Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Abstract

A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.

A kriging approach based on Aitchison geometry for the characterization of particle-size curves in heterogeneous aquifers

“…These types of data are typically based on standard grain sieve analysis of soil samples, yielding a discrete representation of the curves by measuring selected particle diameters which, in turn, correspond to quantiles of the particle-size curve. The information can then be employed to classify soil types [e.g., Riva et al (2006) and references therein], to infer hydraulic parameters such as porosity and hydraulic conductivity [e.g., amongst others, Lemke and Abriola (2003); Riva et al (2006Riva et al ( , 2008Riva et al ( , 2010; Bianchi et al (2011);Tong et al (2010); Barahona-Palomo et al (2011) and references therein], or, in the presence of inorganic compounds, to provide estimates of the porous medium sorption capacity [e.g., Hu et al (2004) and references therein].…”

Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter

“…Although the EnKF was primarily constructed to update model-state variables, in subsurface hydrology it is commonly used to estimate hydraulic conductivity. For this purpose Hendricks , Dré-court et al (2006), Tong et al (2010), Xu et al (2013a, and Panzeri et al (2015), among others, showed that the use of head observations in an EnKF framework can help improve the conductivity estimates, while Crestani et al (2013) and Tong et al (2013), among others, considered tracer tests for the same purpose. Most parameter estimations used 2-D models, as these are conceptually simpler, faster, and easier to constrain and display.…”

Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field

“…Filter divergence is caused by progressive underestimation of the model error covariance magnitude during the integration (Evensen 2009), so the filter becomes ''too confident'' in the model and ''ignores'' the observations in the analysis process. Tong et al (2010) found some problems to use EnKF method to identify a heterogeneous conductivity field using transient head data. One severe problem is that the simulation via data assimilation method cannot be conducted for a very long time.…”

“…Another problem is that the number of the observation wells should not be too large (see Fig. 7 in Tong et al (2010)). Both problems are due to the filter divergence.…”

“…Although they assimilated the observed hydraulic head data, but only to the steady state flow with one kind of boundary. Tong et al (2010) have applied the EnKF method to a synthetic study case of two-dimensional transient groundwater flow considering the effect of boundary conditions on the assimilated results. They found that the EnKF method was efficient to identify a heterogeneous conductivity field in a synthetic case with the Neumann boundary conditions while it was not very efficient with the mixed Neumann/Dirichlet boundary conditions.…”