Journal of Meteorological Research

, Volume 33, Issue 2, pp 190–205 | Cite as

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

  • Xiaolei FuEmail author
  • Zhongbo Yu
  • Ying Tang
  • Yongjian Ding
  • Haishen Lyu
  • Baoqing Zhang
  • Xiaolei Jiang
  • Qin Ju
Special Collection on Development and Applications of Regional and Global Land Data Assimilation Systems


Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter (EnKF) and Simple Biosphere Model (SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation, and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.

Key words

soil moisture Ensemble Kalman Filter (EnKF) Simple Biosphere Model (SiB2) prediction 


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Xiaolei Fu
    • 1
    • 2
    Email author
  • Zhongbo Yu
    • 3
  • Ying Tang
    • 4
  • Yongjian Ding
    • 1
  • Haishen Lyu
    • 3
  • Baoqing Zhang
    • 5
  • Xiaolei Jiang
    • 3
  • Qin Ju
    • 3
  1. 1.State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouChina
  2. 2.College of Civil EngineeringFuzhou UniversityFuzhouChina
  3. 3.State Key Laboratory of Hydrology–Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  4. 4.Department of GeographyMichigan State UniversityEast LansingUSA
  5. 5.College of Earth and Environmental SciencesLanzhou UniversityLanzhouChina

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