Phoenix Regional Heat and Air Quality Knowledge Repository

Given increasing utility of numerical models to examine urban impacts on meteorology and climate, there exists an urgent need for accurate representation of seasonally and diurnally varying anthropogenic heating data, an important component of the urban energy budget for cities across the world. Incorporation of anthropogenic heating data as inputs to existing climate modeling systems has direct societal implications ranging from improved prediction of energy demand to health assessment, but such data are lacking for most cities. To address this deficiency we have applied a standardized procedure to develop a national database of seasonally and diurnally varying anthropogenic heating profiles for 61 of the largest cities in the United Stated (U.S.). Recognizing the importance of spatial scale, the anthropogenic heating database developed includes the city scale and the accompanying greater metropolitan area.

Our analysis reveals that a single profile function can adequately represent anthropogenic heating during summer but two profile functions are required in winter, one for warm climate cities and another for cold climate cities. On average, although anthropogenic heating is 40% larger in winter than summer, the electricity sector contribution peaks during summer and is smallest in winter. Because such data are similarly required for international cities where urban climate assessments are also ongoing, we have made a simple adjustment accounting for different international energy consumption rates relative to the U.S. to generate seasonally and diurnally varying anthropogenic heating profiles for a range of global cities. The methodological approach presented here is flexible and straightforwardly applicable to cities not modeled because of presently unavailable data. Because of the anticipated increase in global urban populations for many decades to come, characterizing this fundamental aspect of the urban environment – anthropogenic heating – is an essential element toward continued progress in urban climate assessment.

The relationship between the characteristics of the urban land system and land surface temperature (LST) has received increasing attention in urban heat island and sustainability research, especially for desert cities. This research generally employs medium or coarser spatial resolution data and primarily focuses on the effects of a few classes of land-cover composition and pattern at the neighborhood or larger level using regression models. This study explores the effects of land system architecture—composition and configuration, both pattern and shape, of fine-grain land-cover classes—on LST of single family residential parcels in the Phoenix, Arizona (southwestern USA) metropolitan area. A 1 m resolution land-cover map is used to calculate land architecture metrics at the parcel level, and 6.8 m resolution MODIS/ASTER data are employed to retrieve LST. Linear mixed-effects models quantify the impacts of land configuration on LST at the parcel scale, controlling for the effects of land composition and neighborhood characteristics. Results indicate that parcel-level land-cover composition has the strongest association with daytime and nighttime LST, but the configuration of this cover, foremost compactness and concentration, also affects LST, with different associations between land architecture and LST at nighttime and daytime. Given information on land system architecture at the parcel level, additional information based on geographic and socioeconomic variables does not improve the generalization capability of the statistical models. The results point the way towards parcel-level land-cover design that helps to mitigate the urban heat island effect for warm desert cities, although tradeoffs with other sustainability indicators must be considered.

This study seeks to determine the role of land architecture—the composition and configuration of land cover—as well as cadastral/demographic/economic factors on land surface temperature (LST) and the surface urban heat island effect of Phoenix, Arizona. It employs 1 m National Agricultural Imagery Program data of land-cover with 120mLandsat-derived land surface temperature, decomposed to 30 m, a new measure of configuration, the normalized moment of inertia, and U.S. Census data to address the question for two randomly selected samples comprising 523 and 545 residential neighborhoods (census blocks) in the city. The results indicate that, contrary to most other studies, land configuration has a stronger influence on LST than land composition. In addition, both land configuration and architecture combined with cadastral, demographic, and economic variables, capture a significant amount of explained variance in LST. The results indicate that attention to land architecture in the development of or reshaping of neighborhoods may ameliorate the summer extremes in LST.