基于历史气象资料和WOFOST模型的区域产量集合预报

Ensemble Forecasting of Regional Yield of Winter Wheat Based on WOFOST Model Using Historical Metrological Dataset

针对基于作物生长模型进行产量预报时气象要素变化对作物生长的实时影响不能得到充分反映,产量预报缺乏量化不确定性信息的突出问题,选择河北省保定市和衡水市冬小麦主产区为研究对象,提出构建历史气象集合作为预报期气象数据输入驱动WOFOST模型的冬小麦生长模拟,并通过实时更新不断向前滚动预报,从传统单一数值的预报转向基于集合的概率预报。结果表明:基于历史气象资料可以进行作物模型的区域产量集合预报,抽穗期至灌浆期是预报精度最高的时期,预报集合中位数与实测产量的皮尔逊相关系数(PCC)最高为0.563,平均绝对误差(MAE)最低为458 kg/hm^2。研究结果表明区域化产量集合预报具有较强的可行性,并为量化作物模拟系统不确定性、数值天气预报与作物模型的结合应用提供了参考。

The crop growth model has distinct advantages of clear mechanism and dynamic serial simulation,and it has been widely used in regional crop production forecasting. The research focused on the major problem that the uncertainty of yield prediction with crop model can not be quantized and the issue that the real-time impact of meteorological driving factors on crop model was not timely reflected.Simulations in the main wheat production areas in Baoding and Hengshui of Hebei Province were conducted and a method was proposed to build a historical meteorological dataset as weather inputs to drive the WOFOST model to simulate wheat growth during the forecasting period. Then the yield was continuously forecasted through real-time updating. In this way,the traditional single-value forecasting of yield was changed to an ensemble-based probabilistic forecasting,and the regional yield ensemble forecast and event possibility forecast can be generated in everyday in the growing season. The validation results indicated that the WOFOST model with historical meteorological data was able to reflect the uncertainty of regional weather data. In the regional ensemble forecasting of wheat yield,the highest yield forecasting accuracy was achieved in the period from heading to grain filling stage. The correlation coefficient（PCC） between the ensemble mean and the measured yields was 0. 563,while the minimum average absolute error（MAE） was 458 kg/hm^2,however,the improvement of yield forecasting accuracy along with forecasting date was slow,because simulation with homogeneous input parameters in potential level was a little coarse. It suggested that with the help of remote sensing data assimilation or medium weather numeric forecasting,yield prediction could achieve better accuracy. The results showed that the regional yield ensemble forecast had strong feasibility,and this research provided a reference for the application of numerical weather forecast and quantification of the uncertainty in crop simulation system.