Urban design choices balance daytime cooling with nighttime warming
Analysis of trade-offs in urban heat mitigation using a Bayesian Optimization framework for an urban canopy layer model
Computational Engineering, Finance, and Science
Summary
Cities face challenges with rising heat that affects people during the day and night. The authors used a computer method called Bayesian optimization to study how different city designs, like building shapes and trees, affect heat at different times. They found that cooling cities during the day often leads to warmer nights, creating a trade-off in comfort. Their work helps city planners see these trade-offs through a tool they made, so they can design neighborhoods that reduce heat stress better.
What this means in practice
- •For urban planners: Use the interactive visualization tool to balance daytime cooling and nighttime heat when planning city layouts and green spaces to reduce heat stress.
- •For city climate modelers: Apply the Bayesian optimization framework to efficiently explore urban design parameters and predict their effects on local thermal comfort metrics.
Authors
Rebekka Walter, Johanna Gelhaus, David Anton, Henning Wessles, Stephan Weber
Abstract
To mitigate the challenges of climate change and intensifying heat stress in urban areas, local adaptation strategies are discussed and introduced in cities worldwide. To understand processes and potential trade-offs of these strategies a Bayesian optimization and surrogate modeling framework was employed to investigate urban parameter ranges of heat mitigation strategies with focus on three thermal metrics: daytime air temperature, Universal Thermal Climate Index (UTCI), and nighttime air temperature. Based on an urban street canyon configuration, it was shown that heat mitigation measures that reduce daytime air temperature and UTCI are often associated with higher nighttime temperatures. This results in a curved Pareto front that reflects the trade-off between daytime and nighttime thermal comfort. Under identical forcing conditions, different urban configurations, varying in geometry, vegetation, and surface characteristics, are shown to alter peak canyon air temperature by up to 5.2~$^\circ$C during the day and 2.6~$^\circ$C at night, while UTCI varies by up to 7.9~$^\circ$C, demonstrating that favorable urban configurations can substantially mitigate microclimatic heat stress. These findings suggest that combining multi-objective Bayesian optimization and surrogate modeling can help bridge the gap between computationally intensive climate simulations and practical decision-making in urban planning. An interactive visualization tool was developed to explore these trade-offs, making the often opposing relationships between urban parameters and the three thermal metrics directly accessible to planners.