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

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).

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

“…Hendricks Franssen and Kinzelbach [18] obtained best results with a combination of all three techniques. Hendricks Franssen et al [20] observed filter inbreeding when analyzing variably saturated flow through a randomly heterogeneous porous medium with NMC = 100 even after dampening log-conductivity fluctuations by a factor of 10. Several authors (e.g., [21][22][23][24]) have seen a reduction in filter inbreeding effects through covariance localization and covariance inflation.…”

Making Use of Online Production Data: Sequential Updating of Mineral Resource Models

“…Applications of Kalman filter approaches in a geo-scientific context can be found in many documented studies. To name just a few: in reservoir engineering, smart well and history matching concepts are designed using Kalman filter approaches (Jansen et al 2009;Heidari et al 2011;Hu et al 2012); in hydrogeology and geosciences, different inverse problems are solved including the updating of permeability patters (Hendricks et al 2011) or the data assimilation applied to a estuarine system (Bertino et al 2002). The application of these techniques is not known in solid mineral resource extraction applications, particularly not for updating resource and reserve models based on sensor data.…”

Data assimilation in integrated hydrological modelling in the presence of observation bias

“…Each numerical element of the groundwater flow model is coupled to a one-dimensional (1-D) model for vertical flow in the vadose zone. For numerical and computational convenience capillary forces are neglected and only gravity-driven flow is considered, which is an option in the MIKE SHE code (Graham and Butts, 2005). Streamflow is simulated using the kinematic routing option.…”

“…This study uses a transient, spatially distributed hydrological model based on the MIKE SHE code (Graham and Butts, 2005). This code considers all major components of the land phase of the hydrological cycle and the code allows the hydrological components to be dynamically coupled, meaning that feedback (i.e.…”