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TitleDirectional geostatistical simulation for regional hydrostratigraphic units uncertainty characterization, Innisfil Creek sub-watershed, Ontario
DownloadDownload (whole publication)
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 J; Ford, D; Priebe, E H; Geological Survey of Canada, Open File 8212, 2017 p. 2,
PublisherNatural Resources Canada
MeetingOntario Geological Survey and Geological Survey of Canada groundwater geoscience open house; Guelph; CA; March 1-2, 2017
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Russell, H A J; Ford, D; Priebe, E H; (2017). Regional-scale groundwater geoscience in southern Ontario: an Ontario Geological Survey, Geological Survey of Canada, and Conservation Ontario open house, Geological Survey of Canada, Open File 8212
File formatpdf
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
Natural Resources Canada Library - Ottawa (Earth Sciences)
ProgramAquifer Assessment & support to mapping, Groundwater Geoscience
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.