ecological and socio -economic vulnerability links closely ......ipcc- socioeconomic scenarios 1....

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

0

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

Anam

oly

( Rai

nfal

l)

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

Seas

onal

Mea

n R

ainf

all

(Obs

) in

mm

(J

JA)

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

Seas

onal

Mea

n R

ainf

all

(obs

) in

mm

( (

SON

)

y = 0.0326x - 61.179 R² = 0.0042

-60

-40

-20

0

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

Ana

mol

y (R

ainf

all)

Temperature Trend : India ( T max)

R² = 0.1053

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.01901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

Tem

prat

ure

(max

) d

iifre

nce

from

the

mea

n

MAM

East Coast

27

28

29

30

31

32

33

34

35

36

3719

01

1906

1911

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

T(m

ax) 0

C

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

Tem

prat

yre

(min

) diif

renc

e fr

om th

e m

ean

DJF

18

20

22

24

26

2819

01

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

0

5

10

15

20

25

30

35

40

Indi

a

Bang

lade

sh

Chin

a

Indo

nesia

Phili

ppin

es

Nig

eria

Viet

nam

Japa

n

Uni

ted

Stat

es

Egyp

t, Ar

ab R

ep.

Uni

ted

King

dom

Kore

a, R

ep.

Mya

nmar

Braz

il

Turk

ey

Mal

aysia

Germ

any

Italy

Moz

ambi

que

Thai

land

Vuln

erab

le P

opul

atio

n in

Mill

ions

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

opportunities 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 0

2

4

6

8

10

12

0

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

0

10

20

30

40

50

60

0

20

40

60

80

2009 2010 2015 2020

%

Peop

le in

mill

ions

Recorded and Estimated Projections for next 10 years after 2010

Estimated People displacement (million)

Total displacement by floods (millions)

%of total population (estimated )FL

OO

D

FLO

OD

FLO

OD

FLO

OD

50 % of the total population is projected to displace

0 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

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Bar

isal

Chi

ttago

ng

Dha

ka

Khu

lna

Raj

asha

hi

Sylh

et

Num

ber

of h

ouse

hold

s (0

00)

Administrative Divisions

Total Households Urban Households

Rural Household Total Landless

0102030405060708090

100

Bar

isal

Chi

ttago

ng

Dha

ka

Khu

lna

Raj

asha

hi

Sylh

et

% o

f th

e to

tal

land

less

hou

seho

lds

% 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 of 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 on 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

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