Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

JOURNAL OF HYDROLOGY

卷:550

Article

Fan, Y. R.¹  Huang, G. H.^1,2  Baetz, B. W.³  Li, Y. P.²  Huang, K.⁴  Chen, X.⁵  Gao, M.⁵ 

[1] Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada

[2] Beijing Normal Univ, Sch Environm, Beijing 100875, Peoples R China

[3] McMaster Univ, Dept Civil Engn, Hamilton, ON L8S 4L8, Canada

[4] Univ Regina, Fac Engn & Appl Sci, Regina, SK S4S 0A2, Canada

[5] Hohai Univ, State Key Lab Hydrol Water Resource & Hydraul Eng, Nanjing 210098, Jiangsu, Peoples R China

关键词: Hydrologic prediction;    Data assimilation;    Ensemble Kalman filter;    Particle filter;    Uncertainty;   

DOI  :  10.1016/j.jhydrol.2017.05.010

来源: Elsevier

【 摘 要 】

This study improved hydrologic data assimilation through integrating the capabilities of particle filter (PF) and ensemble Kalman filter (EnKF) methods, leading to two integrated data assimilation schemes: the coupled EnKF and PF (CEnPF) and parallelized EnKF and PF (PEnPF) approaches. The applicability and usefulness of CEnPF and PEnPF were demonstrated using a conceptual rainfall-runoff model. The performance of two new developed data assimilation methods and traditional EnKF and PF approaches was tested through a synthetic experiment and two real-world cases with one located in the Jing River basin and one located in the Yangtze River basin. The results show that both PEnPF and CEnPF approaches have more opportunities to provide better results for both deterministic and probabilistic predictions than traditional EnKF and PF approaches. Moreover, the computational time of the two integrated methods is manageable. But the proposed PEnPF may need much more time for some large-scale or time-consuming hydrologic models since it generally needs three times of model runs used by EnKF, PF and CEnPF. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free