B A N G L A D E S H : E C O N O M I C S O F A DA P TAT I O N TO C L I M AT E C H A N G E i 70266 v2 Economics of Adaptation to Climate Change A n n exe s BANGL ADESH B A N G L A D E S H : E C O N O M I C S O F A D A P TAT I O N T O C L I M AT E C H A N G E i Economics of Adaptation to Climate Change BANG L ADESH Ministry of Foreign Affairs Government of the Netherlands ii B A N G L A D E S H : E C O N O M I C S O F A D A P TAT I O N T O C L I M AT E C H A N G E © 2010 The World Bank Group 1818 H Street, NW Washington, DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org E-mail: feedback@worldbank.org All rights reserved. This volume is a product of the World Bank Group. The World Bank Group does not guarantee the accuracy of the data included in this work. 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Bangladesh Country Study Annex 1 Major Cyclones Crossing Bangladesh Coast (1960-2009) Wind Storm Number of Landfall Landfall Human Year speed Surges People Specific Economic Loss date Location Casualties (km/hr) (m) Affected Loss of livestock: 27,783. Noakhali 1960 October 31 193 6.1 10,000 200,000 Destruction of Houses: 568,161 Chittagong Vessels capsized: 5–7 Estimated loss: $11.9 billion Meghna Loss of livestock: 25,000 1961 May 9 161 3.1 11,468 NA estuary Railway track between Noakhali and Harinarayanpur was damaged Estimated loss: $46.5 billion Noakhali Loss of livestock: 32,617 1963 May 29 202 6 11,520 1,000,000 Chittagong Destruction of Houses: 376,332 Boats damaged: 4,787 Barisal- Estimated loss: $57.7 billion 1965 May 12 162 3.7 19,279 15,600,000 Noakhali Destruction of Houses: 35,636 Rice harvest loss: 40–50% December Cox’s 1965 184 3.6 873 60,000 Significant damage to fishing boats 15 Bazar and nets Salt beds inundated: 40,000 Estimated loss: $22.4 billion. Loss of livestock: 65,000 1966 October 1 Noakhali 139 6.7 850 1,800,000 Damage to poultry: 185,000. Destruction of Houses: 309,000 Damage to food grains: 5,595 tons November Bhola, Meghna Estimated loss: $86.4 billion 1970 224 10.0 300,000 3,648,000 12 estuary November Chittagong Loss of livestock: 1,000 1974 161 5.1 50 NA Destruction of houses: 2,300 28 Cox’s Bazar Chittagong Destruction of houses: 2,000 1983 November 9 Cox’s Bazar 135 1.5 NA Number of missing boats: 50 near Kutubdia Number of fishermen missing: 300 4,264 1985 May 25 Noakhali 154 4.5 1,810,000 - (6,805 missing) 1986 November 9 Patuakhali 110 0.6 - November Southeast coast 1988 135 10,568,860 - 18 of Teknaf Noakhali Estimated loss: $1,780 billion 1991 April 29 235 7.6 133,882 15,438,849 Chittagong November Gulf of 1995 110 3.6 172 250,000 - 25 Bengal Loss of livestock: 3,118 Chittagong/ Destruction to houses: 211,717 1997 May 19 200 4.6 155 3,052,738 Sitakundu Damage to crops: 97,333 acres, Damage to salt production: 2,232,000 acres September 1997 150 3.0 188 751,529 - 26 1998 May 20 Sitakundu 186 108,944 - November Sundarban Estimated Damage: $2,300 billion 2007 250 6-8 2,388 8,978,541 15 Borguna West Bengal, Estimated Damage: $270 billion 2009 May 5 95 4.0 190 3,935,341 India Source: Quadir and Iqbal (2008) and EMDAT 1 Economics of Adaptation to Climate Change Annex 2 Tracks for Major Cyclones that Crossed the Bangladesh Coast, 1960-2009 Source: IWM 2 Bangladesh Country Study Annex 3 Cyclone Tracks used to Simulate Inundation Risk under the Climate Change Scenario Source: IWM 3 Economics of Adaptation to Climate Change Annex 4 Description of Cyclones & Bay of Bengal Model The storm surge model used in this analysis is a combination of a cyclone model and a hydrodynamic model. The cyclone model simulated a cyclone based on the following parameters: 1. Radius of maximum winds 2. Maximum wind speed 3. Cyclone tracks, forward speed and direction 4. Central pressure 5. Neutral pressure Then the simulated cyclone is run with the hydrodynamic model to generate cyclone-induced storm surge and associated coastal inundation. This analysis used the Bay of Bengal model based on the MIKE21 hydrodynamic modeling system. It is a two-way nested two-dimensional model and its domain extends from Chandpur to 16º latitude in a north-south direction. The grid size of the model is 200m in the Meghna estuary and coastal region of Bangladesh. A description of the bathymetry data used to develop the Bay of Bengal model is as follows: Bathymetry Data The main source of bathymetry data for the modeling is the C-Map (an Electronic Chart System Database), Meghna Estuary Study, Phase II (MES II, 1998-99), Mongla Port Study (2004), IPSWAM (2008) and other projects of Bangladesh Water Development Board (BWDB). Figure A-1 shows the nested bathymetry of the Bay of Bengal Model. In order to simulate realistic flood depths, the following topographic data was used in the analysis: Topographic Data (Digital Elevation Model) The main source of topographic elevation data for the coastal region of Bangladesh is the FINNMAP land survey, FAP 19- National DEM (1952-64), and projects of the Bangladesh Water Development Board (i.e. Khulna Jessore Drainage Rehabilitation Project, 1997; Beel Kapalia project, 2008; and Beel Khuksia project, 2004). The FINNMAP topographic maps and other data were digitized to develop a digital elevation model (DEM) of the coastal region of Bangladesh. The grid size of the model is 50 m x 50 m. The DEM of the coastal region of Bangladesh is shown in Figure A-2. The MIKE 21 modeling system includes dynamic simulation of flooding and drying processes, which is important for a realistic simulation of flooding in the coastal area and inundation. 4 Bangladesh Country Study Figure A4-1 Nested Bathymetry of Bay of Bengal Model Figure A4-2 Digital Elevation Model of Coastal Region of Bangladesh (Below) 5 Economics of Adaptation to Climate Change Annex 5 Polders likely to be overtopped in the Baseline & Climate Change Scenarios Overtopping Sl. Overtopping Depth Length River Dyke Length Base 2050 Sl. No. Sea Dyke Depth Base 2050 Nos. (km) (m) (m) (km) (m) (m) 1 P07/1 32 0 1 1 P40/1-2 59 2.5 2.7 2 P07/2 64 0 1 2 P41-42 81 2 3.25 3 P10-12 67 1 1 3 P45 26 1.25 1.5 4 P13-14/2 122 0 1 4 P46-47 80 1.75 2 5 P23 37 0 1.5 5 P48 38 1.2 2 6 P31 63 0 1 7 P32 49 0 1 6 P50/51 48 1.8 2.25 8 P33 52 0 1 7 P52/53 40 2.5 3 9 P35/1 62 1 2.5 8 P54 60 1.8 2.25 10 P35/2 105 1 2.5 9 P55/1 46 1.4 1.75 11 P35/3 40 0 1.5 10 P55/3 53 1.25 2 12 P36/2 87 0 1.5 11 P55/4 31 2.9 3.75 13 P37 100 1 2.75 12 P56/57(E) 140 2.5 3.25 14 P38 40 2 2.75 13 P56/57(W) 90 1.5 2.5 15 P39/1 96 2 2.5 14 P58/1 32 3.25 5 16 P39/2 109 1.5 3.25 15 P58/2 28 3.3 4.25 17 P41/6-7 95 0.75 1 16 P59/2 93 3.25 4 18 P42 28 2 3.25 17 P59/3B 70 4.5 6 19 P43/1 114 1.8 2 18 P59/3C 42 4 6.5 20 P43/2 121 1.8 2 19 P60 45 4.25 6.5 21 P44 84 1.5 2.2 20 P61/1 26 3.5 6.4 22 P55/2 109 1.9 2.75 21 P61/2 45 1 6.5 23 P63/1A -Battali 38 1.75 5.6 22 P62 22 2.5 5.5 24 P64/2A 59 0 5 P64/2b-joakkhal-Koriardi 17 25 0 3 23 P63/1A-Raipur 20 1.5 4.5 26 P64/2b-Pekua 30 0 3.75 24 P64/1A 54 1.75 6.25 25 P64/2b-Mognama 27 1 3.5 26 P64/2b-Ujantia 22 0 3.5 27 P66/3 37 0 0.5 28 P69 29 0 1.25 29 P70 29 1 1.75 30 P71 41 1.75 4.25 31 P72 62 4 5.5 32 P73/1 89 2.3 3.5 33 P73/2 48 2.6 3 Note: Polders that are not overtopped in both scenarios are not listed. Overtopping height of 0 meters indicates that the polder is not overtopped in that scenario. 