1. Operational vulnerability indicators Anand Patwardhan IIT-Bombay

2. June 10, 2002Anand Patwardhan, IIT-Bombay2 Context and objectives matter

3. June 10, 2002Anand Patwardhan, IIT-Bombay3 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

4. June 10, 2002Anand Patwardhan, IIT-Bombay4 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

5. June 10, 2002Anand Patwardhan, IIT-Bombay5 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

6. June 10, 2002Anand Patwardhan, IIT-Bombay6 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

7. June 10, 2002Anand Patwardhan, IIT-Bombay7 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?

8. June 10, 2002Anand Patwardhan, IIT-Bombay8 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

9. June 10, 2002Anand Patwardhan, IIT-Bombay9 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

10. June 10, 2002Anand Patwardhan, IIT-Bombay10 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)

11. June 10, 2002Anand Patwardhan, IIT-Bombay11 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

12. June 10, 2002Anand Patwardhan, IIT-Bombay12 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

13. June 10, 2002Anand Patwardhan, IIT-Bombay13

14. June 10, 2002Anand Patwardhan, IIT-Bombay14 Cyclone hazard baseline

15. June 10, 2002Anand Patwardhan, IIT-Bombay15 Exposure – typical indicators Population Housing stock, public infrastructure Typically reported along administrative boundaries

16. June 10, 2002Anand Patwardhan, IIT-Bombay16 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

17. June 10, 2002Anand Patwardhan, IIT-Bombay17 Example of impact data – Orissa super cyclone

18. June 10, 2002Anand Patwardhan, IIT-Bombay18 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?

19. June 10, 2002Anand Patwardhan, IIT-Bombay19 Wind and mortality

20. June 10, 2002Anand Patwardhan, IIT-Bombay20 Central pressure and mortality

21. June 10, 2002Anand Patwardhan, IIT-Bombay21 Damage functions for the US

22. June 10, 2002Anand Patwardhan, IIT-Bombay22 Example 1 – similar event & location, different times YearMin. Pres. in mb Wind Speed Km/h Mortal ity Live- stock No. of houses damag e Loss in Rs lakhs Pop. affected 1984AP984.110565890,650320,0 00 226321300,000 1987AP984.31025025,80068000600050,000 1996AP98610068 200060008200

23. June 10, 2002Anand Patwardhan, IIT-Bombay23 Example 2 – similar event, same time, different locations YearPlaceWind Speed (Km/h) Pressure (in mb) No. of Deaths No. of houses damage d 1994Madras12598430485,700 1993Karaikal12098931833,131

24. June 10, 2002Anand Patwardhan, IIT-Bombay24 Example 3 – similar event, same time, different locations YearPress In mb Wind Speed Km/h No. of Death s No. of Houses Loss in Rs Lakhs 1996AP974130 to 150 1677421,00 0 20000 0 1996Guj.972130 to 150 3360008200

25. June 10, 2002Anand Patwardhan, IIT-Bombay25 Mortality associated with heat waves

26. June 10, 2002Anand Patwardhan, IIT-Bombay26 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

27. June 10, 2002Anand Patwardhan, IIT-Bombay27 Flood damage trends

28. June 10, 2002Anand Patwardhan, IIT-Bombay28 Examine scaled (or normalized) impacts

29. June 10, 2002Anand Patwardhan, IIT-Bombay29 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

30. June 10, 2002Anand Patwardhan, IIT-Bombay30 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

31. June 10, 2002Anand Patwardhan, IIT-Bombay31 HDI change in response to a change in the macro-economic environment - liberalization

32. June 10, 2002Anand Patwardhan, IIT-Bombay32 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