1. 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, China 2. Key Lab. of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China 3. Institute of Agriculture Resources and Regional Planning,

2.  Introduction  Study area and data  Methods  Result and discussion  Conclusion

3.  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.

4. Study area Meteorological stations

5.  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.

6. CROP GROWTH MODELING WOFOSR SOIL PARAMETERS CROP 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

7. CROP GROWTH MODELING WOFOSR ADMINISTRATIVE UNITS CROP PARAMETERS SOIL PARAMETERS DAILY METEO DATA SENSITIVITY ANALYSISI of CROP PARAMETERS CROP PARAMETERS INITIALIZATION SIMULATED LAI (LAI sim ) MODIS LAI (LAI obs ) MODIS LAI (LAI obs ) J LAI MINIMUM? OPTIMIZED CROP PARAMETERS NO YES Assimilating MODIS LAI and crop growth model with the Ensemble Kalman Filter for optimizing crop parameters, and improving crop yield forecast

8. GCMs OUTPUT SPATIAL DOWNSCALING MONTHLY WEATHER PARAMETERS TEMPORAL DOWNSCALING DAILY WEATHER PARAMETERS INTERPOLATION 0.5×0.5 GRID INTERPOLATION 0.5×0.5 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)

9. The Global sensitive parameters of winter wheat growth analyzing in EFAST  AMAXTB (maximum leaf CO 2 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 index First-order sensitive index Crop parameters

10.  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

11. 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

12. 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.

13. Jan.Feb.Mar.Apr.MayJun.Oct.Nov.Dec. Precipitation （ n = 735 ） 0.8300.8450.8810.7870.7400.6290.7710.8230.839 Maximum temperature （ n = 178 ） 0.9170.8990.8350.7040.7440.7430.8430.9310.929 Minimum temperature （ n = 178 ） 0.9360.9320.9000.8700.8240.7860.9140.9170.922 Correlation of precipitation, between simulated and measured precipitation, monthly minimum temperature, and monthly maximum temperature

14. MeanSD SkewnessKurtosisWilcoxon P Jan. M3.12.00.7-0.5 0.4086 C3.32.00.9-0.2 Feb. M4.03.92.57.7 0.4721 C3.42.21.96.1 Mar. M4.4 2.89.7 0.3587 C4.24.02.911.4 Apr. M7.67.11.83.8 0.3448 C7.66.61.62.0 May M7.99.22.36.8 0.0047 C9.310.02.58.3 Jun. M11.115.83.619.0 0.0030 C10.116.84.224.3 Oct. M7.39.12.56.8 0.0107 C8.58.92.46.8 Nov. M5.24.91.93.6 0.0849 C5.54.11.63.3 Dec. M2.72.52.98.4 0.4896 C2.31.21.41.3 M: Measured, C: Simulated with CLIGEN Statistics of daily precipitation depths and mean numbers of raindays at Beijing ---for sample

15. MeanSD SkewnessKurtosisWilcoxon P Jan. M1.83.60.10 0.3971 C1.83.60.0-0.1 Feb. M5.14.60.1-0.2 0.4707 C5.04.60.1 Mar. M11.84.90.00.1 0.2262 C11.74.9-0.10.2 Apr. M20.34.6-0.1-0.2 0.4742 C20.34.60.0-0.1 May M26.54.1-0.1 0.2348 C26.44.10.0-0.2 Jun. M30.53.7-0.3-0.1 0.1504 C30.43.70.0-0.1 Oct. M19.14.1-0.1-0.3 0.4854 C19.24.1-0.1 Nov. M10.14.6-0.1-0.4 0.4797 C10.24.70.0-0.1 Dec. M3.43.80.0 0.4799 C3.43.80.1-0.2 Statistics of daily maximum temperature using CLIGEN at Beijing ---for sample M: Measured, C: Simulated with CLIGEN

16. MeanSD SkewnessKurtosisWilcoxon P Jan. M-8.53.4-0.1-0.3 0.2968 C-8.53.4-0.1 Feb. M-5.74.0-0.40.5 0.1225 C-5.84.10.1 Mar. M0.43.80.10.2 0.4716 C0.33.80.00.3 Apr. M7.93.9-0.1-0.3 0.4258 C8.03.90.0 May M13.83.4-0.20.0 0.1107 C13.73.40.0-0.2 Jun. M18.82.8-0.40.0 0.0669 C18.82.80.0-0.2 Oct. M7.84.0-0.1-0.5 0.4297 C7.94.0-0.1 Nov. M10.14.6-0.1-0.4 0.2842 C10.24.70.0-0.1 Dec. M0.31.54.36.1 0.2512 C0.31.53.36.3 Statistics of daily minimum temperature using CLIGEN at Beijing ---for sample M: Measured, C: Simulated with CLIGEN

17. Change of winter wheat growing season length in North China under the IPCC-B1 scenario (2010~2099)

18. Change of winter wheat yield in North China under the IPCC-B1 scenario (2010~2099)

19.  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.