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Record W6904670469 · doi:10.14288/1.0442342

Rain Down the Drain : UBC Vancouver Green Rainwater Infrastructure Performance Monitoring and Future Weather Event Modelling

2024· article· en· W6904670469 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsGreen infrastructureRainwater harvestingStormwaterSurface runoffFlooding (psychology)Urban heat islandClimate changeSwaleClimate resilience

Abstract

fetched live from OpenAlex

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.”

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.474

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.005
GPT teacher head0.155
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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