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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueUrban Stormwater Management Solutions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGreen infrastructureRainwater harvestingStormwaterSurface runoffFlooding (psychology)Urban heat islandClimate changeSwaleClimate resilience

Résumé

récupéré en direct d'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.”

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,235
Score d'incertitude au seuil0,474

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,155
Écart entre enseignants0,150 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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