Pilot Study on the Use of PROMISE Climate Data in a Crop Model  Type and origin and of climate data  Daily, at 2m (Tmax, Tmin, Rs, Hum, wind, rain)  (obs + sim), (sim)  Senegal (6 points sim, 13 stations obs)  Preliminary simulations with ARPEGE (MF Bordeaux)  Station data from Cirad-Agrhymet data base H Syabuddin, JC Combres, JF Royer, M Dingkuhn

 Crop Model  Preliminary version of SARRA-H  Calibrated for peanut using CERAAS data (Senegal)  Photoperiodism inactivated, crop duration = f(Temp)  Sowing date sensitive to rainfall (farmer ’s criteria)  Water and Rs limited growth & yield  Rooting depth limited by wetting front

Georeference for Climate Data Saint Louis Dakar Ziguinchor Bakel Bambey Matam Kolda

Evaluation of ARPEGE Climate Simulations Simulations for Senegal in show… A strong under-estimation of annual rainfall due to an inaccurate positioning of the Inter-Tropical Convergence Zone (ITCZ) An under-estimation of the N-S climatic gradient An over-estimation of the E-W climatic gradient (coast-to- continent) A strong under-estimation of diurnal temperature amplitudes => To permit meaningful test runs of SARRA-H, a latitudinal (north) shift of simulated climate by 2 degrees was performed

Annual rainfall Wrong positioning of ITCZ by about 2° => under-estimation of rainfall

Annual rainfall Comparison of measured and simulated data after north-shift of simulated climate by 2°

Bakel (continental climate) Observations: Simulated rainy season longer « Slow start » of rainy season causes risks of failure of crop establishment Intra-annual(seasonal) rainfall distribution Comparison of measured and simulated data after north-shift of simulated climate by 2° Observed Simulated

Rainfall intensity distribution (daily cumulatives) Measured and simulated data after N-shift of simulated climate by 2° Over-estimation of small rains (1-3 mm), under-estimation of big rains (> 10mm), ca. factor 2 Delay in sowing, smaller fraction of useful precipitation (E!), wetting front remains shallow (rooting depth!)

Problem: ARPEGE over-estimates rain-days by factor 1,5 to 3 Frequency of rain-days Measured and simulated data after N-shift of simulated climate by 2°

SARRA Water Balance: Atmospheric Demand and Soil Reserve Root front Wetting front Rain Sowing Stock ET(pot)=1 Kc 2 compartments simulated SARRA ET(max)=Kc * ET(pot)

Rainfall Evaporation Transpiration Runoff Drainage Infiltration (=> stock) 1 2 3a 3b 4 5 Small rain event: Evaporation Moderate rain event: Stock, Transpiration Big rain event: Runoff, Drainage Partitioning of Precipitation at the Plot Level

Air Temperature Measured and simulated data after N-shift of simulated climate by 2° Maximum temperatures OK Strong over-estimation of minimum temperatures => Over-estimation of daily mean temperatures by 4 to 5 °C => Under-estimation of diurnal temperature amplitudes => Simulated crop duration too short

Mean simulated grain yields and (preliminary) Thiès Dakar Louga Saint Louis Probability for yields to fall below… (%) Causes of yield under-estimation: Stress thru delayed sowing; Short crop duration (high Tmin) Causes of yield decrease:Short crop duration (rising T); Rs lower by 2-3 MJ/d in Sept/Oct

Conclusion 1: ARPEGE climate Latitude of ITCZ wrong by 2° Tmin too high, (Tmax-Tmin) too low Rainfall intensity distribution very different from station data for Too many small rains (1-3 mm) Too few big rains (>10 mm) => problem of scale? Predicted climate change for More rains in Sept/Oct (favorable) Less Rs in Sept/Oct (unfavorable)

Conclusion 2: Test simulations for peanut Simulation results primarily reflect distortions, brought about by… –rainfall intensity distribution (effect of large pixel size?) delay in sowing date, resulting in terminal stress Increased proportion of water evaporated Wetting and root front remain shallow (sensitivity of drought spells) –High Tmin and low (Tmax-Tmin) Short crop duration High respiration rate (yield reduction) No scenario evaluations yet for climate change

Perspectives Adapting crop model to climate data, or vice versa? Adapting the crop model –Would require de-sensitising yield to soil water stock (=> fixed assumptions on useful fraction of rains) –Loss of sensitivity to « Sahel » characteristics Adapting the climate simulations –Smaller pixels ? (200 x 200 km is pretty course anyway for regional forecasting!) Transforming the climate files for station type intensity distributions of rains –Need for parameters -- how to estimate change? –Who will do it and when? (end of project!)