Operational vulnerability
indicators
Anand Patwardhan
IIT-Bombay
Context and objectives matter
Question
What are the physical impacts
of sea level rise?
What are the market & nonmarket losses associated with
sea level rise?
What is the optimal response to
sea level rise?
Which research strategy will
have the largest value of
information?
Which region should be
selected for protection first?
June 10, 2002
Decision context
Input to preliminary impact
assessment
Input to international
negotiations
Input to formulation of
adaptation policies
Input to research
prioritization
Objective
Identifying data needs and
organizing data
Countries have to provide estimates
of abatement costs and climate
damages
Determining the reduction in
damages with responses
Determining the value of reducing
key uncertainties through research
Input to policy
prioritization
Allocating resources efficiently
towards responses to sea level rise
Anand Patwardhan, IIT-Bombay
2
Vulnerability
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A composite measure of the sensitivity of the
system and its adaptive (coping) capacity
Combine hazard, exposure and response layers
The layers (and their interactions) evolve
dynamically (future vulnerability)
Need indicators to represent the layers
How do we represent the interactions?
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For example: damage functions may be used to link
hazard and impacts
June 10, 2002
Anand Patwardhan, IIT-Bombay
3
Hazard – how to represent
climate?
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Climate change or climate variability?
To which variable(s) is the system most
sensitive?
May be a primary (temperature,
precipitation), compound (degree days,
heat index, AISMR) or derived (proxy)
quantity (storm surge)
May be expressed as a statistic – flood
return period
June 10, 2002
Anand Patwardhan, IIT-Bombay
4
Exposure: what is at risk?
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Things we value
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Stocks
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Population
Capital stock – public and private
Land (more correctly, properties of land – fertility)
Flows
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Market & non-market
Services
Environmental amenities
Matters in terms of the impacts being considered
June 10, 2002
Anand Patwardhan, IIT-Bombay
5
Impacts: how is it at risk?
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Empirical
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Response surfaces, reduced-form models,
damage functions
Estimated using historical data
Process-based models
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Mechanistic, capture the essential physical /
biological processes
Crop models, Bruun rule, water balance
models
June 10, 2002
Anand Patwardhan, IIT-Bombay
6
Adaptive capacity
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Autonomous – what responses are
happening (will happen) automatically?
How will impacts be perceived, how will
they be evaluated and how will response
take place?
Who will respond, in what way?
June 10, 2002
Anand Patwardhan, IIT-Bombay
7
Interactions between the layers
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Interactions are dynamic, evolutionary
Path dependency
Specification of scenarios
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Linked and dynamic vs. static
Modeling issues
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An adjustable parameter in an impacts model?
(for example, think of AEEI in energyeconomic models)
Endogenous dynamics, capture the essential
elements of the adaptation process
June 10, 2002
Anand Patwardhan, IIT-Bombay
8
Example: cyclone impacts in India
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Aggregate analysis
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Reduced-form damage functions
Event-wise analysis
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Cross-sectional and time series analysis to
tease out relative importance of event
characteristics, exposure and adaptive
capacity
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Key features (historical baseline)
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Approximately 8-10 cyclonic events make
landfall every year
Maximum activity July – November
No significant secular trends
Significant temporal variability on
interannual and decadal scales
Intraseasonal distribution varies on
decadal time scales
Spatial distribution (location of cyclone
landfall)
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Spatial distribution – a simple
approach
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For cyclones, maximum damage at
landfall
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Wind stress (housing, crops)
Surge & flooding (housing, mortality,
infrastructure)
A monotonic scale is defined as the
distance along the coast of the landfall
location relative to an arbitrary origin
Spatial distribution of storms may then be
described by a cumulative distribution
function
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Spatial distribution
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Shifts in incidence on decadal time scales
ENSO state affects spatial distribution
(cold events tend to favor greater
clustering of storms in TN and Orissa /
WB)
Aggregate seasonal monsoon rainfall
affects spatial distribution – increased
clustering in AP / Orissa during excess
rainfall years
June 10, 2002
Anand Patwardhan, IIT-Bombay
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1
0.8
0.6
El Nino
Normal
La Nina
0.4
0.2
0
0
June 10, 2002
500
1000
Coastal distance scale
Anand Patwardhan, IIT-Bombay
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Cyclone hazard baseline
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Exposure – typical indicators
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Population
Housing stock, public infrastructure
Typically reported along administrative
boundaries
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Cyclone impact indicators
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Deaths
Injuries
Cattle, Poultry and Wildlife
Houses and huts damaged
Crop Area affected
Districts/Villages affected
Population affected and evacuated
Trees uprooted
Infrastructure damaged (Roads, Rails, Dams, Bridges, Irrigation systems,
Electric and Telecommunication poles & lines)
Estimates of property loss (Rupees)
Relief work and compensations made
Damage to ports and boats
Tidal surge and extent of area inundated by the sea
Heavy rains and floods in the interior regions
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Example of impact data – Orissa
super cyclone
No. of affected districts
Population affected (million
Villages
Blocks
Crop Area (million hectares)
Houses (million)
Loss of Human Life
Persons Injured
Missing
Livestock
Fishing boats lost
Fishing nets lost
June 10, 2002
12
12.9
14643
97
1.8
1.6
9887
2507
40 (?)
