by the international community (DOE, 2006). This climate change may include change in temperature, rainfall, and increase in sea level, salinity intrusion into country, etc. But for erosion comparison, only sea level rise has been taken into consideration. There are different studies for the sea level rise scenarios in Bangladesh. For this study, the projected scenarios of the sea level rise in 2030 and 2050 due to climate change in Bangladesh by IPCC and NAPA were considered. Only sea level rise was considered here whereas other values were considered same as current state. Data that is used in SWAN for erosion calculation regarding climate change is given in Appendix 5.10. Figure 5.11 shows comparative erosion scenarios at current climate, and in 2030 and 2050 considering the climate change (sea level rise) along A-A, B-B, and C-C for different wind. Figure 5.11(a) shows that there is no significant change in the rate of erosion due to sea level rise along A-A in 2030 for different winds. Figure 5.11(b) and 5.11(c) also show that there is no significant change in the rate of erosion along B-B and C-C respectively in 2030 for different winds. In 2050, the rate of erosion along A-A, B-B, and C-C also show that there is no significant change in comparison to the current rate of erosion due to same wind (5.11(d), 5.11(e), and 5.11(f)). There are eight lines in each graph but four lines are depicted. Depicted four lines represent the rate of erosion regarding climate change in 2030 and 2050. The current rate of erosion lines are not seen here. That means, the rate of erosion regarding climate change is higher than that in current state for same wind but change is not significant thus the lines overlap and change cannot be seen clearly in this scale. Therefore, it can be concluded that the rate of erosion in coastal waters in Bangladesh in 2030 and 2050 is higher than the current state but the change is not significant.

Chapter 5

Erosion Rate at High Tide in 2030 Considering Climate Change along the Coast of Bangladesh along A-A Erosion Rate at High Tide in 2030 Considering Climate Change along the Coast of Bangladesh along B-B 0.8 0.8 5 m/s, present southern wind 5 m/s, present southern wind 5 m/s, 2030 Southern wind 5 m/s, 2030 Southern wind 0.7 (a) 10 m/s, present southern wind 0.7 (b) 10 m/s, present southern wind 10 m/s, 2030 Southern wind 10 m/s, 2030 Southern wind

0.6 20 m/s, presen Southern wind 0.6 20 m/s, presen Southern wind S

S 20 m/s, 2030 Southern wind 20 m/s, 2030 Southern wind 2 2 30 m/s, presen Southern wind 30 m/s, presen Southern wind 0.5 0.5 30 m/s, 2030 Southern wind 30 m/s, 2030 Southern wind

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude Longitude

Erosion Rate at High Tide in 2030 Considering Climate Change along the Coast of Bangladesh along C-C Erosion Rate at High Tide in 2050 Considering Climate Change along the Coast of Bangladesh along A-A 0.8 0.8 5 m/s, present southern wind 5 m/s, present southern wind 5 m/s, 2030 Southern wind 5 m/s, 2050 Southern wind 0.7 (c) 10 m/s, present southern wind 0.7 (d) 10 m/s, present southern wind 10 m/s, 2030 Southern wind 10 m/s, 2050 Southern wind

0.6 20 m/s, presen Southern wind 0.6 20 m/s, presen Southern wind S

S 20 m/s, 2030 Southern wind 20 m/s, 2050 Southern wind 2 2 30 m/s, presen Southern wind 30 m/s, presen Southern wind 0.5 0.5 30 m/s, 2030 Southern wind 30 m/s, 2050 Southern wind

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Latitude Longitude

Erosion Rate at High Tide in 2050 Considering Climate Change along the Coast of Bangladesh along B-B Erosion Rate at High Tide in 2050 Considering Climate Change along the Coast of Bangladesh along C-C 0.8 0.8 5 m/s, present southern wind 5 m/s, present southern wind 5 m/s, 2050 Southern wind 5 m/s, 2050 Southern wind 0.7 (e) 10 m/s, present southern wind 0.7 (f) 10 m/s, present southern wind 10 m/s, 2050 Southern wind 10 m/s, 2050 Southern wind

0.6 20 m/s, presen Southern wind 0.6 20 m/s, presen Southern wind S

S 20 m/s, 2050 Southern wind 20 m/s, 2050 Southern wind 2 2 30 m/s, presen Southern wind 30 m/s, presen Southern wind 0.5 0.5 30 m/s, 2050 Southern wind 30 m/s, 2050 Southern wind

0.4 0.4

0.3 0.3

Erosion in Kg/m Erosion in Kg/m Erosion 0.2 0.2

0.1 0.1

0 0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 Longitude Latitude

Figure 5.11: Comparison of the rate of erosion at current state and, in 2030 along (a) A-A; (b) B-B; (c) C-C; in 2050 along (d) A-A; (e) B-B; (f) C-C

Chapter 5

5.6.2.2 Change in rate of Erosion due to Climate Change Figure 5.12 shows the change in the rate of erosion due to sea level rise along A-A, B-B, and C-C due to 30 m/s wind in 2030 and 2050 in compare to current states. Graphs are plotted in small scale to see the change in the rate of erosion. Figure 5.12(a) show that the change in the rate of erosion in 2030 and 2050 is positive. That means, the rate of erosion increases in 2030 and 2050 in comparison with the rate of erosion at current seas state along A-A and change in 2050 is higher than that in 2030. Figure 5.12(b) and 5.12(c) also show similar increasing trend along B-B and C-C respectively. Although the change in the rate of erosion along different cross section is less, there is increasing trend. Therefore, it can be concluded that the rate of erosion will be increased due to sea level rise in coastal waters in Bangladesh but the increased rate is not significant in 2030 and 2050.

Change in rate of Erosion at High Tide for 30 m/s Southern Wind along A-A 0.05 Change in rate of Erosion by 2030 along A-A

0.045 (a) Change in rate of Erosion by 2050 along A-A S

2 0.04

0.035

0.03

0.025

0.02

0.015

0.01 Change in Erosion in Kg/m in Change Erosion 0.005

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude

Change in rate of Erosion at High Tide for 30 m/s Southern Wind along B-B 0.05 Change in rate of Erosion by 2030 along B-B

0.045 (b) Change in rate of Erosion by 2050 along B-B S

2 0.04

0.035

0.03

0.025

0.02

0.015

0.01 Change in Erosion in Kg/m in Change Erosion 0.005

0 89 89.25 89.5 89.75 90 90.25 90.5 90.75 91 91.25 91.5 91.75 92 Longitude

Change in rate of Erosion at High Tide for 30 m/s Southern Wind along C-C 0.05 Change in rate of Erosion by 2030 along C-C

0.045 (c) Change in rate of Erosion by 2050 along C-C S

2 0.04

0.035

0.03

0.025

0.02

0.015

0.01 Change in Erosion in Kg/m in Change Erosion 0.005

0 20.75 20.9 21.05 21.2 21.35 21.5 21.65 21.8 21.95 22.1 22.25 Latitude Figure 5.12: Change in erosion due to 30 m/s wind considering SLR along (a) A-A; (b) B-B; (c) C-C 70

Chapter 5

5.6.2.3 Effects of SLR on Erosion From the discussion presented above, it is clear that the rate of erosion will increase due to sea level rise in 2030 and 2050 in Bangladesh but the change is very low. However, the main effect of SLR on erosion is clearly presented in Figure 5.13. Though the rate of erosion will not change significantly, new areas in the coast will start to erode due to SLR- landward coastal retreat (Figure 5.13). Thus, new areas will inundate and erode and the deposition of erosion materials further into the sea will also take place. Therefore, sea will intrude the coastal areas and the country land area will be reduced by a developing new beach profile.

Eroded material moves further into sea with time

Figure 5.13: Simplified model of landward coastal retreat under SLR (modified from UNEP, 2010)

CHAPTER 6: ADAPTATION MEASURES FOR EXTREME EVENTS MANAGEMENT 6.1 Adaptation and Management for Changing Climate IPCC (2012) presents six approaches to adapt and manage the risk of disaster for a changing climate. These approaches are reducing exposure, reducing vulnerability, transformation of disaster management system, preparation, responding and recovering to climate change, risk sharing and transfer, and increasing resilience to climate change. All of these approaches are connected to each other. Exposure and vulnerability are key determinant to reduce the risk of disasters and depend on economic, social, geographic, demographic, culture, institutional, governance and environmental factors. Reducing exposure and vulnerability will significantly reduce the risk of disaster for climate change. Transformation of disaster management system includes altering rules, regulation, legislative, financial institutions, and technological or biological systems to provide legal basis for climate change adaptation. Risk sharing and transfer, which include insurance, micro-insurance, reinsurance at all levels, are important to reduce vulnerability and thereby, increase resilience to climate extreme. Preparation and respond especially at post-disaster to provide an opportunity for recovering by rebuilding houses, reconstructing infrastructures, and rehabilitating livelihood at least as prior to disaster will help to enhance resilience and sustainable development. Therefore, adaptation to climate change is an integrated approach to reduce the climate risk in future (Figure 6.1).

Reduce Exposure

Increase Transfer Resilience and Share to Changing Risks Risks

Approaches

Prepare, Respond, Transformation and Recover

Reduce Vunerability

Figure 6.1: The approaches to adapt and manage for climate change (IPCC, 2012)

UNDP (2005) divided adaptation measures into three groups. The first is sectoral which means adaptations for sectors which may be affected by climate change e.g. in agriculture, for example, due to less rainfall and higher evaporation, extension in irrigation is required. The second is multi-sectoral which means the management of natural resources that cover sectors 72

Chapter 6 e.g. water resources management, river basin management. The third is cross-sectoral which means measures can cover several sectors e.g. education and training, public awareness campaigns, monitoring, observation and communication systems, climate research, and data collection, etc.

