Seasonal Climatic Comfort Variations in Mountain Cities: A Local Climate Zone Perspective

Abstract

Against the backdrop of global climate change, climate change has become a major factor affecting the suitability of living environments. Therefore, in this study, based on multisource remote sensing data and geospatial analysis methods, we selected the annual land surface temperature (LST) and normalized difference vegetation index (NDVI) in 2022 of the Chengdu–Chongqing urban agglomeration in China as driving indicators. By extracting three meteorological indicators of meteorological stations throughout the year (temperature [T], humidity [U], and wind speed [K]) in study area to construct the comfortable index of human body (IBC) to quantify regional climatic comfort. The results were as follows: First, the IBC averages were 75.620 (summer) > 60.448 (autumn) > 59.922 (spring) > 42.743 (winter), showing a spatial pattern of high values in the middle and low values on the four sides. T was the primary factor influencing annual IBC. Second, we discussed the relationship between IBC and NDVI, air quality index (AQI), LST, and relief degree of land surface (RDLS) under ordinary least square method, geographically weighted regression (GWR), and multiscale GWR (MGWR) models. LST is the most important factor affecting the spatial distribution of annual climate comfort, and the other four factors have different effects on IBC in different seasons. Third, the climate comfort of natural-type local climate zone (LCZ) was high in spring and summer, while the IBC of LCZ corresponding to building type was high in autumn and winter. This study is crucial for understanding the suitability of urban living environments and provides a reference for urban development planning.

Keywords

• Climate change
• climate comfort
• geographically weighted regression (GWR)
• remote sensing index