Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2018-74
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
05 Mar 2018
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
Inflation Method for Ensemble Kalman Filter in Soil Hydrology
Hannes H. Bauser1,2, Daniel Berg1,2, Ole Klein3, and Kurt Roth1,3 1Institute of Environmental Physics (IUP), Heidelberg University, Heidelberg
2HGS MathComp, Heidelberg University, Heidelberg
3Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg
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.
Citation: Bauser, H. H., Berg, D., Klein, O., and Roth, K.: Inflation Method for Ensemble Kalman Filter in Soil Hydrology, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-74, in review, 2018.
Hannes H. Bauser et al.
Hannes H. Bauser et al.
Hannes H. Bauser et al.

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