6 Bangladesh Country Study Annex 6 Estimated Cost to Prevent Overtopping of Embankments by 2050 7 Economics of Adaptation to Climate Change Table A6-1 Interior Polders Baseline Scenario Height Existing to be Polder Length Height Raised Cost ( million US$) Earthwork Earthwork Major (300 m to (1.0 km to Plantation Land Protection Toe (m) (m) (m) 1.0 km) 5.0 km) Turfing (Vetivera) Acquisition Work Protection Total 1 P10-12 67,400 4 2.5 8.1 0.9 0.1 0.0 1.4 42.9 0.0 53.3 2 P35/1 61,600 4.5 2.5 8.0 0.9 0.0 0.0 1.3 0.0 32.0 42.2 3 P35/2 104,600 3 2.5 10.5 1.1 0.1 0.1 2.2 0.0 0.0 14.0 4 P37 100,100 3.5 2.5 11.0 1.2 0.1 0.1 2.1 0.0 0.0 14.5 5 P38 39,500 3.5 3.5 6.6 0.7 0.0 0.1 1.2 0.0 0.0 8.6 6 P39/1 96,200 5 3.5 20.1 2.1 0.1 0.1 2.9 10.3 0.0 35.6 7 P39/2 109,200 3 3 13.8 1.5 0.1 0.0 2.8 0.0 0.0 18.2 8 P41/6-7 95,100 4.5 2.25 10.9 1.2 0.1 0.0 1.8 0.0 0.0 14.0 9 P42 28,000 4.5 3.5 5.5 0.6 0.0 0.0 0.8 5.1 0.0 12.1 10 P43/1 113,520 4.5 3.3 20.6 2.2 0.1 0.1 3.2 0.0 0.0 26.2 11 P43/2 120,600 4.5 3.3 21.9 2.3 0.1 0.1 3.4 0.0 0.0 27.8 12 P44 83,700 4.5 3 13.5 1.4 0.1 0.0 2.2 10.3 0.0 27.5 13 P55/2 109,100 0.75 3.4 9.6 1.0 0.1 0.0 3.2 0.0 0.0 13.9 P63/1A - 14 Battali 38,000 5 3.25 7.3 0.8 0.0 0.0 1.1 0.0 0.0 9.1 317.0 8 Bangladesh Country Study Table A6-2 Interior Polders Climate Change Scenario Existing Height to Polder Length Height be Raised Cost ( million US$) Earthwork Earthwork Major (300 m to 1.0 (1.0 km to Plantation Land Protection Toe (m) (m) (m) km) 5.0 km) Turfing (Vetivera) Acquisition Work Protection Total 1 P07/1 31,800 4.5 2.5 4.1 0.4 0.0 0.0 0.7 21.4 0.0 26.7 2 P07/2 64,000 4 2.5 7.7 0.8 0.0 0.0 1.4 25.7 0.0 35.6 3 P10-12 67,400 4 2.5 8.1 0.9 0.1 0.0 1.4 42.9 0.0 53.3 4 P13-14/2 122,400 4.5 2.5 15.9 1.7 0.1 0.0 2.6 0.0 0.0 20.3 5 P23 36,700 4 3 5.5 0.6 0.0 0.0 0.9 21.4 0.0 28.5 6 P31 62,700 4 2.5 7.5 0.8 0.0 0.0 1.3 42.9 0.0 52.6 7 P32 48,800 4 2.5 5.9 0.6 0.0 0.0 1.0 25.7 0.0 33.3 8 P33 51,500 4 2.5 6.2 0.7 0.0 0.0 1.1 25.7 0.0 33.7 9 P35/1 61,600 4.5 4 14.2 1.5 0.1 0.0 2.1 0.0 32.0 49.9 10 P35/2 104,600 3 4 19.3 2.1 0.1 0.1 3.6 0.0 0.0 25.0 11 P35/3 39,700 3 3 5.0 0.5 0.0 0.0 1.0 0.0 0.0 6.6 12 P36/2 87,400 3 3 11.0 1.2 0.1 0.0 2.2 0.0 0.0 14.5 13 P37 100,100 3.5 4.25 21.7 2.3 0.1 0.1 3.6 0.0 0.0 27.8 14 P38 39,500 3.5 4.25 8.6 0.9 0.0 0.1 1.4 0.0 0.0 11.0 15 P39/1 96,200 5 4 23.8 2.5 0.1 0.1 3.3 10.3 0.0 40.0 16 P39/2 109,200 3 4.75 25.4 2.7 0.1 0.0 4.4 0.0 0.0 32.7 17 P41/6-7 95,100 4.5 2.5 12.3 1.3 0.1 0.0 2.0 0.0 0.0 15.8 18 P42 28,000 4.5 4.75 8.1 0.9 0.0 0.0 1.1 5.1 0.0 15.3 19 P43/1 113,520 4.5 3.5 22.2 2.4 0.1 0.1 3.4 0.0 0.0 28.1 20 P43/2 120,600 4.5 3.5 23.6 2.5 0.1 0.1 3.6 0.0 0.0 29.9 21 P44 83,700 4.5 3.7 17.5 1.9 0.1 0.0 2.7 10.3 0.0 32.4 22 P55/2 109,100 0.75 4.25 13.6 1.5 0.1 0.0 4.0 0.0 0.0 19.1 23 P63/1A -Battali38,000 5 7.1 19.9 2.1 0.1 0.0 2.3 0.0 0.0 24.4 24 P64/2A 59,100 4.5 6.5 26.0 2.8 0.1 0.0 3.3 0.0 0.0 32.1 25 17,000 P64/2b-joakkhal-Koriardi 4 4.5 4.3 0.5 0.0 0.0 0.7 0.0 0.0 5.4 26 P64/2b-Pekua 30,000 4.5 5.25 9.9 1.1 0.0 0.0 1.4 0.0 0.0 12.3 706.4 9 Economics of Adaptation to Climate Change Table A6-3 Sea-facing Polders Baseline Scenario Height to Existing be Polder Length Height Raised Cost ( million US$) Earthwork Earthwork Major (300 m to 1.0 (1.0 km to Plantation Land Protection Toe (m) (m) (m) km) 5.0 km) Turfing (Vetivera) Acquisition Work Protection Total 1 P40/1-2 59,000 5 4 27.5 2.9 0.1 0.0 4.0 7.7 16.0 58.3 2 P41-42 80,500 4.5 3.5 29.6 3.2 0.1 0.0 4.8 7.7 16.0 61.3 3 P45 25,800 6 2.75 8.7 0.9 0.0 0.0 1.2 5.7 16.0 32.6 4 P46-47 79,600 5 3.25 28.7 3.1 0.1 0.0 4.4 14.7 0.0 51.0 5 P48 38,100 5.8 2.7 12.2 1.3 0.0 0.0 1.8 24.6 64.0 104.0 6 P50/51 48,300 5.18 3.3 18.2 1.9 0.0 0.0 2.7 0.0 0.0 22.9 7 P52/53 40,300 4.57 4 17.7 1.9 0.0 0.0 2.8 5.1 0.0 27.6 8 P54 60,000 4.5 3.3 20.5 2.2 0.1 0.0 3.4 10.3 0.0 36.4 9 P55/1 45,800 5 2.9 14.4 1.5 0.0 0.0 2.3 28.6 0.0 46.8 10 P55/3 52,700 5 2.75 15.5 1.7 0.0 0.0 2.5 25.7 0.0 45.4 11 P55/4 30,800 5.5 4.4 17.3 1.8 0.0 0.0 2.3 25.7 0.0 47.2 12 P56/57(E) 140,000 6.5 4 78.6 8.4 0.2 0.0 9.6 0.0 160.1 256.8 13 P56/57(W) 90,000 5 3 29.4 3.1 0.1 0.0 4.6 0.0 0.0 37.2 14 P58/1 32,000 4.8 4.75 18.2 1.9 0.0 0.0 2.6 0.0 0.0 22.7 15 P58/2 28,000 4.8 4.8 16.1 1.7 0.0 0.0 2.3 0.0 32.0 52.2 16 P59/2 92,800 6 4.75 61.0 6.5 0.1 0.3 7.6 64.3 64.0 203.8 17 P59/3B 70,100 6 6 62.3 6.7 0.1 0.0 7.2 0.0 0.0 76.3 18 P59/3C 41,800 6 5.5 33.2 3.5 0.1 0.0 3.9 35.7 0.0 76.4 19 P60 44,900 7 5.75 41.8 4.5 0.1 0.0 4.4 0.0 0.0 50.7 20 P61/1 26,000 6 5 18.2 1.9 0.0 0.0 2.2 0.0 0.0 22.4 21 P61/2 45,100 6 2.5 13.6 1.5 0.0 0.0 1.9 0.0 0.0 17.0 22 P62 22,000 6 4 11.7 1.2 0.0 0.0 1.5 0.0 0.0 14.4 23 2 P63/1A-Raipur 0,000 6 3 7.5 0.8 0.0 0.0 1.0 0.0 0.0 9.3 24 P64/1A 54,200 5.5 3.25 20.9 2.2 0.1 0.0 3.0 32.1 38.4 96.8 25 27,000 P64/2b-Mognama 5 2.5 7.1 0.8 0.0 0.0 1.2 19.3 16.0 44.3 26 P70 29,200 6 2.5 8.8 0.9 0.0 0.0 1.3 30.0 0.0 41.0 27 P71 40,500 4.5 3.25 13.6 1.4 0.0 0.0 2.3 64.3 64.0 145.6 28 P72 61,700 5.5 5.5 46.3 4.9 0.1 0.0 5.8 75.7 128.1 260.9 29 P73/1AB 89,400 5.5 3.8 41.8 4.5 0.1 0.0 5.8 6.4 64.0 122.6 30 P73/2 47,600 5 4.1 22.9 2.4 0.1 0.0 3.3 32.1 0.0 60.9 2144.9 10 Bangladesh Country Study Table A6-4 Sea-facing Polders Climate Change Scenario SEA DYKE Height to Existing be Polder Length Height Raised Cost (million US$) Earthwork Earthwork Major (300 m to 1.0 (1.0 km to Plantation Land Protection Toe (m) (m) (m) km) 5.0 km) Turfing (Vetivera) Acquisition Work Protection Total 1 P40/1-2 59,000 5 4.2 29.3 3.1 0.1 0.0 4.2 7.7 16.0 60.5 2 P41-42 80,500 4.5 4.75 43.9 4.7 0.1 0.0 6.6 7.7 16.0 78.9 3 P45 25,800 6 3 9.6 1.0 0.0 0.0 1.3 5.7 16.0 33.7 4 P46-47 79,600 5 3.5 31.4 3.4 0.1 0.0 4.8 14.7 0.0 54.3 5 P48 38,100 5.8 3.5 16.7 1.8 0.0 0.0 2.3 24.6 64.0 109.4 6 P50/51 48,300 5.18 3.75 21.3 2.3 0.1 0.0 3.1 0.0 0.0 26.7 7 P52/53 40,300 4.57 4.5 20.7 2.2 0.0 0.0 3.1 5.1 0.0 31.2 8 P54 60,000 4.5 3.75 24.1 2.6 0.1 0.0 3.9 10.3 0.0 40.8 9 P55/1 45,800 5 3.25 16.5 1.8 0.0 0.0 2.6 28.6 0.0 49.4 10 P55/3 52,700 5 3.5 20.8 2.2 0.1 0.0 3.2 25.7 0.0 52.0 11 P55/4 30,800 5.5 5.25 21.7 2.3 0.0 0.0 2.8 25.7 0.0 52.6 12 P56/57(E) 140,000 6.5 4.75 97.2 10.4 0.2 0.0 11.4 0.0 160.1 279.2 13 P56/57(W) 90,000 5 4 42.0 4.5 0.1 0.0 6.2 0.0 0.0 52.8 14 P58/1 32,000 4.8 6.5 27.7 3.0 0.0 0.0 3.6 0.0 0.0 34.3 15 P58/2 28,000 4.8 5.75 20.5 2.2 0.0 0.0 2.8 0.0 32.0 57.5 16 P59/2 92,800 6 5.5 73.6 7.9 0.1 0.3 8.7 64.3 64.0 218.9 17 P59/3B 70,100 6 7.5 84.1 9.0 0.1 0.0 9.0 0.0 0.0 