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-KopecInternational Research Institute for Climate Prediction

Maxx Dilley, UNDPBradfield Lyon, IRI

Régina Below, CRED

5th EM-DAT Technical Advisory Group MeetingAugust 18-19, 2005

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-DAT2. Advance the understanding of how drought

hazards associated with drought disasters

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

Relating Hazards to Disasters

Drought Disasters

Societal

Vulnerability

Drought

Hazards

Meteorological

Agricultural

Hydrological

EM-DAT

Hazard Indices

PROXY

PROXY

Drought Disasters in EM-DATNumber of Drought Disaster

Events in EM-DAT

0

10

20

30

40

19

00

19

07

19

14

19

21

19

28

19

35

19

42

19

49

19

56

19

63

19

70

19

77

19

84

19

91

19

98

Year

Nu

mb

er

of

ev

en

ts

Hazard data

availability

Consistent

disaster data

~1979 - 2004

360 disaster events

Africa 47%

Asia 22%

Europe 9%

South America 8%

Central America 8%

Australia/Oceania 3%

North America 3%

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)

Drought Hazard Indicators

NDVI PDSI GMSM

WASP (3-month)WRSISPI (3-month)

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

Converting Spatially-Continuous Data to Country-Level Data

Problematic issues with taking a simple average of data for each country1. 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)

Problem 1: Average of Large Countries Not Representative of Hazard

Apply land classification mask to remove areas neither inhabited or used for agriculture

Application of Land Use Mask

Problem 2: Simultaneous Wet and Dry Areas Within a Country

Apply dry mask to remove all anomalously wet areas

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)

---------------

---------------

---------------

---------------

---------------

---------------

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

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

Hazard Indicators for Botswana (1979-2004)

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Feb-8

0

Feb-8

2

Feb-8

4

Feb-8

6

Feb-8

8

Feb-9

0

Feb-9

2

Feb-9

4

Feb-9

6

Feb-9

8

Feb-0

0

Feb-0

2

Feb-0

4

-5

-4

-3

-2

-1

0

1

2

3SPI

WASPSM

PDSI

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

Creating the Contingency Tables

NOYES

YES

NO

a

c

b

d

DISASTER OCCURS

HAZARD

INDEX

DEFINITION

MET

Creating the Contingency Tables

Is H ≤ Thd?

HB=1

ba

dc

Does disaster occur

in same country within

L months of Ti?HB=0

Country-level average

of masked data H(Ti)

Does disaster occur

in same country within

L months of Ti?

YES

YES

NO

NO

NOYES

Repeat

for Ti+1, n

and all countries

Result: Table for each combination of hazard, time scale, threshold and lag

START

Creating the Contingency TablesExample: 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

a b

c d

b

Y

Y

N

N

Disaster

Hazard

Index

Creating the Contingency TablesExample: 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 YES

a b

c d

a

X

Jul 1979

X

Contingency table for

DHI = [WASP6, Thd=-1.25,

Lag=0-4 Months]

Y

Y

N

N

Disaster

Hazard

Index

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)

Contingency Table Statistics

3-Month WASP Skills at Multiple Thresholds

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

T=-0.75 T=-1 T=-1.25 T=-1.5 T=-1.75 T=-2

Threshold

HRTSPODFARHSS

SPI and WASP

Skill Score SPI vs. WASP at Multiple Time Scales

0

0.01

0.02

0.03

0.04

0.05

T=-0.75 T=-1 T=-1.25 T=-1.5 T=-1.75 T=-2

Threshold

HS

S

WASP1WASP3WASP6WASP9WASP12SPI1SPI3SPI6SPI9SPI12

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

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

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 MaproomPotential applications of DHI in EWS and

methodology in regional/country-level case studies

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

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)