Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 100536 |
| Fachzeitschrift | Urban Climate |
| Jahrgang | 31 |
| Frühes Online-Datum | 19 Nov. 2019 |
| Publikationsstatus | Veröffentlicht - März 2020 |
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.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Sozialwissenschaften (insg.)
- Urban studies
- Erdkunde und Planetologie (insg.)
- Atmosphärenwissenschaften
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in: Urban Climate, Jahrgang 31, 100536, 03.2020.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - City-descriptive input data for urban climate models
T2 - Model requirements, data sources and challenges
AU - Masson, Valéry
AU - Heldens, Wieke
AU - Bocher, Erwan
AU - Bonhomme, Marion
AU - Bucher, Bénédicte
AU - Burmeister, Cornelia
AU - de Munck, Cécile
AU - Esch, Thomas
AU - Hidalgo, Julia
AU - Kanani-Sühring, Farah
AU - Kwok, Yu Ting
AU - Lemonsu, Aude
AU - Lévy, Jean Pierre
AU - Maronga, Björn
AU - Pavlik, Dirk
AU - Petit, Gwendall
AU - See, Linda
AU - Schoetter, Robert
AU - Tornay, Nathalie
AU - Votsis, Athanasios
AU - Zeidler, Julian
N1 - Funding Information: Coauthors Burmeister, Esch, Heldens, Kanani-Sühring, Maronga, Pavlik and Zeidler express their gratitude to the German Federal Ministry of Education and Research (BMBF) for funding grant 01LP1601 within the framework of Research for Sustainable Development (FONA; www.fona.de ). Coauthor See would like to acknowledge the support of the FP7-funded ERC project CrowdLand (Grant n° 617754 ). Coauthors Masson, Bocher, de Munck, Lemonsu, Lévy, Schoetter, Tornay, Bonhomme thank the French National Agency of Research for their support through the project applied Modelling and urbAn Planning laws: Urban Climate and Energy (MApUCE) with reference ANR-13-VBDU-0004 . Coauthors Masson, Bocher, de Munck, Lemonsu, Schoetter, Votsis and Bucher express their gratitude to ERA4CS, an ERA-NET initiated by JPI Climate with co-funding from the European Union (Grant n° 690462 ) for the URCLIM project ( www.urclim.eu ).
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075056186&partnerID=8YFLogxK
U2 - 10.1016/j.uclim.2019.100536
DO - 10.1016/j.uclim.2019.100536
M3 - Article
AN - SCOPUS:85075056186
VL - 31
JO - Urban Climate
JF - Urban Climate
SN - 2212-0955
M1 - 100536
ER -