14 November 2011 : Climate Vulnerability Forum, Dhaka Summit, Bangladesh. UNU-WIDER Conference on Climate Change and Development Policy

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1 Ecological and Socio-economic Vulnerability links closely with climate variation: A study exploring adaptation using this connect Addressing Climate Change :It is an imperative which we have to do in all the circumstances 14 November 2011 : Climate Vulnerability Forum, Dhaka Summit, Bangladesh UNU-WIDER Conference on Climate Change and Development Policy Nidhi Nagabhatla 28 th September 2012, Helsinki- Finland

2 What is Vulnerability? Gradient of exposure and proneness to damage/disaster Residual situation after adapting to a risk situation (IPCC) Which Nations Are Most Vulnerable to Climate Change? British firm Maplecroft (top 10) : Bangladesh, India, Madagascar, Nepal, Mozambique, Philippines, Haiti, Afghanistan, Zimbabwe and Myanmar. ( 60% from Asia- S & SEA) Transforming Adaptive Capacity to Adaptation? The way we respond and cope to change to the way it is required to

3 IPCC- Socioeconomic Scenarios 1. Demographics and development: total population, current and projected (2025) population density, urban population, coastal population. 2. Economics: per capita GDP, GDP distribution from agriculture, industry, other sectors, trends in annual GDP growth rate. 3. Land cover/land use: total land area, arable and cropped, pastured, forest and woodlands 4. Water: water resources per capita, annual allocations for different sectors viz., domestic, industrial and agricultural use. 5. Agriculture: food production, irrigated/rain-fed areas, livestock s, agricultural labor markets & production value chains 6. Energy: energy consumption (commercial/domestic), renewables, hydroelectric 7. Biodiversity: floral, faunal, avifaunal and marine diversity

4 First Segment Climate Change Monitoring

5 Scenarios for South Asia (adopted from Cruz et al 2007)

6 Pre Monsoonal Situation (MAM) Drought Flood

7 Winter Rainfall (DJF)

8 Monsoonal Rainfall (JJA) 350 Drought Flood y = x R² = y = -0.03x R² = Seasonal Mean Rainfall (Obs) in mm (JJA) Anamoly ( Rainfall) JJA

9 Post Monsoon (SON) Seasonal Mean Rainfall (obs) in mm ( (SON) y = x R² = y = x R² = Anamoly (Rainfall)

10 Temperature Trend : India ( T max) East Coast DJF MAM JJA SON 1.5 o C T(max) 0C o C Temprature (max) diifrence from the mean R² = MAM

11 Temperature Trend : India ( T min) 28 DJF MAM JJA SON o C R² = Tmin 0 C C Tempratyre (min) diifrence from the mean DJF

12 Normal Monsoon years JJAS (MM)

13 OBS MME

14 Second Segment Climate Change Assessment Socioeconomic consequences of climate variability and its effect on natural/ managed systems

15 Case Study 1 : Climatic variability vis-a vis Fisheries in Bangladesh Why Bangaldesh?

16 Global Vulnerability Profiling (Wheeler and Haddad, 2005) 40 Countries listed in the order of ranking with India stated as most vulnerable Vulnerable Population in Millions India Bangladesh China Indonesia Philippines Nigeria Vietnam Japan United States Egypt, Arab Rep. United Kingdom Korea, Rep. Myanmar Brazil Turkey Malaysia Germany Italy Mozambique Thailand Of Top 20 Countries more than 50 % in South, South East and East Asia

17 Facts.[ More Facts Export value of global fish trade is: US$63 billion in (2003), more than the combined value of net exports of rice, coffee, sugar and tea. (FAO-UN) Half of global fish trade comes from developing countries ] Global consumption increased by 21% between 1992 and 2002 and increases further Nearly 25% of the world's marine fish stocks are overexploited About 50% fully exploited (overfishing and increasing degradation of coastal, marine and freshwater ecosystems and habitats)

18 Total Inland Marine Leading to people migrating for work

19 DJF SON Three Different time periods : MAM, SON and DJF MAM Date Used : Sea Wifs Temporal Span : Standard Trend : declining Decline more pronounced in SON (nearly half of the value at the start end of the temporal scale)

20 DJF SON MAM Increasing trend during SON and DJF

21 DJF SON Increasing trend during DJF and MAM MAM SON shows a declining trend attributed to fresh water turbidity and influx

22 A negative correlation exists between Chl-a and SST during SON and MAM, except in coastal zone

23 Case Study 2 : Climate Change and Human Migration Bangladesh

24 Highlights: Climate Shifts and Migration Flows Currently more 3% of world population migrate for work Stern (2007) estimates million displaced by CC [Christian Aid (2007) reports 1 billion ] Migration driven by push and pull factors Push Country of origin Political Instability Lack of economic growth and opportunities Lack of access to resources Exposure to extreme climate events (high vulnerability to CC) Rate of population growth Socio-economic condition Pull Country of destination Demand for workers /employment Access to resources Political Stability Low vulnerability to CC Regulated or low population growth

25 Disaster drives Migration : IDP s and Refugees 6,000 5,000 5,000 Number of people dead('00) Total number of People Affected (millions) , , ,000 1, , Cyclone Bhola-Nov Severe Cyclone-May Bangladesh Cyclone- April Cyclone Sidr- November Cyclone Alia-May

26 Observation and Projection 50 % of the total population is projected to displace 80 Estimated People displacement (million) Total displacement by floods (millions) 78 R² = %of total population (estimated ) People in millions % 20 0 FLOOD FLOOD FLOOD FLOOD Recorded and Estimated Projections for next 10 years after

27 Spatial Distribution at the national level Ganges Sylhet Brahmaputra Rajashahi Khulna Dhaka 2008 Chittagong 1996 Barisal % of Agriculture Labour Households to total households Exposure to extreme events as a surrogate of ecological and biophysical vulnerability

28 Number of households (000) Barisal Chittagong Total Households Rural Household Dhaka Khulna Administrative Divisions Urban Households Total Landless Rajashahi Sylhet % of the total landless households Barisal % Urban of the total Landless % Rural of the total Landless Chittagong Dhaka Khulna Rajashahi Sylhet Poverty as a surrogate of social vulnerability ( expressed as landless households)

29 Profiling Vulnerability Bangladesh 1(Low)-5 (high) 1(Low)-5 (high) 1(Low)-5 (high) 1(Low)-5 (high) Provincial Divisions Exposure to Extreme Climate Events Sensitivity (Poverty) Adaptive Capacity Ranking Climate Vulnerability Barisal Chittagong Dhaka Khulna Rajashahi Sylhet

30 Case Study 3 : banana rice Arecanut Agro-ecosystem of Wayanad, in Kerela

31 Wayanad Administrative center : Kalpetta Three main blocks Area : 2000 sq km Wayanad Vellamunda Panamaram Census 2011 Wayanad Population : Kerala 31 Wayanad surrounds Western Ghats on the west

32 Temporal trend in rice : what do records say? 32

33 Paddy (rice ) growth trends Paddy (rice) distribution trend in Wayanad Ten years paddy (rice) distribution trend in Kerala 33

34 DTR [Diurnal Temperature Range ) Anomaly Crop growth simulations show that rice yields decrease 9% for each 1 C increase in seasonal average temperature (Kropff et al., 1993). Summer Winter

35 Concluding Remarks Integration of scientifically delineated climate information in decision making is certainly one of the potential ways to attend to address uncertainty associated with climate change Clear understanding of climate interactions with social and environmental varies with scale, season, systems and region is pertinent to assess vulnerability and address adaptation Transdisciplinarity is a point to ponder

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