operational vulnerability indicators
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Operational vulnerability indicators. Anand Patwardhan IIT-Bombay. Context and objectives matter. Vulnerability. A composite measure of the sensitivity of the system and its adaptive (coping) capacity Combine hazard, exposure and response layers - PowerPoint PPT PresentationTRANSCRIPT
Operational vulnerability indicators
Anand PatwardhanIIT-Bombay
June 10, 2002 Anand Patwardhan, IIT-Bombay 2
Context and objectives matter
Question Decision context Objective What are the physical impacts of sea level rise?
Input to preliminary impact assessment
Identifying data needs and organizing data
What are the market & non-market losses associated with sea level rise?
Input to international negotiations
Countries have to provide estimates of abatement costs and climate damages
What is the optimal response to sea level rise?
Input to formulation of adaptation policies
Determining the reduction in damages with responses
Which research strategy will have the largest value of information?
Input to research prioritization
Determining the value of reducing key uncertainties through research
Which region should be selected for protection first?
Input to policy prioritization
Allocating resources efficiently towards responses to sea level rise
June 10, 2002 Anand Patwardhan, IIT-Bombay 3
Vulnerability
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?
For example: damage functions may be used to link hazard and impacts
June 10, 2002 Anand Patwardhan, IIT-Bombay 4
Hazard – how to represent climate?
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 5
Exposure: what is at risk?
Things we value Market & non-market
Stocks Population Capital stock – public and private Land (more correctly, properties of land – fertility)
Flows Services Environmental amenities
Matters in terms of the impacts being considered
June 10, 2002 Anand Patwardhan, IIT-Bombay 6
Impacts: how is it at risk?
Empirical Response surfaces, reduced-form models,
damage functions Estimated using historical data
Process-based models Mechanistic, capture the essential physical /
biological processes Crop models, Bruun rule, water balance
models
June 10, 2002 Anand Patwardhan, IIT-Bombay 7
Adaptive capacity
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 8
Interactions between the layers
Interactions are dynamic, evolutionary Path dependency Specification of scenarios
Linked and dynamic vs. static Modeling issues
An adjustable parameter in an impacts model? (for example, think of AEEI in energy-economic models)
Endogenous dynamics, capture the essential elements of the adaptation process
June 10, 2002 Anand Patwardhan, IIT-Bombay 9
Example: cyclone impacts in India
Aggregate analysis Reduced-form damage functions
Event-wise analysis 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 10
Key features (historical baseline)
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 11
Spatial distribution – a simple approach
For cyclones, maximum damage at landfall 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 12
Spatial distribution
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 13
0
0.2
0.4
0.6
0.8
1
0 500 1000Coastal distance scale
El Nino
NormalLa Nina
June 10, 2002 Anand Patwardhan, IIT-Bombay 14
Cyclone hazard baseline
June 10, 2002 Anand Patwardhan, IIT-Bombay 15
Exposure – typical indicators
Population Housing stock, public infrastructure Typically reported along administrative
boundaries
June 10, 2002 Anand Patwardhan, IIT-Bombay 16
Cyclone impact indicators 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 17
Example of impact data – Orissa super cyclone
No. of affected districts 12Population affected (million 12.9Villages 14643Blocks 97Crop Area (million hectares) 1.8Houses (million) 1.6Loss of Human Life 9887Persons Injured 2507Missing 40 (?)Livestock 440000Fishing boats lost 9085Fishing nets lost 22143
June 10, 2002 Anand Patwardhan, IIT-Bombay 18
What can we do with analysis of impact data?
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? 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 19
Wind and mortality
1
10
100
1000
10000
100000
0 20 40 60 80 100 120 140 160
Wind speed (knots)
De
ath
s
June 10, 2002 Anand Patwardhan, IIT-Bombay 20
Central pressure and mortality
1
10
100
1000
10000
100000
900 920 940 960 980 1000 1020
Min. Press.
