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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-588
https://doi.org/10.5194/hess-2020-588

  04 Dec 2020

04 Dec 2020

Review status: this preprint is currently under review for the journal HESS.

Data assimilation with multiple types of observation boreholes via ensemble Kalman filter embedded within stochastic moment equations

Chuan-An Xia1,2, Xiaodong Luo3, Bill X. Hu1, Monica Riva2,4, and Alberto Guadagnini2,4 Chuan-An Xia et al.
  • 1Institute of Groundwater and Earth Science, Jinan University, Guangzhou, China
  • 2Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milan, Italy
  • 3Norwegian Research Centre (NORCE), Bergen, Norway
  • 4Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, USA

Abstract. We employ an approach based on ensemble Kalman filter coupled with stochastic moment equations (MEs-EnKF) of groundwater flow to explore the dependence of conductivity estimates on the type of available information about hydraulic heads in a three-dimensional randomly heterogeneous field where convergent flow driven by a pumping well takes place. To this end, we consider three types of observation devices, corresponding to (i) multi-node monitoring wells equipped with packers (Type A), (ii) partially (Type B) and (iii) fully (Type C) screened wells. We ground our analysis on a variety of synthetic test cases associated with various configurations of these observation wells. Moment equations are approximated at second order (in terms of the standard deviation of the natural logarithm, Y, of conductivity) and are solved by an efficient transient numerical scheme proposed in this study. The use of an inflation factor imposed to the observation error covariance matrix is also analyzed to assess the extent at which this can strengthen the ability of the MEs-EnKF to yield appropriate conductivity estimates in the presence of a simplified modeling strategy where flux exchanges between monitoring wells and aquifer are neglected. Our results show that (i) the configuration associated with Type A monitoring wells leads to conductivity estimates with the (overall) best quality; (ii) conductivity estimates anchored on information from Type B and C wells are of similar quality; (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when an incomplete/simplified flow model is adopted; and (iv) when compared with the standard Monte Carlo -based EnKF method, the MEs-EnKF can efficiently and accurately estimate conductivity and head fields.

Chuan-An Xia et al.

 
Status: open (until 10 Feb 2021)
Status: open (until 10 Feb 2021)
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Chuan-An Xia et al.

Chuan-An Xia et al.

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