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Toward the Development of a Drought Hazard Index: Methods and Initial Results

Toward the Development of a Drought Hazard Index: Methods and Initial Results. Emily K. Grover-Kopec International Research Institute for Climate Prediction Maxx Dilley, UNDP Bradfield Lyon, IRI Régina Below, CRED. 5 th EM-DAT Technical Advisory Group Meeting August 18-19, 2005.

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Toward the Development of a Drought Hazard Index: Methods and Initial Results

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  1. Toward the Development of a Drought Hazard Index: Methods and Initial Results Emily K. Grover-Kopec International Research Institute for Climate Prediction Maxx Dilley, UNDP Bradfield Lyon, IRI Régina Below, CRED 5th EM-DAT Technical Advisory Group Meeting August 18-19, 2005

  2. Background • Initial analysis of relationship between hydro-meteorological drought hazards and drought disasters highlighted need to review EM-DAT documentation methods • CRED and IRI develop joint project to: • Improve documentation of drought disasters in EM-DAT • Advance the understanding of how drought hazards associated with drought disasters

  3. Characterizing the Hazard • Temporal and spatial nature of the hazard make it difficult to define • Use drought impacts as ground-truth for definition • Develop hazard index for characterizing magnitude, duration, timing and location

  4. Relating Hazards to Disasters Drought Disasters EM-DAT PROXY Societal Vulnerability Drought Hazards Hazard Indices PROXY Meteorological Agricultural Hydrological

  5. Drought Disasters in EM-DAT • 360 disaster events ~1979 - 2004 Hazard data availability Consistent disaster data

  6. Drought Hazard Indicators • Meteorological • SPI (Standardized Pcpn Index) • WASP (Weighted Anomaly Standardized Pcpn) • Agricultural • NDVI (Normalized Difference Vegetation Index) • Soil Moisture • PDSI (Palmer Drought Severity Index) • WRSI (Water Requirement Satisfaction Index)

  7. Drought Hazard Indicators NDVI PDSI GMSM SPI (3-month) WRSI WASP (3-month)

  8. Drought Hazard Indicators SPI WASP NDVI WRSI GMSM PDSI Indicators are a function of: 1. Time Scale 2. Time Lag 3. Threshold Example: 3-Month SPI < -1.0 ; 0-4 months before disaster event

  9. Converting Spatially-Continuous Data to Country-Level Data • Problematic issues with taking a simple average of data for each country 1. Average of large country generally not representative of disaster event in EM-DAT 2. Relatively wet and dry regions in same country can mute drought hazard signal --------------- --------------- --------------- --------------- --------------- --------------- Hazard data = F(X,Y,T) Disaster data = F(Country,T)

  10. Problem 1: Average of Large Countries Not Representative of Hazard • Apply land classification mask to remove areas neither inhabited or used for agriculture

  11. Application of Land Use Mask

  12. Problem 2: Simultaneous Wet and Dry Areas Within a Country • Apply dry mask to remove all anomalously wet areas

  13. Spatially-Continuous Data Converted to Country-Level Data Applying land classification and dry masks to the data and then averaging the result over national boundaries generates hazard data that can be compared to the point disaster data --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- --------------- Hazard data = F(Country,T) Hazard data = F(X,Y,T) Disaster data = F(Country,T)

  14. Analysis Options: Not Regression Regression is not an appropriate analysis technique • Hazard indicators highly correlated • Autocorrelation present in indicators with time scale greater than 1 month J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D Indicator time series with 3-Month time scale

  15. Analysis Options • Condense hazard and disaster data to binary, country-level indicators and then use: • Contingency table statistics and skill scores  Ongoing • Principle Component Analysis  Planned

  16. Creating the Contingency Tables DISASTER OCCURS YES NO a b YES HAZARD INDEX DEFINITION MET NO c d

  17. Creating the Contingency Tables START Country-level average of masked data H(Ti) Does disaster occur in same country within L months of Ti? NO Is H ≤ Thd? HB=0 Repeat for Ti+1, n and all countries YES YES NO HB=1 d c Does disaster occur in same country within L months of Ti? YES NO b a Result: Table for each combination of hazard, time scale, threshold and lag

  18. Creating the Contingency TablesExample: 6-Month WASP, Threshold=-1.25, Lag=4 months 6-Month WASP Data EM-DAT Disaster Afghanistan x x x x x … Albania x x x x x … . . . . . . Zimbabwe x x x x x … X Check EM-DAT for corresponding disaster Y N Jun 1979 Hazard Index b Y Does a disaster start in Afghanistan during Jun-Oct 1979? a b Is value less than or equal to -1.25? c d N NO YES

  19. Creating the Contingency TablesExample: 6-Month WASP, Threshold=-1.25, Lag=0-4 months 6-Month WASP Data EM-DAT Disaster Afghanistan x x x x x … Albania x x x x x … . . . . . . Zimbabwe x x x x x … X Check EM-DAT for corresponding disaster Y N Jul 1979 Hazard Index a Y Does a disaster start in Afghanistan during Jul-Nov 1979? a b Is value less than or equal to -1.25? c d N X Contingency table for DHI = [WASP6, Thd=-1.25, Lag=0-4 Months] YES YES

  20. Making Sense of It All • Statistics can be used to characterize each hazard indicator’s table in terms of how well it “predicts” disasters • Let these statistics tell us which is/are the best indicator(s)

  21. Contingency Table Statistics

  22. SPI and WASP

  23. Initial Results • WASP appears to have closer relationship with disasters at all but shortest time scales • Seasonality important • For these meteorological indices: • Time scale ~ 3-6 months • Country-wide threshold ~-1.0 (moderate drought) • Contingency tables/stats • Will be able to say more about contingency table results after significance testing • Additional motivation for using additional statistical methods

  24. Next Steps • Continue contingency table analysis for remaining hazard indicators • Perform additional statistical methods • PCA  Provide a series of independent, weighted sums of the indicators which maximize the amount of explained variance in the disaster data

  25. Next Steps • Apply above information to formulations of single Drought Hazard Index (DHI) • Most likely a weighted combination of indicators, but may be a single indicator • Make DHI available via the IRI Data Library Maproom • Potential applications of DHI in EWS and methodology in regional/country-level case studies

  26. Principle Component Analysis Basics • Standarizing indicators gives equal weight to all. Otherwise indicators with higher variance have more weight. • Combine indicators so those that are describing similar aspects are described in a single metric • Each combination (principal component): • Measures different aspect of disaster behavior and is completely uncorrelated with the others • Has high variance (i.e., summarizes as much information as possible) • Are weighted sums of original indicators

  27. Contingency Table Statistics HR = (a+d)/n TS = a/(a+b+c) POD = a/(a+c) FAR = b/(a+b) HSS = (ad-bc)/(a+c)(b+d)

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