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

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Operational vulnerability indicators

Operational vulnerability indicators

Anand Patwardhan

IIT-Bombay


Context and objectives matter

Context and objectives matter

Anand Patwardhan, IIT-Bombay


Vulnerability

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

Anand Patwardhan, IIT-Bombay


Hazard how to represent climate

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

Anand Patwardhan, IIT-Bombay


Exposure what is at risk

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

Anand Patwardhan, IIT-Bombay


Impacts how is it at risk

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

Anand Patwardhan, IIT-Bombay


Adaptive capacity

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?

Anand Patwardhan, IIT-Bombay


Interactions between the layers

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

Anand Patwardhan, IIT-Bombay


Example cyclone impacts in india

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

Anand Patwardhan, IIT-Bombay


Key features historical baseline

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)

Anand Patwardhan, IIT-Bombay


Spatial distribution a simple approach

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

Anand Patwardhan, IIT-Bombay


Spatial distribution

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

Anand Patwardhan, IIT-Bombay


Operational vulnerability indicators

Anand Patwardhan, IIT-Bombay


Cyclone hazard baseline

Cyclone hazard baseline

Anand Patwardhan, IIT-Bombay


Exposure typical indicators

Exposure – typical indicators

  • Population

  • Housing stock, public infrastructure

  • Typically reported along administrative boundaries

Anand Patwardhan, IIT-Bombay


Cyclone impact indicators

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

Anand Patwardhan, IIT-Bombay


Example of impact data orissa super cyclone

Example of impact data – Orissa super cyclone

Anand Patwardhan, IIT-Bombay


What can we do with analysis of impact data

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?

Anand Patwardhan, IIT-Bombay


Wind and mortality

Wind and mortality

Anand Patwardhan, IIT-Bombay


Central pressure and mortality

Central pressure and mortality

Anand Patwardhan, IIT-Bombay


Damage functions for the us

Damage functions for the US

Anand Patwardhan, IIT-Bombay


Example 1 similar event location different times

Example 1 – similar event & location, different times

Anand Patwardhan, IIT-Bombay


Example 2 similar event same time different locations

Example 2 – similar event, same time, different locations

Anand Patwardhan, IIT-Bombay


Example 3 similar event same time different locations

Example 3 – similar event, same time, different locations

Anand Patwardhan, IIT-Bombay


Mortality associated with heat waves

Mortality associated with heat waves

Anand Patwardhan, IIT-Bombay


Example flood damage in india

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

Anand Patwardhan, IIT-Bombay


Flood damage trends

Flood damage trends

Anand Patwardhan, IIT-Bombay


Examine scaled or normalized impacts

Examine scaled (or normalized) impacts

Anand Patwardhan, IIT-Bombay


Problems

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

Anand Patwardhan, IIT-Bombay


Adaptive capacity1

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

Anand Patwardhan, IIT-Bombay


Hdi change in response to a change in the macro economic environment liberalization

HDI change in response to a change in the macro-economic environment - liberalization

Anand Patwardhan, IIT-Bombay


Common issues

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

Anand Patwardhan, IIT-Bombay


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