Upscaling and Inverse Modeling of Groundwater Flow and Mass ...

1.1 Motivation and Objectives
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Introduction
In the last several decades, groundwater flow and transport modeling is routinely
utilized to assess groundwater resources, understand the evolution of
contaminant plumes, and further provide the corresponding remediation strategies
to the decision-maker.
In the past, deterministic numerical models were commonly considered
with zoned hydrological parameters (e.g., porosity, conductivity, dispersivity),
which were inversely determined by optimizing the fit between the observed
aquifer response and the simulated values (e.g., piezometric heads, concentrations,
temperatures and others). Trial-and-error at the beginning, and
more advanced automatic matching techniques, later, were used for the inverse
modeling. The main shortcoming of this approach is the loss of small
scale variability of conductivity, which is usually very significant for transport
predictions. Furthermore, if an assessment of the uncertainty is needed, a
stochastic approach is needed instead of this type of deterministic model.
With the advance of geostatistics, stochastic hydrological modeling increasingly
becomes the research focus in the last decennia. Unlike traditional
zoned parameter values, equally-likely high-resolution parameter images are
first generated by means of geostatistical techniques such as sequential Gaussian
simulation or multiple point geostatistics. Then, inverse techniques such
as the self-calibration and the ensemble Kalman filter can be used to calibrate
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