[Simulation of cropland soil moisture based on an ensemble Kalman filter].
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.
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China. kate_12@hotmail.com
Zhao
Z
Liu
Yan-Lian
YL
Zhou
Wei-Min
WM
Ju
Ping
P
Gao
D003198:Computer Simulation; D004463:Ecology / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods; D005544:Forecasting; D008962:Models, Theoretical; D012987:Soil / Q000032:analysis*; D014908:Triticum / Q000254:growth & development*; D014867:Water / Q000032:analysis*
D012987:Soil; D014867:Water