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DHC_CP

DHC_CP. Diagnostic Hydrique des Cultures. Champs Pluviométriques. Crop Water Balance Calculation Using Satellite based Rainfall Estimates. Presented by : Abdallah SAMBA, Agrometeorologist AGRHYMET Regional Centre at Niamey, NIGER Trieste, June 2001. CIRAD. AGRHYMET. Overview.

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DHC_CP

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  1. DHC_CP Diagnostic Hydrique des Cultures Champs Pluviométriques Crop Water Balance Calculation Using Satellite based Rainfall Estimates Presented by : Abdallah SAMBA, Agrometeorologist AGRHYMET Regional Centre at Niamey, NIGER Trieste, June 2001 CIRAD AGRHYMET

  2. Overview • Introduction • Brief presentation of the geoclimatic context • Simulated water balance components • Evolution of the model • DHC_CP functionalities • Simulated results CIRAD AGRHYMET

  3. Introduction • Need to forecast the yields of food crops in order to : • best manage the cereal stocks, • control the fluxes and • start in time the food aids. • Heaviness of the techniques based on the statistical investigations and polls • Using the water balance simulation to obtain parameters allowing to estimate the yields.

  4. The geoclimatic Context • The sudano-sahelian belt • CILSS member countries • Main crops • Average annual rainfall • Local constraints • Rainfall and its interannual variability • Drought spells during the crop cycle • The « Water management » approach AGRHYMET CIRAD

  5. Water fluxes and their effects on agricultural hydrosystem ( ) ( ) Agricultural production Precipitations Soil evaporation Crop transpiration Runoff Erosion Drainage Capilary rise Lixiviation Ground water

  6. Simplification for Water Balance simulation (The DHC4 model ) Agricultural production Precipitation Crop transpiration Soil evaporation Drainage Ground water

  7. The Evolution of the model • Recent history • 1986 : the first surveys ; • 1987-1989 : the ESPACE project • (Evaluating and Monitoring Agricultural Production as related to Climate and Environnement) • DHC4, a first approach • Diagnosis tool • Water balance simulation • Current limitations AGRHYMET CIRAD

  8. n years x stations Agrometeorological Stations n stations DHC4a first approach • DATA BASES • PET (ATLAS) • Daily rainfall data (SUIVI) • Daily historical rainfall data (CLIMBASE) RESULTS • File • Screen • GIS • Spreadsheet • Printer • Available soil water • Crop • Cycle duration • Sowing date • Daily rainfall data WATER BALANCE SIMULATION Modem/Fax AGRHYMET CIRAD

  9. METEOSAT Satellite n years x stations n stations Stochastic Rainfall Generation Parameter Calibration Agrometeorological Stations WATER BALANCE SIMULATION • File • Screen • GIS • Spreadsheet • Printer Rainfall data RESULTS • DATA BASES • PET • Historical rainfall data CIRAD AGRHYMET

  10. The model functionnalities • Input data • Climatic data • Rainfall (satellite estimates) • PET ( ATLASETP, 1951-1980 period) • Agronomic Parameters • Available soil water (spatialised data) • Crop (species and cycle duration, crop coefficients) • Sowing dates (estimated from METEOSATimages , meteorological and field data) • Simulation results • Dates of the beginning of the agricultural season • Actual evapotranspiration • Water requirement satisfaction indices • Potential yields estimated 2 months before harvest • Optional Treatments • Image processing • Raster to Vector conversion AGRHYMET CIRAD

  11. Calcul de l’ETR par l’algorithme d’Eagleman

  12. Modeling water dynamics in the soil and root growth Rooting front Wetting front Time First rain Sowing Root available water Maximum available water mm of water

  13. Les coefficients culturaux (KCs)

  14. Probability of a rain event : Let Aj be the event of rain on day j and Äj the opposite event: p(Aj / Aj-1) = a11 p(Aj / Äj-1) = a01 Ajustment of rainfall amounts : for random rainfall generation, the repartition function is : F(x) = 1 - e-((x- x0 )/a) its reciprocal is : F-1(y) = x0 - a.ln (1-y) Synthesis : we know the three parameters that caracterise a given site for a given month (a01, a11, a). We are therefore able to generate as much likely rainfall sequences as we want. The Rainfall generator : theoretical basis Semi-random Generation probabilistic daily rainfall on a given site Ajustement of daily rainfall to a probabilistic law Data spatialisation using interpolation (logistic regression between stations) at a 25 km ´ 25 km scale. The Simulated Water Balance Components (1) AGRHYMET CIRAD

  15. The Eagleman relationship ETR = 0.732 - 0.05 ´ETM + (4.97 ´ETM - 0.661 ´ETM2) ´HR- (8.57 ´ETM - 1.560 ´ETM2) ´HR2 + (4.35 ´ETM - 0.880 ´ETM2) ´HR3 with HR : fraction of currently available soil water relative to potential ETMp : crop maximum evapotranspiration = Kc´ ETPp IRESP index IRESP % = ETR / ETM cycle´ETR / ETM F sensible the sensible phase corresponds to the flowerins-fruit set period Yield estimation RDT (kg/ha) = 11.3 ´ IRESP -128 r2 = 0.66 Principles of DHC_CP algorithms Calculation of daily crop water consomption using the Eagleman relationship Water satisfaction index IRESP Yield estimation The Simulated Water Balance Components (2) AGRHYMET CIRAD

  16. DHC_CP: An Early Warning System AET: Actual EvapoTranspiration Actual evapotranspiration estimated one month before harvest Potential Yield Estimation Potential Yield in 1994 CIRAD AGRHYMET

  17. Suivi de la campagne agricole

  18. Suivi de la campagne agricole

  19. Suivi de la campagne agricole Satisfaction des besoins en eau du mil pendant la 3ème décade d ’août 2000

  20. Prévision des rendements Rendements du mil estimés au 30 Septembre 2000 dans les pays du CILSS

  21. Prévision des rendements

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