Validation of a 2D modelling approach for urban microclimatic conditions

The mitigation of urban microclimatic deficiencies as a result of the urbanized environment receives increasing attention by cities, municipalities and the planning community. Priority areas for the implementation of multi-beneficial climate change adaptation measures and the development of mitigation plans need to be identified. Therefore modelling methods producing good results while only requiring moderate data input and computational effort are needed for the widespread application in planning processes. In this study, we test the rapid fine-scale methodology for simulating urban bioclimatic conditions in a 2D environment that was previously introduced by Back et al. (Back et al., 2021). The original methodology uses high resolution land cover classification (Hiscock et al., 2021) from multi-spectral aerial imagery, digital elevation data, and a vector layer of buildings to calculate land surface temperature (LST), mean radiant temperature (MRT), and the Universal Thermal Climate Index (UTCI). We apply this methodology to the rural municipality of Feldbach, Austria, using commercially and openly available satellite imagery. The simulated data is validated against the publicly available monitoring data from the WegenerNet climate station networks (Fuchsberger et al., 2022), which provides high spatial and temporal resolution measurements. The results are evaluated regarding the agreement of relative spatial differences of the simulated variables with the observed data. To be suitable for the identification of priority areas for the implementation of climate change adaptation measures, the methodology is expected to accurately reflect the spatial variability of the simulated variables.

Project supported by ESA Network of Resources Initiative.

Back, Y., Bach, P. M., Jasper-Tönnies, A., Rauch, W., & Kleidorfer, M. (2021). A rapid fine-scale approach to modelling urban bioclimatic conditions. Science of The Total Environment, 756, 143732. https://doi.org/10.1016/j.scitotenv.2020.143732

Fuchsberger, J., Kirchengast, G., Bichler, C., Leuprecht, A., & Kabas, T. (2022). WegenerNet climate station network Level 2 data [Text/csv,application/x-netcdf]. Wegener Center for Climate and Global Change, University of Graz. https://doi.org/10.25364/WEGC/WPS7.1:2022.1

Hiscock, O. H., Back, Y., Kleidorfer, M., & Urich, C. (2021). A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management. Water Resources Management, 35(4), 1339–1352. https://doi.org/10.1007/s11269-021-02790-x