Speaker
Description
Global climate change presents a growing challenge, as frequent extreme heat events increasingly harm urban environments. Urban heat resilience refers to a city's capability to respond to extreme heatwaves. As urbanization accelerates and city scale expands, addressing these challenges will require firm solutions and careful consideration of heat resilience. Prior studies explored several different calculating methods on heat resilience and focused on the built environment’s impact on urban climate resilience. Therefore, there are two main research questions: 1. What is the relationship between city scale, urban morphology and urban heat resilience? 2. How a city can be designed for urban heat resilience under climate change? This study aims to clarify its calculation methods on heat resilience index and evaluation criteria in urban context by associating it with the land use land cover and urban morphology design parameters. Moreover, the study will use spatial regression models and random forest algorithms to explore the numerical correlations between city scale, urban morphology, and heat resilience, selecting 70 typical Chinese cities in different climatic zones as case studies. Finally, this study will propose and validate the solutions and design strategies using mesoscale climate simulations and machine learning algorithms. These strategies include nature-based solutions such as vertical vegetation, green walls or roofs, and water bodies in order to mitigate urban overheating and improve heat resilience. The preliminary results have indicated that the typical city scales have been expanding with the rapid urbanization and urban areas impacted by the extreme heat were gradually increasing in size. Also, heat resilience deteriorated with rapid urban expansion, reaching its worst point. After that, it began to show signs of certain improvement and stabilization. The next key step of this research is to investigate future changes in urban heat resilience and to identify design strategies that can enhance heat resilience. Importantly, our study would like to improve the calculation method on urban heat resilience index based on previous research and provide valuable design and planning recommendations for climate-resilient cities under climate change.
| Keywords | Urban heat resilience; urban scale and morphology; machine learning |
|---|---|
| Best Congress Paper Award | No |
