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Development of a combined crop and
climate forecasting system
Tim Wheeler and Andrew Challinor
[email protected]
Crops and Climate Group
A combined crop and climate
forecasting system
Report from:
‘Food Crops in a Changing Climate’
Linking climate information
to crop models
general
circulation model
crop model
At what scale should information pass between crop and climate models?
Development of a combined
crop / climate forecasting system
Find spatial scale
of weather-crop
relationships
Fully coupled cropclimate simulation
Osborne (2004)
Challinor et. al. (2003)
Climate
change
Crop modelling
at the working
spatial scale
Challinor et. al. (2004)
Ensemble
methods
(Challinor et al, 2005b,c)
(2005c,d)
Hindcasts with
observed weather data
(Challinor et al, 2004)
and reanalysis
(Challinor et al, 2005a)
Simple correlations between
rainfall 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
General Large Area Model
for Annual Crops (GLAM)
• 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
Challinor et. al. (2004)
Hindcasts of groundnut yield for
all India using GLAM
-1
)
1200
National Yield Statistics
1100
Groundnut yield (kg ha
1000
GLAM simulation
900
800
700
600
500
400
1965
1970
1975
1980
Year
1985
1990
Capturing the effects of
intra-seasonal variability
1975
Total rainfall: 394mm
Model: 1059 kg/ha
Obs: 1360 kg/ha
1981
Total rainfall 389mm
Model: 844 kg/ha
Obs: 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.
Using probabilistic climate forecasts
Model average
63 ensemble members
25
713 kg ha-1
775 kg ha-1
Frequency
Observed
20
15
10
5
0
200 300 400 500 600 700 800 900 1000 1100 1200
Yield (kg ha-1)
Use of DEMETER multi-model ensemble for groundnut yield
in Gujarat, 1998
from Challinor et al (2005)
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
The impact of water and temperature
stress at flowering under climate change
Hadley Centre PRECIS model, A2 (high emission) scenario
1960-1990
Groundnut
1 = no
impact
2071-2100
0 = max.
impact
• Current risk is dominated by water stress; in the future
climate run temperature stress dominates in the north.
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
Fully coupled crop-climate simulation
Crops ‘growing’ in HadAM3
Fully coupled crop-climate simulation
All-India groundnut yield (red) with simulated mean yield
(black) and spatial standard deviation (grey shading).
Using satellite estimates of rainfall
TAMSAT
Teo Chee-Kiat
David Grimes
Conclusions
• A combined crop and climate modelling
system has been developed and tested for
the current climate.
– It shows skill in seasonal hindcasts and with
climate ensembles
– It has been used to study crop responses to
climate change
– Can be fully coupled to a GCM, and driven by
satellite data