A feasibility study of merits and development strategies for a regional water resources modelling platform for southern Ontario - Great Lakes Basin
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".