Parameterization and Validation of Numerical Transport Models using Hydrogeophysical Estimation Approaches
Creators: name: Hubbard, Susan
name: Chen, Jinsong
name: Scheibe, Timothy
name: Fang, Yilin
name: Williams, Kenneth
name: Linde, Niklas
name: Hubbard, Susan
Publisher: name: XVI International Conference on Computational Methods in Water Resources
Year: 2006

description: Numerical modeling of fluid flow and transport is often used to test hypotheses and
to guide resource management.  For example, numerical models are used to simulate
contaminant plume transport over time and to design effective remediation plans, and
to simulate aquifer depletion versus recharge over time and to design sustainable
resource extraction programs. Effective hydrological hypothesis testing using
mathematical modeling requires accurate and sufficient parameterization. It is well
recognized that physical and chemical properties of aquifer systems (such as
hydraulic conductivity and reactive iron oxide surface area) is great and can have
multiple scales of spatial variability.  As these parameters exert significant
control on transport, natural attenuation, and active remediation processes,
successful modeling requires sufficient information about the distribution of these
properties. Given the difficulty of obtaining characterization data for model
parameterization using conventional wellbore approaches, the predictive capability of
these models for practically guiding water resource or contaminant management is
limited.	
     Geophysical methods hold potential for providing quantitative information about
subsurface hydrogeological parameters or processes that can be used to constrain or
validate transport models. However, to use integrated geophysical and hydrogeological
information for such purposes, we must confront issues of scale, non-uniqueness, and
uncertainty; we must have petrophysical relationships available that link the
disparate measurements; we must have an understanding about the spatial distribution
of measurement and parameter estimate uncertainties; and we must have estimation
approaches that can be used for integration. In this presentation, we will first
review examples of the estimation of hydrological-geochemical properties needed for
model parameterization using geophysical data and stochastic approaches, such as
Bayesian and Monte Carlo Markov Chain. These methods are advantageous in that they
offer a systematic approach for incorporating different types of data and
petrophysical relationships, and provide estimates of uncertainty that can be used
within transport simulation. We also illustrate estimation examples performed using
both sequential processes (whereby the geophysical data are interpreted and converted
to a form in which they can act as a constraint on the hydrogeological inversion) and
joint inversion processes, and describe the benefits and limitations of the different
approaches.
     Advanced numerical models are being developed or used to simulate coupled
biogeochemical-hydrological processes, such as dissolution and precipitation of
minerals, gas evolution, changes in soil water and oxygen levels, sorption,
attachment/detachment, oxidation and reduction, biofilm generation, and changes in
permeability and porosity. We will review several examples of the use of
geophysically-obtained parameter estimates to parameterize or validate numerical flow
and transport models. In the first set of examples, we will illustrate how
geophysically obtained estimates of aquifer zonation, hydraulic conductivity, and
sediment FeII and FeIII concentrations were used within models to explore the impact
of physical and chemical heterogeneity on chemical, bacteria, and uranium transport
and reduction processes. As a final example, we will illustrate the use of time-lapse
geophysical data for assisting in the understanding of the spatiotemporal products
associated with active remediation approaches (such as the generation of gasses and
precipitates or the changes in aqueous concentrations). We will discuss how the dense
geophysical information can be used to explore the capabilities of advanced numerical
models to accurately account for coupled physical, chemical, and biological processes
in the presence of natural heterogeneity at the field scale.