Stormwater Runoff Reduction Achieved by Green Roofs: Comparing SWMM Method to TR-55 Method
Bibliographic record
Abstract
This research uses the EPA Storm Water Management Model (SWMM) to simulate runoff generated by impervious roofs and green roofs. Simulation results are compared with previous simulation results using Natural Resources Conservation Service Technical Release-55 (tr-55) and measurement by the British Columbia Institute of Technology (BCIT). Findings show that SWMM's Green-Ampt method can calculate more accurate runoff coefficients of impervious roofs than tr-55's Curve Number method. Annual runoff coefficients are 0.88 by SWMM, 0.6 by tr-55 and 0.93 by BCIT's measurement. However, using SWMM's Green-Ampt method alone does not accurately simulate green roof runoff. Green roof runoff is more accurately simulated by combining the Green-Ampt method with evapotranspiration of green roofs. According to BCIT's measurement, runoff reduction rates of monitored green roofs are 24% and 21%. As determined by combining the Green-Ampt method with evapotranspiration of green roofs, the potential runoff reduction achieved by green roofs is 20%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".