【摘要】 引入实际土壤水分观测数据,运用基于集合卡尔曼滤波的数据同化方法,利用改进的生态机理模型BEPS(boreal ecosystem productivity simulator)模拟了江苏省徐州农业试验站2000—2004年冬小麦生长季的根层土壤水分动态.结果表明:经过引入观测数据进行同化后由BEPS模型得到的土壤水分模拟结果与观测值之间的决定系数、均方根误差和平均绝对误差分别为0.626～0.943、0.018～0.042和0.021～0.041,模拟精度较同化前有显著提高,验证了利用数据同化算法提高模型对土壤水分模拟精度的可行性.单点试验结果表明,输入变量的误差、观测值的误差、引入观测数据的频率以及引入观测数据的深度等因素对利用集合卡尔曼滤波进行同化后得到的土壤水分模拟结果有较大影响.更多还原

【Abstract】 By using an ensemble Kalman filter(EnKF) to assimilate the observed soil moisture data,the modified boreal ecosystem productivity simulator(BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station,Jiangsu Province of China during the growth seasons in 2000-2004.After the assimilation of observed data,the determination coefficient,root mean square error,and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943,0.018-0.042,and 0.021-0.041,respectively,with the simulation precision improved significantly,as compared with that before assimilation,indicating the applicability of data assimilation in improving the simulation of soil moisture.The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.更多还原