102.2 18 P59/3C 41,800 6 8 54.8 5.8 0.1 0.0 5.7 35.7 0.0 102.1 19 P60 44,900 7 8 64.5 6.9 0.1 0.0 6.2 0.0 0.0 77.6 20 P61/1 26,000 6 7.9 33.5 3.6 0.0 0.0 3.5 0.0 0.0 40.6 21 P61/2 45,100 6 8 59.1 6.3 0.1 0.0 6.2 0.0 0.0 71.7 22 P62 22,000 6 7 24.0 2.6 0.0 0.0 2.6 0.0 0.0 29.3 23 2 P63/1A-Raipur 0,000 6 6 17.8 1.9 0.0 0.0 2.1 0.0 0.0 21.8 24 P64/1A 54,200 5.5 7.75 64.7 6.9 0.1 0.0 7.2 32.1 38.4 149.4 25 27,000 P64/2b-Mognama 5 5 16.8 1.8 0.0 0.0 2.3 9.6 16.0 46.6 26 2 P64/2b-Ujantia2,000 4 5 12.0 1.3 0.0 0.0 1.9 9.6 9.6 34.4 27 P66/3 36,600 6 2 8.5 0.9 0.0 0.0 1.3 0.0 0.0 10.7 28 P69 29,000 6 2.75 9.8 1.0 0.0 0.0 1.4 18.0 0.0 30.2 29 P70 29,200 6 3.25 12.0 1.3 0.0 0.0 1.6 30.0 0.0 44.9 30 P71 40,500 4.5 5.75 28.6 3.0 0.0 0.0 4.0 64.3 64.0 164.0 31 P72 61,700 5.5 7 64.0 6.8 0.1 0.0 7.4 75.7 128.1 282.1 32 P73/1 89,400 5.5 5 59.2 6.3 0.1 0.0 7.7 6.4 64.0 143.7 33 P73/2 47,600 5 4.5 25.8 2.8 0.1 0.0 3.7 32.1 0.0 64.5 2648.2 11 Economics of Adaptation to Climate Change Annex 7 Earthwork Computation with an Illustrative Example Sea-facing Polders Additional Area Additional unit volume of earthwork, A = 0.5x {4.3+4.3+10x (ha+he)} x (ha+he)-0.5x {4.3+4.3+10xhe}xhe = 5 ha2+4.3 ha +10 ha he Where, he =Existing Height ha =Additional Height Interior Polders Additional unit volume of earthwork, A = 0.5x {4.3+4.3+5x (ha+he)} x (ha+he)-0.5x {4.3+4.3+5xhe}xhe = 2.5 ha2+4.3 ha +5 ha he Total earthwork volume is computed by multiplying the unit volume of earthwork with the affected length of polder. 12 Bangladesh Country Study Annex 8 Determining Additional Vents Required to Reduce Drainage Congestion in Coastal Polders The number of additional vents required is based on trial simulations for reducing drainage congestion in four polders. The southwest regional model (SWRM) is used to assess the additional number of vents and the simulation is done for following conditions: 1. Base condition:  The flood of 2005 monsoon (July-September) and precipitation of that time 2. Climate change condition for the year 2050:  The flood of 2005 has been considered as a reference flooding condition to assess the increase of flooding due to increased precipitation for climate change.  GCM (global circulation model) result (i.e. ECHAM5) has been used in basin-level rainfall data for future climate change scenario and projected year 2050.  The sea level rise of 27 cm for the projected year 2050 has been considered in accordance with National Adaptation Programme of Action (NAPA) to assess the effect of sea level rise on coastal inundation. The water level inside polder increases due to climate change condition and it creates drainage congestion in the polder. Trial simulations are done with a different length of opening to reduce the water depth inside the polder to the level of the base condition. The table below shows that polder P10-12 needs 150 m length of opening to reduce the water depth inside the polder to 0.984 meter, which is close to the level of 0.964 meter under the base condition. So the adaptation length of opening is computed by deducting two lengths of opening. Additional numbers of vents are computed by dividing the length of opening with 1.52 m (i.e. width of each vent). For the four simulations, 4 to 51 additional vents are required to reduce congestion from the climate change condition to the levels existing under the base condition. While the range is broad, the average number of vents required in the four simulations, 25, is used for the analysis. A total of 59 polders (33 sea dykes and 26 river dykes) are affected by the overtopping. The total number of vents of size 1.52mX1.83m required to avoid drainage congestion is 1,475. Existing and Base Condition Simulated Adaptation for 2050 Cost for Cost for Adaptation Polder Length of Length of Water Level Water Level Length of Adaptation (Tk.) ( Million $) Openning Openning No. of Vent (m) (m) Openning (m) (m) (m) P10-12 72 0.964 150 0.984 78 51 1020000000 15 P23 19 1.049 30 1.124 11 7 140000000 2 P39_2-1 104 1.455 130 1.517 26 17 340000000 5 P39_2-2 104 1.552 140 1.561 36 24 480000000 7 Note: 1. Vent size of the regulator is considered 5 feet width X 6 feet height (1.52mX1.83m) and 2. Cost per vent is 2 crore BDT. Average Nos. of vents required for each affacted polder: 25 13 Economics of Adaptation to Climate Change Annex 9 Floods in Bangladesh Floods in Bangladesh can be classified into four categories (Ahmad et al. 1994; Ahmad et al. 2000): (1) flash floods, (2) river floods, (3) rainwater floods, and (4) coastal floods. Flash floods: Flash floods usually take place in the hilly areas during the pre-monsoon months of April and May. Runoff due to exceptionally heavy rainfall in upland areas causes flash floods. Flash floods occur within a short period of time and last from a few hours to a few days. Flash floods occur frequently—sometimes several times a year—mauling standing crops and destroying physical infrastructure at the foot of the northern and eastern hills of Bangladesh (Huq et al. 1996). Flash floods cause extensive damages to crops and property, particularly in the haor areas of Bangladesh. River floods: River floods result from snow-melt in the high Himalayas and heavy monsoon rainfall in the foothills of the Himalayas, the Assam Hills, the Tripura Hills, and the upper Brahmaputra and Ganges floodplains outside Bangladesh. River floods generally occur during the monsoon. The timing of the flood—whether early or late in the monsoon period—and duration of flooding are important determinants of the extent of damage. Rainwater floods: Heavy rainfall over the floodplain and terrace areas in Bangladesh causes rainwater floods. Runoff from heavy pre-monsoon rainfall (April-May) accumulates in floodplain depressions and in the lower parts of valleys within the Madhupur Tract. During the monsoon, local rainfall and the rising water level of adjoining rivers add to the pressure. Thus, the extent and depth of rainwater flooding varies within the rainy season and from year to year, depending on the amount and intensity of local rainfall and on contemporary water levels in the major rivers. Coastal floods: Coastal areas of Bangladesh are vulnerable to high tides as well as storm surges during cyclones. In the 1960s, 123 embankments and supporting infrastructure were constructed to protect low-lying coastal areas against tidal floods and salinity intrusion. Marginal areas outside the embankments are prone to tidal inundation. During cyclones, embankments are often overtopped/ breached by storm surges and large areas are flooded. 