A fully integrated groundwater-surface-water modelling platform for southern Ontario
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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; both teacher heads agree on what is shown here.
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".