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Scaling Modeling Effort toward Simple Cost-Effective Solutions
Jason Morrisey, SNC-Lavalin
Evaluation of groundwater issues relies heavily on
mathematical models to estimate solutions. Advances in
technology and computational power have allowed
practitioners to develop and run ever more sophisticated
and complex numerical simulations to evaluate
groundwater problems and solutions. However, there is a
tendency to choose time-consuming and costly evaluation
methods rather than seeking the simplest approach that
meets the modeling objective. Groundwater models used
for flow and contaminant transport are classified as
analytical, semi-analytical or numerical solutions. All of
these classifications require some simplification and each
technique has advantages and disadvantages. Often the
simplest model that meets the objective is the best and
most appropriate model choice.
Analytical solutions are often relied upon when site
characterization data are sparse since the solution often
simulates only part of the groundwater system. Limited
data availability alone can often justify the use of analytical
models. In addition, analytical solutions are computationally
more efficient than numerical solutions and are more
conducive to uncertainty analysis.
Semi-analytical models involve complex analytical solutions
involving numerical (i.e. iterative solution) techniques. Semianalytical methods allow for complex site conditions
compared to simulations based on purely analytical
solutions. For example, semi-analytical methods could
incorporate multiple sources in groundwater recharge and
discharge; however, the methods still require simplifying
assumptions about system geometry and homogeneity.
Numerical models can evaluate further complexity in site
conditions compared to analytical or semi-analytical models.
By defining the model domain as a set of small elements,
numerical models enable consideration of irregular
boundaries and spatial/temporal variations in the system,
and can simulate the flow system more holistically. Since
numerical models solve the defining equations for each
element within the domain, they require significantly more
computing power. Although numerical models can simulate
more complex conditions, they require significantly more
data to adequately characterize these irregularities and
variations.
To select an appropriate model, the complexity of the site
hydrogeology and imposed stress or stresses, along with
data availability, is typically considered with the objectives.
It is noted that increasing the model complexity does not
ensure an increase in the model accuracy. Therefore, if the
site data is sparse (e.g. a site in its infancy with limited
monitoring data to calibrate to), the hydrogeology is
relatively simple, etc., a highly complex numerical model
may not be the best choice. In these cases, analytical or
semi-analytical methods may be more appropriate. Three
case studies are presented to demonstrate the selection and
application of the simplest model to meet a specific
objective.
Jason Morrissey, MEng, CEng(UK), C WEM(UK)
Mr. Jason Morrissey, , is a Senior Groundwater Hydrologist
with SNC-Lavalin with over 16 years of engineering
consultancy experience. He specializes in applied
mathematics and physics of aquifer systems and well
hydraulics. Mr. Morrissey has worked on infrastructure,
mining, environmental and geotechnical projects within
Canada, Europe and the Middle East. His primary role is
Project Manager and Technical Lead on projects which
typically involve engineering analysis and design,
mathematical modeling, design options appraisal, contract
specification, assessment and validation reporting, and
liaison with statutory authorities. Mr. Morrissey’s modeling
and design work includes: disposal injection wells, open
loop geothermal systems, hydraulic barriers, multi-phase
extraction systems, surfactant flushing, and large scale
construction dewatering systems. Mr. Morrissey is a
Member of the United Kingdom Engineering Council.
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