Heatwaves are one of the most common phenomena originating from changes in the urban thermal environment. They are caused mainly by the evapotranspiration decrease of surface impermeable areas from increases in temperature and reflected heat, leading to a dry urban environment that can deteriorate aspects of everyday life. This study aimed to calculate daily maximum ground surface temperature affecting heatwaves, to quantify the effects of urban thermal environment control through water cycle restoration while validating its feasibility. The maximum surface temperature regression equation according to the impermeable area ratios of urban land cover types was derived. The estimated values from daily maximum ground surface temperature regression equation were compared with actual measured values to validate the calculation method’s feasibility. The land cover classification and derivation of specific parameters were conducted by classifying land cover into buildings, roads, rivers, and lands. Detailed parameters were classified by the river area ratio, land impermeable area ratio, and green area ratio of each land-cover type, with the exception of the rivers, to derive the maximum surface temperature regression equation of each land cover type. The regression equation feasibility assessment showed that the estimated maximum surface temperature values were within the level of significance. The maximum surface temperature decreased by 0.0450˚C when the green area ratio increased by 1% and increased by 0.0321˚C when the impermeable area ratio increased by 1%. It was determined that the surface reduction effect through increases in the green area ratio was 29% higher than the increasing effect of surface temperature due to the impermeable land ratio.

This study investigated the relationship between heat-related illnesses obtained from healthcare big data and daily maximum temperature observed in seven metropolitan cities in summer during 2013~2015. We found a statistically significant positive correlation (r = 0.4~0.6) between daily maximum temperature and number of the heat-related patients from Pearson's correlation analyses. A time lag effect was not observed. Relative Risk (RR) analysis using the Generalized Additive Model (GAM) showed that the RR of heat-related illness increased with increasing threshold temperature (maximum RR = 1.21). A comparison of the RRs of the seven cities, showed that the values were significantly different by geographical location of the city and had different variations for different threshold temperatures. The RRs for elderly people were clearly higher than those for the all-age group. Especially, a maximum value of 1.83 was calculated at the threshold temperature of 35℃ in Seoul. In addition, relatively higher RRs were found for inland cities (Seoul, Gwangju, Daegu, and Daejeon), which had a high frequency of heat waves. These results demonstrate the significant risk of heat-related illness associated with increasing daily maximum temperature and the difference in adaptation ability to heat wave for each city, which could help improve the heat wave advisory and warning system.