Ecological and Socio-economic Vulnerability links closely with - - PowerPoint PPT Presentation

Ecological and Socio-economic Vulnerability links closely with climate variation: A study exploring adaptation using this connect

Nidhi Nagabhatla

14 November 2011 : Climate Vulnerability Forum, Dhaka Summit, Bangladesh

28th September 2012, Helsinki- Finland Addressing Climate Change :It is an imperative which we have to do in all the circumstances

UNU-WIDER Conference on Climate Change and Development Policy

What is Vulnerability ?

Which Nations Are Most Vulnerable to Climate Change? Transforming Adaptive Capacity to Adaptation ?

British firm Maplecroft (top 10) : Bangladesh, India , Madagascar, Nepal, Mozambique, Philippines, Haiti, Afghanistan, Zimbabwe and Myanmar. ( 60% from Asia- S & SEA) Residual situation after adapting to a risk situation (IPCC) The way we respond and cope to change to the way it is required to Gradient of exposure and proneness to damage/disaster

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

Climate Change Monitoring First Segment

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

Pre Monsoonal Situation (MAM) 1870-2007

Drought Flood

Winter Rainfall (DJF)

Monsoonal Rainfall (JJA)

y = -0.03x + 61.285 R² = 0.0025

• 100
• 80
• 60
• 40
• 20

20 40 60

1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Anamoly ( Rainfall)

JJA

y = -0.0355x + 312.55 R² = 0.0035 150 200 250 300 350

1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Seasonal Mean Rainfall (Obs) in mm (JJA)

Drought Flood

Post Monsoon (SON)

y = 0.0326x + 32.443 R² = 0.0042

50 70 90 110 130 150 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Seasonal Mean Rainfall (obs) in mm ( (SON) y = 0.0326x - 61.179 R² = 0.0042

• 60
• 40
• 20

20 40 60 80 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Anamoly (Rainfall)

Temperature Trend : India ( T max)

R² = 0.1053

• 2.0
• 1.5
• 1.0
• 0.5

0.0 0.5 1.0 1.5 2.0 1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 Temprature (max) diifrence from the mean

MAM

East Coast

27 28 29 30 31 32 33 34 35 36 37 1901 1906 1911 1916 1921 1926 1931 1936 1941 1946 1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 T(max) 0C

DJF MAM JJA SON

1.5oC 1.27oC

R² = 0.1146

• 1.5
• 1.0
• 0.5

0.0 0.5 1.0 1.5 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003

Tempratyre (min) diifrence from the mean

DJF

18 20 22 24 26 28 1901 1904 1907 1910 1913 1916 1919 1922 1925 1928 1931 1934 1937 1940 1943 1946 1949 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 Tmin 0 C

DJF MAM JJA SON

0.170C

Temperature Trend : India ( T min)

0.97oC

1952 1960 1964 1986 1999 2005 1974

JJAS (MM) Normal Monsoon years

OBS MME

Socioeconomic consequences of climate variability and its effect on natural/ managed systems Second Segment

Climate Change Assessment

Case Study 1 : Climatic variability vis-a vis Fisheries in Bangladesh

Why Bangaldesh ?

Global Vulnerability Profiling (Wheeler and Haddad, 2005)

13.2 27

5 10 15 20 25 30 35 40

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

Vulnerable Population in Millions 2008 2050 Of Top 20 Countries more than 50 % in South , South East and East Asia Countries listed in the order of ranking with India stated as most vulnerable

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)

More Facts

Leading to people migrating for work Total Inland Marine

• Three Different time periods :

MAM, SON and DJF

• Date Used : Sea Wifs
• Temporal Span : 1998-2009
• Standard Trend : declining
• Decline more pronounced in SON
• (nearly half of the value at the start end of the

temporal scale)

MAM SON DJF

Increasing trend during SON and DJF

SON DJF MAM

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

SON DJF MAM

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

Case Study 2 : Climate Change and Human Migration

Bangladesh

• Currently more 3% of world population migrate for work
• Stern (2007) estimates 150-200 million displaced by CC

[Christian Aid (2007) reports 1 billion ]

• Migration driven by ‘push and pull factors’

Highlights: Climate Shifts and Migration Flows

Pull

 Country of destination  Demand for workers /employment  Access to resources  Political Stability  Low vulnerability to CC  Regulated or low population growth

Push

 Country of origin  Political Instability  Lack of economic growth and

• pportunities

 Lack of access to resources  Exposure to extreme climate events (high vulnerability to CC)  Rate of population growth  Socio-economic condition

5,000 110 1,400 30 2 2 4 6 8 10 12 1,000 2,000 3,000 4,000 5,000 6,000 Cyclone Bhola-Nov- 1970. Severe Cyclone-May

• 1985

Bangladesh Cyclone- April-1991 Cyclone Sidr- November -2007 Cyclone Alia-May- 2009 Number of people dead('00) Total number of People Affected (millions)

Disaster drives Migration : IDP’s and Refugees

Observation and Projection

48 49 63 78 39 41 54 68

R² = 0.895

10 20 30 40 50 60

20 40 60 80

2009 2010 2015 2020 % People in millions

Recorded and Estimated Projections for next 10 years after 2010

Estimated People displacement (million) Total displacement by floods (millions) %of total population (estimated )

FLOOD FLOOD FLOOD FLOOD

50 % of the total population is projected to displace

10 20 30 40 50

Barisal Chittagong Dhaka Khulna Rajashahi Sylhet

% of Agriculture Labour Households to total households 2008 1996 1983

Spatial Distribution at the national level

Brahmaputra Ganges

Exposure to extreme events as a surrogate of ecological and biophysical vulnerability

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Barisal Chittagong Dhaka Khulna Rajashahi Sylhet Number of households (000) Administrative Divisions Total Households Urban Households Rural Household Total Landless 10 20 30 40 50 60 70 80 90 100

Barisal Chittagong Dhaka Khulna Rajashahi Sylhet

% of the total landless households % Urban of the total Landless % Rural of the total Landless

Poverty as a surrogate of social vulnerability

( expressed as landless households)

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 4 4 2 5 Chittagong 4 3 2 4 Dhaka 2 1 4 2 Khulna 2 3 4 3 Rajashahi 1 5 5 1 Sylhet 5 1 1 4

Case Study 3 : Agro-ecosystem

• f Wayanad, in

Kerela

rice

banana

Arecanut

Wayanad

31

Vellamunda Panamaram

Kerala Wayanad Administrative center : Kalpetta Three main blocks Area : 2000 sq km

Wayanad surrounds Western Ghats

• n the west

Census 2011 Wayanad Population :816 558

32

Temporal trend in rice : what do records say ?

33

Paddy (rice ) growth trends

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

DTR [Diurnal Temperature Range ) Anomaly

Winter

Summer

Crop growth simulations show that rice yields decrease 9% for each 1°C increase in seasonal average temperature (Kropff et al., 1993).

Concluding Remarks

• Clear understanding of climate interactions with social

and environmental varies with scale, season, systems and region is pertinent to assess vulnerability and address adaptation

• 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

• Transdisciplinarity is a point to ponder

Thank You for your attention

fearbeneath.com