Climate-sensitive Urban Planning through Optimization of Tree Placements
Simon Schrodi Ferdinand Briegel Max Argus Andreas Christen Thomas Brox
Climate change is increasing the intensity and frequency of many extreme
weather events, including heatwaves, which results in increased thermal
discomfort and mortality rates. While global mitigation action is undoubtedly
necessary, so is climate adaptation, e.g., through climate-sensitive urban
planning. Among the most promising strategies is harnessing the benefits of
urban trees in shading and cooling pedestrian-level environments. Our work
investigates the challenge of optimal placement of such trees. Physical
simulations can estimate the radiative and thermal impact of trees on human
thermal comfort but induce high computational costs. This rules out
optimization of tree placements over large areas and considering effects over
longer time scales. Hence, we employ neural networks to simulate the point-wise
mean radiant temperatures--a driving factor of outdoor human thermal
comfort--across various time scales, spanning from daily variations to extended
time scales of heatwave events and even decades. To optimize tree placements,
we harness the innate local effect of trees within the iterated local search
framework with tailored adaptations. We show the efficacy of our approach
across a wide spectrum of study areas and time scales. We believe that our
approach is a step towards empowering decision-makers, urban designers and
planners to proactively and effectively assess the potential of urban trees to
mitigate heat stress.