operational vulnerability indicators anand patwardhan iit-bombay

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


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


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


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


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


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?


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


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


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)


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


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


0

0.2

0.4

0.6

0.8

1

0 500 1000Coastal distance scale

El Nino

NormalLa Nina


Cyclone hazard baseline


Exposure – typical indicators

Population Housing stock, public infrastructure Typically reported along administrative

boundaries


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


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


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?


Wind and mortality

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100000

0 20 40 60 80 100 120 140 160

Wind speed (knots)

De

ath

s


Central pressure and mortality

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10000

100000

900 920 940 960 980 1000 1020

Min. Press.

De

ath

s


Damage functions for the US

1

10

100

1000

888 934 950 957 969 977 989 999

Minimum pressure (mb)

Mo

rta

lity

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1000.00

10000.00

100000.00

Da

ma

ge

(m

illi

on

co

nst

an

t $)

Mortality Series1


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


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


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


Mortality associated with heat waves

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600

800

1000

1200

1400

1600

1800

1978 1983 1988 1993 1998

Mo

rta

lity

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5

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He

at

wa

ve s

pe

lls

Deaths

Number of spells of heat wave


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


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


Examine scaled (or normalized) impacts

0

50

100

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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


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


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


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%


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

operational vulnerability indicators anand patwardhan iit-bombay

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