6.2 Low Regret Adaptation in Bangladesh Low regret adaptation is an option for managing the risks of climate extremes and disasters which provides a benefit now and a range of projected climate scenarios. IPCC (2012) listed few potential low regret measures e.g. early warning systems; risk communication between decisionmakers and local citizens; sustainable land management; and ecosystem management and restoration. Improvements to health surveillance, water supply, sanitation, and irrigation and drainage system; climate proofing of infrastructure; development and enforcement of building codes; and better education and awareness also mentioned as low regret measures. Many of these adaptation provides co-benefits e.g. improvement in livelihoods, human well being, and biodiversity conservation (IPCC, 2012). Bangladesh is the worst victim to climate change. This is why; different adaptation measures are already present here. Both hard infrastructures and soft policy measures jointed with communal practices, sectoral, multi-sectoral, and cross-sectoral adaptation are in place in Bangladesh as adaptation measures to extreme climate events. Hard infrastructures include coastal embankments, foreshore afforestation, cyclone shelters, early warning systems, and relief operations whereas soft measures include design standards for roads and agricultural research and extension like the introduction of high-yielding varieties of crops. Due to the implementation both of adaptation measures, the country has become more resilient in facing hazards that can be evidenced by reducing number of fatalities due to recent disasters (WB, 2010c). Some of these measures are presented below: Coastal embankments: In the early sixties and seventies, 123 polders (of which 49 are sea- facing) were constructed to protect the low-lying coastal areas of Bangladesh from tidal flood and salinity intrusion to reduce the exposure. Although polders are an effective measure for protection against storm surges and cyclones, breaking of embankments due to overtopping, erosion, inadequate operation and maintenance are a common phenomenon (WB, 2010c). Foreshore afforestation to protect sea-facing dikes: Foreshore afforestation is a cost-effective technique to decrease the impacts of cyclonic storm surges by dissipating wave energy and reducing hydraulic load on the embankments during storm surges. This is also an exposure reducing approach. Recently 60 km of forest belts exist on the 49 sea-facing polders with a total combined length of 957 km (WB, 2010c). Cyclone shelters: Although cyclone shelters are currently very important to protect human lives and livestock during cyclones, from the focus group interviews, it is clear that there are a lot of limitations to use the cyclone shelters. These limitations mainly include the lack of convenient facilities in the existing design; distance from the homestead; difficulties in accessing the shelters; the unwillingness to leave livestock behind; deficiencies of user- friendly facilities for women and people with disabilities; overcrowding; and lack of

Chapter 6 sanitation facilities (WB, 2010c). There are total 2133 cyclone shelters in the coastal districts in Bangladesh (Shamsuddoha and Chowdhury, 2007). This is also an exposure reducing and resilience increasing approach. Early warning systems: Early warning and evacuation systems have played a vital role to save lives during cyclones. The BMD tracks cyclones and issues a forewarning to indicate the time and the areas that are likely to be affected by the cyclonic storm. FFWC is authorized to forecast the flood over the country except coastal area. This information of flood or cyclone is broadcast through newspapers, televisions, and through other media to stakeholders (Figure 6.2).

Message Data from Satellite Data from from BMD, 86 Data from imagery 35 field RTH, WL, 56 India Satellite from Radar observatio New RF by (Central images SPARSS Observatio ns Delhi SSB Water from O, Dhaka ns (hourly (hourly) (continuou wireless, Commissi NOAA ½ hourly (3 hourly) s) mobile on) and IMD

Primary connection Secondary connection Bangladesh Meteorological FFWC, Department Radio using (BMD) Bangladesh MIKE 11

Newspapers Storm Warning Center Public Bangladesh Cyclone Television Preparedness (BTV) Forecast Program Warning for 24h, (CPP) Mobile 48h, 72h Company

Relief control T/P Channels DAE

All concern National International Authority Shipping Coordination exchange Authority stations Center

Figure 6.2: Cyclone and Flood information flows in Bangladesh (modified from UNEP, 2010)

Chapter 6

Closure dam: Closure dam is very effective and frequently used technology for flood and erosion protection in Bangladesh. Closure dams are hard engineered structures which main function is to prevent coastal flooding. It is used to shorten the required length of defences behind the barrier. Its construction cost is low because mainly local materials are mainly used to construct closure dam in Bangladesh. Construction materials includes e.g. clay filled sacks bamboo, reed rolls, stell beams, bricks and blocks, palm leaves, reed bundles, timber piles, jute reed bundles, golpata leaves, etc (UNEP, 2010). This is another exposure reducing approach. A picture of closure dam construction is given below (Figure 6.3).

Figure 6.3: Closure dam under construction at Jamuna river, Bangladesh (UNEP, 2010)

Grass plantation at the slope of polders: Vetiver grass is a type of grass that is planted along the slope of polders to protect it from erosion. Vetiver grass is commonly found in different districts of Bangladesh but it is not common in the coastal region including offshore islands. Vetiver is commonly known over the country by different names like Binna or Binnaghas or Khas-khas (common in most of the districts), Binnachoba (Manikgonj, Mymensing, Kishoregonj, and greater Sylhet), Biana (Rajshahi, Chapainawabgonj), Chengamura or Chengamuri (greater Noakhali and greater Comilla) and Bana, Bena, Bena-jhar, Binithoa (southern districts). Vetiver has been integrated for vegetation model in Coastal Embankment Rehabilitation Project (CERP) and it has been introduced in eighteen coastal polders over eighty-seven kilometers of earthen embankment combined with other economic plants. Vetiver has also been planted in different types of low-cost toe-protection trials with soil- cement mixture bags, pre caste concrete frames, zigzag beams, octagonal hollow blocks etc. There are successful cases where the initial protection and watering could be ensured but vertical growth of roots were shorter than expected in some places (Islam, 2003). Islam (2003) has suggested that Vetiver plantation be started by early March with continuous one month irrigation then followed by second stage by end of October with continuous one month irrigation with sweet water to get the better plantation output. This is also to reduce the exposure and increase the resilience. A picture of Vetiver grass is depicted in Figure 6.4. 75

Chapter 6

Figure 6.4: Plantation of vetiver along polder (Islam, 2003)

Decentralization of relief operations: Historical relief operations were centralized in Dhaka which was far away from the actual impacts and affected location and population, resulting in a long chain of command and delayed effective relief. Recently this system has improved by decentralizing the operations. Pre-positioning of emergency relief materials like life-saving drugs and medical supplies are playing an important role in quick response to save lives (WB, 2010c). This is a preparation, respond and recover approach for disaster management. The NAPA suggested few urgent adaptation measures for Bangladesh to address adverse effects of climate change including variability and extreme events based on existing coping mechanisms and practices. These adaptation measures are for capacity building, awareness rising, intervention, and research. Maximum of these suggested measures are Multi-sectoral and cross sectoral (MOEF 2005).

6.3 Costs of Adaptation Measures to Tropical Cyclones and Storm Surges WB (2010c) calculated adaptation cost by 2050 to cyclone in Bangladesh is presented below:

. Table 6.1: Adaptation cost to cyclone and storm surges by 2050 in Bangladesh (WB, 2010c)

Baseline scenario Additional risk due to Climate change scenario (Existing risks) (1) climate change (2) total risk= (1) +(2) Adaptation Annual Annual Annual Investment Investment Investment option maintenance maintenance maintenance cost (USD) cost (USD) cost (USD) cost (USD) cost (USD) cost (USD) million million million million million million Polders 2,462 49 893 18 3,355 67 Afforestation 75 75 Cyclone 628 13 1,219 24 1,847 37 shelters Resistant 200 200 housing Early warning 39 8 39 8 system Toatal 3,090 62 2,426 50 5,516 112

Chapter 6

Table 6.1 presents the cost of adaptation for different adaptation measures to climate change for cyclone and storm surges in Bangladesh by 2050. Bangladesh has already invested in the adaptation to the tropical cyclones and storm surges since 1960. This investment provides for construction of embankments, cyclone shelters; coastal afforestation; and development of early warning systems. Recently climate change e.g. sea level rise adds a new dimension which needs addressing. Embankment and Polder’s height need to increase due to Sea level rise. Cyclone shelters need frequent maintenance; Houses in the coastal areas need cyclone resistance development; more areas in the coast need afforestation. Implementation of all these require a lot of investment to adapt to climate change in Bangladesh. A lot of development is necessary in the forecasting sector for reliable early warning and effective dissemination. World Bank calculated that Bangladesh requires 5,516 million USD to adapt the climate change scenario by 2050 and in addition 112 million USD as annual maintenance cost (WB, 2010c).

CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS 7.1 Conclusions From the cyclonic disaster history of Bangladesh, it is clear that at least 157 cyclones hit Bangladesh and about two million people died along with massive economic damages occurring due to cyclones and cyclone-induced storm surges during 1584-2009, which makes Bangladesh the number one nation at risk of tropical cyclones. Climate change may intensify this severity in future. Extreme events and disasters like irregular or excessive rainfall, temperature extremes, and droughts are already observed in Bangladesh. Natural hazards may not be stopped but they can be managed to reduce the risk. So, disaster risk reduction approach like Hyogo Framework for Action is very important to take into account. Achievements of Bangladesh to implement the disaster risk reduction programmes are significant and Bangladesh achieved a score 3.53 out of 5 to implement HFA during 2009- 2011, which indicates that there is still some commitment and capacities to achieving disaster risk reduction due to lack of resources and research. Research work is very important to know the future scenarios of disasters and to develop a plan of action to manage the new risk scenarios. Recently, a number of institutes and universities of Bangladesh have started climate change and disaster risk reduction related education and research work but this is still insufficient to manage the current and future risks. Coastal erosion is another natural hazard suffered by the coastal population of Bangladesh. This erosion phenomenon along the coast of Bangladesh is also investigated here with the help of SWAN under a number of assumptions below:  There is no influence of currents  Wind condition is considered uniform over the computation grid  Water level over the computation grid is uniform  Only stationary mode has been carried out here  Structured grid has been used The main reason for these assumptions is the lack of data. The study established the following findings by erosion modeling:  In summer the maximum wind speed of daily wind average is the highest along the coast of Bangladesh whereas in winter, it is the lowest.  In summer and monsoon, the wind is mainly blown from south but in winter, it is opposite whereas in autumn, it is from different directions or calm.  Although the trend of forecasted significant wave height Hs and model output Hs is similar, maximum model output value of Hs is lower than the forecasted Hs.  The rate of erosion is increased with the increasing wind speed or wind energy.  Critical bed shear stress for erosion along the coast of Bangladesh is relatively low 2 2 2 = 0.07 N/m , since the usually used range is 0.1 N/m to 0.6 N/m .  The threshold wave orbital velocity near the bottom for erosion along the coast of Bangladesh is 0.0269 m/s.

