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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 1
Hydrol. Earth Syst. Sci., 20, 555–569, 2016
https://doi.org/10.5194/hess-20-555-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 555–569, 2016
https://doi.org/10.5194/hess-20-555-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Feb 2016

Research article | 01 Feb 2016

Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter

D. Erdal and O. A. Cirpka D. Erdal and O. A. Cirpka
  • Center for Applied Geoscience, University of Tübingen, 72074 Tübingen, Germany

Abstract. Regional groundwater flow strongly depends on groundwater recharge and hydraulic conductivity. Both are spatially variable fields, and their estimation is an ongoing topic in groundwater research and practice. In this study, we use the ensemble Kalman filter as an inversion method to jointly estimate spatially variable recharge and conductivity fields from head observations. The success of the approach strongly depends on the assumed prior knowledge. If the structural assumptions underlying the initial ensemble of the parameter fields are correct, both estimated fields resemble the true ones. However, erroneous prior knowledge may not be corrected by the head data. In the worst case, the estimated recharge field resembles the true conductivity field, resulting in a model that meets the observations but has very poor predictive power. The study exemplifies the importance of prior knowledge in the joint estimation of parameters from ambiguous measurements.

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Short summary
Groundwater recharge and hydraulic conductivity are both important properties of a groundwater system. However, models using an erroneous conductivity field can be compensated by a false recharge field to construct the same type of hydraulic head observations. In this work we show that prior knowledge is very important when estimating parameter fields from ambiguous data (such as head observations). If the prior information is reasonable, the joint parameter estimation can be possible.
Groundwater recharge and hydraulic conductivity are both important properties of a groundwater...
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