Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation |
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| Institution: | 1. Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milano, Italy;2. Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721, USA;1. Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;2. Deltares, Rotterdamseweg 185, 2629 HD Delft, The Netherlands;3. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;1. Civil and Environmental Engineering Faculty, Tarbiat Modares University, PO Box 14115-397, Tehran, Iran;2. Department of Water Resources Engineering, Tarbiat Modares University, PO Box 14115-336, Tehran, Iran;1. Department of Geology and Geological Engineering, South Dakota School of Mines and Technology, Rapid City 57701, USA;2. School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, China;1. Water, Energy and Environment Laboratory (LR3E), National School of the Engineers, B.p.w.3038 Sfax, Tunisia;2. University of Nantes, LETG-Nantes Géolittomer, UMR 6554, BP 81 227 Nantes, France |
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| 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. |
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| Keywords: | Ensemble Kalman Filter Random hydraulic conductivity field Moment equations Data assimilation Transient groundwater flow Filter inbreeding |
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