Innovation in Stormwater Management in Canada: The Way Forward
Bibliographic record
Abstract
Abstract Rapid urban expansion, increased traffic, ageing infrastructure, greater climatic variability, and the need for enhanced sustainability of urban water resources pose significant challenges to conventional stormwater management. Innovative approaches are needed in order to mitigate the risk of flooding, pollution, and aquatic ecosystem degradation, and enhance beneficial uses of urban waters. To examine such approaches, a series of three regional conferences on innovative stormwater management were held in Vancouver, Calgary, and Toronto during 2007 to 2008 under the sponsorship of the Canadian Water Network (CWN) and the Canadian Mortgage and Housing Corporation (CMHC). Authors of selected conference papers providing information on innovative approaches to mitigating the risk of flooding and reducing pollution impacts at the property, neighbourhood, and watershed scales were then invited to submit journal papers, and those accepted in the review process were included in this Special Issue of the Water Quality Research Journal of Canada. An overview of the selected papers indicates that no single innovative measure is adequate under all circumstances,and a multibarrier approach is deemed to be most effective. Examples of innovations at the property level include harvesting roof runoff and reusing water, managing rainwater by infiltration in swales and into soils in bioretention areas, minimizing impervious surfaces, and using pervious pavement. At the neighbourhood level, runoff impacts are mitigated by designing roads without curbs, gutters, and drain pipes, and diverting runoff into infiltration channels, swales, and wetlands. Creating roads and parking lots with pervious pavement and draining runoff from such surfaces into infiltration basins is also discussed. Among stormwater quality source controls, potential effects of street sweeping on runoff quality enhancement were assessed. New innovations at the watershed scale include: (a) the creation of wide riparian buffer zones that can detain water, remove sediments, and mitigate nutrient export and other pollutant effects, (b) the minimization of channelization of streams and rivers, and (c) the designation of floodwater storage areas. A new water balance model that is linked to a global information system (GIS) and works at all the three scales offers the best option to conceptualize stormwater problems, and their mitigation, in urban watersheds. Finally, the aim of this Special Issue is to promote examples of successful innovative approaches to improving stormwater management in Canadian cities, hoping that other practitioners will build on this experience and bring stormwater management practice to the next higher level.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".