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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 5
Hydrol. Earth Syst. Sci., 20, 2103–2118, 2016
https://doi.org/10.5194/hess-20-2103-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 2103–2118, 2016
https://doi.org/10.5194/hess-20-2103-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 May 2016

Research article | 30 May 2016

Data assimilation in integrated hydrological modelling in the presence of observation bias

Jørn Rasmussen et al.
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Cited articles  
Albergel, C., Rudiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Anderson, J. and Anderson, S.: A Monte Carlo implementationof the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.
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.
Bishop, C. and Hodyss, D.: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models, Tellus A, 61, 84–89, 2009.
Bosilovich, M. G., Radakovich, J. D., da Silva, A., Todling, R., and Verter, F.: Skin Temperature Analysis and Bias Correction in a Coupled Land–Atmosphere Data Assimilation System, J. Meteorol. Soc. Jpn., 85A, 205–228, https://doi.org/10.2151/jmsj.85A.205, 2007.
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In the paper, observations are assimilated into a hydrological model in order to improve the model performance. Two methods for detecting and correcting systematic errors (bias) in groundwater head observations are used leading to improved results compared to standard assimilation methods which ignores any bias. This is demonstrated using both synthetic (user generated) observations and real-world observations.
In the paper, observations are assimilated into a hydrological model in order to improve the...
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