Selection of appropriate indicators at the micro-scale of urban to evaluate the climate resilience of heat waves

Cities are densely populated and have impervious surfaces due to their building materials. For this reason, cities have different thermal environment than rural ones, and 'urban heat islands (UHIs)' occurs in which the atmosphere becomes warmer than the surrounding environment (Stewart and Oke, 2012). In times of climate crisis, urban systems become increasingly vulnerable due to extreme weather events of unpredictable intensity and frequency. Urban heatwave is one of them. The increasing urban heat is leading to increased exposure and damage to dangers and vulnerabilities. Accordingly, we are necessary to prepare and adapt through the urban thermal environment evaluation.

For high accuracy in this evaluation, it is necessary to develop a model that can evaluate the climatic resilience of heat waves in a city's 'micro'-scale space, and Indicators applicable to the micro-scale model should be selected first. Indicators related to thermal comfort(PET, UTCI, SET, etc.) are mainly used for micro-scale, but they reflect individual tendencies. Therefore, it is necessary to seek ways to secure objectivity. Indicators related to heat wave and thermal environments (HI, MRT, etc.) are often used on a macro scale. So, it is necessary to confirm whether they can be applied on a micro scale as well. Furthermore, It is important to understand the threshold value of each indicator to prepare for the risk and vulnerability caused by the heat wave. The threshold value (tipping point) means a point at which damage increases rapidly, and the original state cannot be restored beyond this value.

Therefore, this study investigates the indicators related to heat wave and thermal comfort. Then, by applying the indicators to the micro-scale urban heat model, we select methods and indicators suitable for evaluating the micro-scale climate resilience of cities. We identify the variable that has the greatest influence on the evaluation result of each indicator exceeding the threshold. By analyzing the relationship (such as the similarity) between the variables, we confirm whether data can be constructed on a micro-scale.