Operational real-time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream-aquifer interaction and parameter updating

Hendricks Franssen, H.J.; Kuhlmann, U.; Müller, R.; Kinzelbach, W.; Kaiser, H.P.; Stauffer, F.; Bauser, G.

doi:10.1029/2010WR009480

Journal Article

Operational real-time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream-aquifer interaction and parameter updating

Hendricks Franssen, H. J.FZJ* ; Kaiser, H. P. ; Kuhlmann, U. ; Bauser, G. ; Stauffer, F. ; Müller, R. ; Kinzelbach, W.

2011
AGU Washington, DC

Water resources research 47, W02532 (2011) [10.1029/2010WR009480]

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Please use a persistent id in citations: http://hdl.handle.net/2128/20593 doi:10.1029/2010WR009480

Abstract: Urban groundwater is frequently contaminated, and the exact location of the pollution spots is often unknown. Intelligent monitoring of the temporal variations in groundwater flow in such an area assists in selectively extracting groundwater of drinking water quality. Here an example from the city of Zurich (Switzerland) is shown. The monitoring strategy consists of using the ensemble Kalman filter (EnKF) for optimally combining online observations and online models for the real-time characterization of groundwater flow. We conducted numerical simulation experiments for the period January 2004 to December 2007 with a 3-D finite element model for variably saturated groundwater flow. It was found that the daily assimilation of piezometric head data with EnKF results in a better characterization of piezometric heads than does a model which is inversely calibrated with historical data but not updated in real time. The positive impact of model updating with observations can still be observed 10 days after the update. These simulations also suggest that parameters (hydraulic conductivity and leakage) are successfully updated: 1 and 10 day piezometric head predictions are better with than without updating of parameters. Additional experiments with a synthetic model for the same site, in which the only difference is that certain parameter values are selected as the unknown "true" conditions, show that EnKF also successfully updates unknown parameters. However, this is only the case if spatially distributed hydraulic conductivities and leakage coefficients are jointly updated and if a damping parameter is used. The mean absolute error of estimated log leakage coefficients decreased by up to 63%; for log hydraulic conductivity a decrease of up to 27% was observed. From January 2009 the method has been operational at the Water Works Zurich and showed a remarkable performance until present (October 2010).

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Note: The study was performed within the project "Real-Time Control of a Well-Field Using a Groundwater Model," a cooperation between ETH Zurich, Zurich Water Supply, and TK Consult Zurich. This project is funded by the Swiss Innovation Promotion Agency CTI under contract 7608.2 EPRP-IW.

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