Analysing heat exposure in two German cities by using meteorological data from both within and outside the urban area

As many cities are increasingly affected by heat waves, knowledge regarding those parts of cities most susceptible to heat exposure is essential for the implementation of directed adaptation measures. The frequency of heat waves is projected to increase in both German cities considered for this study: Karlsruhe and Berlin. By aggregating temperature data from meteorological stations within the two cities and their hinterlands, the local temperature distribution within the administrative city boundaries was assessed. A multiple regression approach was used to reveal the regional inter‐relationship between non‐meteorological factors such as altitude, population density and land use, on the one hand, and the heat distribution, on the other. This functional relationship was then applied at the city quarter level for the two cities. A model selection process was undertaken to find the most significant models describing the heat exposure of two heat indicators: heat wave days (HWDs) and tropical nights (TRNs). While altitude and population density were found to be the most significant explanatory variables for Karlsruhe, population density had a dominating influence on the distribution of heat at the city quarter level for Berlin. In Karlsruhe, models describing the daytime temperature performed best, whereas in Berlin those describing the night time temperature distribution had the highest statistical significance. This method could be used with relatively low financial and material expense to assess heat exposure in different city quarters even if there are insufficient meteorological stations within a city.