Influence of urban land-use change on cold-air path occurrence and spatial distribution

Abstract

Abstract The urban population is predicted to reach a 70% share of global population by mid-century. Future urbanization might be directed along several development typologies, e.g. sprawling urbanization, more compact cities, greener cities, or a combination of different typologies. These developments induce urban land-use change that will affect urban climate and might reinforce phenomena such as the urban heat island and thermal discomfort of urban residents. A planning-based mitigation approach to ensure thermal comfort of residents are urban cold-air paths, i.e. low-roughness areas enabling drainage and transport of colder air masses from rural surroundings. We study how urban land-use change scenarios influence cold-air path occurrence probability and spatial distribution in a mid-European city using a machine learning approach, i.e. boosted regression trees. The Urban Sprawl Scenario results in the strongest reduction of cold-air path area by 3.6% in comparison to the reference case. The Green City Scenario gives evidence for an increase of cold-air path area (2.2%) whereas the Compact Green City Scenario partly counteracts the negative influence of urban densification by increased fractions of vegetated areas. The proposed method allows for the identification of priority areas for cold-air path preservation in urban planning.

Keywords

• Urban cold-air paths
• Machine learning
• Urban climate
• Land-use change
• Urban development