Implementing a neighbourhood scale stormwater retrofit : effect of self-draining rain barrels on an urban stream
Bibliographic record
Abstract
Over the past 50 years, as North America has become more urbanized, extensive research has been done to understand the impact of urbanization on the hydrological cycle. Specifically, land development is known to significantly alter the hydrological cycle and consequently the aquatic habitat by increasing the magnitude and frequency of flooding events, by increasing storm flow flashiness and by altering base flow regimes in streams. The current approach to mitigating the negative impacts of land development on receiving streams involves decentralized treatment of frequently occurring rainfall events at the source in other words on-site stormwater management. Stormwater practitioners and researchers have identified the need for pilot scale research projects to improve the current understanding of on-site stormwater management techniques. The objectives of the current study were to determine the level of effort and methods required to gain volunteer participation for on-site stormwater management retrofit projects as well as to determine if retrofitting single family lots on a neighbourhood scale can have a an effect on the hydrological response of the receiving stream. The objectives were achieved through collaborating with the City of Burnaby, to plan and implement a pilot project in two residential neighbourhoods in the Beecher Creek Watershed. It was hypothesized that with the cooperation of the municipality, sufficient landowner participation (at least 30% of the study area residents) could be gained through door-to-door meetings with residents and through offering incentives for participation in the study. It was also hypothesized that retrofitting the houses in the study area with self-draining rain barrels that detain roof runoff could have a regulating effect on the stream response. Two sub-catchments in the Beecher Creek watershed were chosen as the sites of the study and flow-monitoring stations were set up at sub-catchments’ outfalls. A communication strategy was developed and executed over a seven-month period that resulted in participation of 26 (out of 77 possible) residents. Overall, 40 rain barrels were installed to capture the runoff from about 3.5% to 7% of the catchment area. Analysis of the initial collected data indicated that the rain barrels had a regulating effect on the stream response.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".