Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-1689-2021
https://doi.org/10.5194/hess-25-1689-2021
Research article
 | 
06 Apr 2021
Research article |  | 06 Apr 2021

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

Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, and Alberto Guadagnini

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Revised manuscript not accepted
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Cited articles

Alfonzo, M. and Oliver, D. S.: Seismic data assimilation with an imperfect model, Comput. Geosci., 24, 889–905, https://doi.org/10.1007/s10596-019-09849-0, 2020. 
Bauser, H. H., Berg, D., Klein, O., and Roth, K.: Inflation method for ensemble Kalman filter in soil hydrology, Hydrol. Earth Syst. Sci., 22, 4921–4934, https://doi.org/10.5194/hess-22-4921-2018, 2018. 
Bianchi Janetti, E., Riva, M., Straface, S., and Guadagnini, A.: Stochastic characterization of the Montalto Uffugo research site (Italy) by geostatistical inversion of moment equations of groundwater flow, J. Hydrol., 381, 42–51, 2010. 
Bianchi Janetti, E., Guadagnini, L., Riva, M., Guadagnini, A.: Global sensitivity analyses of multiple conceptual models with uncertain parameters driving groundwater flow in a regional-scale sedimentary aquifer, J. Hydrol., 574, 544–556, 2019. 
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteorol. Soc., 140, 1521–1535, 2014. 
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Our study shows that (i) monitoring wells installed with packers provide the (overall) best conductivity estimates; (ii) conductivity estimates anchored on information from partially and fully screened wells are of similar quality; (iii) inflation of the measurement-error covariance matrix can improve conductivity estimates when a simplified flow model is adopted; and (iv) when compared to the MC-based EnKF, the MEs-based EnKF can efficiently and accurately estimate conductivity and head fields.
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