Ecological and Socio -economic Vulnerability links closely ......IPCC- Socioeconomic Scenarios 1....
Transcript of 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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