Regional Variations in Temperatures

Abstract

Recent evidence suggests that urban forms and materials can help to mediate temporal variation of microclimates and those landscape modifications can potentially reduce temperatures and increase accessibility to outdoor environments. To understand the relationship between urban form and temperature moderation, we examined the spatial and temporal variation of air temperature throughout one desert city—Doha, Qatar—by conducting vehicle traverses using highly resolved temperature and GPS data logs to determine spatial differences in summertime air temperatures. To help explain near-surface air temperatures using land-cover variables, we employed three statistical approaches: ordinary least squares (OLS), regression tree analysis (RTA), and random forest (RF). We validated the predictions of the statistical models by computing the root mean square error (RMSE) and discovered that temporal variations in urban heat are mediated by different factors throughout the day. The average RMSE for OLS, RTA, and RF are 1.25, 0.96, and 0.65 (in Celsius), respectively, suggesting that the RF is the best model for predicting near-surface air temperatures at this study site. We conclude by recommending the features of the landscape that have the greatest potential for reducing extreme heat in arid climates.