| Simulation of soil moisture based on ensemble Kalman filter assimilation method and HYDRUS-1D model |
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| DOI：10.7606/j.issn.1000-7601.2023.02.16 |
| Key Words: soil moisture simulation data assimilation HYDRUS-1D model ensemble Kalman filter |
| Author Name | Affiliation | | WANG Chunjuan | College of Water Conservancy and Civil Engineering， Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018， China | | LIU Quanming | College of Water Conservancy and Civil Engineering， Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018， China
Autonomous Regional Colluborative Innovation Center for Intergrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot， Inner Mongolia 010018, China | | YIN Chengshen | College of Water Conservancy and Civil Engineering， Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018， China | | WANG Fuqiang | College of Water Conservancy and Civil Engineering， Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018， China |
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| Abstract: |
| In this study, a data assimilation scheme based on the combination of the ensemble Kalman filter method and HYDRUS-1D model was established. The soil moisture observation data of 0~80 cm from April 26 to October 5 in 2021 in Wuyuan Station of Bayannur were used for simulation verification，in order to improve the simulation accuracy of soil moisture. The results showed that: (1) The size of set number and the selection of observation error had great influence on the performance of assimilation system. The accuracy of soil moisture simulation on longer significantly improved when the set number was more than 100. The smaller the observation error was,the higher the simulation accuracy of soil moisture was, so the assimilation accuracy was the highest when the observation error was 0.025. (2) After data assimilation, the simulation accuracy of soil water in each layer was significantly improved compared with that before assimilation. The relative error, root mean square error and mean absolute error between the assimilation value and the observation value of soil water in each layer were reduced to 0.025~0.063,0.01~0.017 cm3·cm-3,0.008~0.016 cm3·cm-3, respectively. It is proved that the data assimilation can effectively improve simulation effect of soil moisture. (3) The soil water assimilation effect of 0~20cm was the best, followed by 20~40 cm, and the soil water assimilation effect of 40~80 cm was poor. In terms of simulation accuracy, the analysis value is better than the assimilation forecast value, and the assimilation forecast value was better than HYDRUS-1D forecast value. |
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