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| DOI | 10.1016/j.agrformet.2023.109882 |
| Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework | |
| 发表日期 | 2024 |
| ISSN | 0168-1923 |
| EISSN | 1873-2240 |
| 起始页码 | 346 |
| 卷号 | 346 |
| 英文摘要 | Recently, data assimilation (DA) has garnered significant attention. Integration of DA approaches and crop models could diminish model uncertainties and improve the precision of model simulations. While previous research extensively focused on assimilating leaf area index (LAI) or soil moisture (SM), the feasibility and effectiveness of assimilating evapotranspiration (ET) have been rarely explored. In this study, we proposed a novel framework of ET assimilation. Then, together with commonly assimilated LAI and SM, we evaluated the performance of this new method in simulating the key indicators (i.e., LAI and ET in daily and interannual scales, and the crop yield) based on the long-term eddy covariance observations and well-calibrated crop model. DA strategies we utilized to evaluate consist of two approaches (i.e., Ensemble Kalman filter (EnKF) and EnKF with simultaneous state-parameter estimation (EnKF-SSPE)) and combinations of three assimilated observations (i.e., LAI, SM, and ET). Our results demonstrate that joint assimilation of LAI and ET with EnKF-SSPE performs best for wheat while joint assimilation of SM and ET with EnKF-SSPE is the best for maize. For a single observation, LAI and ET play a dominant role in DA for wheat and maize, respectively. This is because the interannual variability of wheat growth is primarily influenced by agricultural management (e.g., cultivar change) and can be represented by LAI. For maize which is mostly rainfed, water stress usually occurs. Therefore, ET, with its ability to reflect the water stress status, proves to be effective. EnKF-SSPE outperforms EnKF, exhibiting potential in revealing the parameter evolution during long-term crop modeling, especially when crop cultivars are regularly renewed. This study evaluates different observations and methods through DA based on a newly proposed sequential ET assimilation framework, which might be illuminating for future applications of DA. |
| 英文关键词 | Data assimilation; Crop growth; Evapotranspiration; Crop modeling; Ensemble Kalman filter; Simultaneous state -parameter estimation |
| 语种 | 英语 |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| WOS类目 | Agronomy ; Forestry ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001166050100001 |
| 来源期刊 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/304852 |
| 作者单位 | Tsinghua University |
| 推荐引用方式 GB/T 7714 | . Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework[J],2024,346. |
| APA | (2024).Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework.AGRICULTURAL AND FOREST METEOROLOGY,346. |
| MLA | "Evaluation of data assimilation strategies on improving the performance of crop modeling based on a novel evapotranspiration assimilation framework".AGRICULTURAL AND FOREST METEOROLOGY 346(2024). |
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