基于冬小麦农业气候分区的WOFOST模型参数标定

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

以1981—2010年河南省113个气象观测站影响冬小麦生长及产量形成的主要气象因素为区划指标,利用K均值聚类算法,将河南省划分为5个农业气候生态区。根据2013—2017年地面农业气象观测数据,利用Sobol全局敏感性分析方法,各分区选择总敏感指数大于0.01的作物参数,得到9种敏感参数。以产量与叶面积指数为代价函数,采用差分进化马尔科夫链蒙特卡洛方法对敏感参数进行分区标定,并使用2018—2019年观测数据进行验证。结果表明:分区进行参数标定时,叶面积指数动态模拟精度和产量模拟精度均显著优于使用默认参数或整个研究区使用同一套优化参数时的精度,其中,使用分区调参后验平均值模拟关键生育期叶面积指数的总均方根误差为0.655,其模拟产量的均方根误差为672.016 kg·hm-2。该方法将农业气候学知识与差分进化马尔科夫链蒙特卡洛优化算法相结合,通过合理、高效地分区域标定作物模型参数,可为作物模型区域应用和模型参数调整优化提供科学依据。

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 agro-climatic 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 agro-climatology 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.