Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 17, issue 1 | Copyright
Hydrol. Earth Syst. Sci., 17, 21-38, 2013
https://doi.org/10.5194/hess-17-21-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Jan 2013

Research article | 10 Jan 2013

Operational hydrological data assimilation with the recursive ensemble Kalman filter

H. K. McMillan1, E. Ö. Hreinsson1, M. P. Clark2, S. K. Singh1, C. Zammit1, and M. J. Uddstrom1 H. K. McMillan et al.
  • 1National Institute of Water and Atmospheric Research, Christchurch, New Zealand
  • 2National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract. This paper describes the design and use of a recursive ensemble Kalman filter (REnKF) to assimilate streamflow data in an operational flow forecasting system of seven catchments in New Zealand. The REnKF iteratively updates past and present model states (soil water, aquifer and surface storages), with lags up to the concentration time of the catchment, to improve model initial conditions and hence flow forecasts. We found the REnKF overcame instabilities in the standard EnKF, which were associated with the natural lag time between upstream catchment wetness and flow at the gauging locations. The forecast system performance was correspondingly improved in terms of Nash–Sutcliffe score, persistence index and bounding of the measured flow by the model ensemble. We present descriptions of filter design parameters and explanations and examples of filter behaviour, as an information source for other groups wishing to assimilate discharge observations for operational forecasting.

Publications Copernicus
Download
Citation
Share