Download A GIS based environment for the implementaiton of

Pilot Study on the Use of
PROMISE Climate Data in a
Crop Model
H Syabuddin, JC Combres, JF Royer, M Dingkuhn
 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
 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
Bambey
1
2
Matam
3
Bakel
Dakar
5
Ziguinchor
6
7
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-tocontinent)
• 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 1950-79
Wrong positioning of ITCZ by about 2°
=> under-estimation of rainfall
17,0
16,0
15,0
Latitude (°)
Pluie_Obs-195079
(Pdr)
(SL-Aero)
(Mtm)
(1)(2)(3) (Lou) (Bkl)
(Th)
Pluie_Sml-195079
(Dkr-Y)(Diour)
14,0
Puissance (Pluie_Obs195079)(Kld)
(Zig)
Polynomial
(Kdg)
(Pluie_Sml-195079)
13,0
(5) (6)
12,0
Pluie_Sml-201039
(7)
11,0
10,0
(9) (10) (11)
9,0
8,0
0
250
500
750
1000
Annual cumulative rainfall (mm)
1250
1500
Annual rainfall 1950-79
Comparison of measured and simulated data after
north-shift of simulated climate by 2°
20,0
Latitude (°)
18,0
Pluie_Obs-195079
Pluie_Sml-195079
Puissance (Pluie_Obs-195079)
Polynomial (Pluie_Sml-195079)
(1)(2) (3)
(Pdr)
(SL-Aero)
(Mtm)
(6) (Bkl)
(7)(Th)
(5)(Lou)
(Dkr-Y) (Diour)
16,0
14,0
(Kld)
(9) (10) (11)
12,0
(Zig)
(Kdg)
10,0
8,0
0
250
500
750
1000
Annual rainfall (mm)
1250
1500
Pluie
décadaire observée
et simulée
Intra-annual(seasonal)
rainfall
distribution1950-79
Probabilité humide 80 % 1950-1979
Comparison of measured and simulated data after
north-shift of simulated climate by 2°
140,0
120,0
Observed
Bakel
100,0
mm par décade
(continental climate)
80,0
Simulated
Bakel
Simulé PG7 80 %
60,0
40,0
20,0
0,0
0
5
10
15
20
25
30
35
40
décade
Observations: • Simulated rainy season longer
•« Slow start » of rainy season causes risks of
failure of crop establishment
Rainfall intensity distribution (daily cumulatives) 1950-79
Measured and simulated data after N-shift of simulated climate by 2°
Frequency (%)
45
40
Obs_StLouis195079
35
Sml-PG5_195079
30
25
20
15
10
5
0
1à3
3 à 10
10 à 25
25 à 50
50 à 100
> 100
Rainfall(1-3
per day
(mm) under-estimation
• Over-estimation of small rains
mm),
of big rains (> 10mm), ca. factor 2
• Delay in sowing, smaller fraction of useful precipitation (E!),
wetting front remains shallow (rooting depth!)
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)
Nom de station
Pluie
Pluie
No.
Jours
No. PG
(mm)
(mm)
1.
Saint Louis AERO
298.5
34
300.9
5
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
418.6
6
7.
Podor
284.8
35
8.
Bakel
585.2
45
566.6
7
9.
Kolda
1204.2
83
796.1
9
10. Kedougou
1293.2
84
873.2
10
11. Ziguinchor
1424.8
96
993.8
11
Jours
61
101
133
126
142
182
Problem: ARPEGE over-estimates rain-days by factor 1,5 to 3
SARRA Water Balance:
Atmospheric Demand and Soil Reserve
ET(pot)=1
ET(max)=Kc * ET(pot)
SARRA
Sowing
Kc
Stock
Rain
2 compartments
simulated
Root front
Wetting front
Partitioning of Precipitation at the Plot Level
1 Rainfall
4
3a
Transpiration
Evaporation
Moderate
rain event:
Stock,
Transpiration
2 Runoff
3b Infiltration
Big rain event:
Runoff,
Drainage
(=> stock)
Drainage
Small rain event:
Evaporation
5
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
Mean simulated grain yields 1950-79 and 2010-39
(preliminary)
1600.0
PG5_195079
1400.0
Thiès
PG5_201039
Obs-SL_195079
1200.0
Obs-Lou_195079
Dakar
Louga
Saint Louis
Obs-Th_195079
Rendement (kg/ha)
Obs-Dkr_195079
1000.0
800.0
600.0
400.0
200.0
0.0
20
0.2
50
0.5
80
0.8
Probability forProbabilite
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
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!)