14 Bangladesh Country Study Annex 10 Spatial Vulnerability of Bangladesh to Various Types of Floods So Source: WARPO-Halcrow et al., 2004 15 Economics of Adaptation to Climate Change Annex 11 Classification of Floods in Bangladesh Parameters Types of Flooded Percent Probability of Physical parameters affected floods area Range inundated occurrence* (sq. km) Range Normal 31,000 21 50 Hampers normal human activities flood Cropping pattern is adjusted with inundation May increase soil fertility Economic loss is minimum Moderate 31,000– 21–26 30 Hampers human activity moderately flood 38,000 Damage limited to crops Economic loss is moderate Evacuation not necessary; people take their own measures Severe flood 38,000– 26–34 10 Hampers human activities severely 50,000 Damage is mainly to crops, infrastructure (roads, railways, power, telecommunications, etc.) and certain urban centers Economic loss is higher Requires evacuation Requires relief operation Catastrophic 50,000– 34–38.5 5 Hampers human activities very severely flood 57,000 Extensive damage to crops of all types of lands, cultured fisheries, lives and property in both urban and rural centers, all types of infrastructure, etc. Requires extensive relief operation Very high economic loss Requires international support Exceptional 57,000+ 38.5+ 5 Hampers human activities exceptionally flood Extensive damage to crops of all types of lands, cultured fisheries, lives and property in both urban and rural centers, all types of infrastructure, etc. Requires extensive relief operation Disrupts communication Closing of educational institutions Exceptional economic loss Usually requires international support Source: Mirza 2002 16 Bangladesh Country Study Annex 12 Flood-affected Areas in Bangladesh, 1954-2007 Year Inundated Area (Sq. km) Inundated Area (%) Flood Classification 1954 36,800 25 Moderate 1955 50,500 34 Severe 1956 35,400 24 Moderate 1960 28,400 19 Normal 1961 28,800 20 Normal 1962 37,200 25 Moderate 1963 43,100 29 Severe 1964 31,000 21 Normal 1965 28,400 19 Normal 1966 33,400 23 Moderate 1967 25,700 17 Normal 1968 37,200 25 Moderate 1969 41,400 28 Severe 1970 42,400 29 Severe 1971 36,300 25 Moderate 1972 20,800 14 Normal 1973 29,800 20 Normal 1974 52.6 36 Catastrophic 1975 16,600 11 Normal 1976 28,300 19 Normal 1977 12,500 8 Normal 1978 10,800 7 Normal 1980 33,000 22 Moderate 1982 3,140 2 Normal 1983 11,100 8 Normal 1984 28,200 19 Normal 1985 11,400 8 Normal 1986 6,600 4 Normal 1987 57,300 39 Exceptional 1988 89,970 61 Exceptional 1989 6,100 4 Normal 1990 3,500 2 Normal 1991 28,600 19 Normal 1992 2,000 1 Normal 1993 28,742 20 Normal 1994 419 0 Normal 1995 32,000 22 Moderate 1996 35,800 24 Moderate 1998 1,00,250 68 Exceptional 1999 32,000 22 Moderate 2000 35,700 24 Moderate 2001 4,000 2.8 Normal 2002 15,000 10 Normal 2003 21,500 14 Normal 2004 55,000 38 Catastrophic 2005 17,850 12 Normal 2006 16,175 11 Normal 2007 62,300 42 Exceptional 17 Economics of Adaptation to Climate Change Annex 13 Chronology of Above Normal Floods in Bangladesh 1781 Serious flood, which was more pronounced in the western part of SYLHET district. The CATTLE suffered much from the loss of fodder. 1786 Floods in the Meghna wrought havoc to the crops and immense destruction of the VILLAGEs on the banks. It was followed by a FAMINE, which caused great loss of life at BAKERGANJ. At Tippera the embankment along the GUMTI gave way. At Sylhet the PARGANAs were entirely under water, the greater part of the cattle drowned and those surviving were kept on BAMBOO rafts. 1794 The Gumti embankment burst again, causing much damage around Tippera. 1822 Bakerganj division and Patuakhali subdivision were seriously affected, 39,940 people died and 19,000 cattle perished and properties worth more than 130 million taka were destroyed. BARISAL, Bhola, and MANPURA were severely affected. 1825 Destructive floods occurred at Bakerganj and adjoining regions. There were no important embankments or other protective works against inundation in the district. 1838 Heavy rainfall caused extensive inundation at RAJSHAHI and a number of other districts. The cattle suffered much from loss of fodder and the people were greatly inconvenienced when driven to seek shelter on high places and when the water subsided. CHOLERA broke out in an epidemic form. 1853 Annual inundation was more pronounced than usual in the west of Sylhet district, partly the result of very heavy local rainfall and partly caused by the overflow of the Meghna. 1864 Serious inundation when the embankment was breached and the water of the Ganges flooded the greater part of Rajshahi town. There was much suffering among the people who took shelter with their cattle on the embankment. 1865 Extensive inundation caused by the annual rising of the Ganges flooded Rajshahi district. Excessive rainfall seriously affected Rajshahi town. 1867 Destructive flood also affected Bakerganj. Crop was partially destroyed, but no general distress resulted. 1871 Extensive inundation in Rajshahi and a few other districts. Crops, cattle, and valuable properties were damaged. This was the highest flood on record in the district. Cholera broke out in an epidemic form. 1876 Barisal and PATUAKHALI were severely affected. Meghna overflowed by about 6.71m from the SEA LEVEL. Galachipa and Bauphal were damaged seriously. A total of about 215,000 people died. Cholera broke out immediately after flood. 1879 Flooding of the Teesta when the change in the course of the Brahmaputra began. 1885 Serious floods occurred due to the bursting of an embankment along the Bhagirathi, affected areas of Satkhira subdivision of KHULNA district. 1890 Serious flood at SATKHIRA caused enormous damage to cattle and people. 1900 Due to the bursting of an embankment along the Bhagirathi, Satkhira was affected. 1902 At Sylhet the general level of the river went so high that there was terrible flood. Crops and valuable properties were damaged. 1904 The crops in some parts of COX'S BAZAR subdivision and KUTUBDIA island were damaged due to an abnormally high tide. This flood was exceptional in severity in MYMENSINGH. The distress caused on this occasion is probably the nearest parallel to that which resulted from the flooding of the Teesta in 1879, when the change in the course of Brahmaputra began. 1954 On August 2, Dhaka district went under water. On August 1 flood peak of the JAMUNA River at 18 Bangladesh Country Study Sirajganj was 14.22m and on August 30 flood peak of the Ganges River at HARDINGE BRIDGE was 14.91m. 1955 More than 30 percent of Dhaka district was flooded. The flood level of the BURIGANGES exceeded the highest level of 1954. 1962 The flood occurred twice, once in July and again in August and September. Many people were affected and crops and valuable properties were damaged. 1966 One of the most serious floods that ever visited Dhaka occurred on 8 June 1966. The flood level was almost the highest in the history of Sylhet district too. A storm on the morning of 12 June 1966 made the situation grave. About 25 percent of houses were badly damaged, 39 people died and 10,000 cattle were lost, and about 1,200,000 people were affected. On September 15 Dhaka city became stagnant due to continuous rainfall for 52 hours, which resulted in pools of water 1.83m deep for about 12 hours. 