Thermal loads in two different urban quarters -- perspectives from mobile measurements and mental maps.

The fact that different urban structures have different climatic effects and therefore differ in their thermal loads for people is well known. However, there is a lack of quantitative and qualitative surveys in specific districts that are suitable to derive accepted adaptation measures. This paper addresses the research questions where thermally stressed areas in public space are identified by mobile measurements and by mental maps and what are the causes of each, where both methods agree or disagree, and what are the benefits and the limitations of using both methods for prioritizing adaptation measures. Mobile measurements in an urban quarter over a whole day can supply needed data for determining thermal loads of urban structures and their temporal development. Mental maps give information about the perception of urban dwellers and -- based on the spatial distribution of obtained data -- the user frequency. Both methods provide information concerning where and when measures should be taken in order to facilitate adaptation to heat. The paper presents the results of mobile measurements and mental maps of two different urban quarters in Germany. Thermal loads were assessed by using the Universal Thermal Climate Index (UTCI) for three selected summer days. Results indicated that the different urban structures can differ by up to 7K or by two stages of thermal stress during a hot summer day. Surface material with high albedo can overcompensate smaller sky view factors resulting in high thermal loads. Street trees caused changing thermal loads but reduced them on average. Identified hot spots based on mobile measurements mostly correlated with hot spots identified by mental maps, if they were frequently used. However, hottest spots identified by measurements were not necessarily most frequently named as hot spots in the mental maps.Most often named hot spots of mental maps coincided with major traffic routes suggesting that user frequency is important. We conclude that the combination of both methods can be valuable for identifying locations with high priority for climate adaptation in cities. [ABSTRACT FROM AUTHOR]