Chapter 7

 For 5 m/s and 10 m/s wind speed, the rate of erosion is very low but for 15 m/s or higher wind speeds, the rate of erosion increases dramatically.  The rate of erosion along a cross section at nearshore is significantly influenced by the water depth along that cross section.  The rate of erosion in 2030 and 2050 considering climate change (SLR) is higher in comparison with the current rate of erosion in the coastal waters in Bangladesh but the increased rate is not significant. New areas in the country will be affected by erosion. Generally, it can be concluded that SWAN can describe 2D effects along the coast of Bangladesh satisfactorily even with the aforementioned assumptions. However, it can also be derived from the study that SWAN gives the overall scenarios of erosion correctly but for characterization of the beach profile due to erosion, detailed input data and sediment transport model (morphodynamics model) are required.

7.2 Recommendations Based on this study the following recommendations can be suggested:  Integration, cooperation, coordination and harmonization among different DRR institutions in Bangladesh are very important to ensure the sustainability to manage the future disaster risk in Bangladesh.  There is a significant overlap between DRR and CCA. So, to implement any DRR activities, it needs to take into account the shifting risks associated with climate change and ensure that DRR activities will not increase the medium or long term vulnerability to climate change.  Pre-disaster approaches like door-to door awareness campaigns for capacity building, early warning and dissemination systems, and research on forecasting natural disasters will be focused and funds for those activities will be ensured whereas implementation of relief and rehabilitation programmes with accountability must be ensured at post- disaster.  Recent bathymetry of higher resolution and unstructured grid should be used in SWAN for better prediction of erosion.  For an improvement of the results, future research should try to consider the current along the coast of Bangladesh and a variable wind field in the computational grid.  Morphodynamics’ model needs to consider getting the real profile along Bangladesh’s coast.  For climate change analysis, long term authentic data is necessary which is absent in this study. So, a digital system to collect the required data should be established.  Insurance for coastal population must be enforced. Special provision must be made for women, children, the aged and disabled people.  Agricultural/development time schedule should be arranged in such a way that cyclone period may be avoided.  Education and training is very important. Bangladesh is a democratic country and local level election is normally held in every five years in which a lot of new officials

Chapter 7 are elected as local level public authority. Therefore, continuous training for public sector is very important to ensure the sustainability of DRR and CCA programmes.

REFERENCES

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Appendix

Storm Wind Damage Si. Nature of the Surge Year Month Date Speed Death (US $ No. phenomena Height (km/h) Million) (m) 1 1584 200,000

2 1585 Severe Cyclonic Storm 3 1789 20,000 4 1797 November Severe Cyclonic Storm 5 1822 May Severe Cyclonic Storm 40,000 6 1831 October Cyclonic Storm 7 1864 Severe Cyclonic Storm 100,000 8 1872 October Cyclonic Storm 270 100,000- 9 1876 October 31 Super Cyclonic Storm 12-14 400,000 10 1895 October Cyclonic Storm Severe Cyclonic Storm 11 1897 October 24 175,000 with Hurricane 12 1898 May Cyclonic Storm 13 1901 November Cyclonic Storm 14 1904 November Cyclonic Storm 143 15 1909 October 16 Cyclonic Storm 698 16 1909 December Cyclonic Storm 17 1911 April Severe Cyclonic Storm 120,000 18 1912 Severe Cyclonic Storm 40,000 19 1913 October Cyclonic Storm 500 20 1917 May Severe Cyclonic Storm 70,000 21 1917 September 24 Cyclonic Storm 432 22 1919 September 20-25 Severe Cyclonic Storm 40,000 23 1922 April Cyclonic Storm 24 1923 May Cyclonic Storm 25 1926 May Cyclonic Storm 606 7,000- 26 1941 May 26 Cyclonic Storm 7,500 27 1942 October Severe Cyclonic Storm 28 1948 May 17-19 Cyclonic Storm 1,200 29 1950 November 15-20 Cyclonic Storm 30 1955 October Cyclonic Storm 1700 63 31 1958 May 16-19 Cyclonic Storm 870 32 1958 October 21-24 Severe Cyclonic Storm 89 2.0 12,000 33 1959 October 10 Cyclonic Storm 14,000 34 1960 May 25-29 Cyclonic Storm 3.2 106 Severe Cyclonic Storm 5149- 35 1960 October 9-11 160-201 6.6 with Hurricane 6,000 Severe Cyclonic Storm 8149- 36 1960 October 30-31 161-210 4.5-8.8 with Hurricane 15,000 Severe Cyclonic Storm 1,000- 37 1961 May 6-9 145-160 4.5-7.5 with Hurricane 11,468 Severe Cyclonic Storm 38 1961 May 27-30 95-160 7.0-9.0 10,466 with Hurricane Severe Cyclonic Storm 39 1962 October 26-30 200 5.8 50,000 with Hurricane Severe Cyclonic Storm 11520- 40 1963 May 28-29 201-209 5.0-8.1 50 with Hurricane 50,000 41 1963 June 5-8 Cyclonic Storm 3.1 42 1963 October 25-29 Cyclonic Storm 105 2.2 43 1964 April 11 Cyclonic Storm 196 Severe Cyclonic Storm 12,000- 44 1965 May 10-12 162 6.0 58 with Hurricane 19,279

Appendix

45 1965 May 31 Severe Cyclonic Storm 6.0-7.1 12,000 46 1965 November 5 Severe Cyclonic Storm 160 3.5 Severe Cyclonic Storm 870- 47 1965 December 14-15 200-210 4.0-6.1 with Hurricane 1,000 Severe Cyclonic Storm 48 1966 October 1 146 4.7-9.1 500-850 with Hurricane Severe Cyclonic Storm 6.7- 49 1966 October 27-31 120-145 850 with Hurricane 10.0 50 1966 December 12 Cyclonic Storm 51 1967 May 18 Cyclonic Storm 0.9 Severe Cyclonic Storm 52 1967 October 9-11 160 3.0 with Hurricane Severe Cyclonic Storm 53 1967 October 23-24 130 2.0-7.6 128 with Hurricane 54 1968 April 14 Cyclonic Storm 850 55 1968 May 10 Cyclonic Storm 56 1969 April 14-17 Cyclonic Storm 75-922 57 1969 October 10-11 Cyclonic Storm 8.0 175 Severe Cyclonic Storm 58 1970 May 5-7 148 5.0 18 with Hurricane Severe Cyclonic Storm 59 1970 October 22-23 118-163 5.5 300 with Hurricane 5.6- 300,000- 60 1970 November 12-13 Super Cyclonic Storm 222-241 63-86.40 10.6 500,000 61 1971 May 7-8 Cyclonic Storm 80 5.0-5.5 163 62 1971 September 28-30 Cyclonic Storm 5.0 63 1971 November 5-6 Severe Cyclonic Storm 105 5.5 64 1971 November 28-30 Severe Cyclonic Storm 110 1.0 11,000 65 1973 April 9 Cyclonic Storm 700 66 1973 April 12 Cyclonic Storm 200 Severe Cyclonic Storm 67 1973 November 16-18 165 3.8 with Hurricane Severe Cyclonic Storm 183- 68 1973 December 6-9 118-122 4.5-6.2 with Hurricane 1,000 600- 69 1974 August 13-15 Severe Cyclonic Storm 80-100 4.5-6.5 2,500 Severe Cyclonic Storm 70 1974 November 24-29 161 6.0 20-200 with Hurricane 71 1975 May 9-12 Severe Cyclonic Storm 110 5 72 1975 June 5-7 Cyclonic Storm 4.0 Severe Cyclonic Storm 73 1975 June 24-28 161 4.8 with Hurricane Severe Cyclonic Storm 74 1975 November 8-12 143 3.1 with Hurricane 75 1976 October 18-21 Severe Cyclonic Storm 105 5.0 76 1976 November 20 Severe Cyclonic Storm 111 3.1 77 1977 April 1 Severe Cyclonic Storm 600 78 1977 April 24 Severe Cyclonic Storm 13 79 1977 May 9-13 Severe Cyclonic Storm 113-122 1.3 80 1978 April 9 Severe Cyclonic Storm 1,000 81 1978 May 5 Cyclonic Storm 30 82 1978 October 1-3 Cyclonic Storm 74 83 1979 May 2 Cyclonic Storm 3 84 1979 August 17 Cyclonic Storm 50 85 1980 April Cyclonic Storm 11 86 1981 March 6 Cyclonic Storm 15 87 1981 December 10 Severe Cyclonic Storm 80-120 2 15 88 1983 March 21 Cyclonic Storm 6 Severe Cyclonic Storm 89 1983 October 14-15 93-122 43-600 with Hurricane 87

Appendix

Severe Cyclonic Storm 90 1983 November 9-13 122-136 2.5 67-300 with Hurricane 91 1985 March 28 Cyclonic Storm 50 Severe Cyclonic Storm 4,264- 92 1985 May 24-25 154 5.0 50 with Hurricane 11,069 93 1985 July 5 Cyclonic Storm 27 94 1985 October 16 Cyclonic Storm 71 95 1986 March Cyclonic Storm 19 96 1986 April 4 Severe Cyclonic Storm 100 97 1986 September 26 Cyclonic Storm 40 98 1986 November 8-9 Severe Cyclonic Storm 110 25 99 1987 June 4 Cyclonic Storm 12 100 1988 May 23 Cyclonic Storm 28 101 1988 June 13 Cyclonic Storm 5 102 1988 October 19 Cyclonic Storm 31 Severe Cyclonic Storm 1,498- 103 1988 November 24-30 162 5.0 310 with Hurricane 9,590 104 1989 April 26 Severe Cyclonic Storm 800 16.2 105 1989 May 26 Cyclonic Storm 15 106 1990 October 7-8 Cyclonic Storm 2.0 370 107 1990 December 18-21 Severe Cyclonic Storm 115 250 138,000- 1780- 108 1991 April 29 Super Cyclonic Storm 225-235 7.5 150,000 3000 109 1991 June 2 Severe Cyclonic Storm 100-110 2.0 110 1992 January 31 Cyclonic Storm 7 111 1992 April 22 Cyclonic Storm 16 112 1993 January 9 Cyclonic Storm 50 113 1993 January 12 Cyclonic Storm 31 114 1993 February 19 Cyclonic Storm 8 115 1993 March 27 Severe Cyclonic Storm 300 116 1993 May 7 Cyclonic Storm 9 117 1993 May 9 Cyclonic Storm 15 118 1993 May 13 Cyclonic Storm 14 119 1993 May 17 Cyclonic Storm 25 120 1994 March 28 Cyclonic Storm 40 121 1994 April 2 Cyclonic Storm 20 Severe Cyclonic Storm 122 1994 May 2 210 130-400 with Hurricane 123 1994 May 18 Cyclonic Storm 15 124 1995 April 12 Cyclonic Storm 69 125 1995 May 15 Severe Cyclonic Storm 525 Severe Cyclonic Storm 126 1995 November 21-25 210 172-650 with Hurricane 127 1996 April 23 Cyclonic Storm 17 128 1996 May 8 Severe Cyclonic Storm 140 129 1996 May 13 Severe Cyclonic Storm 525 130 1996 July 27 Cyclonic Storm 60 131 1996 October 29 Cyclonic Storm 24 132 1997 March 23 Cyclonic Storm 11 133 1997 May 18-19 Super Cyclonic Storm 225 5.00 111-200 134 1997 August 27 Cyclonic Storm 100 Severe Cyclonic Storm 135 1997 September 25-27 150 3.05 155-188 with Hurricane 136 1998 March 23 Cyclonic Storm 28 137 1998 April 23 Cyclonic Storm 14 Severe Cyclonic Storm 138 1998 May 16-20 150-165 2.5 12 with Hurricane 139 1998 July 3 Cyclonic Storm 60 140 1998 November 19-25 Severe Cyclonic Storm 90 2.44 200

