Applications of a regional-scale integrated modelling platform towards watershed-level hydrologic insights
Bibliographic record
Abstract
Persistent questions exist on how a regional scale model, such as the one developed for Southern Ontario, can be employed as a tool to address local level questions. While the spatial resolution of the regional model may be high enough for big-picture applications, it is arguably not high enough to provide useful insights on highly-dynamic small-scale hydrologic behavior or operational aspects of watershed management. In order to meet the needs of smaller scale applications, a set of HydroGeoSphere (HGS) fully-integrated groundwater - surface water models has been constructed for the 12 major watersheds that lie within the bounds of the regional model. These models have been constructed with a much higher level of spatial resolution than the regional model (i.e. with Strahler order 2 stream networks as opposed to Strahler order 3) and with full representation of the surface water features as channel elements within the model, which creates a more representative depiction of watershed and sub-watershed scale hydrologic behavior. As the underlying database upon which the watershed models are constructed is amalgamated across Southern Ontario, these models all share a consistent hydrostratigraphy, soil, and landcover representation, which in turn creates a uniform simulation framework that aligns with regional model behavior. However, in recognition of the fact that higher resolution model construction data may be available and/or required for smaller scale applications, the watershed models can be efficiently reconstructed using data in standard GIS formats. In addition to being able to use each of the 12 watershed models as a pre-built standalone fully-integrated model for individual watersheds, the full set of models is now running operationally as a surface water forecasting system for the whole of the Southern Ontario model domain. Using an ensemble set of weather forecasts and an advanced monitoring data assimilation scheme, surface water forecasts are being generated at daily frequency for a two week forecast interval for 100's of locations across Southern Ontario. Already, a demonstrated strength of the platform is that flows at ungauged locations (as well as known gauging locations) can be predicted with some certainty given that HGS employs a physics-based mass conservative approach for simulating water movement within the highly dynamic GW-SW system in Southern Ontario. In order to disseminate output from the hydrologic forecasting platform to watershed stakeholders, a cloud based portal has been developed with watershed-level dashboards and on-the-fly analytic functionality. While surface water flows are currently the only forecast endpoint, platform development is ongoing, with plans to release operational forecasts for other components of the hydrologic cycle in future releases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".