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应用生态学报 ›› 2011, Vol. 22 ›› Issue (11): 2943-2953.

• 研究论文 • 上一篇    下一篇

基于集合卡尔曼滤波同化方法的农田土壤水分模拟

刘昭1,周艳莲1**,居为民2,高苹3   

  1. 1南京大学地理与海洋科学学院, 南京 210093;2南京大学国际地球系统科学研究所, 南京 210093;3江苏省气象台, 南京 210008
  • 出版日期:2011-11-18 发布日期:2011-11-18

Simulation of cropland soil moisture based on an ensemble Kalman filter. 

LIU Zhao1, ZHOU Yan-lian1, JU Wei-min2, GAO Ping3   

  1. 1School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China;2International Institute for Earth System Science, Nanjing University, Nanjing 210093, China; 3Jiangsu Meteorological Bureau, Nanjing 210008, China
  • Online:2011-11-18 Published:2011-11-18

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.

关键词: 土壤水分, BEPS模型, 数据同化, 集合卡尔曼滤波

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.

Key words: soil moisture, BEPS model, data assimilation, ensemble Kalman filter