Hydrological Efficacy of Ontario’s Bioretention Cell Design Recommendations: A Case Study from North York, Ontario
Bibliographic record
Abstract
Use of sustainable stormwater technologies such as bioretention cells (BRCs) is gaining in popularity across the world as episodes of severe flooding are becoming more frequent due to increased urbanization, and associated costs are rising due to decaying infrastructure and insufficient flood management. The aim of this study is to use numerical modeling to expand the understanding of BRC systems across the Toronto region. There is no one universally accepted approach to designing BRC systems. Local sensitivity analysis (LSA) with the one-factor-at-a-time method and global sensitivity analysis (GSA) with factorial design were conducted to identify the most influential components of BRC design for overflow reduction. Eight different model scenarios were used in a long term simulation to determine the efficacy of Ontario’s BRC design standards for meeting Toronto’s runoff volume control target (RVCT) of 27 mm. LSA shows that the highest reduction in overflow can be achieved by increasing BRC surface area, the saturated hydraulic conductivity (BSM Ksat) of bioretention soil media, or BRC ponding depth. On the other hand, GSA suggests that the most effective BRC performance can be achieved by simultaneously increasing the area of BRC, BSM Ksat, and BRC storage depth. Continuous simulation results show that Ontario’s minimum BRC design guideline does not meet Toronto’s RVCT. However, small adjustments to the baseline design, such as a 0.4% increase in BRC surface area, a 5 cm increase in ponding depth, or a 3 cm/h increase in BSM Ksat, can reduce the number of storm events causing overflow by up to 50% and meet RVCT.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".