Towards a climate-driven simulation of coupled surface-subsurface hydrology at the continental scale: a Canadian example
Bibliographic record
Abstract
The development of new, large-scale tools to evaluate water resources is critical to understanding the long-term sustainability of this resource under future land use, climate change, and population growth. In cold and humid regions it is imperative that such tools consider the hydrologic complexities associated with permafrost and groundwater-surface water (GW-SW) interactions, as these factors are recognized to have significant influence on the global water cycle. In this work we present a physics-based, three-dimensional, fully-integrated GW-SW model for Continental Canada constructed with the HydroGeoSphere simulation platform. The Canadian Continental Basin Model (CCBM) domain, which covers approximately 10.5 million km2, is discretized using an unstructured control-volume finite element mesh that conforms to key river basin boundaries, lakes, and river networks. In order to construct the model, surficial geology maps were assembled, which were combined with near-surface information and bedrock geology into a seven-layer subsurface domain. For the large-scale demonstration, the model was used to simulate historic groundwater levels, surface water flow rates (R2=0.85), and lake levels (R2=0.99) across the domain, with results showing that these targets are well reproduced. To demonstrate the regional-scale utility, simulation results were used to perform a regional groundwater flow analysis for western Canada and a water balance analysis for the Laurentian Great Lakes (Superior, Michigan, Huron, Erie and Ontario). The outcome of this work demonstrates that large-scale fully-integrated hydrologic modeling is possible and can be employed to quantify components of a large-scale water balance that are otherwise difficult or impossible to obtain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".