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Development of a combined crop and climate forecasting system
Tim Wheeler and Andrew Challinor
Crops and Climate Group
Linking climate informationto crop models
general circulation model
crop model
At what scale should information pass between crop and climate models?
Find spatial scale of weather-crop
relationships
Crop modelling at the working
spatial scale Hindcasts with observed weather data
Ensemble methodsClimate
change
Challinor et. al. (2003)
Development of a combinedcrop / climate forecasting system
Challinor et. al. (2004)(Challinor et al, 2004)
and reanalysis(Challinor et al, 2005a)
(Challinor et al, 2005b,c)
(2005c,d)
Fully coupled crop-climate simulation
Osborne (2004)
Simple correlations betweenrainfall and crop yield
Seasonal rainfall and groundnut yields for all India.
Time trend removed. rainfall yield
Patterns of seasonal rainfall and yield of groundnut in India
District level groundnut yields (kg ha-1)
Mean of 1966 - 1990
Data source: ICRISAT
Patterns of seasonal rainfall and yield of groundnut in India
Sub-divisional level seasonal rainfall (JJAS, cm)
Mean of 1966 - 1990
Data source: IITM
• Aims to combine:– the benefits of more empirical approaches (low
input data requirements, validity over large spatial scales) with
– the benefits of a process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates)
• Uses a Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management to simulate farm yields
General Large Area Modelfor Annual Crops (GLAM)
Challinor et. al. (2004)
Hindcasts of groundnut yield forall India using GLAM
400
500
600
700
800
900
1000
1100
1200
1965 1970 1975 1980 1985 1990
Year
Gro
un
dn
ut
yie
ld (
kg
ha
-1)
National Yield Statistics
GLAM simulation
Capturing the effects ofintra-seasonal variability
1975Total rainfall: 394mmModel: 1059 kg/haObs: 1360 kg/ha
1981Total rainfall 389mmModel: 844 kg/haObs: 901 kg/ha
Using ERA40 reanalysis data
Andhra Pradesh
Gujarat
• Gujarat: bias correction of climatological mean rainfall works well- Correlation with observed yields 0.49 0.60
• Andhra Pradesh: simulated mean yield < observed, variability >> observed- Incorrect seasonal cycle (both mean and variability) though Jun and Sept good. This is harder to correct.
0
5
10
15
20
25
200 300 400 500 600 700 800 900 1000 1100 1200
Yield (kg ha-1)
Fre
qu
ency
Using probabilistic climate forecasts
Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)
Model average 63 ensemble members
Observed
775 kg ha-1
713 kg ha-1
Probabilistic forecasting of crop failure
• The number of ensemble members predicting yield below a given threshold is an indication of probability of occurrence
• Found predictability in crop failure
• Current risk is dominated by water stress; in the future climate run temperature stress dominates in the north.
The impact of water and temperature stress at flowering under climate change
1960-1990 1 = no impact
0 = max. impact
2071-2100
Hadley Centre PRECIS model, A2 (high emission) scenario
Groundnut
Variety response to temperature stress alone under climate change
Hadley Centre PRECIS model, A2 (high emission) scenario 2071-2100
Number of years when the total number of pods setting is below 50%.
Sensitive variety Tolerant variety
An integrated approach to climate impact assessments
• Crops can modify their own environment
– The water cycle and surface temperatures vary according to land use
• Integrate biological and physical modelling– By working on common spatial scale– By fully coupling the models
All-India groundnut yield (red) with simulated mean yield (black) and spatial standard deviation (grey shading).
Fully coupled crop-climate simulation