Prediction change of winter wheat in North China by
using IPCC-AR4 model data
Zhang Mingwei 1, Deng Hui 2,3, Ren Jianqiang 2,3, Fan Jinlong 1 , Li Guicai 1, Chen Zhongxin 2,3
1. National satellite Meteorological Center, Beijing, China2. Key Lab. of Resources Remote Sensing & Digital Agriculture,
Ministry of Agriculture, Beijing, China3. Institute of Agriculture Resources and Regional Planning,
IntroductionStudy area and dataMethodsResult and discussionConclusion
Outline
Predict the change of winter wheat yield in North China by using IPCC-AR4 model data using WOFOST model. Based on the output of IPCC AR4 model and observation data,
statistical downscaling of precipitation, minimum temperature, and maximum temperature in North China was analyzed.
With the combination crop model and climate model, the effects of climate change on the winter wheat production of North China were simulated.
1. Introduction
2. Study area and data
Study areaMeteorological stations
Remote sensing data 8-day MODIS LAI from 2007 to 2010
Climate data The climate change scenario of IPCC-B1, projected under IPCC
SRES B1 using the CMIP3 multi-model, was used in this study.
The 0.5°by 0.5° (latitude by longitude) daily mean, maximum, minimum temperature, and precipitation dataset for the period of 1971-2000 over mainland China were acquired from the National Climate Center of China.
The daily mean, maximum, minimum temperature, and precipitation data of 301 meteorological stations were acquired from China Meteorological Administration from 2007 to 2010 .
2. Study area and data
3. Methods
CROP GROWTH MODELING
WOFOSR
SOIL PARAMETERSCROP
PARAMETERS
ADMINISTRATIVE UNITS
DAILY METEO DATA TO GRID
YIELD FORECASTING
Crop yield forecast
WOFOST model
Meteorological data
Crop parameters
Soil parameters
……
For improving regional crop yield forecasts
Optimize regional crop parameters
Downscale GCMS output
3. Methods---Optimized WOFOSR parameters
CROP GROWTH MODELING
WOFOSR
ADMINISTRATIVE UNITS
CROP PARAMETERS
SOIL PARAMETERS
DAILY METEO DATA
SENSITIVITY ANALYSISI of
CROP PARAMETERS
CROP PARAMETERS
INITIALIZATION
SIMULATED LAI (LAIsim)
MODIS LAI (LAIobs)
JLAI MINIMUM?
n
ii
t
titsimtobsLAI LAILAIJ
1
2)()( )(
OPTIMIZED CROP PARAMETERS
NO
YESAssimilating MODIS LAI and crop growth model with the Ensemble Kalman Filter for optimizing crop parameters, and improving crop yield forecast
3. Methods---Spatiotemporal downscaling of GCMs output
GCMs OUTPUT
SPATIAL DOWNSCALING
MONTHLY WEATHER PARAMETERS
TEMPORAL DOWNSCALING
DAILY WEATHER PARAMETERS
INTERPOLATION0.5×0.5 GRID
DAILY METEO DATA TO GRID
Spatial downscaling a statistically downscaling GCM monthly output
Temporal downscaling monthly data were disaggregated to daily weather series using the stochastic weather generator (CLIGEN)
4. Result and discussionThe Global sensitive parameters of winter wheat growth analyzing in EFAST AMAXTB (maximum leaf CO2
assimilation rate)
SPAN (life span of leaves growing at 35 Celsius)
CVO (efficiency of conversion into storage organization)
SLATB (specific leaf area) with total sensitivity index exceeding 0.1 were the key parameters which effected the yield estimation of winter wheat at regional scale.
Crop parameters
Total sensitive indexF
irst-order sensitive index
Crop parameters
4. Result and discussion
Assimilating MODIS LAI and WOFOST with the Ensemble Kalman Filter (ENKF) for LAI simulation Influence of ensemble size
LOGISTIC model was used to correct MODIS LAI
4. Result and discussion
Divergence point diagram between simulated and statistic yields for Daxing of Beijing, Gucheng of Shandong province, and Dezhou of Shandong province (1993~2000, data is missing in 1996)
Validation of simulated winter wheat yield with WOFOST
0 1 2 3 4 5 60
1
2
3
4
5
6
f(x) = 0.127435203 exp( 0.764585325 x )R² = 0.881936746149874
GCM monthly precipi-tation（mm/d）
Measure
d m
onth
ly p
recip
itati
on
（m
m/d）
Spatial downscaling of GCMs output
-2 3 8 13 18-2
3
8
13
18
f(x) = 0.65531677397 x + 2.95451744111R² = 0.835175517223562
GCM monthly maximum temperature（℃）
Measure
d m
onth
ly m
axim
um
te
mpera
ture（℃）
-15 -5 5 15-15
-10
-5
0
5
10f(x) = 1.0216160355 x − 1.26980547804R² = 0.900379051902988
GCM monthly minimum temperature（℃）
Measure
d m
onth
ly m
inim
um
te
mpera
ture（℃）
Divergence point diagram between simulated and measured precipitation, monthly minimum temperature, and monthly maximum temperature at March.
A simple univariate linear and non-linear function were fitted to obtain transfer functions for each month. Those transfer functions were used to downscale the monthly GCM outputs.
