Testing a modified environmental flows framework for a Southern Ontario (Canada) river system: assessing hydrological alteration and management recommendations
Bibliographic record
Abstract
To date, environmental flow strategies have predominantly been used in cases where a lack of available water has degraded the ecological quality and natural functioning of a river system. In this study, we used environmental flows on an urbanized watershed where flow volumes and flow rates have increased, and large (e.g. 100-year) event return periods become more frequent (∼10 years). Using an environmental flows strategy to model the amount of hydrological alteration, we assessed whether urban development and land cover changes have impacted the flow regime and the ecological health of this watershed. We reconstructed the flow regime at Lovers Creek (Barrie, Ontario, Canada) using three scenarios of urban development: a pre-settlement reference condition with 100% natural cover, mid-development with 6% urban cover and the current condition with 21% urban cover. We found that, in contrast to many other studies, increased urban cover has coincided with increased baseflow volumes, as well as increased volumes in other components of the flow regime. Of particular concern were the increased flow rate and volume of large events such as channel forming and riparian flows; an increased rate of change in event hydrographs, and a much shorter return period of large event storms (e.g. a 100-year event under reference conditions, is now a 10-year event in the current urbanized condition). In this watershed, restoration of flow to the reference condition is not practical; however, using improved stormwater management such as low-impact development technologies and green infrastructure may offset some of the alteration to the flow regime and mitigate future further alterations due to increased urbanization. As Lovers Creek has been identified as a critical coldwater stream habitat with brook trout, the results of this study are being used to assess changes in the ecological health due to increased urban cover, and to develop management strategies that assist in restoring a more natural flow regime in order to better protect both natural habitats and human infrastructure that are vulnerable to flooding caused by more frequent, increased volume flow events.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".