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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 Maxx Dilley, UNDP Bradfield Lyon, IRI Régina Below, CRED. 5 th EM-DAT Technical Advisory Group Meeting August 18-19, 2005. - PowerPoint PPT Presentation
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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)