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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 4927–4958, 2017
https://doi.org/10.5194/hess-21-4927-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Sep 2017

Research article | 29 Sep 2017

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Hongjuan Zhang et al.

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Cited articles

Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Bailey, R. T. and Baù, D.: Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother, Hydrol. Earth Syst. Sci., 16, 287–304, https://doi.org/10.5194/hess-16-287-2012, 2012.
Bateni, S. M. and Entekhabi, D.: Surface heat flux estimation with the ensemble Kalman smoother: Joint estimation of state and parameters, Water Resour. Res., 48, W08521, https://doi.org/10.1029/2011wr011542, 2012.
Bertoldi, G.: The water and energy balance at basin scale: a distributed modeling approach, University of Trento, Monograph of the School of Doctoral Studies in Environmental Engineering, 202 pp., ISBN 88-8443-069-0, 2004.
Blondin, C.: Parameterization of land-surface processes in numerical weather prediction, in Land Surface Evaporation: Measurements and Parameterization, Springer-Verlag, New York, 31–54, 1991.
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Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most...
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