1/13/2003 IRI Presentation

E D C

F E W S

An empirical study of the

links between NDVI and

atmospheric variables in Africa with

applications to forecasting vegetation change and

precipitation

Chris FunkUCSB, Climate Hazard Group

Molly Brown,NASA Global

Inventory Modeling and

Mapping

Systems

Objective: Improved FEWS NET Executive Summaries

Background – Useful NDVI projections

• NDVI is a satellite measurement of vegetation used to monitor drought

• NDVI linked to locusts (Tucker, 1985; Hielkema et al., 1986), Malaria (Hay et al., 1998), and Rift Valley Fever (Linthicum et al., 1999)

– RVF in 1998/1999 cost the Greater Horn ~$100 million

• NDVI is linked lagged precipitation– (e.g. Nicholson, 1990; Potter and Brooks, 1998; Richard and

Poccard, 1998, and others)

• It seems logical to try to use lagged rainfall to project future NDVI

– These projections are distinct from and compatible with NDVI forecasts based on downscaled climate information

– e.g. the work of Matayo Indeje at the IRI

• Future work should look at combining approaches• Best Skill = Persistence + lagged Rain + Climate Forecast

Monthly Data

• NDVIe data from NASA GIMMS (1 global and 0.1 degree African)

• GPCP rainfall rescaled to 1 degree• Tim Love’s CPC FEWS NET African Rainfall

Climatology data (0.1 degree, Africa)– Cold cloud duration precipitation estimates blended

with automatic gauge data (Love et al., 2004)

• NCAR Reanalysis relative humidity fields– Class ‘C’ variable (Kalnay et al., 1996)

Talk Overview• Empirical Models of NDVI Change

– Describe model– Test model

• Works in most semi-arid regions• But, only some regions have decent cross-validated skill when the seasonal

cycle is removed• However, most of Africa is explained well by either the seasonal cycle or

the projection model

– From a decision support perspective we can tell people what the NDVI conditions will be a few months in advance

• Empirical analysis of lagged NDVI/precipitation relationships– NDVI can maybe help predict precipitation in a few regions

• Brazil and Eastern Australia (perhaps).

Month ahead Max-to-Min NDVI Change Formulation

1 min 1(100 )t tLoss N N RH

max 1 1ln(1 )t tGrowth N N P

We assume geographicallyvarying Nmin and Nmax are fixed.

These have been shown to be strongly related to average precipitation, temperature and latitude (Potter and Brooks, 1998)

More veg higherevapotranspiration

Higher RH lessevapotranspiration

Less veg higherrainfall efficiency

More rain moreveg

Month ahead Max-to-Min NDVI Change Formulation

1 max 1 1 2 1 min 1ˆ ln(1 ) (100 )t t t t tN b N N P b N N RH

We modelNDVI change

‘Growth’ term ‘Loss’ term

Growth stops whenwe reach historic maxNDVI Growth assumed to

log-linear with precipitation

Loss stops whenwe reach historic minNDVI

Loss assumed linearlyrelated to 100-RH

Zimbabwe test site revisited

GPCP-based NDVI Growth estimate and NDVI change

R2 = 0.6

-0.2

0

0.2

0 0.1 0.2 0.3 0.4 0.5

NDVI Growth Estimate

ND

VI

Ch

ang

e

NDVI Loss estimate and NDVI Change

R2 = 0.5-0.2

-0.1

0

0.1

0.2

0 0.05 0.1 0.15

NDVI Loss EstimateN

DV

I Cha

nge

Observed and Estimated NDVI Change for Zimbabwe Test Site

-0.1

-0.05

0

0.05

0.1

0.15

Observed NDVI Change Estimated NDVI Change

Extremes under-estimated – could consider extending max/minbeyond historic values

Observed and Estimated NDVI for Zimbabwe Test Site

¤ Extremes under-estimated – could consider extending max/min beyond historic values¤ Some inter-annual variability is captured

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Observed NDVI Change Estimated NDVI Change

1-month max-to-min NDVI Change models

Future

Climatological Averages

Past

Observed Data

5-month max-to-min NDVI change models

G1 G2 G3 G4 G5L1 L2 L3 L4 L5

FoL1

G1 G2 G3 G4 G5L1 L2 L3 L4 L5

FoL2

G1 G2 G3 G4 G5L1 L2 L3 L4 L5

FoL3

G1 G2 G3 G4 G5L1 L2 L3 L4 L5

FoL4

¤ Climatological averages could be replaced with forecast precipitation and relative humidity¤ This effort meant to complement forecasting efforts by the IRI, CPC and others

Cross-validation results for Africa

¤ R2 images contain the seasonal cycle¤ Skill = 1.0 – Var(Nobs- Nest)/Var(Nobs), Michaelsen, 1987¤ Some drought-prone semi-arid locations show good skill

Focus on 4 month forecast

¤ In southern Africa low rainfall regions predicted okay, but seasonal cycle appears dominant¤ In eastern Greater Horn region, good skills found – applications to pasture, malaria and RVF feasible

Livestock dependentRVF-prone

Sample Application – NE Kenya NDVI projections

• In 1997/98 an extensive outbreak of Rift Valley Fever occurred in northeastern Kenya

• Apx. 27,500 cases occurred in Garissa district, making this the largest recorded outbreak in East Africa, (Woods, Karpati and others, 2002)

• Early warning can allow prevention, monitoring and mitigation activities

Sample Application – NE Kenya NDVI projections

NE Kenya Test Site - Observed and forecast monthly NDVI anomalies

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1995 1996 1997 1998 1999 2000 2001 2002

Year-month

ND

VI

ano

mal

y

Obs Lag1 Lag2 Lag3 Lag4

TestSite

Sample Application – NE Kenya NDVI projections

TestSite

O - Lag 1- R2 = 0.86

X - Lag 4 - R2 = 0.83

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

Caveats:Large area increases accuracy of estimatesARC rainfall known to be accurate in KenyaStill … this analysis bodes well for RVF detection

-0.07

-0.02

0.03

0.08

0.13

0.18

1995 1996 1997 1998 1999 2000 2001 2002Year

Dec

emb

er N

DV

I A

no

mal

ies

Obs Lag4

Conlcusions

•  We can model NDVI change in semi-arid regions with a simple max-to-min growth/loss formulation

• Skill levels are high in semi-arid, low in tropical forests and places with a strong seasonal cycle

• Future work will look at incorporating forecast information

• We hope to create integrated monitoring/projection information products