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Enregistrement W2520638894 · doi:10.17918/etd-6654

Assessing green infrastructure as an effective strategy to help cities to build resilience to climate change

2015· dissertation· en· W2520638894 sur OpenAlexfundno aff
Maria Raquel Catalano de Sousa

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueUrban Stormwater Management Solutions
Établissements canadiensnon disponible
Organismes subventionnairesNorthern Research StationU.S. Forest ServiceSociety of Wetland ScientistsGovernment of CanadaDrexel UniversityU.S. Department of Agriculture
Mots-clésClimate changeEnvironmental scienceGreenhouse gasPrecipitationContext (archaeology)Deforestation (computer science)ClimatologyNatural resource economicsGeographyMeteorologyGeology

Résumé

récupéré en direct d'OpenAlex

The increase of greenhouse gases (GHG) emissions in the atmosphere due to human activities such as fossil fuel burning, deforestation and land-use changes, is causing increases in surface temperature. From the pre-industrial era to the current days, the carbon dioxide (CO₂) concentration has increased from 280 ppm to 398.17 ppm (NOAA, 2015) and according to the Intergovernmental Panel on Climate Change (IPCC, 2014) globally averaged combined land and ocean surface temperature data show a warming of 0.85°C per decade over the period 1880 to 2012. Under higher temperatures, the atmosphere has a higher capacity to hold water vapor (Horton et al, 2010), increasing the time interval between rain events and the magnitude of precipitation especially in extreme events. Thus, at higher temperatures the frequency at which precipitations occur tends to decrease while the intensity of such precipitations tends to increase. In the urban environment, where typically the majority of surfaces are impermeable, the impact of climate change tends to exacerbate the occurrence and the intensity of floods as well as droughts and heat waves. Within this context, there has been much discussion about strategies that could effectively help cities to reduce their CO₂ emissions to the atmosphere (mitigation strategies ) and to adapt to the impacts caused by climate change (adaptation strategies ). Regarding the impacts caused by urban floods, a decentralized approach, known as green infrastructure (GI) has been proposed as an alternative to the traditional concrete infrastructures (gray infrastructures [GR]). GI sustains, or attempts to replicate pre-development site hydrology in the post-development condition (Montalto, 2007), taking advantage of natural processes like infiltration, interception and evapotranspiration to manage stormwater (Davis et al, 2012). Beside capturing precipitation and reducing the amount of runoff that is convened to the sewer systems, GI can provide other benefits such as reduction of heat island effects, increased air and water quality, carbon sequestration, expansion of recreational spaces, increased habitat for flora and fauna among others (Wise et al, 2010). Because of their capacity to deliver multiple benefits, GI has been proposed as a sustainable alternative for cities to mitigate and adapt to climate change (Mason & Montalto, 2014; Union European, 2010). Several government grants have been launched recently to focus on the development, application and evaluation of methodologies for integrating GI into urban spaces as adaptation efforts to climate change (DOI, 2014; NOAA, 2014). Nevertheless, the body of literature that assesses GI as an effective strategy to help cities to build resilience to climate change remains small. For instance, the performance of designed urban green spaces under climate change is still poorly understood. In addition, the comparison between potential benefits of GI applied to urban watershed scale with the environmental costs associated with their installation and maintenance is still poorly supported by research (Pataki et al 2011). In order to better explore these research gaps, this thesis aims to evaluate GI as a means of reducing climate risks in the urban northeast environment. To reach out this main objective, we propose three different hypotheses: Hypothesis #1: Green infrastructure can reduce GHG emissions associated with urban drainage infrastructure · Compared to grey (stormwater management) infrastructure strategies, GI releases lesser GHG emissions over its entire useful life Hypothesis #2: GI can help cities adapt to extreme precipitation · GI facilities can significantly reduce urban runoff even during extreme precipitation Hypothesis #3: GI vegetation is not vulnerable to climate change, especially to floods and droughts · The risk of plant mortality within the expected envelope of climate variability (floods and droughts) is insignificant. The three hypotheses, as well as, a preliminary chapter that introduces the thesis topic, are presented separately in a scientific journal format. Chapter 1 reviews literature about the leading climate risks facing the Northeast Region (NE) of Unites States of America (USA), while provides an overview of the ongoing GI initiatives in the USA and their potential value for reducing vulnerability to the key climate risks faced by the urban northeast region. Chapter 2 addresses hypothesis #1 and includes a study conducted at the watershed scale level that used life cycle assessment techniques to compare the carbon footprint of a green and a grey strategy to reduce combined sewer overflow occurrence (CSO) in a highly urbanized watershed. Chapter 3 addresses hypothesis #2 via an investigation at the site scale that evaluated the performance of a bioretention installed in an urban watershed during extreme events including Hurricane Sandy and Hurricane Irene. Chapter 4 addresses hypothesis #3 and presents an experiment conducted in a greenhouse that evaluated the response of two species commonly used in vegetated GI sites to consecutive periods of floods and drought. This thesis finalizes with a general conclusion section for all the chapters.

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)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,005
Science ouverte0,0010,002
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,314
Écart entre enseignants0,292 · 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'étudeThéorique ou conceptuel
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

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

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