Appendix

141 1999 April 7 Cyclonic Storm 7 142 1999 April 10 Cyclonic Storm 66 143 1999 May 7 Cyclonic Storm 3 144 1999 October 28 Cyclonic Storm 145 2000 October 28 Cyclonic Storm 83 146 2002 November 12 Cyclonic Storm 65-85 2.0 147 2003 April 21 Severe Cyclonic Storm 230 148 2004 April 18-19 Cyclonic Storm 15 149 2004 May 19 Cyclonic Storm 65-90 1.5 150 2005 March 20-23 Cyclonic Storm 83 151 2005 May 6-23 Severe Cyclonic Storm 80 152 2005 September 19-21 Cyclonic Storm 153 2007 June 8-17 Severe Cyclonic Storm 130 3,363- 154 2007 November 15-17 Super Cyclonic Storm 223 6.0 3775 3,500 155 2008 October 26 Cyclonic Storm 7 156 2009 April 17 Cyclonic Storm 5 157 2009 May 25 Cyclonic Storm 70-90 2.0 190-500 270

Appendix

Area (km2) Upazilas District Total Exposed Interior Exposed Interior Mongla, Saran Khola, Bagerhat Sadar, Chitalmari, Bagerhat 3,959 2,679 1,280 Morrelganj Fakirhat, Kachua, Mollahat

Rampal Amtali, Barguna Sadar Barguna 1,831 1,663 168 Betagi Patharghata, Bamna Agailjhara, Babuganj, Bakerganj, Barisal Gaurnadi, Hizla, Mehendiganj, 2,785 2,785 Muladi, Wazirpur, Banari Para, Barisal Sadar Bhola Sadar, Manpura, Bhola Lalmohan, Daulatkhan 3,403 3,403 Burhanuddin, Char Fasson, Tazumuddin Chandpur Sadar, Faridganj, Chandpur 1,704 1,704 Haimchar, Hajiganj, Kachua,

Matlab, Shahrasti Anowara, Banshkhali, Chittagong port, Boalkhali, Chandanaish, Double Mooring, Lohagara, Rangunia, Chandgaon, Chittagong Mirsharai, Pahartali, Fatikchhari, 5,283 2,413 2,870 Panchlaish, Sandwip, Hathazari, Patiya, Raozan, Sitakunda, Patenga, Satkania, Bakalia, Karanaphuli, Halisahar, Kotwali, Kulshi Boijid Bostami, Chakaria, Cox’s Bazar Sadar, Kutubdia, Cox's Bazar 2,492 2,492 Ukhia,

Maheshkhali, Ramu, Teknaf Feni Chhagalnaiya, Feni Sadar, 928 235 693 Sonagazi Parshuram, Daganbhuiyan Gopalganj Gopalganj Sadar, Kotali Para, 1,490 1,490 Muksudpur, Kashiani,, Tungipara Bagher Para, Chaugachha, Jessore Jhikargachha, Manirampur, 2,567 2,567 Abhaynagar, Keshabpur, Jessore Sadar, Sharsha Jhalokati Jhalokati Sadar, Kanthalia, 749 749 Nalchity, Rajapur Batiaghata, Daulatpur, Dumuria, Dighalia, Khalishpur, Khan Jahan Khulna 4,394 2,767 1,627 Dacope, Koyra Ali, Khulna Sadar, Paikgachha,

Phultala, Rupsha, Sonadanga, Terokhada Lakshmipur Lakshmipur Sadar, Raipur, 1,456 571 885 Ramgati Ramganj Narail Lohagara, Narail Sadar, Kalia, 990 990 Narigati Companiganj, Hatiya, Noakhali 3,601 2,885 716 Chatkhil, Senbagh, Begumganj Noakhali Sadar Patuakhali Dashmina, Rangabali, Bauphal, Mirzaganj, Patuakhali 3,221 2,103 1,118 Galachipa, Kala Para Sadar Bhandaria, Kawkhali, Nazirpur, Pirojpur 1,308 353 955 Mathbaria Pirojpur Sadar, Nesarabad

(Swraupkati) Satkhira Debhata, Kalaroa, Kaliganj, 3,858 2,371 1,487 Assasuni, Shyamnagar Satkhira Sadar, Tala Shariatpur Bhederganj, Damudya, Palong 1,182 1,182 Goshairhat, Naria, , Zanjira Total 47,201 23,935 23,266

Appendix 3.2: Districts and Upazilas of Bangladesh’s coastal zone (MoEF, 2007) 90

Appendix

Crops Crops No. of No. of Dead No. of House No. of No. Affected damaged damaged House Livestock, Year Damaged Dead Fully Partially damage Cattles and (Partially) People District People (Acre) (Acre) Fully Goats 1970 5 1100000 - 3350000 3350000 - 250000 470000 1985 9 167500 39500 86590 10095 7135 10 2020 1986 7 238600 17800 84837 1116 3446 12 1050 1988 21 1006536 2316042 1597780 788715 863837 9590 386766 1989 33 346087 38712 38629 12173 20008 573 2065 1990 39 1015866 171099 242897 75085 63562 132 5326 1991 33 121229 11760 8725 34791 20274 76 25 1991 19 13798275 133272 791621 819608 882750 138882 1061029 1994 2 422020 23986 57912 52057 17476 134 1296 1995 28 305953 2593 42644 22395 44664 91 1838 1996 2 81162 - 2431 15868 15976 545 4933 1997 10 3784916 254755 59788 290320 452886 127 7960 1997 12 2015669 16537 72662 51435 163352 78 3196 2007 30 8923259 743322 1730317 564967 957110 3363 1778507 2009 11 3928238 77486 245968 243191 370587 190 150131

Table 3.3 is continued

No. of No. of Road Road No. of No. Affected Damaged Damaged Damaged Damaged Damaged Embankment Year Institution Institution Fully Partially Bridge/ Damaged District People (Fully) (Partially) (Km) (km) Culvert 1970 5 1100000 ------1985 9 167500 - - 32 - 11 10 1986 7 238600 2 47 132 1 1988 21 1006536 2442 5444 515 976 39 18 1989 33 346087 74 166 - - - - 1990 39 1015866 233 461 - - - - 1991 33 121229 62 151 - - - - 1991 19 13798275 3865 5801 - 764 496 707 1994 2 422020 96 98 169 - 83 97 1995 28 305953 127 537 - - - - 1996 2 81162 85 64 - - - - 1997 10 3784916 1824 3000 174 1527 527 122 1997 12 2015669 2500 2256 218 2379 85 280 2007 30 8923259 4231 12723 1714 6361 1687 1875 2009 11 3928238 445 4588 2233 6621 157 1742.53

Appendix 3.3: Detailed damages by selected cyclones that hit Bangladesh recently (MoWCA, 2010; DMB)

Appendix

Area in Total Population District Name sq. km Households Total Male Female Sex Ratio density BARISAL Division 13297 M*100/F sq. km BARGUNA 1831 215842 892781 437413 455368 96 488 BARISAL 2785 513673 2324310 1137210 1187100 96 835 BHOLA 3403 372723 1776795 884069 892726 99 522 JHALOKATI 749 158139 682669 329147 353522 93 966 PATUAKHALI 3221 346462 1535854 753441 782413 96 477 PIROJPUR 1308 256002 1113257 548228 565029 97 871

CHITTAGONG 33771 Division BANDARBAN 4479 80102 388335 203350 184985 110 87 BRAHMANBARIA 1927 538937 2840498 1366711 1473787 93 1510 CHANDPUR 1704 506521 2416018 1145831 1270187 90 1468 CHITTAGONG 5283 1532014 7616352 3838854 3777498 102 1442 COMILLA 3085 1053572 5387288 2575018 2812270 92 1712 COX'S BAZAR 2492 415954 2289990 1169604 1120386 104 919 FENI 928 277665 1437371 694128 743243 93 1451 KHAGRACHHARI 2700 133792 613917 313793 300124 105 223 LAKSHMIPUR 1456 365339 1729188 827780 901408 92 1200 NOAKHALI 3601 593918 3108083 1485169 1622914 92 843 RANGAMATI 6116 128496 595979 313076 282903 111 97

DHAKA Division 31120 DHAKA 1464 2786133 12043977 6555792 5488185 119 8229 FARIDPUR 2073 420174 1912969 942245 970724 97 932 GAZIPUR 1800 826458 3403912 1775310 1628602 109 1884 GOPALGANJ 1490 249872 1172415 577868 594547 97 798 JAMALPUR 2032 563367 2292674 1128724 1163950 97 1084 KISHOREGONJ 2689 627322 2911907 1432242 1479665 97 1083 MADARIPUR 1145 252149 1165952 574582 591370 97 1036 MANIKGANJ 1379 324794 1392867 676359 716508 94 1007 MUNSHIGANJ 955 313258 1445660 721552 724108 100 1439 MYMENSINGH 4363 1155436 5110272 2539124 2571148 99 1163 NARAYANGANJ 700 675652 2948217 1521438 1426779 107 4308 NARSINGDI 1141 477976 2224944 1102943 1122001 98 1934 NETRAKONA 2810 479146 2229642 1111306 1118336 99 798 RAJBARI 1119 238153 1049778 519999 529779 98 961 SHARIATPUR 1182 247880 1155824 559075 596749 94 984 SHERPUR 1364 341443 1358325 676388 681937 99 995 TANGAIL 3414 870102 3605083 1757370 1847713 95 1056

