Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 22, issue 9
Articles | Volume 22, issue 9
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 9
Research article
 | 
21 Sep 2018
Research article |  | 21 Sep 2018

Inflation method for ensemble Kalman filter in soil hydrology

Hannes H. Bauser, Daniel Berg, Ole Klein, and Kurt Roth

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Short summary
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and measurements to estimate states and parameters, but require a proper representation of uncertainties. In soil hydrology, model errors typically vary rapidly in space and time, which is difficult to represent. Inflation methods can account for unrepresented model errors. To improve estimations in soil hydrology, we designed a method that can adjust the inflation of states and parameters to fast varying errors.
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