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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 7
Hydrol. Earth Syst. Sci., 19, 2999–3013, 2015
https://doi.org/10.5194/hess-19-2999-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 2999–3013, 2015
https://doi.org/10.5194/hess-19-2999-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 02 Jul 2015

Research article | 02 Jul 2015

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance

J. Rasmussen1, H. Madsen2, K. H. Jensen1, and J. C. Refsgaard3 J. Rasmussen et al.
  • 1Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark
  • 2DHI, Hørsholm, Denmark
  • 3Geological Survey of Denmark and Greenland, Copenhagen, Denmark

Abstract. Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only.

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