Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-2103-2016
https://doi.org/10.5194/hess-20-2103-2016
Research article
 | 
30 May 2016
Research article |  | 30 May 2016

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

Jørn Rasmussen, Henrik Madsen, Karsten Høgh Jensen, and Jens Christian Refsgaard

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Short summary
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.