Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model

Martyn P. Clark, David E. Rupp, Ross A. Woods, Xiaogu Zheng, Richard P. Ibbitt, Andrew G. Slater, Jochen Schmidt, Michael J. Uddstrom

Research output: Contribution to journalArticle (Academic Journal)peer-review

282 Citations (Scopus)


This paper describes an application of the ensemble Kalman filter (EnKF) in which streamflow observations are used to update states in a distributed hydrological model. We demonstrate that the standard implementation of the EnKF is inappropriate because of non-linear relationships between model states and observations. Transforming streamflow into log space before computing error covariances improves filter performance. We also demonstrate that model simulations improve when we use a variant of the EnKF that does not require perturbed observations. Our attempt to propagate information to neighbouring basins was unsuccessful, largely due to inadequacies in modelling the spatial variability of hydrological processes. New methods are needed to produce ensemble simulations that both reflect total model error and adequately simulate the spatial variability of hydrological states and fluxes. (C) 2008 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)1309-1324
Number of pages16
JournalAdvances in Water Resources
Issue number10
Publication statusPublished - Oct 2008

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