Characterization of Urban Green Roofs’ Stormwater Runoff
Bibliographic record
Abstract
Green roofs are considered as one of the green technologies towards the development of sustainable urban environment and the building of ecofriendly cities. The benefits of implementing green roofs are numerous such as increase of biodiversity and reduction of urban heat island effect, stormwater management, energy saving, air quality improvement, etc. From the perspective of stormwater control, as more and more rooftops in the urban area turns into green spaces, characteristics of stormwater runoff from the drainage area may undergo a significant change which inevitably exerts potential impacts on the existing and yet-to-be-built drainage systems. However, in addition to peak flow reduction, basic understanding of the characteristics of green roof stormwater runoff is either lacking or very limited, and referenced green roof studies are very few in the literature. In this study, based on a set of rooftop-monitored data including the antecedent soil moisture content, rainfall, runoff volume and peak flow rates, stormwater runoff from both the roof garden area and the control area (the original rooftop without greening) was analyzed for its characteristics. It is well known that the antecedent soil moisture content is an important parameter associated with runoff generation, whereas runoff coefficient is a direct measure of the rainfall-runoff transformation process. These fundamental relationships are characterized in an attempt to better understand runoff generation mechanisms from green roofs. As part of this study for stormwater runoff characterization, probability distributions were
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".