Summer daytime cooling efficiency of various land cover is investigated for the urban core of Phoenix, Arizona, using the Local-Scale Urban Meteorological Parameterization Scheme (LUMPS). We examined the urban energy balance for 2 summer days in 2005 to analyze the daytime cooling-water use tradeoff and the timing of sensible heat reversal at night. The plausibility of the LUMPS model results was tested using remotely sensed surface temperatures from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and reference evapotranspiration values from a meteorological station. Cooling efficiency was derived from sensible and latent heat flux differences. The time when the sensible heat flux turns negative (sensible heat flux transition) was calculated from LUMPS simulated hourly fluxes. Results indicate that the time when the sensible heat flux changes direction at night is strongly influenced by the heat storage capacity of different land cover types and by the amount of vegetation. Higher heat storage delayed the transition up to 3 h in the study area, while vegetation expedited the sensible heat reversal by 2 h. Cooling efficiency index results suggest that overall, the Phoenix urban core is slightly more efficient at cooling than the desert, but efficiencies do not increase much with wet fractions higher than 20%. Industrial sites with high impervious surface cover and low wet fraction have negative cooling efficiencies. Findings indicate that drier neighborhoods with heterogeneous land uses are the most efficient landscapes in balancing cooling and water use in Phoenix. However, further factors such as energy use and human vulnerability to extreme heat have to be considered in the cooling-water use tradeoff, especially under the uncertainties of future climate change.

We quantified the spatio-temporal patterns of land cover/land use (LCLU) change to document and evaluate the daytime surface urban heat island (SUHI) for five hot subtropical desert cities (Beer Sheva, Israel; Hotan, China; Jodhpur, India; Kharga, Egypt; and Las Vegas, NV, USA). Sequential Landsat images were acquired and classified into the USGS 24-category Land Use Categories using object-based image analysis with an overall accuracy of 80% to 95.5%. We estimated the land surface temperature (LST) of all available Landsat data from June to August for years 1990, 2000, and 2010 and computed the urban-rural difference in the average LST and Normalized Difference Vegetation Index (NDVI) for each city. Leveraging non-parametric statistical analysis, we also investigated the impacts of city size and population on the urban-rural difference in the summer daytime LST and NDVI. Urban expansion is observed for all five cities, but the urbanization pattern varies widely from city to city. A negative SUHI effect or an oasis effect exists for all the cities across all three years, and the amplitude of the oasis effect tends to increase as the urban-rural NDVI difference increases. A strong oasis effect is observed for Hotan and Kharga with evidently larger NDVI difference than the other cities. Larger cities tend to have a weaker cooling effect while a negative association is identified between NDVI difference and population. Understanding the daytime oasis effect of desert cities is vital for sustainable urban planning and the design of adaptive management, providing valuable guidelines to foster smart desert cities in an era of climate variability, uncertainty, and change.

Cities are developing innovative strategies to combat climate change but there remains little knowledge of the winners and losers from climate-adaptive land use planning and design. We examine the distribution of health benefits associated with land use policies designed to increase vegetation and surface reflectivity in three US metropolitan areas: Atlanta, GA, Philadelphia, PA, and Phoenix, AZ. Projections of population and land cover at the census tract scale were combined with climate models for the year 2050 at 4 km × 4 km resolution to produce future summer temperatures which were input into a comparative risk assessment framework for the temperature-mortality relationship. The findings suggest disparities in the effectiveness of urban heat management strategies by age, income, and race. We conclude that, to be most protective of human health, urban heat management must prioritize areas of greatest population vulnerability.

We use the Northeast US Urban Climate Archipelago as a case study to explore three key limitations of planning and policy initiatives to mitigate extreme urban heat. These limitations are: (1) a lack of understanding of spatial considerations—for example, how nearby urban areas interact, affecting, and being affected by, implementation of such policies; (2) an emphasis on air temperature reduction that neglects assessments of other important meteorological parameters, such as humidity, mixing heights, and urban wind fields; and (3) too narrow of a temporal focus—either time of day, season, or current vs. future climates. Additionally, the absence of a direct policy/planning linkage between heat mitigation goals and actual human health outcomes, in general, leads to solutions that only indirectly address the underlying problems. These issues are explored through several related atmospheric modeling case studies that reveal the complexities of designing effective urban heat mitigation strategies. We conclude with recommendations regarding how policy-makers can optimize the performance of their urban heat mitigation policies and programs. This optimization starts with a thorough understanding of the actual end-point goals of these policies, and concludes with the careful integration of scientific knowledge into the development of location-specific strategies that recognize and address the limitations discussed herein.

The impacts of land-cover composition on urban temperatures, including temperature extremes, are well documented. Much less attention has been devoted to the consequences of land-cover configuration, most of which addresses land surface temperatures. This study explores the role of both composition and configuration—or land system architecture—of residential neighborhoods in the Phoenix metropolitan area, on near-surface air temperature. It addresses two-dimensional, spatial attributes of buildings, impervious surfaces, bare soil/rock, vegetation and the “urbanscape” at large, from 50 m to 550 m at 100 m increments, for a representative 30-day high sun period. Linear mixed-effects models evaluate the significance of land system architecture metrics at different spatial aggregation levels. The results indicate that, controlling for land-cover composition and geographical variables, land-cover configuration, specifically the fractal dimension of buildings, is significantly associated with near-surface temperatures. In addition, statistically significant predictors related to composition and configuration appear to depend on the adopted level of spatial aggregation.