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TitreDirectional geostatistical simulation for regional hydrostratigraphic units uncertainty characterization, Innisfil Creek sub-watershed, Ontario
TéléchargerTéléchargement (publication entière)
AuteurBenoit, 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; par Russell, H A J; Ford, D; Priebe, E H; Commission géologique du Canada, Dossier public 8212, 2017 p. 2,
ÉditeurRessources naturelles Canada
RéunionOntario Geological Survey and Geological Survey of Canada groundwater geoscience open house; Guelph; CA; mars 1-2, 2017
Documentdossier public
Mediaen ligne; numérique
Référence reliéeCette publication est contenue dans 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, Commission géologique du Canada, Dossier public 8212
SNRC31D/04; 31D/05
Lat/Long OENS -80.0000 -79.5000 44.3333 44.0000
Sujetseau souterraine; aquifères; régimes des eaux souterraines; écoulement de la nappe d'eau souterraine; unités hydrostratigraphiques; géostatistiques; modèles; simulations par ordinateur; gestion des ressources; hydrogéologie; géomathématique; stratigraphie; géologie de l'environnement
Bibliothèque de Ressources naturelles Canada - Ottawa (Sciences de la Terre)
ProgrammeAquifer Assessment & support to mapping, Géoscience des eaux souterraines
Diffusé2017 02 22
Résumé(disponible en anglais seulement)
The 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.