edu.ucar.opensky::articles:12575 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-26T03:50:13.454211 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7qr4xx1 Operational hydrological data assimilation with the recursive ensemble Kalman filter 2013-01-10T00:00:00Z publication https://n2t.org/ark:/85065/d7qr4xx1 https://doi.org/10.5194/hess-17-21-2013 McMillan, H. author Hreinsson, E. author Clark, Martyn P. author Singh, S. author Zammit, C. author Uddstrom, M. author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License. None eng geoscientificInformation