The future changes in spatio-temporal distribution of urban heat load and factors that affect its variability

crossref(2024)

引用 0|浏览2
暂无评分
摘要
Summer temperature extremes are increasing rapidly under the current global climate change. Urban environments are among those most exposed to temperature extremes due to the urban heat island, and these exacerbated conditions significantly affect human health and activities, making urban heat load one of the most fundamental concerns for people living in cities. Our research quantifies spatio-temporal changes in urban heat load in two Central-European cities (Brno and Ostrava, Czech Republic) in different geographical configurations. We applied the urban climate model MUKLIMO_3, combined with the cuboid method, to simulate recent and future distributions of four summer climate indices. The simulation results clearly indicate continuous climate warming and project a significant increase in the mean annual values of summer climate indices by the end of the 21st century, particularly in the built-up areas with a predominance of impervious surfaces. Both model simulations and in-situ observations confirm that the magnitude of these changes can differ significantly from city to city suggesting the distribution of urban heat load is not only influenced by climate change, but also by local geography and anthropogenic factors. To determine the causes of the differences in urban heat load variability, we applied land use/land cover configuration metrics and correlation analysis using various geographical factors. Our results show that a compact and less fragmented land use/land cover structure can significantly increase the urban heat load. Altitude also has a strong influence on the heat load pattern in complex terrain. Therefore, some cities are and may continue to be extremely vulnerable to adverse summer temperature extremes. We suggest that urban planners should take into account the current impact of land use/land cover structure on temperature conditions when designing effective adaptation measures to mitigate urban heat load.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要