Installation of blue-green solutions at large scale to mitigate pluvial floods
Bibliographic record
Abstract
The growth of urbanization and the intensification of extreme rainfall events, that has characterized the last century, are leading to an increase of pluvial floods, which are becoming a significant problem in many cities. Among the different solutions proposed and developed to mitigate flood risk in urban areas, green roofs and rainwater harvesting systems have been deeply investigated to reduce the runoff contribution generated from rooftops. These tools have been largely studied at small scale, analysing the flood reduction that can be achieved from one single building or in a small neighbourhood, without considering the large-scale effects. In this work, the potential impact of the installation of green-blue solutions on all the rooftops of a city is evaluated, assuming to place green roofs on flat roofs and rainwater harvesting systems on sloped ones. We investigated nine cities from 5 different countries (Canada, Haiti, United Kingdom, Italy and New Zealand), representing different climatological and geomorphological characteristics. The behaviour of the blue-green solution was estimated with the help of a conceptual lumped ecohydrological model and the mass conservation, using rainfall and temperature time series as climatological input to derive the discharge reduction for different scenarios. Due to the high percentage of sloped roofs in most of the investigated locations, the cost-efficiency analysis highlights that the large-scale installation of rainwater harvesting tanks enables to achieve higher mitigation capacity than green roofs at lower cost. Green roofs, however, present many additional benefits (such as biodiversity contribution, thermal insulation for buildings, pollution reduction and increase of aesthetic added value) that need to be evaluated by urban planners and policy makers. The best achievable performance is given by the coupled system of rainwater harvesting tanks and intensive green roofs: for extreme rainfall events this solution guarantees a discharge reduction up to 20% in most of the cities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".