No abstract

Urban heat is a growing environmental concern in cities around the world. The urban heat island effect, combined with warming effects of climate change, is likely to cause an increase in the frequency and intensity of extreme heat events. Alterations to the physical, built environment are a viable option for mitigating urban heat, yet few studies provide systematic guidance to practitioners for adapting diverse land uses. In this study, we examine the use of green infrastructure treatments to evaluate changes in ambient temperatures across diverse land uses in the city of Portland, Oregon. We apply ENVI-met® microclimate modeling at the city-block scale specifically to determine what built environment characteristics are most associated with high temperatures, and the extent to which different physical designs reduce ambient temperature. The analysis included six green infrastructure interventions modeled across six different land-use types, and indicated the varying degrees to which approaches are effective. Results were inconsistent across landscapes, and showed that one mitigation solution alone would not significantly reduce extreme heat. These results can be used to develop targeted, climate- and landscape-specific cooling interventions for different land uses, which can help to inform and refine current guidance to achieve urban climate adaptation goals.

We introduce and evaluate three methods for modeling the spatial distribution of multiple land-cover classes at subpixel scales: (a) sequential categorical swapping, (b) simultaneous categorical swapping, and (c) simulated annealing. Method 1, a modification of a binary pixel-swapping algorithm, allocates each class in turn to maximize internal spatial autocorrelation. Method 2 simultaneously examines all pairs of cell-class combinations within a pixel to determine the most appropriate pairs of sub-pixels to swap. Method 3 employs simulated annealing to swap cells. While convergence is relatively slow, Method 3 offers increased flexibility. Each method is applied to a classified Landsat-7 ETMϩ dataset that has been resampled to a spatial resolution of 210 m, and evaluated for accuracy performance and computational efficiency.

No abstract

Recent evidence suggests that urban forms and materials can help to mediate temporal variation of microclimates and that 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 is 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.