Low impact development technologies for mitigating climate change: Summary and prospects
Notice bibliographique
Résumé
\nMany cities are adopting low impact development (LID) technologies (a type of nature-based solution) to sustainably manage urban stormwater in future climates. LIDs, such as bioretention cells, green roofs, and permeable pavements, are developed and applied at small-scales in urban and peri-urban settings. There is an interest in the large-scale implementation of these technologies, and therefore assessing their performance in future climates, or conversely, their potential for mitigating the impacts of climate change, can be valuable evidence in support of stormwater management planning. This paper provides a literature review of the studies conducted that examine LID function in future climates. The review found that most studies focus on LID performance at over 5 km2 scales, which is quite a bit larger than traditional LID sizes. Most paper used statistical downscaling methods to simulate precipitation at the scale of the modelled LID. The computer model used to model LIDs was predominantly SWMM or some hybrid version of SWMM. The literature contains examples of both vegetated and un-vegetated LIDs being assessed and numerous studies show mitigation of peak flows and total volumes to high levels in even the most extreme climates (characterized by increasing rainfall intensity, higher temperatures, and greater number of dry days in the inter-event period). However, all the studies recognized the uncertainty in the projections with greatest uncertainty in the LID’s ability to mitigate storm water quality. Interestingly, many of the studies did not recognize the impact of applying a model intended for small-scale processes at a much larger scale for which it is not intended. To explore the ramifications of scale when modelling LIDs in future climates, this paper provides a simple case study of a large catchment on Vancouver Island in British Columbia, Canada, using the Shannon Diversity Index. PCSWMM is used in conjunction with providing regional climates for impacts studies (PRECIS) regional climate model data to determine the relationship between catchment hydrology (with and without LIDs) and the information loss due to PCSWMM’s representation of spatial heterogeneity. The model is applied to five nested catchments ranging from 3 to 51 km2 and with an RCP4.5 future climate to generate peak flows and total volumes in 2022, and for the period of 2020–2029. The case study demonstrates that the science behind the LID model within PC stormwater management model (PCSWMM) is too simple to capture appropriate levels of heterogeneity needed at larger-scale implementations. The model actually manufactures artificial levels of diversity due to its landuse representation, which is constant for every scale. The modelling exercise demonstrated that a simple linear expression for projected precipitation vs. catchment area would provide comparable estimates to PCSWMM. The study found that due to the spatial representation in PCSWMM for landuse, soil data and slope, slope (an important factor in determining peak flowrates) had the highest level of information loss followed by soil type and then landuse. As the research scale increased, the normalized information loss index (NILI) value for landuse exhibited the greatest information loss as the catchments scaled up. The NILI values before and after LID implementation in the model showed an inverse trend with the predicted LID mitigating performance.\n
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».