1968 Severe flood in Sylhet district and about 700,000 people were badly affected. 1969 Chittagong district fell in the grip of flood caused by heavy rainfall. Crops and valuable property were damaged 1974 In Mymensingh about 10,360 sq km area was flooded. People and cattle were severely affected and more than 100,000 houses were destroyed. 1987 Catastrophic flood occurred in July-August. Affected 57,300 sq km (about 40 percent of the total area of the country) and estimated to be a once in 30–70 year event. Excessive rainfall both inside and outside of the country was the main cause of the flood. The seriously affected regions were on the western side of the Brahmaputra, the area below the confluence of the Ganges and the Brahmaputra, considerable areas north of Khulna, and finally some areas adjacent to the Meghalaya hills. 1988 Catastrophic flood occurred in August-September. Inundated about 82,000 sq km (about 60 percent of the area) and its return period is estimated to be 50–100 years. Rainfall together with synchronization of very high flows of all the three major rivers of the country in only three days aggravated the flood. Dhaka, the capital of Bangladesh, was severely affected. The flood lasted 15 to 20 days. 1989 Flooded Sylhet, SIRAJGANJ and MAULVI BAZAR and 600,000 people were trapped by water. 1993 Severe rains all over the country, thousands of hectares of crops went under water. Twenty-eight districts were flooded. 1998 Over two-thirds of the total area of the country was flooded. It compares with the catastrophic flood of 1988 so far as the extent of flooding is concerned. A combination of heavy rainfall within and outside the country, synchronization of peak flows of the major rivers and a very strong backwater effect coalesced into a mix that resulted in the worst flood in recorded history. The flood lasted for more than two months. 2000 Five southwestern districts of Bangladesh bordering India were devastated by flood rendering nearly 3 million people homeless. The flood was caused due to the outcome of the failure of small river dykes in West Bengal that were overtopped by excessive water collected through heavy downpour. 2004 The floods spread, eventually impacting Dhaka and other central districts. Nationwide, 36 million people (about 25 percent of the population) across 39 districts were affected by the floods many of which were rendered homeless. Approximately 38 percent of Bangladesh was inundated by the time the waters began to recede in late August, including 800,000 hectares of agricultural land. As of mid- September, the death toll had reached almost 800. 2007 The 2007 floods came in two waves. The first wave commenced around the 24th July to 6th August. The second wave commenced on the 5th September to 15th September. A total of 46 districts were affected to varying degrees during both flood waves. The flooded area in the peak period of August was 62,300 Sq. km during the flood season of 2007. From 30 July to 5 September, a total of 808 people had died during the floods. Eighty-five-thousand (85,000) houses were completely damaged, while almost 1 million suffered partial damages. 19 Economics of Adaptation to Climate Change Annex 14 Damages from Major Floods in Bangladesh This study estimated the cost of adaptation to prevent further damage to roads, railways, embankments and drainage structures from intensification of monsoon floods due to climate change. However, policy makers need to compare this adaptation cost with potential benefit, i.e., averted damages for investment decisions. Averted damages include (but are not limited to): (a) direct physical damages to the above-mentioned infrastructure due to intensification of monsoon flood, (b) indirect losses due to drop in economic activities caused by, for example, damages to roads and railways; and (c) damages to other economic activities and assets, such as crop loss, or damages inflicted on buildings due to failure of embankments. While damages have not been estimated for the climate scenarios, some of the damages from the other major floods are summarized below. Monsoon Flood of 1987 1988 1998 2004 2007 Road (km) 8,500 3,770 9,622 6,728 16,638 Rural Road (km) 3,105 29,154 19,882 Bridges & Culverts along Roads 225 600 1,204 265 858 Railway Tracks (km) 650 1,053 349 415 Bridges & Culverts along Railway Tracks 167 74 86 128 Embankments (#) 2,800 3,800 4,329 Embankments (km) 1,990 2,990 2,964 2,243 Irrigation / Drainage Canal (km) 283 373 752 366 Water Management Structures * (#) 1,465 1,031 1,041 River Bank Protection Works (km) 265 187 129 * Water management structures include sluices or regulators, weir, dam, siphon, aqueduct, barrage, drainage outlet, irrigation inlet etc Source: CEGIS The flood of 1998 is the longest lasting and most devastating flood Bangladesh has experienced in 100 years. Floods of differing magnitude affected 53 of the 64 districts, with the most severe effects in Comilla, Chitagong, Cox's Bazar, Faridpur, Feni, Gaibandha, Hobiganj, Jamalpur, Khagrachari, Kurigram, Lalmonirhat, Manikganj, Mymensingh, Natore, Nilphamari, Pabna, Rajbari, Rajshahi, Rangpur, Sherpur, Sirajangj, and Tangail districts. Over two-thirds of Bangladesh was inundated for an average duration of 59 days, and about 50 per cent was inundated for up to 67 days to depths of up to 3 m. Flooding prevailed at a stretch from July to mid-September, becoming critical on (a) July 28 (30 per cent of the country was inundated), (b) August 30 (41 per cent of the country was inundated), and (c) September 7 (when 51 per cent of the country was inundated). The extent of the flooding in Bangladesh on 7 September was probably the greatest of the twentieth century. 