Appendix 3.4A: Population census in Bangladesh (BBS, 2011)

Appendix

Area Population Total District Name in sq. Sex Households density km Total Male Female Ratio sq. km M*100/F KHULNA Division 22272 BAGERHAT 3959 354223 1476090 740138 735952 101 1027 CHUADANGA 1177 277464 1129015 564819 564196 100 962 JESSORE 2567 656413 2764547 1386293 1378254 101 1060 JHENAIDAH 1961 422332 1771304 886402 884902 100 902 KHULNA 4394 547347 2318527 1175686 1142841 103 1046 KUSHTIA 1601 477289 1946838 973518 973320 100 1210 MAGURA 1049 205902 918419 454739 463680 98 884 MEHERPUR 716 166312 655392 324634 330758 98 872 NARAIL 990 162607 721668 353527 368141 96 746 SATKHIRA 3858 469890 1985959 982777 1003182 98 1044

RAJSHAHI Division 18197 BOGRA 2920 867137 3400874 1708806 1692068 101 1173 JOYPURHAT 965 242556 913768 459284 454484 101 903 NAOGAON 3436 655801 2600157 1300227 1299930 100 757 NATORE 1896 423875 1706673 854183 852490 100 898 CHAPAI NABABGANJ 1703 357982 1647521 810218 837303 97 968 PABNA 2372 590749 2523179 1262934 1260245 100 1062 RAJSHAHI 2407 633758 2595197 1309890 1285307 102 1070 SIRAJGANJ 2498 714971 3097489 1551368 1546121 100 1290

RANGPUR Division 16317 DINAJPUR 3438 715773 2990128 1508670 1481458 102 868 GAIBANDHA 2179 612283 2379255 1169127 1210128 97 1125 KURIGRAM 2296 508045 2069273 1010442 1058831 95 922 LALMONIRHAT 1241 290444 1256099 628799 627300 100 1007 NILPHAMARI 1580 421572 1834231 922964 911267 101 1186 PANCHAGARH 1405 228581 987644 496725 490919 101 703 RANGPUR 2368 720180 2881086 1443816 1437270 100 1200 THAKURGAON 1810 320786 1390042 701281 688761 102 780

SYLHET Division 12596 HABIGANJ 2637 393302 2089001 1025591 1063410 96 792 MAULVIBAZAR 2799 361177 1919062 944728 974334 97 686 SUNAMGANJ 3670 440332 2467968 1236106 1231862 100 659 SYLHET 3490 596081 3434188 1726965 1707223 101 995

Total 147570 32173630 144043697 72109796 71933901 100,2 976

Appendix 3.4B: Population census in Bangladesh (BBS, 2011)

Appendix

Area in Total Population % of population in the age group District Name sq. km Households Total 0-4 5-9 10-14 65+ Disable% BARGUNA 1831 215842 892781 9,9 12,4 11,5 6 2,10 BARISAL 2785 513673 2324310 9,8 12,9 13 5,8 1,30 BHOLA 3403 372723 1776795 12,1 15,2 13,4 4,8 1,50 JHALOKATI 749 158139 682669 9,3 12,5 13,1 6,6 1,90 PATUAKHALI 3221 346462 1535854 10,4 13,4 12,3 5,6 1,6 PIROJPUR 1308 256002 1113257 9,6 12,2 12,1 6,5 2,00 CHANDPUR 1704 506521 2416018 10,9 13,2 13 5,9 1,90 CHITTAGONG 5283 1532014 7616352 10 11,9 12 3,8 1,30 COX'S BAZAR 2492 415954 2289990 13,3 15,8 13,9 3,1 1,50 FENI 928 277665 1437371 10,6 12,4 12,7 5,4 1,30 LAKSHMIPUR 1456 365339 1729188 11,9 14,6 13 5,2 1,30 NOAKHALI 3601 593918 3108083 12,3 14,9 13,5 4,9 1,40 GOPALGANJ 1490 249872 1172415 10,7 13,7 12,8 5,5 1,40 SHARIATPUR 1182 247880 1155824 11,3 14,3 13,4 5,9 1,30 BAGERHAT 3959 354223 1476090 9 11,5 11,8 6,3 1,70 JESSORE 2567 656413 2764547 8,9 10,7 11 5,3 1,30 KHULNA 4394 547347 2318527 8,5 10,4 10,9 5,3 1,70 SATKHIRA 3858 469890 1985959 8,6 10,9 11 5,7 1,70 NARAIL 990 162607 721668 10,3 12,8 11,9 5,9 1,60 Total 47201 8242484 38517698 Literacy Area in Total Population % Type of Structure (%) District Name sq. km Households Semi- Total Both Pucka Kutcha Jhupri pucka BARGUNA 1831 215842 892781 57,6 2 4,8 89,6 3,6 BARISAL 2785 513673 2324310 61,2 7,3 10,9 80 1,8 BHOLA 3403 372723 1776795 43,2 1,7 7,6 86,3 4,5 JHALOKATI 749 158139 682669 66,7 6,7 11,4 79,5 2,5 PATUAKHALI 3221 346462 1535854 54,1 2,6 5,7 86,6 5 PIROJPUR 1308 256002 1113257 64,9 4 8 86,2 1,8 CHANDPUR 1704 506521 2416018 56,8 7,3 8,8 83,3 0,6 CHITTAGONG 5283 1532014 7616352 58,9 25 20,6 48,3 6,1 COX'S BAZAR 2492 415954 2289990 39,3 6,2 11,6 68,9 13,3 FENI 928 277665 1437371 59,6 16,6 17,8 64,3 1,3 LAKSHMIPUR 1456 365339 1729188 49,4 7,6 7,4 82,6 2,4 NOAKHALI 3601 593918 3108083 51,3 7,6 7,6 80,6 4,2 GOPALGANJ 1490 249872 1172415 58,1 4 12,3 82,7 1 SHARIATPUR 1182 247880 1155824 47,3 2,8 8,4 87,7 1 BAGERHAT 3959 354223 1476090 59 5,1 11,8 78,3 4,8 JESSORE 2567 656413 2764547 56,5 16,4 33,6 44,9 5,2 KHULNA 4394 547347 2318527 60,1 18,3 23 56,6 2 SATKHIRA 3858 469890 1985959 52,1 14,3 28,5 55,8 1,4 NARAIL 990 162607 721668 61,3 6,4 24,3 68,3 1

Appendix 3.5: Population and household scenarios in the c oastal area of Bangladesh ( BBS, 2011)

Appendix

Total Population Number of Child Old Total District Name Household s Total 0-4 5-9 10-14 65+ BARGUNA 215842 892781 88385 110705 102670 53567 355327 BARISAL 513673 2324310 227782 299836 302160 134810 964589 BHOLA 372723 1776795 214992 270073 238091 85286 808442 JHALOKATI 158139 682669 63488 85334 89430 45056 283308 PATUAKHALI 346462 1535854 159729 205804 188910 86008 640451 PIROJPUR 256002 1113257 106873 135817 134704 72362 449756 CHANDPUR 506521 2416018 263346 318914 314082 142545 1038888 CHITTAGONG 1532014 7616352 761635 906346 913962 289421 2871365 COX'S BAZAR 415954 2289990 304569 361818 318309 70990 1055685 FENI 277665 1437371 152361 178234 182546 77618 590759 LAKSHMIPUR 365339 1729188 205773 252461 224794 89918 772947 NOAKHALI 593918 3108083 382294 463104 419591 152296 1417286 GOPALGANJ 249872 1172415 125448 160621 150069 64483 500621 SHARIATPUR 247880 1155824 130608 165283 154880 68194 518965 BAGERHAT 354223 1476090 132848 169750 174179 92994 569771 JESSORE 656413 2764547 246045 295807 304100 146521 992472 KHULNA 547347 2318527 197075 241127 252719 122882 813803 SATKHIRA 469890 1985959 170792 216470 218455 113200 718917 NARAIL 162607 721668 74332 92374 85878 42578 295162 Total 8242484 38517698 4008377 4929878 4769531 1950728 15658514 Child 35,6 Total Dependent 40,7 15658514 No. of Total Population Literature Rate % Disable Vulnerable District Name Vuln. Households People House % Total Male Female House BARGUNA 215842 892781 59,2 56,1 18748 93,2 201165 BARISAL 513673 2324310 61,9 60,6 30216 81,8 420185 BHOLA 372723 1776795 43,6 42,9 26652 90,8 338432 JHALOKATI 158139 682669 67,6 65,8 12971 82 129674 PATUAKHALI 346462 1535854 56,2 52 24574 91,6 317359 PIROJPUR 256002 1113257 65 64,7 22265 88 225282 CHANDPUR 506521 2416018 56,1 57,3 45904 83,9 424971 CHITTAGONG 1532014 7616352 61,1 56,7 99013 54,4 833416 COX'S BAZAR 415954 2289990 40,3 38,2 34350 82,2 341914 FENI 277665 1437371 61,1 58,3 18686 65,6 182148 LAKSHMIPUR 365339 1729188 48,9 49,8 22479 85 310538 NOAKHALI 593918 3108083 51,4 51,2 43513 84,8 503642 GOPALGANJ 249872 1172415 60,3 56 16414 83,7 209143 SHARIATPUR 247880 1155824 48 46,6 15026 88,7 219870 BAGERHAT 354223 1476090 60 58 25094 83,1 294359 JESSORE 656413 2764547 59,4 53,7 35939 50,1 328863 KHULNA 547347 2318527 64,3 55,9 39415 58,6 320745 SATKHIRA 469890 1985959 56,1 48,2 33761 57,2 268777 NARAIL 162607 721668 63,3 59,3 11547 69,3 112687 576566 Vulnerable 5983170 Total 8242484 38517698 Disable % 1,5 House 72,6%

Appendix 3.6: Population and households vulnerable to the natural hazards (BBS, 2011)

