Application of the Multimodel Ensemble Kalman Filter Method in Groundwater System

Water

Liang Xue¹ 

[1]Department of Oil-gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum

[2] No. 18 Fuxue Road, Changping District, Beijing 102249, China

[3] E-Mail

关键词: groundwater modeling;    data assimilation;    Bayesian model averaging;    the ensemble Kalman filter;   

DOI  :  10.3390/w7020528

来源: mdpi

【 摘 要 】

With the development of in-situ monitoring techniques, the ensemble Kalman filter (EnKF) has become a popular data assimilation method due to its capability to jointly update model parameters and state variables in a sequential way, and to assess the uncertainty associated with estimation and prediction. To take the conceptual model uncertainty into account during the data assimilation process, a novel multimodel ensemble Kalman filter method has been proposed by incorporating the standard EnKF with Bayesian model averaging framework. In this paper, this method is applied to analyze the dataset obtained from the Hailiutu River Basin located in the northwest part of China. Multiple conceptual models are created by considering two important factors that control groundwater dynamics in semi-arid areas: the zonation pattern of the hydraulic conductivity field and the relationship between evapotranspiration and groundwater level. The results show that the posterior model weights of the postulated models can be dynamically adjusted according to the mismatch between the measurements and the ensemble predictions, and the multimodel ensemble estimation and the corresponding uncertainty can be quantified.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.