Jan. Feb. Mar. Apr. May Jun. Oct. Nov. Dec.
Precipitation
（ n = 735）0.830 0.845 0.881 0.787 0.740 0.629 0.771 0.823 0.839
Maximum
temperature（ n =
178）0.917 0.899 0.835 0.704 0.744 0.743 0.843 0.931 0.929
Minimum
temperature（ n =
178）0.936 0.932 0.900 0.870 0.824 0.786 0.914 0.917 0.922
Spatial downscaling of GCMs output
Correlation of precipitation, between simulated and measured precipitation, monthly minimum temperature, and monthly maximum temperature
Mean SD Skewness Kurtosis Wilcoxon P
Jan.M 3.1 2.0 0.7 -0.5
0.4086C 3.3 2.0 0.9 -0.2
Feb.M 4.0 3.9 2.5 7.7
0.4721 C 3.4 2.2 1.9 6.1
Mar.M 4.4 4.4 2.8 9.7
0.3587C 4.2 4.0 2.9 11.4
Apr.M 7.6 7.1 1.8 3.8
0.3448C 7.6 6.6 1.6 2.0
MayM 7.9 9.2 2.3 6.8
0.0047C 9.3 10.0 2.5 8.3
Jun.M 11.1 15.8 3.6 19.0
0.0030C 10.1 16.8 4.2 24.3
Oct.M 7.3 9.1 2.5 6.8
0.0107C 8.5 8.9 2.4 6.8
Nov.M 5.2 4.9 1.9 3.6
0.0849C 5.5 4.1 1.6 3.3
Dec.M 2.7 2.5 2.9 8.4
0.4896C 2.3 1.2 1.4 1.3
Temporal downscaling of GCMs output
M: Measured , C: Simulated with CLIGEN
Statistics of daily precipitation depths and mean numbers of raindays at Beijing ---for sample
Mean SD Skewness Kurtosis Wilcoxon P
Jan.M 1.8 3.6 0.1 0
0.3971C 1.8 3.6 0.0 -0.1
Feb.M 5.1 4.6 0.1 -0.2
0.4707C 5.0 4.6 0.1 0.1
Mar.M 11.8 4.9 0.0 0.1
0.2262C 11.7 4.9 -0.1 0.2
Apr.M 20.3 4.6 -0.1 -0.2
0.4742C 20.3 4.6 0.0 -0.1
MayM 26.5 4.1 -0.1 -0.1
0.2348C 26.4 4.1 0.0 -0.2
Jun.M 30.5 3.7 -0.3 -0.1
0.1504C 30.4 3.7 0.0 -0.1
Oct.M 19.1 4.1 -0.1 -0.3
0.4854C 19.2 4.1 -0.1 -0.1
Nov.M 10.1 4.6 -0.1 -0.4
0.4797C 10.2 4.7 0.0 -0.1
Dec.M 3.4 3.8 0.0 0.0
0.4799C 3.4 3.8 0.1 -0.2
Temporal downscaling of GCMs outputStatistics of daily maximum temperature using CLIGEN at Beijing ---for sample
M: Measured , C: Simulated with CLIGEN
Mean SD Skewness Kurtosis Wilcoxon P
Jan.M -8.5 3.4 -0.1 -0.3
0.2968C -8.5 3.4 -0.1 -0.1
Feb.M -5.7 4.0 -0.4 0.5
0.1225C -5.8 4.1 0.1 0.1
Mar.M 0.4 3.8 0.1 0.2
0.4716C 0.3 3.8 0.0 0.3
Apr.M 7.9 3.9 -0.1 -0.3
0.4258C 8.0 3.9 0.0 0.0
MayM 13.8 3.4 -0.2 0.0
0.1107C 13.7 3.4 0.0 -0.2
Jun.M 18.8 2.8 -0.4 0.0
0.0669C 18.8 2.8 0.0 -0.2
Oct.M 7.8 4.0 -0.1 -0.5
0.4297C 7.9 4.0 -0.1 -0.1
Nov.M 10.1 4.6 -0.1 -0.4
0.2842C 10.2 4.7 0.0 -0.1
Dec.M 0.3 1.5 4.3 6.1
0.2512C 0.3 1.5 3.3 6.3
Temporal downscaling of GCMs outputStatistics of daily minimum temperature using CLIGEN at Beijing ---for sample
M: Measured , C: Simulated with CLIGEN
4. Result and discussion
Change of winter wheat growing season length in North China under the IPCC-B1 scenario (2010~2099)
4. Result and discussion
Change of winter wheat yield in North China under the IPCC-B1 scenario (2010~2099)
WOFOST The global sensitive analysis in EFAST is effective for parameter
selection in crop growth model optimization for improving its performance at regional scale.
The crop parameters of WOFOST model can be calibrated by the approach which minimizes the difference between LAI from MODIS and the predicted one from WOFOST by adjusting model parameters.
GCMs The method of linear or non-linear univariate regressions is simple to use
and viable for downscaling GCM output. The daily time series meteorological data generated using the stochastic weather generator (CLIGEN) based on monthly data is feasible for assessment of climate change impacting on crop growth.
Winter wheat Under the IPCC-B1 Scenario, the length of winter wheat growing season
in North China would be shortened from 2010 to 2099, and its yield would be decreased.
5. Conclusion
Thank you for your attention!
