Cities play an increasingly significant role in the challenges posed by climate change, mainly due to their role in economic and demographic drivers. It is generally agreed that the intensification of climate change effects, such as extreme weather events, requires strengthening in the mechanisms of adaptation and the endogenous self-organization of urban systems. An operative way to adapt is by mainstreaming climate resilience, i.e., the iterative process of integrating climate change considerations into policymaking, budgeting, implementation, and monitoring processes at national and subnational levels. This paper falls under this heading, and it aims at building an innovative methodology to experiment with data-driven approaches to support the resilient transition of the city of Turin in Italy. The process aims to create territorial knowledge of specific weather phenomenon, that cloudburst events are, by filling the gap of existing hazard information with original vulnerability datasets. The proposed approach will create a hydraulic vulnerability map by identifying cloudburst vulnerable areas with a GIS-based spatial overlay. The paper will employ an array of datasets combined with original modelling techniques elaborated with the help of the open-source InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) software program. The results allow us to understand what would happen if the urban water network failed to discharge during a phenomenon of intense rain and, consequently, which city areas should undergo adaptation and transformation to reduce their flooding vulnerability.
Mainstreaming climate resilience: A GIS-based methodology to cope with cloudbursts in Turin, Italy / Brunetta, Grazia; Caldarice, Ombretta; Faravelli, Martino. - In: ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE. - ISSN 2399-8083. - STAMPA. - (2022), pp. 1-17. [10.1177/23998083221076500]
|Titolo:||Mainstreaming climate resilience: A GIS-based methodology to cope with cloudbursts in Turin, Italy|
|Data di pubblicazione:||2022|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1177/23998083221076500|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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