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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

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

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

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 capacity30
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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