Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system
Simultaneous state-parameter estimation supports the evaluation of data assimilation performance and measurement design for soil-water-atmosphere-plant system
复制标题
同步状态参数估计支持土壤-水-大气-植物系统的数据同化性能评估和测量设计
DOI：
10.1016/j.jhydrol.2017.10.061
复制
发表时间：
2017-12
影响因子：
6.4
通讯作者：
Lin Lin
中科院分区：
文献类型：
--
作者：
Hu Shun;Shi Liangsheng;Zha Yuanyuan;Williams Mathew;Lin Lin
改善农业水和作物管理需要关于作物和土壤状况及其演变的详细信息。数据同化通过以顺序的方式将测量数据与模式相结合，提供了一种获得这些信息的有吸引力的方法。然而，由于土壤-水-大气-植物(SWAP)系统中存在大量的变量和参数，数据同化还缺乏全面的探索。在这项研究中，使用集合卡尔曼滤波(EnKF)的同时状态参数估计来评估数据同化性能，并为SWAP系统的测量设计提供建议。结果表明，状态向量的选择对有效的数据同化是至关重要的。特别是，更新发育阶段可以避免由于不同成员之间物候阶段的差异而造成的物候变化的负面影响。同时状态参数估计(SSPE)同化策略优于仅更新状态(USO)同化策略，因为它能够缓解模式变量和参数之间的不一致性。然而，由于土壤分层和对作物参数的了解有限，SSPE同化策略的性能可能会随着不确定参数的增加而恶化。除了最容易获得的表层土壤水分(SSM)和叶面积指数(LAI)测量外，还需要深层土壤水分、谷物产量或其他辅助数据来为参数估计提供足够的约束条件，并确保数据同化性能。该研究为土壤水分和粮食产量对交换系统数据同化的响应提供了一个新的视角，对实际应用中土壤水分运动和作物生长模型及测量设计具有一定的指导意义。
Improvements to agricultural water and crop managements require detailed information on crop and soil states, and their evolution. Data assimilation provides an attractive way of obtaining these information by integrating measurements with model in a sequential manner. However, data assimilation for soil-water-atmosphere-plant (SWAP) system is still lack of comprehensive exploration due to a large number of variables and parameters in the system. In this study, simultaneous state-parameter estimation using ensemble Kalman filter (EnKF) was employed to evaluate the data assimilation performance and provide advice on measurement design for SWAP system. The results demonstrated that a proper selection of state vector is critical to effective data assimilation. Especially, updating the development stage was able to avoid the negative effect of “phenological shift”, which was caused by the contrasted phenological stage in different ensemble members. Simultaneous state-parameter estimation (SSPE) assimilation strategy outperformed updating-state-only (USO) assimilation strategy because of its ability to alleviate the inconsistency between model variables and parameters. However, the performance of SSPE assimilation strategy could deteriorate with an increasing number of uncertain parameters as a result of soil stratification and limited knowledge on crop parameters. In addition to the most easily available surface soil moisture (SSM) and leaf area index (LAI) measurements, deep soil moisture, grain yield or other auxiliary data were required to provide sufficient constraints on parameter estimation and to assure the data assimilation performance. This study provides an insight into the response of soil moisture and grain yield to data assimilation in SWAP system and is helpful for soil moisture movement and crop growth modeling and measurement design in practice.
登录
查看更多内容
影响因子：
4.7
作者：
Moradkhani, H;Sorooshian, S;Houser, PR
通讯作者：
Houser, PR
影响因子：
6.2
作者：
de Wit, Allard;Duveiller, Gregory;Defourny, Pierre
通讯作者：
Defourny, Pierre
DOI：
--
发表时间：
2009
期刊：
--
影响因子：
--
作者：
T. Lakhankar;N. Krakauer;R. Khanbilvardi
通讯作者：
T. Lakhankar;N. Krakauer;R. Khanbilvardi
影响因子：
6.2
作者：
Curnel, Yannick;de Wit, Allard J. W.;Defourny, Pierre
通讯作者：
Defourny, Pierre
影响因子：
8.1
作者：
Ge, Qiao;Menendez, Monica
通讯作者：
Menendez, Monica