The application of climate forecasts and agrometeorological information for agriculture, food security, forestry, livest

1. The application of climate forecasts and agrometeorological information for agriculture, food security, forestry, livestock and fisheries G. Maracchi, F. Meneguzzo, M. Paganini Banjul, Gambia, 9-13 December, 2002

2. Information needs ofFOOD SECURITY Availability of input data Appropriate location Appropriate spatial resolution Timely information

3. Existing Food Security Systems

4. Existing Food Security Systems - AGRHYMET SISP • Base parameters • statistical analysis procedures on rainfall for ecological zoning; • a millet simulation model to estimate millet crop conditions and the effect of rainfall distribution; • statistical analysis of the yields.

5. Growth at five days interval Varieties Phases length Initial Kc Growing Flowering Grain filling 75 days 50 15 15 0.2 0.08 90 days 65 15 15 0.15 0.06538 120 days 80 15 20 0.1 0.0562 120 days JFL - JSEM 15 20 0.1 (1-0.1)/(IPFL-IPSEM) phot. Existing Food Security Systems - AGRHYMET SISP Parameterization of crop phenology

6. Existing Food Security Systems - AGRHYMET SISP Correlation between the production SISP indexes and the agricultural statistics

8. Existing Food Security Systems - USAID FEWS • The analysis is organised in three sections: • Vulnerability/Baseline Information • Hazard/Shock Information • Risk/Outcome Analysis

11. Existing Food Security Systems - DHC-Champs pluviaux • The CCD are used in the crop water diagnostic (DHC) in order to produce: • maps of the crop water satisfaction • maps of the crop water needs • maps of crop yields

12. Existing Food Security Systems - DHC-Champs pluviaux

13. Existing Food Security Systems -World Food Programme -Vulnerability Analysis & Mapping • WFP has produced vulnerability assessment maps in 3 stages: • identifying the income sources for each relevant group • analysing the causal structure of vulnerability • reconciling the analysis of risk and coping capacity

15. Existing Food Security Systems - FAO GIEWS • monitors food supply and demand • analyses information on productionstocks, trade and foodaid • monitors export prices • reacts to natural disasters • issues Special Alerts and up-to-date reports

16. Existing Food Security Systems - FAO GIEWS • web pages on the Internet • develops new approaches for early warning • cultivates and maintains information-sharing between governmental and private actors • depends on the free exchange of information

17. Existing Food Security Systems - FAO GIEWS

18. Solving the problem FOOD SECURITY INFORMATION • Information available on Internet • More appropriate to the decision makers information needs • Improved survey methods and operations for monitoring actual and potential outbreak areas • Create interaction between producers of information CLIMATE PREDICTION INFORMATION

19. The local CLIMATE • Decreasing annual pluviometry S-N • Alternation of dry season (9-5 months) and rainy season • The monsoon is the main defining factor • Unimodal distribution of the rain

20. Link between climate and teleconnections CLIMATE DEFINITION The average of the weather over periods The effects of changes in sea surface temperatures in the Pacific Ocean on temperature and rainfall patterns in regions that are far away from the Pacific TELECONNECTIONS DEFINITION

23. Teleconne-ctions in Sahel

24. Simultaneous Correlation of Sahel Rainfall with SST (June, July)

25. Simultaneous Correlation of Sahel Rainfall with SST (August, September)

26. Correlation of Sahel Rainfall in June and July with SST in May

27. INTERTROPICAL CONVERGENCE ZONE - location Drought years are associated with the ITCZ being south of its normal position, while wet years are associated with the ITCZ north of normal Warmer SST in Guinea Gulf lead to higher precipitation over Guinea coast (increased moisture) and lesser over Sahel (northerly flow, sinking at low levels)

28. INTERTROPICAL CONVERGENCE ZONE - location Rapidly increasing SST in May over Guinea cause delayed monsoon in Sahel (June and July)

29. Synthetic descriptions of atmospheric teleconnection patterns • Can be found at following addresses: • The Climate Diagnostics Center (NOAA)http://www.cdc.noaa.gov/Teleconnections • Climate Precition Center (NOAA): http://www.cpc.noaa.gov/data/teledoc/telecontents.html

30. Existing climate predictions Amount of rainfall • IRI Net assessments • PRESAO outlook • CLIMAG WA enhanced methodology Onset of the growing season • IBIMET methodology (Maracchi/Pini) • Omotosho method • CLIMAG WA enhanced methodology

31. Applications for 2001 & 2002 • Comparison of results per single zone for each year • BUT • Each methodology has its own spatial resolution • Each methodology has its own temporal resolution

32. Data formats

33. The IRI Forecast Process (1) • Forecasting the tropical SST anomalies using dynamical and statistical models • Using the predicted SST for atmospheric general circulation models (GCMs) • Estimating the expected skill

34. The IRI Forecast Process (2) • Statistical postprocessing of model output • Putting all the indications together a final IRI forecast called net assessment • issued in the form of maps that show regions having homogeneous forecast probabilities for the below, near and above normal terciles

35. Examples of Net Assessments

36. Omotosho methodology Onset of the growing season • The method is empirical/dynamical and uses the following requirements: • Difference between the U-component of the wind at 3000 m and at the surface must be between –20 m/s and –5 m/s • Difference between the U-component of the wind at 7500 m and at 3000 m must be between 0 and 10 m/s

37. Omotosho methodology Onset of the growing season

38. IBIMET method Onset of the growing season • Predict the seeding decades for the different zones in order to produce advises to peasants • The philosophy is to utilise the information already available on INTERNET (NOAA, IGES COLA, ADDS)

39. Exercise for the agricultural season 2001 1 – Rainfall Forecasting section NOAA - Climate Prediction Center, Prediction of the rainfall quantity at 24-96 hours 2 – Rainfall Estimation section ADDS - Africa Data Dissemination Service, Decadal rainfall estimation images 3 – Field data section Real sowing dates in different areas in Mali collected by local institutions

40. Forecasting Section = Daily forecast images Total rainfall of the decade Through the daily images it is possible to forecast the amount of rainfall expected in the decade and give the advise of the sowing date to farmers

41. Estimation Section Precipitation Estimate based on GPI, SSM/I, AMSU and GTS The image has been utilised to validate the information prepared by the forecasting information

42. Field Observation Data Section Field observation areas Data collected by local institutions The collected information are related to the real sowing date

43. The information of the different three sections has been compared in order to evaluate the process Results-2001 42A

44. Results-2002 42B

45. Comparison between predictions

46. Zone 3 Zone 2 Zone 1 PRESAO FORECAST Examples of comparison2002 wet season CLIMAG WA FORECAST Zone 3 Zone 2 Zone 1

47. MONITORING ACTIVITIES - an added value • Allows to evaluate the conditions of the wet season on the agricultural and food situation • Allows to evaluate the conditions and the effectiveness of the Early Warning Systems and of the mechanisms of crisis management

48. MONITORING ACTIVITIES - operational tools Bulletins • Ex. AGRHYMET Regional Centre

49. MONITORING ACTIVITIES - operational tools Space-borne information • Satellite images (METEOSAT, NOAA,...)

50. MONITORING ACTIVITIES - operational tools INTERNET • Warnings diffused by Internet