1. Using Precipitation and Temperature to Model Agriculture Conditions in Africa Eric Wolvovsky NOAA/FEWS-NET July 1, 2008

2. Overview  Introduction to FEWS-NET  Methodology  Output  Applications  Potential Future Work  Conclusion

3. Introduction  Famine Early Warning System Network Early warning on food security concerns Early warning on food security concerns US Agencies involved US Agencies involved USAID (Lead)USAID (Lead) USDAUSDA USGSUSGS NASANASA NOAANOAA ChemonicsChemonics USGS and Chemonics have staff in country USGS and Chemonics have staff in country

4. Introduction

5. Introduction  NOAAs role in FEWS-NET Analyze and track meteorological phenomenon as it relates to food security Analyze and track meteorological phenomenon as it relates to food security Tropical cyclonesTropical cyclones Large scale severe weatherLarge scale severe weather Extreme heatExtreme heat FreezesFreezes Rainfall for crops, pastures and drinking waterRainfall for crops, pastures and drinking water

6. Introduction  Goals for model: Analyze individual crops Analyze individual crops Analyze regionally Analyze regionally High resolution High resolution Simple metric Simple metric Light weight Light weight Relates temperature and rainfall Relates temperature and rainfall

7. Methodology  Blaney-Criddle Formula E is seasonal moisture required K is crop coefficient T ai is mean monthly temperature d i is monthly fraction of annual daylight hours n is number of months

8. Methodology  Data Challenges Of the 1000 weather stations in Africa ~500 report daily Of the 1000 weather stations in Africa ~500 report daily Data is not filtered Data is not filtered May have bad dataMay have bad data May have reported -999.0May have reported -999.0

9. Methodology  CPC RFE 2.0 Uses 3 satellite inputs and daily station data Uses 3 satellite inputs and daily station data Daily temporal resolution Daily temporal resolution 0.1 degree spatial resolution 0.1 degree spatial resolution Struggles Struggles CoastsCoasts MountainsMountains Areas with few station reportsAreas with few station reports

10. Methodology  NCEP/NCAR Reanalysis Uses: Uses: StationStation ShipShip AircraftAircraft SatelliteSatellite Monthly Temporal Resolution Monthly Temporal Resolution 2.5 degree spatial resolution 2.5 degree spatial resolution Temperatures have a warm bias at higher elevations Temperatures have a warm bias at higher elevations

11. Methodology  Monthly Fractional Hours of Annual Daylight Developed as a function of latitude based on fixed values Developed as a function of latitude based on fixed values Monthly temporal resolution Monthly temporal resolution 0.1 degrees resolution 0.1 degrees resolution Hours of daylight varies only with latitude Hours of daylight varies only with latitude

12. Methodology  FAO Crop shapefiles Monthly temporal resolution Monthly temporal resolution  Crop Coefficient Determined by US Soil Conservation Service field tests Determined by US Soil Conservation Service field tests Values used Values used Maize 2.2Maize 2.2 Sorghum 2Sorghum 2 Wheat 1.8Wheat 1.8 Millet 1.4Millet 1.4

13. Methodology  Blaney-Criddle Formula * Crop Coefficient *

14. Methodology

15. Methodology  Conditions are determined by comparing required rainfall with received rainfall Percent of Required Rainfall Classification Less than 50% Failure Between 50% and 75% Poor Between 75% and 125% Below Average Between 125% and 175% Average Between 175% and 225% Good Greater than 225% Excellent Required Rainfall CPC RFE 2.0 * 100 = Percent of Required Rainfall Received

16. Methodology

17. Output

18. Output

19. Output

20. Output

21. Output

22. Output

23. Applications  Hazards assessments  Weekly weather briefings  Use by decision makers

24. Potential Future Work  Beyond Africa  Beyond Grains  Increase temporal resolution  Better method of validation

25. Conclusion  Light weight agriculture model  Method uses inputs that are known  Method is expandable  Will support FEWS-NET

26. Thank You Eric.Wolvovsky@noaa.gov