Knoxville Microclimates: Spatial and Temporal Variability in Ambient Air Temperature Across Four Urban Neighborhoods

The urban heat island (UHI), has been studied extensively over the past 30 to 40 years, yet questions remain regarding the spatiotemporal variability of the UHI, and what factors influence this variability. More recent studies have emphasized the microclimates within an urban setting, but most do not have the high resolution climate or land cover data necessary to truly understand the interactions taking place at a neighborhood-level scale. This study used a network of 10 identical weather stations and high resolution land use data in Knoxville, Tennessee to analyze the microclimates of a medium-sized city with a temperate climate over the course of an entire year. Two stations were installed in each of four urban neighborhoods, in locations with varying localized tree cover, in addition to two control locations in the center of downtown and in a nearby urban nature center. The goal of the study was to observe the spatial and temporal patterns of temperature in these neighborhoods and analyze what land cover characteristics best explain those patterns. The intra-neighborhood results (Clear vs. Tree) suggest that there is significant temperature variability within a single neighborhood, based on the land use characteristics immediately surrounding a given weather station. However, the inter-neighborhood variability (differences between neighborhoods) was greater in magnitude, which suggests that the overall differences in neighborhood characteristics has a higher effect on climate than more local characteristics. Temperature variability was also found to be greater during the warm seasons (Spring and Summer) and during days with dry air masses. Land cover at the neighborhood scale (impervious cover and tree canopy percentages at the 500 meter radius) had the highest correlation with the minimum daily temperature (Tmin) during the Summer season. Tmax had the highest correlation with the distance of each station from Downtown, but was also significantly correlated with land use. This work demonstrates the need for high-resolution climate and land cover data to truly understand the interactions of urban characteristics and the microclimates within a city. These data can be used to better inform planning strategies to build resiliency to extreme heat into urban environments.