A development of a fully integrated groundwater-surface-water modelling platform for the Phanerozoic basin region of southern Ontario
Bibliographic record
Abstract
In early 2018, construction began on a HydroGeoSphere (HGS) fully-integrated GW-SW modelling platform for Southern Ontario, with high resolution subsurface hydro-stratigraphy based on the GSC-OGS 3D geological modelling initiative. In total, four HGS models were constructed, using a combination of coarse (41,000 nodes per layer) and fine (133,000 nodes per layer) unstructured finite element meshes, and with the full Phanerozoic sequence (represented by 21 hydrostratigraphic/soil layers) as well as a version with the Phanerozoic sequence cut off at the sulfur-brine interface (represented by 16 hydrostratigraphic/soil layers). During model construction, extensive effort was devoted to characterizing and reducing model structural uncertainty, with the end result being a heterogeneous subsurface parameterization (which includes effective representation of key karst units) underlying a temporally and spatially varying land surface that incorporates key driving factors for overland flow and evapotranspiration. Results from validation and testing demonstrate that the models can successfully capture the spatially varying transient behavior of the coupled SW and GW flow system at the regional scale, based on simulated vs observed surface water flows and groundwater heads at a respective 27 hydrograph locations and 300 monitoring well locations dispersed across the model domain. Further comparison of the simulation results from the different models combined with the different levels of temporal forcing (steady-state vs. transient) provides insight on how model spatial and temporal resolution can influence simulated heads and stream flows, and can thus be used to help determine prudent use cases for models incorporating varying levels of detail. With the proof-of-concept now near completion, there are numerous applications for the modelling platform and its underlying database to be used to further develop our understanding of regional scale hydrologic processes within Southern Ontario, as well as to inform smaller scale investigations that would benefit from insight on how regional flow systems may be influencing localized areas of interest. Furthermore, the platform will also serve as a tool to help identify current limitations in our knowledge of regional groundwater flow, to help guide future data collection initiatives, and to quantitatively test hypotheses relating to how different conceptual model realizations of subsurface hydrostratigraphy can influence hydrologic behavior. This presentation will provide a brief overview of the methodology employed towards model construction, followed by a discussion of model performance and spatial-temporal sensitivities, and will finish with a set of example applications targeting questions relating to how groundwater - surface water interactions govern inflow to the Great Lakes under different climate conditions.
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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 | 0.000 | 0.000 |
| 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 teacher head, 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".