# Presentation: 394457

## An empirical study of the links between NDVI and atmospheric variables in Africa with applications to forecasting vegetation change and precipitationChris FunkUCSB, Climate Hazard GroupMolly Brown,NASA Global Inventory Modeling and Mapping Systems 

- 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 Funk****UCSB, 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

- 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  higher
- evapotranspiration

- Higher RH  less
- evapotranspiration

- Less veg  higher
- rainfall efficiency

- More rain  more
- veg

## Month ahead Max-to-Min NDVI Change Formulation

- We model
- NDVI 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

## Observed and Estimated NDVI Change for Zimbabwe Test Site

- 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

## 1-month max-to-min NDVI Change models

## 5-month max-to-min NDVI change models

-     Climatological Averages

- Past
- Observed Data

- 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

- Test
- Site

## Sample Application – NE Kenya NDVI projections

- Test
- Site

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

## 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