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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 12 | Copyright
Hydrol. Earth Syst. Sci., 20, 4999-5014, 2016
https://doi.org/10.5194/hess-20-4999-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Dec 2016

Research article | 19 Dec 2016

EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology

Hannes H. Bauser1,2, Stefan Jaumann1,2, Daniel Berg1,2, 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 representation of soil water movement exposes uncertainties in all model components. We assess the key uncertainties for the specific hydraulic situation of a 1-D soil profile with TDR (time domain reflectometry)-measured water contents. The uncertainties addressed are initial condition, soil hydraulic parameters, small-scale heterogeneity, upper boundary condition, and the local equilibrium assumption by the Richards equation. We employ an ensemble Kalman filter (EnKF) with an augmented state to represent and estimate all key uncertainties, except for the intermittent violation of the local equilibrium assumption. For the latter, we introduce a closed-eye EnKF to bridge the gap. Due to an iterative approach, the EnKF was capable of estimating soil parameters, Miller scaling factors and upper boundary condition based on TDR measurements during a single rain event. The introduced closed-eye period ensured constant parameters, suggesting that they resemble the believed true material properties. This closed-eye period improves predictions during periods when the local equilibrium assumption is met, but requires a description of the dynamics during local non-equilibrium phases to be able to predict them. Such a description remains an open challenge. Finally, for the given representation our results show the necessity of including small-scale heterogeneity. A simplified representation with Miller scaling already yielded a satisfactory description.

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The representation of soil water movement comes with uncertainties in all model components. We assess the key uncertainties for the case of a one-dimensional soil profile with measured water contents. We employ a data assimilation method to represent and reduce the key uncertainties. For intermittent phases where model assumptions are violated, we introduce a "closed-eye period" to bridge the gap. We also demonstrate the need to include heterogeneity.
The representation of soil water movement comes with uncertainties in all model components. We...
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