Runoff Reduction Effects of Green Roofs in Vancouver, BC, Kelowna, BC, and Shanghai, P.R. China
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This research examines how distinct climatic conditions affect the runoff reduction functions of green roofs by comparing performance in Vancouver, BC, Kelowna, BC and Shanghai, P.R. China. To quantify the reduction in runoff volume effectuated by green roofs, both the Soil Conservation Service Curve Number (SCS-CN), crop coefficient method and the Hargreaves-Samani method are applied in calculating the annual water gains and losses of green roofs during a year of average precipitation, using local climate data such as precipitation, evapotranspiration, and temperature. Using a soil water balance model, the research also analyzes the change in soil water content of a typical green roof with a soil depth of 150 mm, and compares the potential irrigation requirements of plants with low versus high water requirements in each of the three cities. The calculation results show that the typical green roof could reduce annual rooftop runoff by 29% in Vancouver, 55% in Shanghai, and 100% in Kelowna. Furthermore, these results illustrate the important role that soil properties, soil depth, and plant selection play in maintaining growth of plants and minimizing green roof irrigation requirements. L'étude dont il est question ici a pour objectif d'examiner l'influence des conditions climatiques sur la fonction de rétention des eaux de ruissellement par les toits verts. Cet objectif est effectué par une comparaison de performance d'un toit vert de spécification typique dans les villes de Vancouver et Kelowna en Colombie Britannique ainsi que Shanghai en R.P. de Chine. Pour quantifier la réduction des eaux de ruissellement effectué par les toits verts, l'étude applique la "Soil Conservation Service Curve Number" (SCS-CN), la méthode "Crop Coefficients" (coefficients de cultures) ainsi que la méthode Hargreaves-Samani pour calculer les gains et pertes annuelles en eau par un toit vert pendant une année de précipitations moyennes, basé sur les donnés climatiques locales, comme les précipitations atmosphériques, l'évapotranspiration et la température. Se servant d'un modèle d'équilibre aquatique cette recherche explore d'avantage le changement du contenu d'eau d'un toit vert typique avec un substrat de croissance d'une épaisseur de 150 mm, et compare le besoin d'irrigation de plantes à haut et bas niveau de demande d'eau dans chaque ville. Les résultats montrent qu'un toit vert typique pourrait réduire la quantité les eaux de ruissellement annuels de 29% à Vancouver, de 55% à Shanghai et de 100% à Kelowna. De plus, il s'avère que les spécificités du toit vert, en particulier, la qualité du sol, l'épaisseur du substrat de croissance et la séléction des plantes jouent un role important pour assurer la bonne croissance des plantes et amoindrir le besoin d'irrigation du toit vert.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it