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City-descriptive input data for urban climate models: Model requirements, data sources and challenges

Abstract : Cities are particularly vulnerable to meteorological hazards because of the concentration of population, goods, capital stock and infrastructure. Urban climate services require multi-disciplinary and multi-sectorial approaches and new paradigms in urban climate modelling. This paper classifies the required urban input data for both mesoscale state-of-the-art Urban Canopy Models (UCMs) and microscale Obstacle Resolving Models (ORM) into five categories and reviews the ways in which they can be obtained. The first two categories are (1) land cover, and (2) building morphology. These govern the main interactions between the city and the urban climate and the Urban Heat Island. Interdependence between morphological parameters and UCM geometric hypotheses are discussed. Building height, plan and wall area densities are recommended as the main input variables for UCMs, whereas ORMs require 3D building data. Recently, three other categories of urban data became relevant for finer urban studies and adaptation to climate change: (3) building design and architecture, (4) building use, anthropogenic heat and socio-economic data, and (5) urban vegetation data. Several methods for acquiring spatial information are reviewed, including remote sensing, geographic information system (GIS) processing from administrative cadasters, expert knowledge and crowdsourcing. Data availability, data harmonization, costs/efficiency trade-offs and future challenges are then discussed.
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https://hal.archives-ouvertes.fr/hal-02391022
Contributor : Aude Lemonsu <>
Submitted on : Tuesday, December 3, 2019 - 1:42:28 PM
Last modification on : Friday, February 5, 2021 - 3:24:09 AM

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Valéry Masson, Wieke Heldens, Erwan Bocher, Marion Bonhomme, Bénédicte Bucher, et al.. City-descriptive input data for urban climate models: Model requirements, data sources and challenges. Urban Climate, Elsevier, 2020, Urban Data and Climate Information Services, 31, pp.100536. ⟨10.1016/j.uclim.2019.100536⟩. ⟨hal-02391022⟩

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