Prediction change of winter wheat in North China by using IPCC-AR4 model data

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. Outline • Introduction • Study area and data • Methods • Result and discussion • Conclusion

3. 1. Introduction • 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. 2. Study area and data Study area Meteorological stations

5. 2. Study area and data • 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. 3. Methods • Crop yield forecast • WOFOST model • Meteorological data • Crop parameters • Soil parameters • …… • For improving regional crop yield forecasts • Optimize regional crop parameters • Downscale GCMS output CROP PARAMETERS SOIL PARAMETERS DAILY METEO DATA TO GRID CROP GROWTH MODELING WOFOSR ADMINISTRATIVE UNITS YIELD FORECASTING

7. 3. Methods---Optimized WOFOSR parameters SENSITIVITY ANALYSISI of CROP PARAMETERS SOIL PARAMETERS CROP 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

8. 3. Methods---Spatiotemporal downscaling of GCMs output GCMs OUTPUT DAILY WEATHER PARAMETERS • Spatial downscaling • a statistically downscaling GCM monthly output • Temporal downscaling • monthly data were disaggregated to daily weather series using the stochastic weather generator (CLIGEN) SPATIAL DOWNSCALING INTERPOLATION 0.5×0.5 GRID MONTHLY WEATHER PARAMETERS DAILY METEO DATA TO GRID TEMPORAL DOWNSCALING

9. 4. Result and discussion • The Global sensitive parameters of winter wheat growth analyzing in EFAST • AMAXTB (maximum leaf CO2assimilation 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. First-order sensitive index Crop parameters Total sensitive index Crop parameters

10. 4. Result and discussion • LOGISTIC model was used to correct MODIS LAI • Assimilating MODIS LAI and WOFOST with the Ensemble Kalman Filter (ENKF) for LAI simulation • Influence of ensemble size

11. 4. Result and discussion Validation of simulated winter wheat yield with WOFOST • 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)

12. 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.

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

14. 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

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

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

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

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

19. 5. Conclusion • 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.

20. Thank you for your attention!