大区域尺度WOFOST(world food studies)模型的动态模拟是作物模型区域应用的重要基础.该文以中国冬小麦主产区为研究对象,利用中国冬小麦主产区内174个农业气象站多年观测数据以及气象站点观测数据,重点优化WOFOST模型中与品种相关的积温参数,即出苗至开花有效积温与开花至成熟有效积温.在冬小麦主产区分区的基础上,以2012—2015年气象数据驱动WOFOST模型,在站点尺度进行冬小麦的物候期、叶面积指数(leaf area index,LAI)和单产动态模拟和精度分析.结果表明:WOFOST模型模拟出苗至开花天数的决定系数R2为0.89~0.94,均方根误差RMSE为7.87~11.52 d,模型模拟开花至成熟天数的R2为0.63~0.77,RMSE为2.99~4.65 d;模型模拟LAI的R2为0.70~0.83,RMSE为0.89~1.46 m2/m2;灌溉区WOFOST模拟的单产精度R2为0.45~0.59,RMSE为734~1421 kg/hm2;雨养区WOFOST模拟的单产精度R2为0.48~0.61,RMSE为1046~1329 kg/hm2.结果表明,WOFOST模型在全国尺度取得了较高模拟精度,为区域尺度作物模型的农业应用提供了坚实的过程模型基础.%Crop model calibration and parameterization are essential for model evaluation and agricultural application. It is important for model application to accurately estimate the values of crop model parameters and further improve the performance of model prediction. WOFOST (world food studies) is a well-known, widely applied simulation model to analyze quantitatively the growth and production of field crops, which was originally developed for crops in European countries. It is the base model for Monitoring Agricultural Resources (MARS) Crop Growth Monitoring System (CGMS) in operational use for yield estimation in European Union. Dynamic simulation of WOFOST model in large regional scale is an important basis for regional crop modeling. In this study, we selected the main winter wheat production areas of China as the study area, and the data from 174 agricultural meteorological stations from 2011 to 2014 were used to calibrate several key WOFOST input parameters, especially 2 parameters related with variety, namely the effective accumulated temperature from emergence to flowering (TSUM1) and the effective accumulated temperature from flowering to maturity (TSUM2). On the basis of the zoning of the main winter wheat production areas, we used the meteorological data from 2012 to 2015 to drive the WOFOST model at a single-point scale, to simulate the winter wheat growth and dynamic development. The simulated phenology, LAI (leaf area index) and yield at the station level were evaluated with the field measured data. Results showed that the NRMSE (normalized root mean square error) of LAI ranged from 50% to 63%. The NRMSE of simulated days was 4%-7% from emergence to anthesis period and 8%-12% from anthesis to maturity period, and then CV (coefficient of variation) of the phenology was between 14% and 20%, which meant significant spatial variability. We simulated the yield respectively in irrigated area and rainfed area. And the NRMSE of simulated yield in irrigated area ranged from 11% to 23%, while the NRMSE of simulated yield in rain-fed area ranged from 22% and 28%, and the CV ranged from 14% to 22% for irrigated areas and from 25% to 40% for rain-fed areas, which exhibited significant spatial variability. The NRMSE of simulated LAI was between 50% and 63%, which could be explained that the LAI during different growth stages was all included into the accuracy analysis. Several important input parameters (such as TDWI (initial biomass) and SPAN (leaf senescence coefficient)) could be optimized through assimilating remote sensing data into crop model, which could greatly improve the performance of crop model at the regional scale. Our results showed that the WOFOST model is of great potential for simulating the dynamic growth process of winter wheat in China. The calibrated WOFOST provides the dynamic model basis for regional applications, such as assimilating remote sensing data into crop model for crop yield estimation and climate change prediction with crop model.
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