Appendix

Tide Levels in May, 2012 at Cox's Bazar Water Water Water Day Time Day Time Day Time Level (m) Level (m) Level (m) 1 6:00 2,5 11 1:30 2,9 21 4:05 0,7 11:50 1,1 7:35 0,8 10:30 3,4 18:25 2,7 13:55 3,1 16:35 0,7 2 0:35 0,9 20:20 0,8 22:40 3,1 7:10 2,7 12 2:30 2,7 22 4:35 0,6 13:05 0,9 8:30 1 11:00 3,4 19:25 2,9 14:55 2,8 17:05 0,7 3 1:35 0,7 21:20 1 23:10 3,1 8:00 3,1 13 3:45 2,5 23 5:05 0,7 14:05 0,7 09:40 1,1 11:30 3,4 20:20 3,1 16:15 2,7 17:40 0,7 4 2:25 0,5 22:35 1 23:40 3,1 8:45 3,4 14 5:20 2,5 24 5:40 0,7 14:55 0,6 11:05 1,2 12:00 3,3 21:05 3,3 17:45 2,6 18:15 0,8 5 3:10 0,4 23:55 1 25 0:15 3 9:30 3,6 15 6:35 2,6 6:15 0,8 15:40 0,4 12:35 1,1 12:35 3,2 21:45 3,4 18:55 2,6 18:50 0,8 6 3:55 0,3 16 1:05 1 26 0:55 2,9 10:15 3,7 7:35 2,7 6:55 0,8 16:25 0,4 13:40 1,1 13:15 3,1 22:30 3,5 19:45 2,7 19:35 0,9 7 4:35 0,3 17 1:55 0,9 27 1:35 2,8 10:55 3,8 8:15 2,9 7:40 0,9 17:10 0,4 14:25 1 14:00 3 23:10 3,5 20:25 2,8 20:20 0,9 8 5:20 0,3 18 2:30 0,8 28 2:30 2,7 11:35 3,7 8:55 3,1 8:35 1 17:50 0,4 15:00 0,9 14:55 2,9 23:55 3,4 21:05 2,9 21:20 1 9 6:00 0,4 19 3:05 0,8 29 3:45 2,7 12:20 3,6 9:25 3,1 9:45 1,1 18:35 0,5 15:35 0,8 16:10 2,8 10 0:40 3,1 21:35 3 22:30 1 6:45 0,6 20 3:35 0,7 30 5:10 2,7 13:05 3,4 9:55 3,3 11:05 1,1 19:25 0,7 16:05 0,7 17:30 2,8 22:10 3,1 23:40 0,9 31 6:25 2,9 12:25 1 18:45 2,9

Maximum tide level in May 3,8 Model Minimum tide level in May 0,3 Application

Tide Levels for first half of June, 2012 at Cox's Bazar (Data used for Model calibration by interpolation, connected to Appendix 5.4 for water level data) Water Water Water Day Time Day Time Day Time Level (m) Level (m) Level (m) 1 0:50 0,8 6 5:05 0,5 11 3:05 2,7 7:30 3,1 11:25 3,8 9:00 1,1 13:35 0,9 17:45 0,5 15:25 2,8 19:45 3,1 23:45 3,4 21:40 1 2 1:50 0,7 7 5:50 0,5 12 4:20 2,7 8:20 3,4 12:10 3,6 10:00 1,2 14:35 0,8 18:25 0,6 16:35 2,7 20:40 3,2 8 0:30 3,3 22:40 1,1 3 02:45 0,6 6:35 0,7 13 5:35 2,7 96

Appendix

09:10 3,6 12:50 3,5 11:15 1,3 15:25 0,6 19:10 0,7 17:50 2,6 21:30 3,4 9 1:15 3,1 23:50 1,1 4 3:35 0,5 7:20 0,8 14 6:45 2,7 9:55 3,7 13:40 3,3 12:40 1,2 16:15 0,5 19:55 0,8 18:55 2,7 22:15 3,4 10 2:10 2,9 15 0:55 1,1 5 4:20 0,5 8:05 1 7:40 2,9 10:40 3,8 14:30 3 13:45 1,2 17:00 0,5 20:45 0,9 19:50 2,7 23:00 3,4

Tide Level at Cox's Bazar in May, 2012

3.5

2.5

1.5 Water (m) Water Level 1

0.5

Time

Tide Level at Cox's Bazar in June (1st fort), 2012

3.5

2.5

1.5

Water (m) Water Level 1

0.5

Time Appendix 5.1: Tide levels that have been considered in SWAN model 97

Appendix

Country Side Mean Wind blows wind from direction (Degree) GRID AREA N 0 NNE 22.5 NE 45 ENE 67.5 270° 90° E 90 ESE 112.5 247.5° 112.5° SE 135 SSE 157.5 135° 225° S 180 202.5° SSW 202.5 157.5° 18 0° SW 225 WSW 247.5 Sea Side W 270 WNW 292.5 NW 315 Among these 9 directions, only seasonal dominant NNW 337.5 direction has been taken into account. In summer, CLM calm monsoon and autumn, southern wind is dominant. For winter additionally western wind has been also considered to look the directional effect.

Season wise number of days of wind blowing from a wind direction Winter Summer Monsoon Autumn Wind Wind Wind Wind Days Days Days Days blows from blows from blows from blows from N 1157 N 132 N 21 N 469 NNE 199 NNE 31 NNE 8 NNE 144 NE 155 NE 36 NE 13 NE 155 ENE 29 ENE 12 ENE 4 ENE 27 E 66 E 91 E 223 E 139 ESE 14 ESE 20 ESE 38 ESE 21 SE 32 SE 107 SE 491 SE 67 SSE 17 SSE 89 SSE 439 SSE 60 S 280 S 2058 S 3140 S 241 SSW 51 SSW 246 SSW 285 SSW 60 SW 40 SW 175 SW 173 SW 32 WSW 38 WSW 110 WSW 73 WSW 28 W 308 W 415 W 171 W 145 WNW 122 WNW 70 WNW 15 WNW 54 NW 475 NW 210 NW 32 NW 226 NNW 427 NNW 138 NNW 18 NNW 153 CLM 558 CLM 108 CLM 224 CLM 663 Total 3968 4048 5368 2684

Appendix 5.2: Number of days of wind blowing from a direction along the coast of Bangladesh for the period 2001-2011 (BMD)

Appendix

Nearshore Forecasted Nearshore Forecasted Conditi Model Results at Model Results at Data at (91.25, 21.00) Data at (88.75, 21.00) on (91.25, 21.00) Point-1 (88.75, 21.00) Point-2 Point-1 Point-2 Tp Hs Tp Tp Hs Tp Hs (m) Direction Direction Hs (m) Direction Direction (s) (m) (s) (s) (m) (s) 2.2-2.9 9 202.5 1.96 9.23 200.76 2.3-3 9 202.5 1.98 9.23 195.56 Only Buoy-1 2.2-2.8 9 202.5 1.96 9.23 198.86 2.3-2.9 9 202.5 1.99 9.23 192.44 CON.

2.2-2.9 9 202.5 2.02 9.23 197.38 2.3-3 9 202.5 2.05 9.23 191.23 Only Buoy-2 2.2-2.8 9 202.5 1.95 9.23 195.4 2.3-2.9 9 202.5 2 9.23 188.3 CON.

2.2-2.9 9 202.5 2.02 9.23 197.38 2.3-3 9 202.5 2.06 9.23 191.23 Buoy-1 & 2 2.2-2.8 9 202.5 1.95 9.23 195.4 2.3-2.9 9 202.5 2 9.23 188.3 VAR.

Tp Hs Tp Tp Hs Tp Hs (m) Direction Direction Hs (m) Direction Direction Buoy-1, (s) (m) (s) (s) (m) (s) Without 2.2-2.9 9 202.5 1.99 9.23 201.7 2.3-3 9 202.5 2.03 9.23 196.1 Bottom Friction. 2.2-2.8 9 202.5 2.01 9.23 200.5 2.3-2.9 9 202.5 2.04 9.23 193.17

Tp Hs Tp Tp Hs Tp 10 Hs (m) Direction Direction Hs (m) Direction Direction (s) (m) (s) (s) (m) (s) Iteration s, Acc= 2.2-2.9 9 202.5 2.01 9.23 200.73 2.3-3 9 202.5 2.03 9.23 194.23 98.97

2.2-2.9 9 202.5 1.9 9.23 199.56 2.3-3 9 202.5 1.94 9.23 196.06 With 2.2-2.8 9 202.5 1.91 9.23 197.5 2.3-2.9 9 202.5 1.96 9.23 192.65 Nesting

Appendix 5.3: The results of sensitivity analysis for different condition by using two boundary conditions (Table 5.4)

