Insights from a "one water" integrated approach to Tier 3 water budget analysis
Bibliographic record
Abstract
One of the significant benefits of the tiered Ontario Source Water Protection water budget approach was the opportunity for significant improvement in numerical model analysis at each progressive level. The concurrent improvements in water use data, advances in computing and storage, and the release of a practical, open-source integrated surface water/groundwater model in 2008 (USGS GSFLOW) further supported the technical advancements. Most important, however, was the recognition that a holistic "one water" approach, addressing the entire hydrologic cycle, was necessary to address the cumulative effects of increased water use, drought, storage, and land use change on groundwater levels, streamflow, and wetland viability. Recognizing this challenge and opportunity, Earthfx strongly advocated conducting fully-integrated surface water and groundwater modelling studies for all the Tier 3 studies. A number of common response patterns and insights emerged from the six fully integrated Tier 3 and Lake Simcoe Protection models that we created. First, we found that groundwater feedback (Dunnian rejected recharge) was the dominant form of interaction, occurring in as much as 30 percent of the watershed areas. Hortonian runoff was found to be relatively rare, due to the infrequency of intense storms, summer ET deficits, and actively-vegetated loose soil conditions. Fully represented headwater streams and springs, high resolution surface topography, and detailed land cover were needed to represent spatially variable and often highly-focussed recharge. The need for detail extended into the conceptualization of the shallow subsurface, where detailed representations of the soil zone and shallow geology were needed to properly simulate subsurface stormflow and seasonal flow through highly permeable shallow aquifer units (weathered tills, epi-karst, etc.). Detailed representations of reservoir operations, quarry dewatering, irrigation water takings, and return flow were also found to be important to simulate overall watershed functions and, ultimately, producing a defensible risk assessment. Based on this experience and insight gained, we are convinced that the key to successful integrated modelling is in the details. Perhaps the most significant conclusion is that practical, engineering-scale integrated analysis can be accomplished within a watershed context. At too large a scale, many of the key process details and complex shallow system interactions would be oversimplified and generalized. Similarly, at too small a scale, such as limiting the model to the extended area of influence of a wellfield, would require oversimplification of model boundaries and neglect of the transient nature of surface and groundwater flow in the surrounding area. In 2010, Refsgaard et al. predicted that by 2020 all modelling in Denmark would consist of fully integrated analysis. Perhaps, due to the challenges and opportunities of the Tier 3 process, that future has arrived early in Ontario.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".