WOFOST Model Parameter Calibration Based on Agro-climatic Division of Winter Wheat

Crop model parameter calibration is an important work of extending point-scale crop model to regional application.Using K-means method with the main meteorological factors affecting the growth and yield formation of winter wheat obtained from 113 meteorological stations from 1981 to 2010 as zoning indicators,Henan Province is divided into five different agro-climatic ecological zones and the cumulative temperature parameters are calculated for each zone.Based on the observations during 2013-2017,nine sensitive parameters are obtained by using Sobol global sensitivity analysis method to analyze and select crop parameters with total sensitivity index greater than 0.01.The sensitive parameters selected from different agroclimatic ecological zones of different winter wheat varieties are highly consistent.A cost function is constructed with yield and leaf area index（LAI）,and each partition is calibrated for sensitive parameters using Differential Evolution Markov Chain method.The results show that the simulated leaf area index in the different agro-climatic ecological zones are in good agreement with the observed values,the root mean square error（RMSE） using the posterior mean value of regional parameters adjustment to simulate the LAI of key growth periods is 0.655,which is obviously higher than that of using default parameters or using the same set of optimized parameters in the whole study area.Results show that the WOFOST model based on agro-climatic division can accurately simulate the growth process of crops.In terms of yield estimation accuracy,the yield simulation accuracy of regional parameter adjustment is also significantly improved.The best accuracy of simulated yield is achieved by using the posterior mean of regional parameters and RMSE is 672.016 kg·hm-2,70.55 % reduction than the yield simulation error when using the default parameters,or 48.75% reduction than the yield simulation error when the same set of optimized parameters（posterior mean） are used for the entire area.The method takes advantage of the knowledge of agroclimatology with the scientific and efficient Differential Evolution Markov Chain optimization algorithm to provide a scientific and theoretical basis for the application of crop models and optimization of regional model parameters through rational and efficient zonal calibration of the study area.