De
ath
s
June 10, 2002 Anand Patwardhan, IIT-Bombay 21
Damage functions for the US
1
10
100
1000
888 934 950 957 969 977 989 999
Minimum pressure (mb)
Mo
rta
lity
1.00
10.00
100.00
1000.00
10000.00
100000.00
Da
ma
ge
(m
illi
on
co
nst
an
t $)
Mortality Series1
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Example 1 – similar event & location, different times
Year Min. Pres.in mb
Wind SpeedKm/h
Mortality
Live-stock
No. of houses damage
Loss in Rslakhs
Pop. affected
1984 AP 984.1 105 658 90,650 320,000
22632 1300,000
1987 AP 984.3 102 50 25,800 68000 6000 50,000
1996 AP 986 100 68 2000 6000 8200
June 10, 2002 Anand Patwardhan, IIT-Bombay 23
Example 2 – similar event, same time, different locations
Year Place Wind Speed(Km/h)
Pressure(in mb)
No. of Deaths
No. of houses damaged
1994 Madras 125 984 304 85,700
1993 Karaikal 120 989 318 33,131
June 10, 2002 Anand Patwardhan, IIT-Bombay 24
Example 3 – similar event, same time, different locations
Year PressIn mb
WindSpeed Km/h
No. of Deaths
No. of Houses
Loss in RsLakhs
1996 AP 974 130 to150
1677 421,000
200000
1996 Guj. 972 130 to150
33 6000 8200
June 10, 2002 Anand Patwardhan, IIT-Bombay 25
Mortality associated with heat waves
0
200
400
600
800
1000
1200
1400
1600
1800
1978 1983 1988 1993 1998
Mo
rta
lity
0
5
10
15
20
25
30
35
40
He
at
wa
ve s
pe
lls
Deaths
Number of spells of heat wave
June 10, 2002 Anand Patwardhan, IIT-Bombay 26
Example: flood damage in India
Hazard: occurrence of floods, proxy – total summer monsoon rainfall 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 “flood-
prone” areas, and total affected Impacts: mortality, crop damage
June 10, 2002 Anand Patwardhan, IIT-Bombay 27
Flood damage trends
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
3500.00
4000.00
4500.00
1953 1958 1963 1968 1973 1978 1983
To
tal d
am
ag
e (
cro
res)
0
2000
4000
6000
8000
10000
12000
Mo
rtalit
y
Total damage (crores) Mortality
June 10, 2002 Anand Patwardhan, IIT-Bombay 28
Examine scaled (or normalized) impacts
0
50
100
150
200
250
1953 1958 1963 1968 1973 1978 1983
Mo
rta
lity
/ p
op
ula
tio
n a
ffe
cte
d
(mil
lio
ns)
0
100
200
300
400
500
600
Da
ma
ge
(cr
ore
Rs/
Mh
a o
f a
rea
)
Scaled mortality Scaled damage
June 10, 2002 Anand Patwardhan, IIT-Bombay 29
Problems
Data availability Reporting and comparability Relating event characteristics to impact –
multiple pathways, initiators and end-points Accounting for interdependence:
The values of two damage categories, viz. Households and crop area may be area dependent
Accounting for controlling factors: The number of deaths and value of property loss is
decided by factors other than area
June 10, 2002 Anand Patwardhan, IIT-Bombay 30
Adaptive capacity
Examine in an empirical sense What can we infer from the past history of
events and responses? Theoretical underpinnings, in terms of
determinants Indicators
State vs. process, input vs. outcome Developmental indicators – HDI itself, or
change in HDI? Linkage with broader socio-economic development issues
June 10, 2002 Anand Patwardhan, IIT-Bombay 31
HDI change in response to a change in the macro-economic environment - liberalizationState 1987-1993 1993-1997West 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 32
Common issues
Scale across different dimensions – temporal, spatial
Unit of analysis (individual – household – community – region – national)
Capturing the perception – evaluation – response process
Data availability and measurability