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

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

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

Introduction

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

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

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

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 May have reported

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

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

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

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

Methodology  Blaney-Criddle Formula * Crop Coefficient *

Methodology

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

Methodology

Output

Output

Output

Output

Output

Output

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

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

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

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