Formulation, Calibration and Testing of a Coarse-Grained Heating Model for Urban Resilience Assessment

Climate change causes a multitude of challenges, not only for nature but also for human lifestyle.One significant consequence of climate change, expected to increasingly threaten urban resilience, is the phenomenon of heat accumulation in cities during summer heat waves.Although small-scaled countermeasures, like cooling rooms for elderly and other vulnerable people, help to dampen the worst consequences of heat waves, the whole architecture of cities needs to change to preserve the general quality of life.An increase of urban vegetation or the installation of large water basins represent typical procedures at this point.Given that the implementation of such interventions costs time and money, simulation tools are needed to predict and compare the benefits of different implementation strategies in order to enable an effective increase of the urban resilience against heat.Still, established simulation frameworks, like PALM and ENVI-met, need elaborate input and are computationally demanding making it complicated and tedious to apply them to whole cities and multiple different scenarios.Addressing this complexity problem, this contribution presents a model framework which predicts the evolution of the air temperature in a coarse-grained manner based on large-scale factors, like the atmospheric temperature, sun radiation and local heat diffusion.The framework can be run on conventional laptops/PCs predicting a few days of temperature evolution within seconds to minutes.It will be shown how ESA satellite data is used to transfer local characteristics of the (urban) environment under study into a model grid of numerous small tiles.Types like "builtup", "tree cover" or "water" are assigned to the tiles, each type is characterized by a unique parameter set representing the response to incoming heat.Using an exemplary model of Basel, Switzerland, it will be shown how those parameters are calibrated and the performance of the model is assessed.