A fully integrated groundwater-surface-water modelling platform for southern Ontario
Notice bibliographique
Résumé
Encompassing a land area of approximately 80,000 km2, the sedimentary basin that lies within Southern Ontario, holds groundwater and surface water resources crucial to both the sustainability and the future development of this nationally important economic and agricultural region. While future water resource availability within Southern Ontario is of obvious concern, both the groundwater and surface water flow systems act as transport pathways for a suite of potential contaminants sourced from waste water treatment plants, industry, and agriculture to reach the Great Lakes. It is also widely recognized that the Great Lakes are under stress, and are now the focal point of numerous provincial, national, and international initiatives to promote surface water quality. These types of water resources challenges are not unique to Ontario, and world-wide there is a growing need to utilize 'big data' and advanced technology to help quantify and understand the risks faced by our global water resources. In this context, efforts underway in Southern Ontario are leading to the development of a globally 'best-in-class' water resources characterization, quantification, and risk management modelling platform. The Southern Ontario model is currently being constructed with the HydroGeoSphere (HGS) platform, a 3D fully integrated groundwater - surface-water (GW-SW) flow and transport simulator. When completed, the model will incorporate several million computational nodes in a high-resolution 3D unstructured finite element mesh, and will facilitate dynamic representation of GW-SW interactions with daily temporal resolution. Development of a model of this scale and complexity is facilitated by ongoing advances in numerical methods, and by the increasing availability of detailed geological, hydrological and land surface datasets that are required when simulating such an expansive model domain. In particular, the recent development of a 60 layer bedrock lithostratigraphic model for Southern Ontario, and the contiguous quaternary hydrostratigraphic data, have made the task of characterizing the subsurface component of the model manageable. When combined with existing highly detailed soils, land cover, and hydrology data, as well as data from the Province's network of surface water and groundwater hydrometric monitoring stations, the hydrostratigraphic data will support an unprecedented level of detail within such a large, regional-scale integrated model. We will present the physical framework of the 3D Southern Ontario HGS model, the principle data sets that are being employed, and the development progress to-date. It is anticipated that this proof-of-concept modelling platform will serve a strategic array of objectives, including the provision of regional boundary conditions for local scale models, and assessments of: Climate change impacts on surface water and groundwater resources; Surface water and groundwater stresses induced by population growth; Impact of large-scale water extraction on regional flow systems; Cumulative impact of agricultural nutrient and WWTP effluent on Great Lake water quality.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».