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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 10
Hydrol. Earth Syst. Sci., 20, 4341–4357, 2016
https://doi.org/10.5194/hess-20-4341-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Modeling hydrological processes and changes

Hydrol. Earth Syst. Sci., 20, 4341–4357, 2016
https://doi.org/10.5194/hess-20-4341-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Oct 2016

Research article | 26 Oct 2016

Multivariate hydrological data assimilation of soil moisture and groundwater head

Donghua Zhang et al.
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Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003.
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Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow, Water Resour. Res., 45, W10421, https://doi.org/10.1029/2008wr007031, 2009b.
Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107–1122, https://doi.org/10.1016/j.advwatres.2005.09.007, 2006.
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We present a method to assimilate observed groundwater head and soil moisture profiles into an integrated hydrological model. The study uses the ensemble transform Kalman filter method and the MIKE SHE hydrological model code. The proposed method is shown to be more robust and provide better results for two cases in Denmark, and is also validated using real data. The hydrological model with assimilation overall improved performance compared to the model without assimilation.
We present a method to assimilate observed groundwater head and soil moisture profiles into an...
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