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Toward the Development of a Drought Hazard Index: Methods and Initial Results Emily K. Grover-Kopec International Research Institute for Climate Prediction.

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Presentation on theme: "Toward the Development of a Drought Hazard Index: Methods and Initial Results Emily K. Grover-Kopec International Research Institute for Climate Prediction."— Presentation transcript:

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 5 th 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: 1.Improve documentation of drought disasters in EM-DAT 2.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 Societal Vulnerability Drought Hazards Meteorological Agricultural Hydrological EM-DAT Hazard Indices PROXY

5 Drought Disasters in EM-DAT Hazard data availability Consistent disaster data ~  360 disaster events Africa47% Asia22% Europe9% South America8% Central America8% Australia/Oceania3% North America3%

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 PDSIGMSM WASP (3-month) WRSISPI (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 Hazard data = F(X,Y,T) Disaster data = F(Country,T) 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)

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

15 Analysis Options  Condense hazard and disaster data to binary, country-level indicators and then use: 1.Contingency table statistics and skill scores  Ongoing 2.Principle Component Analysis  Planned

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

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

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

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

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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