1. Prediction change of winter wheat in North China by using IPCC-AR4 model data Zhang Mingwei1, Deng Hui2,3, RenJianqiang2,3, Fan Jinlong1 , Li Guicai1, Chen Zhongxin2,3 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 Outline

4. 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.

5. 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

6. 2. Study area and data Study area Meteorological stations

8. 8-day MODIS LAI from 2007 to 2010

9. Climate data

10. The climate change scenario of IPCC-B1, projected under IPCC SRES B1 using the CMIP3 multi-model, was used in this study.

11. 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.

12. 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

14. Meteorological data

15. Crop parameters

17. Downscale GCMS outputSOIL PARAMETERS CROP PARAMETERS DAILY METEO DATA TO GRID CROP GROWTH MODELING WOFOSR ADMINISTRATIVE UNITS YIELD FORECASTING

18. 3. Methods---Optimized WOFOSR parameters SENSITIVITY ANALYSISI of CROP PARAMETERS CROP PARAMETERS SOIL PARAMETERS ADMINISTRATIVE UNITS CROP GROWTH MODELING WOFOSR CROP PARAMETERS INITIALIZATION DAILY METEO DATA NO JLAI MINIMUM? SIMULATED LAI (LAIsim) MODIS LAI (LAIobs) Assimilating MODIS LAI and crop growth model with the Ensemble Kalman Filter for optimizing crop parameters, and improving crop yield forecast YES OPTIMIZED CROP PARAMETERS

21. SPAN (life span of leaves growing at 35 Celsius)

22. CVO (efficiency of conversion into storage organization)

23. 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.First-order sensitive index Crop parameters Total sensitive index Crop parameters

25. Assimilating MODIS LAI and WOFOST with the Ensemble Kalman Filter (ENKF) for LAI simulation

27. Spatial downscaling of GCMs output 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. Divergence point diagram between simulated and measured precipitation, monthly minimum temperature, and monthly maximum temperature at March.

28. Spatial downscaling of GCMs output Correlation of precipitation, between simulated and measured precipitation, monthly minimum temperature, and monthly maximum temperature

29. Temporal downscaling of GCMs output Statistics of daily precipitation depths and mean numbers of raindays at Beijing ---for sample M: Measured , C: Simulated with CLIGEN

30. Temporal downscaling of GCMs output Statistics of daily maximum temperature using CLIGEN at Beijing ---for sample M: Measured , C: Simulated with CLIGEN

31. Temporal downscaling of GCMs output Statistics of daily minimum temperature using CLIGEN at Beijing ---for sample M: Measured , C: Simulated with CLIGEN

32. 4. Result and discussion Change of winter wheat growing season length in North China under the IPCC-B1 scenario (2010~2099)

33. 4. Result and discussion Change of winter wheat yield in North China under the IPCC-B1 scenario (2010~2099)

35. The global sensitive analysis in EFAST is effective for parameter selection in crop growth model optimization for improving its performance at regional scale.

36. 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.

37. GCMs

38. 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.

39. Winter wheat

40. 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

41. Thank you for your attention!