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)

1950-79 (obs + sim), 2010-39 (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

21 3

5 6 7

Georeference for Climate DataSaintLouis

Dakar

Ziguinchor

Bakel

Bambey

Matam

Kolda

Evaluation of ARPEGE Climate Simulations

Simulations for Senegal in 1950-79 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

(Kld)

(Pdr)(SL-Aero)

(Mtm)(Lou)

(Dkr-Y)

(Bkl)

(Diour)

(Th)

(Kdg)

(Zig)

(11)(10)(9)

(7)(6)

(3)(2)(1)

(5)

8,0

9,0

10,0

11,0

12,0

13,0

14,0

15,0

16,0

17,0

0 250 500 750 1000 1250 1500

Annual cumulative rainfall (mm)

La

titu

de

(°)

Pluie_Obs-195079

Pluie_Sml-195079

Pluie_Sml-201039

Puissance (Pluie_Obs-195079)Polynomial(Pluie_Sml-195079)

Annual rainfall 1950-79 Wrong positioning of ITCZ by about 2° => under-estimation of rainfall

(Kld)

(Pdr)(SL-Aero)(Mtm)

(Lou)

(Dkr-Y)

(Bkl)

(Diour)

(Th)

(Kdg)

(Zig)(11)(10)(9)

(7)(6)

(3)(2)(1)

(5)

8,0

10,0

12,0

14,0

16,0

18,0

20,0

0 250 500 750 1000 1250 1500

Annual rainfall (mm)

La

titu

de

(°)

Pluie_Obs-195079Pluie_Sml-195079Puissance (Pluie_Obs-195079)Polynomial (Pluie_Sml-195079)

Annual rainfall 1950-79 Comparison of measured and simulated data after north-shift of simulated climate by 2°

Pluie décadaire observée et simuléeProbabilité humide 80 % 1950-1979

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0 5 10 15 20 25 30 35 40

décade

mm

par

dé

cad

e

Bakel

Simulé PG7 80 %

Bakel(continental climate)

Observations: • Simulated rainy season longer•« Slow start » of rainy season causes risks of failure of crop establishment

Intra-annual(seasonal) rainfall distribution1950-79 Comparison of measured and simulated data after north-shift of simulated climate by 2°

Observed

Simulated

0

5

10

15

20

25

30

35

40

45

1 à 3 3 à 10 10 à 25 25 à 50 50 à 100 > 100

Rainfall per day (mm)

Fre

quen

cy (

%)

Obs_StLouis195079

Sml-PG5_195079

Rainfall intensity distribution (daily cumulatives) 1950-79 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 1950-79 Measured and simulated data after N-shift of simulated climate by 2°

Pluviométrie et nombre de jours de pluie par an (moyenne 1950-1979)

No. Nom de station Pluie(mm)

Jours No. PG Pluie(mm)

Jours

1. Saint Louis AERO 298.5 34 5 300.9 61

2. Louga 427.1 36

3. Dakar YOFF 510.7 51

4. Thies 627.9 75

5. Diourbel 665.3 54

6. Matam 450.6 40 6 418.6 101

7. Podor 284.8 35

8. Bakel 585.2 45 7 566.6 133

9. Kolda 1204.2 83 9 796.1 126

10. Kedougou 1293.2 84 10 873.2 142

11. Ziguinchor 1424.8 96 11 993.8 182

SARRA Water Balance:Atmospheric Demand and Soil Reserve

Root front

Wetting front Rain

Sowing

Stock

ET(pot)=1

Kc

2 compartments

simulated

SA

RR

A

ET(max)=Kc * ET(pot)

Rainfall

EvaporationTranspiration

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 1950-79 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

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

0.2 0.5 0.8Probabilite (%)

Ren

dem

ent (

kg/h

a)

PG5_195079PG5_201039Obs-SL_195079Obs-Lou_195079Obs-Th_195079Obs-Dkr_195079

Mean simulated grain yields 1950-79 and 2010-39 (preliminary)

Thiès

DakarLougaSaint Louis

20 50 80

1950

-79

2010

-39

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 1950-79

• Too many small rains (1-3 mm)

• Too few big rains (>10 mm) => problem of scale?

• Predicted climate change for 2010-39

• 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

PerspectivesAdapting 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!)