Next Article in Journal
An Innovative Approach for Drainage Network Sizing
Previous Article in Journal
Automatic Calibration Tool for Hydrologic Simulation Program-FORTRAN Using a Shuffled Complex Evolution Algorithm
Article Menu

Export Article

Open AccessArticle
Water 2015, 7(2), 528-545; doi:10.3390/w7020528

Application of the Multimodel Ensemble Kalman Filter Method in Groundwater System

Department of Oil-gas Field Development Engineering, College of Petroleum Engineering, China University of Petroleum Engineering, China University of Petroleum; No. 18 Fuxue Road, Changping District, Beijing 102249, China
Academic Editor: Athanasios Loukas
Received: 7 November 2014 / Revised: 9 January 2015 / Accepted: 26 January 2015 / Published: 4 February 2015
View Full-Text   |   Download PDF [810 KB, uploaded 9 June 2015]   |  

Abstract

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. View Full-Text
Keywords: groundwater modeling; data assimilation; Bayesian model averaging; the ensemble Kalman filter groundwater modeling; data assimilation; Bayesian model averaging; the ensemble Kalman filter
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xue, L. Application of the Multimodel Ensemble Kalman Filter Method in Groundwater System. Water 2015, 7, 528-545.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top