Operational vulnerability indicators

1. Operational vulnerability indicators Anand Patwardhan IIT-Bombay

2. Context and objectives matter 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 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 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 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 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? 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 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 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) 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 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 Anand Patwardhan, IIT-Bombay

13. Anand Patwardhan, IIT-Bombay

14. Cyclone hazard baseline Anand Patwardhan, IIT-Bombay

15. Exposure – typical indicators • Population • Housing stock, public infrastructure • Typically reported along administrative boundaries 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 Anand Patwardhan, IIT-Bombay

17. Example of impact data – Orissa super cyclone 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? Anand Patwardhan, IIT-Bombay

19. Wind and mortality Anand Patwardhan, IIT-Bombay

20. Central pressure and mortality Anand Patwardhan, IIT-Bombay

21. Damage functions for the US Anand Patwardhan, IIT-Bombay

22. Example 1 – similar event & location, different times Anand Patwardhan, IIT-Bombay

23. Example 2 – similar event, same time, different locations Anand Patwardhan, IIT-Bombay

24. Example 3 – similar event, same time, different locations Anand Patwardhan, IIT-Bombay

25. Mortality associated with heat waves 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 Anand Patwardhan, IIT-Bombay

27. Flood damage trends Anand Patwardhan, IIT-Bombay

28. Examine scaled (or normalized) impacts 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 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 Anand Patwardhan, IIT-Bombay

31. HDI change in response to a change in the macro-economic environment - liberalization 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 Anand Patwardhan, IIT-Bombay