Hydrologic and Thermal Performance of a Full-Scale Farmed Blue–Green Roof
Bibliographic record
Abstract
Conventional green roofs have been widely accepted as a climate change adaptation strategy. However, little is known about the potential of blue–green roofs and rooftop farms to control urban stormwater and improve microclimates. This study evaluates a farmed blue–green roof’s hydrologic and thermal performance over an entire growing season in Toronto, Ontario, Canada. The runoff discharge from three plots planted with various crops was monitored. The substrate and air temperatures at two elevations of different cultivated and self-sowing plant species were collected and compared to a control roof. Results indicate that planting and harvesting activities impacted the hydrologic performance. Mean values for retention ranged from 85–88%, peak attenuation ranged from 82–85%, and peak delay ranged from 7.7 to 8 h. At the lower elevation, the mean air temperature difference above okra, tobacco, and beet was 2.5 °C, whereas, above squash, potato, and milkweed, it was 1.4 °C. Maximum and moderate air-cooling effects were observed in the afternoon and evening, but a warming effect was observed in the early morning. Farmed blue–green roof evaluated in this study provides a runoff control and microclimate improvement comparable to or better than conventional green roofs, in addition to other benefits such as improving food security.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".