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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 9 | Copyright
Hydrol. Earth Syst. Sci., 22, 4921-4934, 2018
https://doi.org/10.5194/hess-22-4921-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Sep 2018

Research article | 21 Sep 2018

Inflation method for ensemble Kalman filter in soil hydrology

Hannes H. Bauser1,2, Daniel Berg1,2, Ole Klein3, and Kurt Roth1,3 Hannes H. Bauser et al.
  • 1Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg, Germany
  • 2HGS MathComp, Heidelberg University, Heidelberg, Germany
  • 3Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany

Abstract. The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology. In this context, it is used to estimate states and parameters simultaneously. Due to unrepresented model errors and a limited ensemble size, state and parameter uncertainties can become too small during assimilation. Inflation methods are capable of increasing state uncertainties, but typically struggle with soil hydrologic applications. We propose a multiplicative inflation method specifically designed for the needs in soil hydrology. It employs a Kalman filter within the EnKF to estimate inflation factors based on the difference between measurements and mean forecast state within the EnKF. We demonstrate its capabilities on a small soil hydrologic test case. The method is capable of adjusting inflation factors to spatiotemporally varying model errors. It successfully transfers the inflation to parameters in the augmented state, which leads to an improved estimation.

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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.
Data assimilation methods like the ensemble Kalman filter (EnKF) can combine models and...
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