Appendix

Modeled Nearshore Forecasted Offshore Forecasted Nearshore Forecasted

Wind Data at Point-1 Data at Buoy-1 Data at Point-2

Water Wind Dir. Tp Dir. Hs Tp Dir. Tp Dir. Date and Time Level Speed Hs (m) Hs (m) (Naut.) (s) (Naut.) (m) (s) (Naut.) (s) (Naut.) No (m) (m/s) 1 08.06.12 06:00 0.80 2.84 157.50 2.1-2.7 9.00 202.50 1.95 9.20 212.00 2.2-2.8 9.00 202.50 2 08.06.12 12:00 3.30 3.86 146.25 2.0-2.6 9.00 202.50 1.90 9.10 208.00 2-2.6 9.00 202.50 3 08.06.12 18:00 1.00 6.95 213.75 2.0-2.6 9.00 202.50 1.81 9.00 208.00 1.9-2.5 9.00 202.50 4 09.06.12 00:00 2.90 7.97 202.50 1.9-2.4 9.00 202.50 1.91 9.30 210.00 1.9-2.5 9.00 202.50 5 09.06.12 06:00 1.10 2.06 146.25 1.8-2.3 9.00 202.50 1.84 9.10 210.00 1.9-2.4 9.00 180.00 6 09.06.12 12:00 2.95 5.15 180.00 1.8-2.3 9.00 202.50 1.67 8.90 210.00 1.8-2.4 9.00 202.50 7 09.06.12 18:00 1.20 5.53 191.25 1.8-2.3 9.00 202.50 1.61 8.70 210.00 1.8-2.3 9.00 202.50 8 10.06.12 00:00 2.45 6.95 180.00 1.7-2.2 9.00 202.50 1.68 8.80 212.00 1.8-2.3 9.00 202.50 9 10.06.12 06:00 1.50 6.31 168.75 1.6-2.1 8.00 202.50 1.95 9.00 215.00 1.8-2.3 9.00 202.50 10 10.06.12 12:00 2.30 7.47 157.50 1.6-2.1 8.00 202.50 2.03 9.00 215.00 1.7-2.2 9.00 202.50 11 10.06.12 18:00 2.00 6.69 202.50 1.6-2.1 9.00 202.50 1.81 8.70 216.00 2-2.5 9.00 202.50 12 11.06.12 00:00 1.90 7.21 180.00 1.7-2.2 9.00 202.50 1.70 8.50 217.00 2.1-2.7 9.00 202.50 13 11.06.12 06:00 2.00 6.31 168.75 1.8-2.3 8.00 202.50 1.73 8.50 215.00 1.9-2.5 9.00 202.50 14 11.06.12 12:00 1.90 8.10 157.50 1.7-2.2 8.00 202.50 1.84 8.90 210.00 1.8-2.3 9.00 202.50 15 11.06.12 18:00 2.10 7.08 157.50 1.7-2.2 8.00 202.50 1.85 9.00 206.00 1.8-2.3 9.00 202.50 16 12.06.12 00:00 1.48 8.75 146.25 1.8-2.3 9.00 202.50 1.79 9.10 203.00 1.9-2.5 9.00 180.00 17 12.06.12 06:00 2.35 6.69 157.50 1.9-2.5 9.00 202.50 1.51 9.70 189.00 1.9-2.5 10.00 180.00 18 12.06.12 12:00 1.45 5.80 157.50 1.9-2.5 9.00 202.50 1.95 9.20 199.00 1.8-2.4 10.00 180.00 19 12.06.12 18:00 2.50 6.18 146.25 2.1-2.7 9.00 202.50 1.84 9.00 196.00 1.8-2.3 10.00 180.00 20 13.06.12 00:00 1.25 7.08 168.75 2.2-2.8 9.00 202.50 2.04 9.90 193.00 1.8-2.3 15.00 202.50 21 13.06.12 06:00 2.65 6.05 168.75 2.2-2.9 9.00 202.50 2.77 9.90 200.00 1.8-2.3 14.00 202.50 22 13.06.12 12:00 1.35 4.50 213.75 2.3-3 9.00 202.50 3.22 9.70 205.00 1.9-2.5 14.00 202.50 23 13.06.12 18:00 2.60 9.00 225.00 2.5-3.3 10.00 202.50 3.24 9.60 210.00 2.4-3.1 11.00 180.00 24 14.06.12 00:00 1.15 10.04 225.00 2.8-3.7 10.00 202.50 3.20 9.50 211.00 3-3.9 10.00 180.00 25 14.06.12 06:00 2.60 9.65 225.00 3-3.9 9.00 202.50 3.11 9.30 213.00 3.1-4 10.00 180.00 26 14.06.12 12:00 1.30 9.78 236.25 3.1-4 9.00 202.50 2.77 9.10 211.00 3.1-4 10.00 180.00 27 15.06.12 00:00 1.15 10.68 213.75 2.8-3.6 8.00 202.50 2.72 9.00 212.00 3.1-4 8.00 202.50 28 15.06.12 06:00 2.55 9.78 225.00 2.8-3.6 8.00 202.50 2.74 8.80 214.00 3.1-4 9.00 202.50 29 15.06.12 12:00 1.40 10.04 213.75 2.8-3.6 8.00 202.50 2.67 8.60 215.00 2.9-3.7 9.00 202.50 30 15.06.12 18:00 2.50 10.80 225.00 2.7-3.5 8.00 202.50 2.54 8.70 217.00 2.8-3.6 9.00 202.50

Appendix 5.4: The data that is considered for the model calibration and comparison of the results at point- 1 & 2

100

Appendix

Nearshore Forecasted Nearshore Model Result at Nearshore Forecasted Data at Nearshore Model Result at Data at Point- 1 Point-1 Point- 2 Point- 2 Tp Tp Tp Tp No Hs (m) Direction Hs (m) Direction Hs (m) Direction Hs (m) Direction (s) (s) (s) (s) 1 2.1-2.7 9 202.50 1.78 9.23 201.84 2.2-2.8 9 202.50 1.72 9.23 189.85 2 2.0-2.6 9 202.50 1.7 9.23 194.96 2-2.6 9 202.50 1.74 9.23 187.81 3 2.0-2.6 9 202.50 1.88 9.23 202.86 1.9-2.5 9 202.50 1.96 9.23 197.91 4 1.9-2.4 9 202.50 2.16 9.23 202.62 1.9-2.5 9 202.50 2.17 9.23 196.15 5 1.8-2.3 9 202.50 1.71 9.23 203.43 1.9-2.4 9 180.00 1.66 9.23 189.83 6 1.8-2.3 9 202.50 1.65 9.23 195.65 1.8-2.4 9 202.50 1.69 9.23 189.44 7 1.8-2.3 9 202.50 1.63 8.38 196.85 1.8-2.3 9 202.50 1.67 8.38 190.99 8 1.7-2.2 9 202.50 1.79 8.38 191.11 1.8-2.3 9 202.50 1.83 9.23 185.58 9 1.6-2.1 8 202.50 1.87 9.23 192.93 1.8-2.3 9 202.50 1.93 9.23 186.87 10 1.6-2.1 8 202.50 2 9.23 185.54 1.7-2.2 9 202.50 2.03 9.23 179.81 11 1.6-2.1 9 202.50 1.81 8.38 202.65 2-2.5 9 202.50 1.82 8.38 197.96 12 1.7-2.2 9 202.50 1.8 8.38 194.63 2.1-2.7 9 202.50 1.81 8.38 188.57 13 1.8-2.3 8 202.50 1.72 8.38 191.35 1.9-2.5 9 202.50 1.77 8.38 184.53 14 1.7-2.2 8 202.50 1.95 9.23 177.5 1.8-2.3 9 202.50 2.03 9.23 170.58 15 1.7-2.2 8 202.50 1.91 9.23 185.3 1.8-2.3 9 202.50 1.97 9.23 177.86 16 1.8-2.3 9 202.50 2.12 9.23 168.67 1.9-2.5 9 180.00 2.19 9.23 164.26 17 1.9-2.5 9 202.50 1.83 9.23 180.2 1.9-2.5 10 180.00 1.9 9.23 172.02 18 1.9-2.5 9 202.50 1.85 9.23 188.56 1.8-2.4 10 180.00 1.93 9.23 181.05 19 2.1-2.7 9 202.50 1.82 9.23 183.21 1.8-2.3 10 180.00 1.88 9.23 176.3 20 2.2-2.8 9 202.50 2.12 10.2 185.74 1.8-2.3 15 202.50 2.23 10.2 177.53 21 2.2-2.9 9 202.50 2.4 10.2 190.83 1.8-2.3 14 202.50 2.5 10.2 183.08 22 2.3-3 9 202.50 2.56 10.2 194.75 1.9-2.5 14 202.50 2.64 10.2 186.66 23 2.5-3.3 10 202.50 2.82 10.2 205.27 2.4-3.1 11 180.00 2.86 10.2 200.38 24 2.8-3.7 10 202.50 2.91 9.23 207.95 3-3.9 10 180.00 2.96 9.23 203.01 25 3-3.9 9 202.50 2.81 9.23 209.75 3.1-4 10 180.00 2.8 9.23 203.53 26 3.1-4 9 202.50 2.63 9.23 216.26 3.1-4 10 180.00 2.61 9.23 210.52 27 2.8-3.6 8 202.50 2.79 8.38 205.09 3.1-4 8 202.50 2.82 8.38 202.52 28 2.8-3.6 8 202.50 2.54 8.38 211.01 3.1-4 9 202.50 2.53 9.23 206.57 29 2.8-3.6 8 202.50 2.5 7.61 205.97 2.9-3.7 9 202.50 2.55 7.61 202.13 30 2.7-3.5 8 202.50 2.68 7.61 216.25 2.8-3.6 9 202.50 2.61 8.38 210.7

Appendix 5.5: SWAN calibration results and forecasting data at point- 1& 2 for the period 08.06.12 06:00 to 15.06.12 18:00

Water Level Modeled Wind Modeled Offshore Wave Climate Case Tide (m) Wind (m/s) Direction Hs (m) Tp (s) Direction 1 W=270 High Tide 3.8 5 2.17 9.1 208 2 S=180 3 W=270 Low Tide 0.3 5 2.17 9.1 208 4 S=180 5 W=270 High Tide 3.8 10 2.94 9.05 213 6 S=180 7 W=270 Low Tide 0.3 10 2.94 9.05 213 8 S=180 9 High Tide 3.8 15 S=180 3.98 9.6 180 10 Low Tide 0.3 15 S=180 3.98 9.6 180 11 High Tide 3.8 20 S=180 5.95 11.75 180 12 Low Tide 0.3 20 S=180 5.95 11.75 180 13 High Tide 3.8 30 S=180 9.5 13.25 180 14 Low Tide 0.3 30 S=180 9.5 13.25 180 Appendix 5.6: The data which is used for model application at current satate 101

Appendix

Downloading Date: 08-06-2012 Time: 14:00 (Wave data for Model Application)