20 Bangladesh Country Study The major factors contributing to the devastating flood of 1998 are (a) constant and intensive rain throughout Bangladesh during July and August; (b) simultaneous and constant above-danger-level flows of the three major rivers (Ganges, Brahmaputra, Meghna); (c) backwater effects resulting from the synchronization of the peak flow of the three major rivers between September 7 and September 9; and (d) La Niña situation. The 1988 floods affected 30 million people in 100,000 sq km, damaged 500,000 homes, and caused 1,100 deaths. It resulted in damages and losses of $2.2 billion, or 4.8 percent of GDP. About a third of the damages were to infrastructure, a third to industry, and the remainder consisted of losses to crops, fisheries, and livestock. Damage to RHD Road Infrastructure and Embankment in 1998 Flood Length Length Damaged Number of Bridges & Submerged (km) Culvert Damaged (km) RHD Roads 9,622 4.244 1,204 RHD Embankments 4,329 LGED rural roads 29,154 21,308 Railway 349 86 21 Economics of Adaptation to Climate Change Annex 15 Models Used for Flood Hydrology GBM Basin Model The GBM basin model was developed to simulate transboundary water runoff into Bangladesh, and provides the boundary conditions for the super model or national flood model. It was developed in 2006 at the Flood Forecasting and Warning Centre (FFWC) of Bangladesh Water Development Board (BWDB) using the MIKE BASIN platform,1 and has subsequently been updated.2 The model separately tracks 95 subcatchments—33 in the Brahmaputra basin, 55 in the Ganges basin, and 7 in the Meghna basin. The primary inputs into the GBM model include topographical, meteorological, and hydrological information. River alignments for the GBM basin were determined using physical maps for India, Nepal, and Tibet. Subcatchments were delineated based on topography defined by shuttle radar topography mission (SRTM) data available from the National Aeronautics and Space Administration (NASA)3 at a horizontal grid spacing of 90m. When needed, data gaps were filled in with GTOPO30, a global digital elevation model (DEM) available from the U.S. Geological Survey (USGS) with a resolution of 30-arc seconds (approximately 1 km). The model was calibrated with historical average monthly or yearly discharge data at three locations near the border between India and Bangladesh in the GBM basin.4 Irrigation withdrawals and river management controls in upstream catchments were not considered in calibrating the model. 5 However, the following data, where available, were used as additional boundary conditions for calibration: (a) rainfall and evaporation data for rainfed subcatchments; (b) monthly temperature data for snowfed catchments; (c) actual daily rainfall data from meteorological stations within the GBM basin supplemented with satellite rainfall data (0.25 × 0.25 horizontal resolution) measured by the tropical rainfall measurement mission (TRMM), and expanded to a 30-year record covering 1978–2008 using a bootstrapping weather generator; and (d) measured evaporation data in the basin. The base model was simulated for three rainfall data sets: TRMM satellite rainfall, GATECH rainfall used in CFAN project, and mixed rainfall data obtained through processing of ground- measured rainfall and filling the missing period or location with TRMM rainfall. The model simulation was continued from 2004 to 2007. Using daily website data of IMD, the model was also simulated for the 2009 monsoon. 1 MIKE BASIN is a versatile GIS-based water resource and environmental modeling package from DHI Water and Environment. MIKE BASIN represents all elements of water resource modeling: users, reservoirs, hydropower, surface water, groundwater, rainfall-runoff, and water quality. 2 Recent updates include (1) river alignments using available physical maps of India, Nepal, and Tibet with all tributaries of the Ganges and Brahmaputra rivers identified using the tool of MIKE BASIN; and (2) re-delineation of subcatchments of the GBM basin using digital elevation model (DEM) developed by USGS based on SRTM (Version 3) data. Subcatchments were delineated considering variability of topography, hydrometeorology, and land use patterns in the basin area. 3 Farr et al., 2007 4 The three locations are Hardinge Bridge on the Ganges, Bahadurabad on the Brahmaputra, and Amalshid on the Meghna. 5 Rainfall and evaporation are requisite boundaries for rainfed subcatchments. Rainfed catchments subjected to irrigation require irrigation records, which were ignored in this study. 22 Bangladesh Country Study Super Model or National Flood Model The super model or national flood model was developed by the FFWC of Bangladesh Water Development Board for routine flood forecasting in Bangladesh. The FFWC operates a real-time numerical model based on a one-dimensional fully hydrodynamic model (MIKE 11 HD), which includes tabular water level variations and inundation maps of the forecast period. The hydrodynamic model is linked to a lumped conceptual rainfall-runoff model (MIKE 11 RR), which generates inflows from catchments within the country. FFWC usually collects real-time hydrometeorological data and simulates the numerical model routinely throughout the monsoon season. The model covers almost all the major river networks of the country except some parts in the coastal areas and eastern hilly region and most of the flood-prone areas of the country. The rainfall runoff model of the national flood model comprises 157 subcatchments having a total area of 122,437 sq. km. The rainfall-runoff model receives precipitation from 40 stations within the country where real-time measurements are available. Evaporation was taken from BWDB measuring stations. Abstraction data processed by WARPO (Water Resources Planning Organization) was based on National Minor Irrigation Development Plan census, which is usually used in the rainfall- runoff model: NAM.6 The hydrodynamic model component of the super model includes regular rivers/khals (10235 km), floodplain routing channels (1,147 km), and link channels. Bathymetries of most of the rivers are updated, incorporating the available latest cross-sections. Bathymetries of floodplain routing channels are taken from a national land terrain model developed in FAP 19. Geometries of link channels are computed based on topography, infrastructure, and intervention. The model comprises 85 boundaries, of which 18 points are water-level boundaries and the rest are inflow boundaries. There are 3,800 water-level grid points and 2,890 discharge grid points where the model can generate data once the boundary data are available. The basic meteorological input data requirements are (a) rainfall data from more than 200 rainfall stations, and (b) potential evapotranspiration data from around 30 evaporation stations. The basic requirements of hydrodynamic model are (a) cross sections—the topographical description of the area to be modeled is achieved through the specification of cross-sections of the channel and flood plain (there are around 2,500 rivers cross-sections incorporated in the model set-up); and (b) boundary data—a time series of upstream and downstream boundaries is required for the model simulation period (there are 85 boundaries in the model). The MIKE 11 GIS is generally used for flood map generation. It is an interface between the MIKE11 modeling system and Arc-View geographic information system (GIS). It has the potential to assist in clarifying and disseminating information through enhanced mapping of impacts on flood levels, communities, agriculture, fisheries and the environment. It simulates flood-related information (depth, duration, and extent of flooding) on the flood plain. The additional data required is the topographic elevation of the area in the form of a digital elevation model (DEM). During each flood season, a flood is forecasted routinely at 69 locations on the major rivers throughout the country. Performance of the model for generating the water levels at the monitoring stations are being analyzed regularly and found satisfactory. Model-generated flood inundation for the 2007 monsoon with the satellite image for the same time was satisfactory. Inflows at the validated locations from the GBM basin model are used as boundary inflows into the national hydrologic model. This detailed model is the primary tool used by the government to make 6 Nedbor Afstomings model. 