Transcript 17:00-17:30 Sanai Li: Impacts of climate change on Chinese

GLAM-wheat modelling in
China
Sanai Li
Supervisors: Prof. Tim Wheeler, Dr Andrew Challinor
Prof. Julia Slingo,
Crops and Climate Group
Outline
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Background
GLAM-Wheat model evaluation
Impact of temperature on wheat
Impact of future climate change on
wheat
Background 1
Assessment of regional crop production is
critical, especially for China
Background 2
Crop model at the field level
Complexity of incorporating the spatial variability of input
High input data requirement
Climate model output is coarse compared with input to the
dynamic crop model
This method is difficult
to apply at
a regional level
(Yang.P).
Development of a large area wheat model-GLAM-Wheat
GLAM-General Large-Area Model for annual crops
\
 Defining the wheat parameter sets
 Quantifying the impact of temperature on crop
 Parameterising the CO2 fertilisation effect
Hadley Centre RCM system-Providing Regional
Climates for Impacts Studies - PRECIS
Observed Annual Tmean (oC), 1961-90
Simulated Annual Tmean (oC), 1961-90
Observed Annual Precipitation (mm/day), 1961-90 Simulated Annual Precipitation (mm/day), 1961-90
From the first China-UK Collaboration Project
Comparison of wheat yield between
observations and simulations at the
county level
Correlation between observed and simulated
yield at the county/city (70-129km) level and
field level
0.8
Correlation(R)
0.7
0.6
Rainfedcounty(city)
Rainfed-field
0.5
0.4
0.3
0.2
Significant level
0.1
0
Guyuan
Guyang
Spring wheat
Huma
Zhengzhou Beijing
Winter wheat
Comparison of simulated and
observed wheat yield (kg/ha) at 0.5o
scale across China
(a) Observations
(b) Simulations
Difference between observed and simulated
mean wheat yield (%)
(correlation r= 0.83,p<0.001)
Cardinal temperature values
for selected annual crops under
conditions in which temperature is the
only limiting variable
The observed impact of temperature on wheat
Tendency of temperature from 1951 to 2001(Ren et al, 2003)
The simulated response of wheat yield (%)
to an increase in temperature (oC) in China
T+1
T+2
Wheat yield change (%) for per
1 oC rise in temperature
Methods
Location
Observations
Global
(1961-2002)
Yield
change(%)
-3.2 to -8.4
South Asia
-2.6 ± 0.7
CERES-Wheat Central America -5.1 ±1.8
model
Brazil
-7.1 ±2.4
GLAM-Wheat
model
China
-4.6 to -5.7
Lobell and Field,
2007
Lobell et al.,
2008
Changes in average precipitation (mm day-1) from
PRECIS for 2071 to 2099 under the A2 scenario
relative to the baseline (1961-1990)
(b) A2 DJF
(c) A2 MAM
(a) A2 Annual
(d) A2 JJA
(e) A2 SON
Changes in the annual and summer mean temperature
(oC) (relative to 1961-1990) for the A2 and B2 scenarios
during 2071 to 2099.
(a) A2 Annual
(b) B2 Annual
(c) A2 JJA
(d) B2 JJA
The predicted change in the average winter yield during
2072 to 2100 for the A2 and B2 scenarios, relative to the
baseline (1961-1990)
A2 without the CO2 effect
B2 without the CO2 effect
A2 with the CO2 effect
B2 with the CO2 effect
Predicted changes (relative to baseline) in the coefficient of
the variation of winter wheat yield for 2072 to 2100 in the
North China Plain
Conclusions
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The GLAM model is suitable to simulate crop yield at large scales
(approximately 100 km) for regional area, county and global studies
of the impacts of climate change
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Across China, the simulated average wheat yield would reduce by
4.6-5.7% for each 1 oC rise in mean temperature.
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Without the CO2 effect, wheat yield by 2100 is expected to increase
by 20-50% in the north of the North China plain, and reduce by 1040% in the south. The CO2 effect tends to offset the negative impact.
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The variability of wheat yield is expected to increase due to an
increase in climate variability.
Thanks