Rain Down the Drain : UBC Vancouver Green Rainwater Infrastructure Performance Monitoring and Future Weather Event Modelling
Bibliographic record
Abstract
Green rainwater infrastructure (GRI) plays an important role in urban stormwater management by mimicking natural hydrological processes and reducing the adverse impacts of runoff on the environment. GRI includes various infrastructure such as green roofs, rain gardens, permeable pavements, tree plantings, and constructed wetlands. GRIs can bring multiple benefits to urban areas. It helps manage stormwater, prevent flooding and erosion, and improve water quality through natural filtration. Furthermore, it could reduce urban heat island effects, enhance air quality, and support biodiversity by providing habitat for various species. Additionally, the implementation of GRIs could yield multiple collateral advantages such as the enhancement of visual appeal and the facilitation of recreational, social, and public spaces. As anthropogenic climate change impacts and environmental degradation due to urban expansion continue to intensify, the hydrological systems of the University of British Columbia’s (UBC) Vancouver campus will face increasingly significant challenges over time. In the face of these challenges, understanding the performance of existing GRIs on campus is crucial to achieving and planning for effective stormwater management at UBC. To investigate the performance of existing GRIs on campus, this project assessed the effectiveness of the Campus Energy Centre (CEC) rain gardens (RGs) by evaluating their capacity for peak flow reduction during precipitation events between January and February 2024. Based on the outcomes, site-specific recommendations to enhance performance and build resilience were made, along with more general recommendations that are widely applicable across campus. Three objectives were identified to evaluate the effectiveness of the GRI and to make recommendations: 1. How did the CEC RGs perform, in terms of peak flow reduction, during rainfall events between January 2024 to February 2024? 2. To what extent were the CEC RGs expected to mitigate flooding posed by climate-adjusted rainfall projections under storm event scenarios with a frequency of 2-year, 10-year, and 100-year return periods and varying storm durations of 5 minutes to 24 hours? 3. What practices can be employed on the CEC RGs to improve their overall ability to manage projected future extreme weather events, based on existing literature around GRI maintenance guidelines?To meet the stated objectives, the project was organized into three distinct phases. Firstly, the peak flow reduction of the CEC RGs was investigated by monitoring the difference between total inflow and outflow, which reflected the site’s water infiltration and storage capacities. To track the flow of water through the RGs, HOBO U20 water level loggers were installed in manholes upstream and downstream of the system, and, within the curbside ‘inlets’ of the RGs to measure the inflow of rainwater from surrounding pavements and the roof of the CEC building (Fig. 1). Data collection occurred between January 28 to February 20, 2024, and results showed that the system effectively managed stormwater inflows without reaching capacity limits. To test the study sites’ ability to withstand increased projections of precipitation, the system of RGs was modelled using the United States Environmental Protection Agency’s Storm Water Management Model (SWMM) 5.2 software, where designed storm events for various return periods and durations were ran through the model to forecast its future performance in 25 to 50 years. Similar to trends observed from the field data collection, the RGs were found to effectively manage stormwater which entered the system by reducing peak flow. However, separate from the effectiveness of the rain gardens, runoff from the surrounding pavement still occurred in all the model scenarios which indicated that a portion of the rainfall impacting the pavements did not enter the rain garden system. The final phase of the project involved developing recommendations to enhance the garden’s functionality, including systematic debris removal to prevent blockages, a strategic approach to fertilization and low-phosphorus products, and advocating for the use of water and environmentally safe cleaning agents in line with UBC’s sustainability targets. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".