Joint characterization of non-Gaussian hydraulic conductivities and non-Gaussian porosities by the normal-score ensemble Kalman filter

Abstract:

Reliable characterization of hydraulic parameters is important for the understanding of groundwater flow and solute transport. The normal-score ensemble Kalman filter (NS-EnKF) has proven to be an effective inverse method for characterization of non-Gaussian hydraulic parameter by assimilating transient piezometric head data, or solute concentration data. Groundwater temperature, an easily captured state variable, has not drawn much attention as an additional state variable useful for the characterization of aquifer parameters. In this work, we jointly estimate non-Gaussian aquifer parameters (hydraulic conductivity field and porosity field) by assimilating three kinds of state variables (piezometric head, solute concentration, and groundwater temperature) via NS-EnKF. A synthetic example including seven tests is designed and used to evaluate the ability to characterize hydraulic conductivity and porosity in a non-Gaussian setting by assimilating different numbers and types of hydraulic variables. The results show that characterization of aquifer parameters can be improved by assimilating groundwater temperature data and that the main patters of the non-Gaussian reference fields can be retrieved with more accuracy and higher precision if multiple state variables are assimilated.