Limits of Blue and Green Infrastructures to Adapt Actual Urban Drainage Systems to the Impact of Climate Change
Bibliographic record
Abstract
Urbanization over the last few decades has resulted in a rise of impervious surfaces in municipalities worldwide. This rise has led to an increase in stormwater runoff and a decrease in the capacity of existing urban drainage systems. Additionally, the projected increase in frequency and intensity of extreme rainfall events due to climate change further exacerbates the risk of urban flooding. In response to this challenge, many municipalities have begun implementing blue and green infrastructures (BGI) to mimic the natural hydrologic cycle and manage stormwater at its source. Although the benefits of BGI, such as bioretention cells, permeable pavement, blue roofs, and green roofs, have been demonstrated, their full potential remains uncertain. This raises the question of whether BGI, when utilized to the maximum potential, can effectively adapt our existing drainage infrastructures to the projected increases in extreme rainfall in a warmer climate. To address this question, a case study was conducted in the Pointes-aux-Trembles District, a 20-km2 urban catchment in Montreal. A calibrated stormwater management model [personnel computer storm water management model (PCSWMM)] was used to simulate various scenarios of BGI implementation, both individually and in combination without considering economic constraints. An extreme rainfall event was simulated under a warming climate to compare urban flooding between the existing urban drainage system and the different BGI scenarios. The results demonstrated the significant potential of BGI in adapting our existing drainage systems to climate change. In the simulated scenario of climate change impact, which resulted in a 136% increase in flood volume, the individual implementation scenarios offset between 20% and 118% of this increase. Furthermore, the combination scenarios achieved offsets of 162% and 167%, resulting in a better performance of the urban drainage systems (UDS) under climate change conditions with BGI practices than in historical conditions without any BGI practice. These findings strongly suggest that BGI practices should be considered as a crucial part of the adaptation solution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".