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TitleDirectional geostatistical simulation for regional hydrostratigraphic units uncertainty characterization, Innisfil Creek sub-watershed, Ontario
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LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorBenoit, N; Marcotte, D; Boucher, A; Bajc, A F
SourceRegional-scale groundwater geoscience in southern Ontario: an Ontario Geological Survey, Geological Survey of Canada, and Conservation Ontario open house; by Russell, H A JORCID logo; Ford, D; Priebe, E H; Geological Survey of Canada, Open File 8212, 2017 p. 2, https://doi.org/10.4095/299758 Open Access logo Open Access
Year2017
PublisherNatural Resources Canada
MeetingOntario Geological Survey and Geological Survey of Canada groundwater geoscience open house; Guelph; CA; March 1-2, 2017
Documentopen file
Lang.English
Mediaon-line; digital
RelatedThis publication is contained in Regional-scale groundwater geoscience in southern Ontario: an Ontario Geological Survey, Geological Survey of Canada, and Conservation Ontario open house
File formatpdf
ProvinceOntario
NTS31D/04; 31D/05
AreaInnisfil Creek
Lat/Long WENS -80.0000 -79.5000 44.3333 44.0000
Subjectshydrogeology; mathematical and computational geology; stratigraphy; environmental geology; groundwater; aquifers; groundwater regimes; groundwater flow; hydrostratigraphic units; geostatistics; models; computer simulations; resource management; Innisfil Creek subwatershed
ProgramGroundwater Geoscience, Aquifer Assessment & support to mapping
ProgramGroundwater Geoscience, Aquifer Assessment & support to mapping
Released2017 02 22
AbstractThe uncertainty characterization of hydrostratigraphic systems is important for risk assessment in hydrogeology. A complex system is difficult to characterize due to limited sampling. Geostatistical simulation methods aim at representing the underlying modeling uncertainties about hydrostratigraphic units proportions, properties and spatial arrangement through an ensemble of equally probable models. These models control the connectivity and, ultimately, the response of the system under an external stimulus. It is then critical to have geostatistical algorithms that can accurately reproduce the geological controls on the flow, such as the directional ordering of units. The latter is an important and common feature of sedimentary environments. Recent developments in geostatistics have improved the realism of hydrostratigraphic units simulation. In that regard, we revisited the Markovian Categorical Prediction (MCP) simulation method to allow for trends and directional ordering in simulation. The adapted MCP approach was applied to determine the uncertainty at a regional scale within the Innisfil Creek subwatershed with its 18 hydrostratigraphic units. The trend in unit occurrences is accounted for by locally confining the geostatistical algorithm to a deterministic model, without resorting to auxiliary variables. The presented methodology allows characterization of the uncertainty of the hydrostratigraphic model.
The outputs are a suite (ensemble) of models, which provide a variability assessment of the spatial units arrangement and are an alternative to the deterministic one that complies with known data and unit ordering and trends. The alternative models propose a realistic variation for the thicknesses of the different units. Results suggest that the updated MCP methodology is highly flexible and efficient for closely replicating hydrostratigraphic units ordination, even for units occurring with low frequency. These geostatistical models are then appropriate for characterizing the uncertainty of groundwater flow and transport as well as aquifer vulnerability and the delineation of wellhead protection areas.
Summary(Plain Language Summary, not published)
Proceedings for Regional-Scale Groundwater Geoscience in Southern Ontario open house organized by the Ontario Geological Survey, Geological Survey of Canada and Conservation Ontario Geoscientists. Open house is on 2017-03-01 and 02. Purpose is public engagement and dissemination of geoscience completed in Southern Ontario during the past year.
GEOSCAN ID299758

 
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