Laboratory Study on the Hydraulic Performance of Bioretention for Stormwater Management in Cold Climates
Bibliographic record
Abstract
Bioretention has shown effective stormwater peak flow and volume reduction in warm and temperate climates. However, the applicability of bioretention for successful stormwater management in cold and semi-arid regions such as Edmonton is still not well understood. Four large bioretention columns were designed for this study and set up in a temperature-controlled laboratory with the capacity of lowering temperature to – 20 °C. Designed storm events were applied and monitored for 1st summer operation, one winter exposure and 2nd summer operation. Synthetic stormwater was applied weekly in summer conditions to investigate the hydraulic performance of two different soil types, with and without an internal water storage layer. Column 1 and Column 3, with less porous soil media (50.8% sand, 29.4% silt, and 19.8% clay), were shown to effectively attenuate peak flow for 1:2 year events, with a mean peak flow reduction of 83% and 91% respectively in 1st summer, and 77% and 73% respectively in 2nd summer. Column 2 and Column 4, with more porous soil media (67.2% sand, 19.6% silt, and 13.2% clay), maintained high hydraulic conductivity (9.6 cm/hr and 9.1 cm/hr respectively) after 2nd summer operation. Under winter conditions, columns with more porous soil media retained more volume of water within the columns, took less time for soil thawing and water breakthrough, and ponding vanished faster over frozen soil than columns with less porous soil media. After columns underwent an extreme winter condition of columns frozen at 20 °C air temperature three times, their hydraulic performance was able to rebound quickly. All columns successfully managed 1:2 year events in terms of the infiltration rate, ponding depths and durations. Preliminary results also showed that both less and more porous soil media have the potential to accept and drain the less frequent, large volume events (1:5 and 1:10 year 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".