Assimilation of soil moisture in LPJ-DGVM

Process-oriented dynamic vegetation models are effective tools to assess carbon and water exchanges between vegetation and environment for different scales. Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) is one of the well-established, process-oriented dynamic vegetation models. It can simulate seasonal trends of EvapoTranspiration (ET) and Net Ecosystem Exchange (NEE) forced by weather data. In this study, LPJ-DGVM was employed to simulate the ET and NEE in Yingke (YK) oasis station and A'Rou (AR) freeze/thaw observation station. The results indicate that LPJ-DGVM could not make good estimations in both YK station and AR station. The simulation results were validated with the water and CO 2 flux observation from Eddy Covariance (EC). The freeze-thaw phenomenon and irrigation have great impacts on soil water content dynamic in arid region, but they are not considered in LPJ-DGVM. In order to improve the simulation accuracy, a soil water content data assimilation scheme was designed. The observed soil water content was assimilated into LPJ-DGVM with Ensemble Kalman Filter (EnKF) algorithm. The simulation accuracy of LPJ-DGVM was improved obviously when soil water content was assimilated into LPJ-DGVM. The EnKF is effective for assimilating in situ observation.