Combining CFD and GIS software capabilities to enhance rapid fine-scale urban micro- and bioclimatic modelling

Cities around the world are under constant change. Population growth is leading to an increasing demand for residential, commercial and traffic areas and thus leading to progressive surface sealing and urban densification. Adding on the existing and growing challenging situation, the Earth’s climate is undergoing dramatical changes. Globally affecting cities by altering local temperature patterns, enhancing the occurrence of dry periods and increasing the frequency of Excessive Heat Events (EHE) as well as tropical nights per year, urban planning is becoming increasingly demanding. These consequences put cities (citizens and infrastructure) at risk, amplifying urban heat and the Urban Heat Island (UHI) effect. To ensure anticipatory and holistic planning approaches to counteract the consequences of climate change, specific tools must be developed enabling consideration of different aspects and boundary conditions as well as analysis of crucial processes and complex relationships within the urban environments. Therefore, we introduce a simple and fast spatial GIS-based modelling approach to carry out fine-scale simulations for land surface temperature (LST), mean radiant temperature (MRT) and Universal Thermal Climate Index (UTCI) in a 2D urban environment. This modelling approach combines a fine-scale surface classification, comprised of eight different surface classes, thermal characteristics (global radiation, direct radiation and diffuse radiation), surface characteristics (Emissivity and Bowen-Ratio values) and meteorological input data. Based on this combined dataset and well-established physical relations in the model set-up, the model uses an adapted approach to first evaluate LST, followed by the MRT and finally the UTCI. A DEM (Digital Elevation Model), a CIR-Image (Coloured Infrared Image) and a vector layer depicting building geometry are required as model input datasets. The accuracy of the input datasets determines the accuracy of the output datasets including the three main indicators. To improve this modelling approach and to consider the effects of climate change, we combine this spatial GIS-approach with the capabilities of computational fluid dynamics (CFD). We use CFD software to simulate wind velocities as well as air temperatures based on certain input parameters. Simulation time strongly depends on the complexity of the urban form within the area of interest. Therefore, a specific urban area was selected and the building structure, as well as the tree structure, was approximated by a self-designed 3D model. An additional input data set containing LST is provided by the modelling approach described above. Temperature data of the building envelope was conducted using a thermal infrared camera, with on-site measurements in the study area carried out during the summer of 2020. Among other settings, an initial wind speed and air temperature define the boundary conditions. Transferring calculated wind speed and air temperature datasets for different heights across the study area using CFD into the GIS based approach, leads to improved spatial LST, MRT and UTCI calculations and results and thus enhanced urban micro- and bioclimatic modelling.