Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation

M. Panzeri, M. Riva, A. Guadagnini, Shlomo P Neuman

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Traditional Ensemble Kalman Filter (EnKF) data assimilation requires computationally intensive Monte Carlo (MC) sampling, which suffers from filter inbreeding unless the number of simulations is large. Recently we proposed an alternative EnKF groundwater-data assimilation method that obviates the need for sampling and is free of inbreeding issues. In our new approach, theoretical ensemble moments are approximated directly by solving a system of corresponding stochastic groundwater flow equations. Like MC-based EnKF, our moment equations (ME) approach allows Bayesian updating of system states and parameters in real-time as new data become available. Here we compare the performances and accuracies of the two approaches on two-dimensional transient groundwater flow toward a well pumping water in a synthetic, randomly heterogeneous confined aquifer subject to prescribed head and flux boundary conditions.

Original languageEnglish (US)
Pages (from-to)8-18
Number of pages11
JournalAdvances in Water Resources
Volume66
DOIs
StatePublished - Apr 2014

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Kalman filter
data assimilation
inbreeding
groundwater flow
groundwater
simulation
confined aquifer
transient flow
sampling
pumping
boundary condition
filter
method
comparison
water

Keywords

  • Data assimilation
  • Ensemble Kalman Filter
  • Filter inbreeding
  • Moment equations
  • Random hydraulic conductivity field
  • Transient groundwater flow

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation. / Panzeri, M.; Riva, M.; Guadagnini, A.; Neuman, Shlomo P.

In: Advances in Water Resources, Vol. 66, 04.2014, p. 8-18.

Research output: Contribution to journalArticle

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