23 Economics of Adaptation to Climate Change annual flood forecasts and issue warnings. See Hopson and Webster (2007) for an application of this model using satellite imagery to improve the forecast from three days to ten days. This model is combined with gridded precipitation and temperature data and predicts water levels and discharges throughout the country. This model, which uses the MIKE 117 platform, predicts daily water levels and discharges throughout the country covering most of the major river networks (except for some parts in the coastal areas and eastern hills). A separate coastal model, the southwest region model (SWRM), was used for the southeastern part of the country. The temporal characteristics of the floods can be analyzed based on daily time-series of water levels and discharge points at a network of 3,800 points. Moreover, monthly flood maps can be prepared using a three-dimensional GIS tool to interpolate the flood surface while taking into account the presence of flood protection works (e.g. roads, embankments, polders). The area under different flood land type classes can then be calculated. Digital Elevation Model (DEM) The flood depth duration map is prepared using the national digital elevation model (DEM) of 300m resolution. The national DEM has been developed on the basis of contour/topographic maps of Bangladesh Water Development Board under Flood Action Plan 19.8 The national DEM was later updated for the coastal area by IWM using FINNmap’s topographic maps of 1991. This updated DEM is instrumental for flood depth and duration analysis using flood modeling results. 7 MIKE 11 is a system for the one-dimensional, dynamic modeling of rivers, channels, and irrigation systems, including rainfall-runoff, advection-dispersion, morphological, and water quality. The complete St. Venant equations can be solved, so the model can be applied to any flow regime where the flow can be assumed one-dimensional. Diffusive wave, kinematic wave, and quasi-steady state options are also available. Flow over weirs, through culverts and user-defined structures, and over the flood plain can be simulated. Output from the hydrodynamic module can be routed to additional modules that simulate the transport of cohesive and non-cohesive sediment, dissolved oxygen, nutrients, heavy metals, and eutrophication. 8 The spot elevation data from the BWDB topographic maps were interpreted and digitized by manually superimposing a transparent 500-m grid template on the maps and recording the elevation point nearest each grid intersection. A total of 432,775 elevation points were digitized from 2106 nos. of BWDB maps. Data entry was expedited with an interactive database application program. The data were checked for validity and combined into a single data file with national coverage. After digitizing, these data were converted into GIS file format and using spatial interpolation the elevation points were transformed into a virtual surface, or raster GIS image. 24 Bangladesh Country Study Annex 16 Social Component Methodology The social component sought to highlight how local populations’ vulnerability to climate change is socially differentiated, what types of resources local populations will need to strengthen their adaptive capacity, and how governments can support adaptation that addresses the needs of the poorest and most vulnerable and maximizes co-benefits with sustainable development. In addition, the social lens drew attention to 'soft' or institutional and policy measures in adaptation that would best complement the cost estimates of 'hard' infrastructure investments generated under other components of the EACC study. The social component set out to answer the following research questions on vulnerability to climate change and potential pro-poor adaptation responses.  What factors make particular individuals, households, or sub-national regions more vulnerable to the negative impacts of climate change?  What has been people’s experience of climate events to date and what adaptation measures have they taken (both autonomous and planned)?  How do different groups and local and national representatives judge various adaptation options and pathways?  How do identified adaptation priorities align with existing development strategies and policy emphases? To answer these questions, the following five-step methodology was adopted:  Primary and secondary literature review supplemented by stakeholder interviews, to identify both existing knowledge about vulnerability in the country, and current and planned efforts to reduce that vulnerability;  Selection of socio-geographic hotspots where both elements of exposure and sensitivity were present based on primary and secondary literature review. Local experts were interviewed and consultation workshops were conducted in order to validate selection of climate vulnerability ―hotspots‖  Fieldwork in vulnerability hotspots to validate results of the initial literature review and develop more detailed vulnerability assessments. The fieldwork comprised three elements: key informant interviews with representatives from local organizations and other leaders; focus group discussions with men and women from different socio-economic strata; and household surveys with a stratified sampling approach. This approach aimed to identify livelihood and adaptation strategies of households in different income tiers; identify sensitivity factors contributing to vulnerability such as gender, migrant status and age; and assess the presence of formal and informal institutions operating in these areas  Participatory Scenario Development (PSD) workshops at local, regional and national levels in order to identify local development visions, expected impacts of climate change on these visions, and preferred adaptation options and combinations of options over time. This analysis included identifying the types of adaptation measures that would benefit different vulnerable groups in each country; the trade-offs between these adaptation strategies; and what types of policies and pre-conditions would maximize co-benefits with sustainable development 