Normalisation of Thermal Indices in the Context of Urban Environments

Global climate change and its thermal implications on cities makes it necessary to react with long- and short-term climate-adapted urban planning and action. This should be organised and implemented by municipalities as part of heat action plans, to minimise future risks of overheated city districts on the city dwellers and especially on vulnerable groups. The evaluation of the thermal impact, based on thermal indices (depicting human thermoregulation) is most important in order to allow for a safe and risk-minimised but also human-adapted urban planning. Out of more than 200 thermal indices, the three most important ones have emerged in the literature (Physiologically Equivalent Temperature, Universal Thermal Climate Index and Perceived Temperature). These indices contain the complete energy balance equation of a human body under prevailing meteorological conditions.

This contribution demonstrates the thermal vulnerabilities, strengths and similarities of the indices. Min-Max-Normalisation was applied to relate and spatially compare the indices, independently of the physical unit and range. Subsequent regression analysis revealed the relationship between each index in turn. In this context, the indices were calculated for the urban district Rieselfeld in Freiburg, Germany, using the numerical, urban microscale model SkyHelios. The model is suitable to predict the meteorological outdoor conditions of future building- and local climate- scenarios.

Our investigation showed that the different thermal indices are not so different and differ mainly in the areas where the modification of radiation and wind is most prominent. These are also precisely the zones in which automated clothing becomes a key driving factor and differs among the indices.

We want to emphasise, that in future it will be necessary to compare not only the thermal indices with each other, but also the underlying implementations of the indices and the higher-level urban microscale models. This will increase confidence of these models, providing additional information for future action in heat action plans.