440000
9085
22143
Anand Patwardhan, IIT-Bombay
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What can we do with analysis of
impact data?
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Effect of multiple stresses
Process understanding – capture through
empirical (damage functions) or analytical
models
Can we get a better handle on an operational
view of adaptive capacity?
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Effectiveness (or lack thereof) of responses
Responses at different scales:
• Individual, family (household), community, region
• Who are the actors, what are the decisions they can
make, how do these interact?
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Wind and mortality
100000
10000
Deaths
1000
100
10
1
0
20
40
60
80
100
120
140
160
Wind speed (knots)
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Central pressure and mortality
100000
10000
Deaths
1000
100
10
1
900
920
940
960
980
1000
1020
Min. Press.
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Damage functions for the US
Mortality
Series1
100000.00
Mortality
10000.00
100
1000.00
100.00
10
10.00
1
Damage (million
constant $)
1000
1.00
888
934
950
957
969
977
989
999
Minimum pressure (mb)
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Example 1 – similar event &
location, different times
Year
Min.
Pres.
in mb
Wind Mortal LiveSpeed ity
stock
Km/h
1984
AP
984.1
105
658
90,650
1987
AP
984.3
102
50
25,800
1996
AP
986
100
68
2000
June 10, 2002
No. of Loss
houses in Rs
damag lakhs
e
Pop.
affected
320,0 22632 1300,000
00
68000
6000
6000
8200
Anand Patwardhan, IIT-Bombay
50,000
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Example 2 – similar event, same
time, different locations
Year
Place
1994 Madras
Wind
Pressure No. of No. of
Speed (in mb) Deaths houses
damage
(Km/h)
d
125
984
304
85,700
1993 Karaikal 120
989
318
33,131
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Example 3 – similar event, same
time, different locations
Year
Press Wind No. of No. of Loss in
In mb Speed Death Houses Rs
Lakhs
Km/h s
1996 AP
974
1996 Guj. 972
June 10, 2002
130 to 1677
150
421,00 20000
0
0
130 to 33
150
6000
Anand Patwardhan, IIT-Bombay
8200
24
Mortality associated with heat
waves
1800
40
1600
Deaths
35
Number of spells of heat wave
1400
1200
Mortality
25
1000
20
800
15
600
Heat wave spells
30
10
400
200
5
0
0
1978
June 10, 2002
1983
1988
1993
Anand Patwardhan, IIT-Bombay
1998
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Example: flood damage in India
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Hazard: occurrence of floods, proxy –
total summer monsoon rainfall
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The India Meteorological Department has
created an All-India Summer Monsoon Rainfall
Series since 1871 (area-averaged measure of
total rainfall)
Or perhaps, the number of “wet spells”?
Exposure: area / population in “floodprone” areas, and total affected
Impacts: mortality, crop damage
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Flood damage trends
Total damage (crores)
Mortality
4500.00
12000
4000.00
3000.00
8000
2500.00
6000
2000.00
1500.00
Mortality
Total damage (crores)
10000
3500.00
4000
1000.00
2000
500.00
0.00
0
1953
June 10, 2002
1958
1963
1968
1973
1978
1983
Anand Patwardhan, IIT-Bombay
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Examine scaled (or normalized)
impacts
Scaled mortality
Scaled damage
600
500
200
400
150
300
100
200
50
100
0
Damage (crore Rs/Mha of area)
Mortality / population affected
(millions)
250
0
1953
June 10, 2002
1958
1963
1968
1973
1978
1983
Anand Patwardhan, IIT-Bombay
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Problems
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Data availability
Reporting and comparability
Relating event characteristics to impact –
multiple pathways, initiators and end-points
Accounting for interdependence:
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The values of two damage categories, viz. Households
and crop area may be area dependent
Accounting for controlling factors:
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The number of deaths and value of property loss is
decided by factors other than area
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Adaptive capacity
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Examine in an empirical sense
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What can we infer from the past history of
events and responses?
Theoretical underpinnings, in terms of
determinants
Indicators
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State vs. process, input vs. outcome
Developmental indicators – HDI itself, or
change in HDI? Linkage with broader socioeconomic development issues
June 10, 2002
Anand Patwardhan, IIT-Bombay
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HDI change in response to a change in
the macro-economic environment liberalization
State
1987-1993 1993-1997
West Bengal
11%
4%
Orissa
12%
21%
Andhra Pradesh
10%
26%
Tamil Nadu
15%
11%
Kerala
6%
4%
Karnataka
2%
15%
Maharashtra
11%
15%
Gujarat
11%
20%
June 10, 2002
Anand Patwardhan, IIT-Bombay
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Common issues
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Scale across different dimensions –
temporal, spatial
Unit of analysis (individual – household –
community – region – national)
Capturing the perception – evaluation –
response process
Data availability and measurability
June 10, 2002
Anand Patwardhan, IIT-Bombay
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