Drainage Discharge Design for Improved Hydrologic Performance of a Blue-Green Roof
Bibliographic record
Abstract
Urban development has led to increased impervious surfaces, disrupting the natural hydrological cycle, and necessitating effective stormwater management solutions. Green stormwater infrastructure, such as green roofs, offers a sustainable approach to mitigate runoff volume and peak flow rates. However, their hydrological performance can be limited during significant storm events. Blue-green roofs, which incorporate an additional water storage layer beneath the growth medium, have emerged as a promising solution. This study aims to develop a discharge strategy for blue-green roofs tailored to the marine pacific west coast climate, maximizing water retention during wet seasons, and ensuring irrigation during dry periods. Using a continuous SWMM hydrological model calibrated with data from a pilot-scale blue-green roof in Vancouver, various discharge designs in the blue storage layer were assessed for their hydrological performance. The calibrated SWMM blue-green model demonstrated a good fit for wet seasons. Different discharge designs significantly impacted the detention and retention performance of blue-green roofs during wet seasons. Active water level control designs, in particular, showed improved hydrological performance compared to passive drainage designs in the storage layer. The study suggests that future blue-green roof designs should consider alternative drainage methods to achieve improvements in annual retention and detention performance.
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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".