Wind Wind Duration (Hours) Property km/h 6 12 18 25 35 45 55 70 80 90 100 120 140 1.74 2.38 2.74 3.05 3.35 3.66 3.66 3.66 3.66 3.66 3.66 3.66 3.66 height (m) 41 6 7 8 9 10 11 11.5 12 12.5 12.5 13 13 13 period (s) 80 185 296 463 741 1019 1296 1852 2222 2593 2871 3611 4352 fetch (km) 2.13 3.05 3.66 3.96 4.27 4.57 4.88 4.88 4.88 5.18 5.33 5.33 5.33 height (m) 48 6.6 8 9 10 11 12 13 13.5 14 14.5 15 15 15.5 period (s) 89 204 315 519 759 1111 1482 2037 2500 2871 3426 4167 4815 fetch (km) 2.29 3.66 4.27 4.88 5.49 6.1 6.1 6.71 6.71 6.71 7.01 7.01 7.01 height (m) 56 7.2 9 10 11 12 13 14 15 16 16 16.5 17 17.5 period (s) 94 232 389 556 926 1296 1667 2222 2778 3241 3704 4630 5556 fetch (km) 3.54 4.88 5.79 6.71 7.62 8.38 8.84 9.14 9.14 9.45 9.45 9.45 9.45 height (m) 67 8 10 11.5 13 14 15 16 17.2 18 18.5 19 19.5 20 period (s) 111 259 435 667 1000 1482 1852 2593 3148 3704 4260 5371 6297 fetch (km) 4.27 5.79 7.01 7.92 8.84 9.75 10.36 10.97 11.28 11.58 11.89 12.19 12.5 height (m) 74 8.8 11 12.5 14 15 16.2 17 19 19.5 20 21 21 22 period (s) 119 278 482 741 1093 1630 2222 2778 3334 4074 4630 5741 7038 fetch (km) 4.88 7.01 8.23 9.45 10.67 11.89 12.5 13.72 13.72 14.33 14.94 15.24 15.24 height (m) 83 9.3 12 13.5 15 16 18 18.5 20 21 22 22.5 23 24 period (s) 130 315 528 787 1167 1759 2315 2963 3704 4260 5000 6667 7593 fetch (km) 5.79 8.23 9.45 11.3 13.11 14.02 14.63 16.46 16.76 17.68 17.98 18.29 18.29 height (m) 93 10 12.5 14.5 16 17.5 19 21 22 23 23 24 25.5 26.5 period (s) 139 333 556 833 1296 1945 2500 3241 3889 4630 5371 7038 7871 fetch (km) 6.86 9.14 10.97 13.4 15.24 16.76 17.98 18.9 19.81 20.12 21.03 21.34 21.34 height (m) 102 11 13 15 17 19 21 22 23 24 25 26 27 28 period (s) 148 352 593 926 1408 2130 2685 3519 4260 4815 5741 7223 8519 fetch (km) 7.62 10.67 12.8 15.2 17.07 20.42 21.34 22.86 24.08 24.38 24.38 24.99 25.91 height (m) 111 11.5 14 16.5 18 20 22 23.5 25 26 28 28 30 30 period (s) 154 370 648 945 1482 2222 2778 3704 4537 5186 6019 7408 9260 fetch (km) 8.38 11.89 14.63 16.8 19.81 22.86 24.38 25.91 27.43 28.04 28.96 30.48 30.48 height (m) 120 12 15 17 19 21 22 25 26.5 28 28.5 30 31 33 period (s) 163 407 704 1037 1574 2315 2963 3889 4630 5463 6297 7778 9445 fetch (km) 9.14 13.11 16.76 18.9 21.64 24.99 27.43 29.87 30.48 31.7 33.22 35.05 36.27 height (m) 130 13 16 18 20 22 25 26 29 29.5 30.5 31 32.5 35 period (s) 169 435 732 1111 1630 2454 2963 4167 4815 5649 6667 8334 10371 fetch (km) 10.36 15.24 18.29 21.3 24.38 27.43 30.18 32 33.53 35.97 36.58 38.1 39.62 height (m) 139 14 17 19 21 23 25.5 27 29 31 32 33 34 36 period (s) 178 454 750 1148 1667 2593 3148 4260 5000 5834 7038 8890 11112 fetch (km) 11.28 16.46 19.81 22 25.91 30.48 32.61 36.27 36.88 40.54 41.45 42.67 42.67 height (m) 148 14.5 17.5 20 22 23.5 26.5 28 30 32 33 34 35 36.5 period (s) 185 472 787 1185 1806 2685 3334 4445 5278 6112 7223 9167 11297 fetch (km) 12.19 17.37 22.56 24.4 28.96 33.22 37.19 40.54 42.37 42.67 44.2 47.24 48.77 height (m) 157 15 18 21 22 25 27.5 30 32 33.5 35 35.5 37.5 39.5 period (s) 191 482 824 1259 1852 2778 3519 4630 5556 6482 7501 9353 12038 fetch (km) 13.72 19 24.38 28 32.61 36.58 39.62 42.67 44.81 47.24 50.29 51.82 57.91 height (m) 167 16 19 22 24 26.5 29 31.5 33 34.5 36.5 37 40 44 period (s) 204 500 852 1296 2037 2871 3704 4815 5741 6945 7871 9630 12594 fetch (km)

Appendix 5.7: Significant wave height and wave period for different wind speeds and durations

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Appendix

$*************HEADING**************************************** $ PROJECT 'swanbangladesh' '01' $'Sensitivity analysis' $'Hs=6.0 Tp=10 Wave angle=190 Wind=41.50m/s' $ SET LEVEL=3.80 NOR=90.00 DEPMIN=0.05 MAXMES=200 MAXERR=1 _ GRAV=9.81 RHO=1025.00 INRHOG=1 HSRERR=0.10 NAUT $ MODE STAT TWOD $ COORD SPHERICAL $ $ --|------|-- $ | This SWAN input file is part of the bench mark tests for | $ | SWAN. | $ --|------|-- $ $***********MODEL INPUT************************************** $ CGRID REGULAR 83.00 18.00 0. 12.00 5.00 720 300 CIRCLE 36 0.05 1.00 31 $ INPGRID BOTTOM REGULAR 83.00 18.00 0. 720 300 0.016667 0.016667 READINP BOTTOM -1.0 'swanbangladesh.bot' 1 0 FREE $ WIND VEL=15.00 DIR=180.00 $ BOUN SHAPE JONSWAP 3.30 PEAK DSPR DEGR BOUN SIDE S CON PAR 3.98 9.60 180 30 $ GEN3 BREAK CONSTANT 1.00 0.73 FRICTION JONSWAP 0.067 TRIAD 0.1 2.20 0.2 0.01 $ NUM DIR cdd=0.50 SIGIM css=0.50 NUM ACCUR 0.02 0.02 0.02 98.50 15 $ $************ OUTPUT REQUESTS ************************* $File name CTA11 should be same otherwise it will not work $ BLOCK 'COMPGRID' NOHEAD 'UBOT_1.mat' LAY-OUT 1 UBOT RTP HS XP YP DIR $ CURVE 'CTA11' 88.75 18.00 10 88.75 21.00 SPEC 'CTA11' SPEC1D 'swanbangladesh01.spc' TABLE 'CTA11' HEAD 'swanbangladesh01.tab' DIST HS RTP DIR DSPR DEP DISSIP WLEN UBOT $ CURVE 'CTA12' 91.25 18.00 10 91.25 21.00 SPEC 'CTA12' SPEC1D 'swanbangladesh02.spc' TABLE 'CTA12' HEAD 'swanbangladesh02.tab' DIST HS RTP DIR DSPR DEP DISSIP WLEN UBOT $ CURVE 'CTA13' 89.00 18.00 12 89.00 21.60 SPEC 'CTA13' SPEC1D 'swanbangladesh03.spc' TABLE 'CTA13' HEAD 'swanbangladesh03.tab' DIST HS RTP DIR DSPR DEP DISSIP WLEN UBOT $ POINTS 'POINT1' 91.25 21.00 88.75 21.00 SPEC 'POINT1' SPEC1D 'swanbangladesh04.spc' TABLE 'POINT1' HEAD 'swanbangladesh04.tbl' XP YP DIST DEPTH HS RTP TM01 WLENGTH DIR UBOT $ TEST 0,0 COMPUTE STOP $ Appendix 5.8: A typical command file for SWAN computation

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Appendix

Critical bed shear stress Dyne/cm^2 N/m^2 1 0.441 0.0441 2 0.464 0.0464 3 0.425 0.0425 4 0.531 0.0531 5 0.445 0.0445 6 0.957 0.0957 7 0.943 0.0943 8 0.784 0.0784 9 0.943 0.0943 10 0.942 0.0942 11 1.017 0.1017 12 1 0.1 13 0.478 0.0478 14 0.531 0.0531 15 0.911 0.0911 16 0.872 0.0872 17 0.982 0.0982 18 0.469 0.0469 19 0.95 0.095 20 0.432 0.0432 21 0.413 0.0413 22 0.561 0.0561 Average 0.704136364 0.070413636

Appendix 5.9: Critical bed shear of soil along the coast of Bangladesh (Barua et al., 1994)

Sea Level Rise for Bangladesh (in cm) 3rd IPCC NAPA Year SMRC Upper Range Scenario 2030 14 18 14 For the Calculation 2050 32 30 32 For the Calculation 2100 88 60 88

Water Sea Water Level Modeled Offshore climate Wind Case Level Level after SLR Wind Direction Hs Tp Wave (m) Rise (m) (m) (m/s) (m) (s) Direction 1 High Tide 3.8 0.14 3.94 5 S=180 2.17 9.1 208 Sea Level 2 High Tide 3.8 0.14 3.94 10 S=180 2.94 9.05 213 Rise Upto 3 High Tide 3.8 0.14 3.94 20 S=180 5.95 11.75 180 2030 4 High Tide 3.8 0.14 3.94 30 S=180 9.5 13.25 180

5 High Tide 3.8 0.32 4.12 5 S=180 2.17 9.1 208 Sea Level 6 High Tide 3.8 0.32 4.12 10 S=180 2.94 9.05 213 Rise Upto 7 High Tide 3.8 0.32 4.12 20 S=180 5.95 11.75 180 2050 8 High Tide 3.8 0.32 4.12 30 S=180 9.5 13.25 180

Appendix 5.10: Data has been used for the future projections along the coast of Bangladesh

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List of Files in CD

LIST OF FILES IN CD

Serial Number Type of File 1 All Matlab plots including Individual mfile 2 SWAN input files for each Run Individually 3 Population Analysis in Bangladesh 4 All Gis Graphs 5 Full Master Thesis 6 Bathymetry Raw Data 7 Bathymetry plotting by Matlab 8 All required Wind and Wave Data

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DECLARATION

DECLARATION I, Mohammad Mahtab Hossain declare that I have written this Master’s Thesis independently. No other that the given sources and resources were used. The quotations for the consulted materials have been identified as such. I declare that this research paper for my degree of Master of Water Resources and Environmental Management, Faculty of Civil Engineering at Leibniz University Hannover, Hereby submitted has not been submitted by me or anyone else for a degree to any recognized institution. This is my own work and that material consulted have been properly acknowledged.

Hannover, 13.09.2012 Signature: ......

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