期刊
JOURNAL OF HYDROLOGY
卷 595, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jhydrol.2020.125681
关键词
Contaminant source identification; Data assimilation; Ensemble smoother with multiple data assimilation; Restart ensemble Kalman filter
资金
- Spanish Ministry of Economy and Competitiveness [CGL2014-59841-P]
- Spanish Ministry of Education, Culture and Sports [PRX17/00150]
- Fundamental Research Funds for the Central Universities [B200201015, B200204002]
- Jiangsu Specially-Appointed Professor Program [B19052]
- National Natural Science Foundation of China [51679067, 51879088]
Understanding a contaminant source is crucial for managing a polluted aquifer, but source information may be unavailable when pollutants are detected. The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is proposed as a more efficient solution than the restart Ensemble Kalman Filter (r-EnKF), but requires a large number of assimilations to achieve the same level of accuracy.
Understanding a contaminant source may help in a better management and risk assessment of a polluted aquifer. However, contaminant source information may not be available when a pollutant is detected in a drinking well. The restart ensemble Kalman filter (restart EnKF, also named r-EnKF) has been demonstrated in synthetic and laboratory experiments as an efficient solution for the identification of a contaminant source. Recently, the ensemble smoother with multiple data assimilation (ES-MDA) has been proposed as an alternative to the r-EnKF as a more efficient solution given that the r-EnKF needs to restart the simulation of the state equation from time zero after each data assimilation step. An analysis, in a synthetic aquifer, of the accuracy of the ES-MDA for the simultaneous identification of a contaminant source and the spatial distribution of hydraulic conductivity by assimilating both piezometric head and concentration observations is carried out using the r-EnKF as a benchmark. The conclusion is that the ES-MDA can outperform the r-EnKF, but the expected speed advantage, associated with the possibility of assimilating all data at once, does not exist. For the ES-MDA to reach the same level of accuracy as the r-EnKF, the number of multiple data assimilations must be large, and final computing time is similar for both approaches. However, the ES-MDA can do much better than the r-EnKF if the number of iterations increases even further, with the consequent increase of computational cost.
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