Optimising pavement watering with a fast urban climate model

Pavement watering has been identified as a promising strategy to mitigate urban heat and support climate adaptation in cities. However, there is a lack of effective modelling tools, particularly for large-scale planning of such measure. This research introduces the incorporation of pavement watering dynamics into an existing urban climate model (TARGET). The updated model was tested against real-world measurements and previous simulation data, showing a good alignment. To verify the model's robustness, we explored various input scenarios, demonstrating that wetting impervious surfaces can lead to a surface temperature reduction of up to 15 °C and an air temperature drop of as much as 2 °C. Building on this foundation, we now implement a multi-objective optimization algorithm to address the trade-offs between cooling performance and water use, enabling the identification of optimal water application strategies based on factors such as timing, intensity and frequency. The results provide actionable insights into resource-efficient pavement watering practices and allows for incorporating aspects of water availability, potential water reuse, and comparison with other heat mitigation measures (such as greenery). Hence, this study enables the design of tailored pavement watering strategies to maximize cooling impact while minimizing resource demand, offering new opportunities for sustainable, smarter, scalable urban heat mitigation solutions.