25 Economics of Adaptation to Climate Change Fieldwork Fieldwork was undertaken at eight study sites, using qualitative and quantitative tools. Sites were selected from among major socio-geographic zones identified, with a view to covering key hazard types, a mix of urban and rural sites, and to ensure alignment with Bank or other donor projects operating in the country. Participatory Rural Appraisal (PRA) exercises (village history/ timelines; focus group discussions of men, women and different age groups; wealth ranking; mapping of institutional and tenure issues; impact diagrams of climate events and community risk mapping, matrix ranking of adaptation options) were undertaken, as well as key informant interviews with local government, NGOS and traditional leaders. Finally, 170 household interviews were undertaken (10-20 per site from different income tiers, with questionnaire modules covering household composition, labor allocation, asset base, livelihood sources, ethnicity, migration, patterns of income and expenditure, agricultural practices, landholdings and land tenure regimes, responses to climate and other shocks participation in formal organizations, local governance, adaptation practices, collective action and risk-sharing, and current access to public investments and services). Participatory Scenario Development (PSD) The use of participatory scenario development as a tool to elicit stakeholder preferences on adaptation is a distinguishing feature of the EACC study. The aim of the Participatory Scenario Development (PSD) workshops was to help local and national actors explore different climate futures, and engage in structured debates around development priorities and preferred adaptation responses. Participants included national and local government representatives, academic and civil society members, donors, and representatives from vulnerable communities, and were held at local/ regional and national levels. Five local/regional and two national workshops with a total of 234 participants were carried out across the country. Each workshop was 1-2 days in length. Components of PSD include:  Process-oriented and collaborative approaches that involves stakeholders participating in exploring the future in creative and policy-relevant ways;  Climate and development baseline projection information which is used for developing ―visualizations‖;  Structured debates around development priorities and relevant adaptation responses and well as trade-offs and synergies among adaptation options or policy reforms; and  A focus on strengthening the inter-sectoral linkages between adaptation and development priorities that are not specific to climate change (Kuriakose et al. 2009; Bizikova et al. 2009). The objective of these PSD workshops was to foster a structured discussion and identification of adaptation pathways that:  Identified the most important impacts of future climate change and climate variability on local populations as understood by them, taking into account baseline and projection scenarios presented by modelers;  Assessed the probable impacts of these identified climate changes on particularly vulnerable people and livelihoods and what the expected associated adaptation responses are likely to be;  Noted the preferred pathways for adaptation and policy response that are pro-poor and cost effective;  Identified key areas of integration and trade�offs across sectors and/or regions in the country, in which adaptation to climate change goes hand�in�hand with other development priorities (Kuriakose et al. 2009; Bizikova et al. 2009). 26 Bangladesh Country Study PSD Workshop Steps The national workshops began with presentations by local experts to characterize current climate and socioeconomic projections for the coming decades, as inputs to participants creating visions of a ―preferred future‖ for 2050. This was followed by considering the specific impacts of climate change on the future vision, and then identifying adaptation options necessary to reach the desired vision (see Figure. Finally, participants created an adaptation pathway showing diverse priorities for adaptation actions over time. They also identified prerequisites, synergies and trade-offs among their adaptation options, and with other known development priorities. The PSD workshops drew from down-scaled climate and poverty scenarios offered as graphic ―visualizations‖ used in handouts, presentations and posters. The process allowed for joint assessment of required interventions and distribution of benefits, and also pointed to key political economy issues in adaptation planning and implementation. National PSD workshop invitees included government, NGO and donor representatives, as well as academics, researchers, and World Bank staff. Local PSD workshops included local government, male and female farmers and representatives of other livelihood groups, local and international NGOs, and researchers, and World Bank staff. Local-level PSD workshops followed similar approaches, with some modifications of materials and exercises, depending on the audience (e.g., adding a timeline exercise of past adaptation practices in responding to extreme events; or matrix ranking of different adaptation options). The PSD approach was particularly effective in identifying multi-causal linkages and drivers of vulnerability in climate-affected regions. Questions Addressed in PSD Workshop  What is the local vision of the future, in terms of development priorities, perceived climate change impacts, and feasible response strategies?  Which areas/sectors are viewed as most vulnerable? What are the key drivers contributing to that vulnerability?  What specific adaptation option investments and sequenced combinations of investments (pathways) are needed to respond to climate change impacts at national and sub-national levels? o How pro-poor are these identified options? o Where in the region should these be applied and who are the vulnerable groups? o What are the preconditions including policy elements needed to implement these effectively? o What are the synergies and trade-offs among these options? 27 Economics of Adaptation to Climate Change REFERENCES Bizikova, Livia, Boardley, Samantha, and Simon Mead. 2009. Participatory Scenario Development for Costing Climate Change Adaptation – Climate Visioning. SEL: 100018962. Prepared by ESSA Technologies Ltd Vancouver BC and International Institute for Sustainable Development (IISD) Winnipeg, Manitoba, Canada. Final Report prepared for the World Bank Economics of Adaptation to Climate Change Study. Kuriakose, Anne T, Livia Bizikova and Carina A. Bachofen. (2009). Assessing Vulnerability and Adaptive Capacity to Climate Risk: Methods for Investigation at Local and National Levels. Social Development Working Paper 116, World Bank. 28