Hydrologic Impacts of Detention Layers within Extensive Vegetated Roof Assemblies
Bibliographic record
Abstract
First-generation extensive green roof systems included only vegetation, growing media, and drainage materials. However, these simple systems can be too heavy for roofs that were not originally intended to support green infrastructure, and the growing media may not provide adequate stormwater detention to meet desired flooding mitigation targets. Today, many commercial green roof manufacturers and suppliers aim to increase the retention and detention provided by green roofs by using lightweight growing media alternatives. This study evaluates the hydrologic impacts of generic vegetated roof assemblies (VRA) that use ultra-lightweight and soilless materials—fleece, mineral wool, and a combined reservoir-detention system—throughout a complete growing season under the natural rainfall conditions in Toronto, Ontario. Discharge from test beds was continuously measured from July to November 2022 and compared to discharge produced by a conventional green roof assembly and an impervious roof covered with stone ballast. The total rainfall for the monitoring period was 219.2 mm, and the stone control retained 48% (105.4 mm). The green roofs improved retention, retaining an additional 73–95 mm of rainfall depending on the roof assembly. All green roofs provided some detention and decreased peak discharge. The extent of detention provided ranged substantially based on the VRA, particularly for large rain events. All green roofs drastically reduced peak flow, averaging greater than 95% regardless of the assembly. The best-performing system (combined reservoir-detention) retained 95% of all received rainfall, reduced peak flows by 98%, and had the greatest discharge delay (9.6 hrs) and duration (15 hrs). This work demonstrates that detention layers improve the hydrologic performance of a green roof system to varying degrees.
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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".