A feasibility study of merits and development strategies for a regional water resources modelling platform for southern Ontario - Great Lakes Basin
Notice bibliographique
Résumé
Water resources within Southern Ontario and the Great Lakes Basin (GLB) are a focal point for a wide range of stakeholders who are faced with addressing climate change impacts and resiliency, surface water and groundwater sustainability, and Great Lakes water quality. Because of the complexity of these challenges, modern science-based decision support tools are required. As demonstrated by water resources management projects underway in the Canadian Prairies and Europe, fully-integrated groundwater-surface water models are increasingly being used as multi-stakeholder decision support tools for demanding hydrologic problems. The centralized high-performance modelling platforms and associated databases are being developed through a collaboration of platform end users and requisite specialists. The multi-stakeholder functionality of this next generation of water resource simulation tools is primarily possible because fully-integrated hydrologic models seamlessly couple surface water (SW) and groundwater (GW) flow systems, including the unsaturated zone, and are driven by spatio-temporal precipitation events that are either derived from observational data or climate system projections. As such, traditional groundwater-only and surface-water-only models can now be replaced by single simulation platforms that employ holistic physics-based approaches for emulating the entire terrestrial water cycle, with full accounting of water balances within and between the various hydrological compartments. Furthermore, fully-integrated physics-based modelling provides additional benefit when simulating hydrologically complex settings such as the GLB because crucial GW-SW interaction processes are inherently captured. While fully-integrated models have been commonly employed on local-scale academic problems (10's to 100's of km2) for more than 10 years, their application to 3D water resources problems at the scale of Southern Ontario or the GLB has only been recently demonstrated. This increase in model scale, as well as complexity and spatial resolution has evolved because of a number of factors, including the mainstream accessibility to high-performance computing resources, improved numerical techniques, and the increasing availability of the large spatially-distributed datasets required to construct these models. While the movement towards open data is recognized as a major impetus for basin-scale model development, some of the datasets required to construct large-scale integrated models are still not widely available. Based on a preliminary investigation of data availability for the GLB and Southern Ontario, it is apparent that the principle data gap relates to the lack of spatially extensive and vertically resolved hydrostratigraphic characterization within the Phanerozoic and Quaternary sedimentary units. Accordingly, a GLB or Southern Ontario focused integrated hydrologic modelling initiative would need strong collaborative support from specialists familiar with the regional geology.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».