Land cover, climate, and the summer surface energy balance in Phoenix, AZ, and Portland, OR

Middel, Ariane; Brazel, Anthony J.; Gober, Patricia; Myint, Soe W.; Chang, Heejun; Duh, Jiunn-Der

doi:10.1002/joc.2408

Cited by 41 publications

(22 citation statements)

References 42 publications

(59 reference statements)

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“…2 provide an overview of the four sites included in this work. SUEWS has mostly been applied in temperate climates though the simpler model LUMPS has been applied in arid environments before (Middel et al, 2012). Here we applied SUEWS to a newly (b 2 years) instrumented site in Dublin, Ireland; a long term (5-10 years) site in Hamburg; Germany; a long term site in Melbourne, Australia; and an established site (2-4 years) in Phoenix, USA.…”

Section: −2mentioning

confidence: 99%

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

et al. 2016

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There are a number of models available for examining the interaction between cities and the atmosphere over a range of scales, from small scales -such as individual facades, buildings, neighbourhoods -to the effect of the entire conurbation itself. Many of these models require detailed morphological characteristics and material properties along with relevant meteorological data to be initialised. However, these data are difficult to obtain given the heterogeneity of built forms, particularly in newly emerging cities. Yet, the need for models which can be applied to urban areas (for instance to address planning problems) is increasingly urgent as the global population becomes more urban. In this paper, a modeling approach which derives the required land cover parameters for a mid-complex urban energy budget and water budget model (SUEWS) in a consistent manner is evaluated in four cities (Dublin, Hamburg, Melbourne and Phoenix). The required parameters for the SUEWS model are derived using local climate zones (LCZs) for land cover, and meteorological observations from off-site synoptic stations. More detailed land cover and meteorological data are then added to the model in stages to examine the impact on model performance with respect to observations of turbulent fluxes of sensible (Q H ) and latent (Q E ) heat. Replacing LCZ land cover with detailed fractional coverages was shown to marginally improve model performance, however the performance of model coupled with 'coarse' LCZ data was within

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Section: −2mentioning

confidence: 99%

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

et al. 2016

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“…So, while an agency may be pursuing deployment of green roofs or rain gardens for storm water management, it may miss the implications of such technologies for urban heat, building energy, and air quality. Taking urban vegetation as an example, there is ample empirical and modeling evidence of the potential for urban vegetation to significantly reduce urban air temperatures (e.g., [11][12][13]), although water availability is a key determinant of performance [14]. Some important implementation issues, however, are often not considered.…”

Section: Interactions Within the Climate Systemmentioning

confidence: 99%

“…While an extensive amount of literature has examined the influence of land cover changes in cities on heat island formation [1,3,14], and the health impacts of high temperatures [31], few studies have explicitly sought to link UHI formation directly to human health outcomes. One of the first studies to address this question [33] used a global circulation model (GISS) coupled with a regional climate model (MM5) to assess the impact of globally-and regionally-driven warming on heat-related mortality in New York by 2050.…”

Section: Reducing Heat-related Mortality In Future Climatesmentioning

confidence: 99%

Improving Heat-Related Health Outcomes in an Urban Environment with Science-Based Policy

et al. 2016

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Abstract: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.

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“…Building upon the literature, we detail how our place-based research has advanced knowledge as well as strategies for dealing with complexity and uncertainty through adaptive approaches. While most DCDC research has been centered on the desert study region of metropolitan Phoenix, some studies-leveraged with additional resources from other grants and projects-have been comparative in nature [40,44]. For example, we focus in particular in this analysis on how a National Oceanographic and Atmospheric Administration (NOAA) sponsored study of arid Phoenix, Arizona and humid Portland, Oregon has expanded knowledge through comparative analysis of land-water-climate dynamics and how they play out across these rather different biomes.…”

Section: Context: the Decision Center For A Desert Citymentioning

confidence: 99%

Decision-Making under Uncertainty for Water Sustainability and Urban Climate Change Adaptation

et al. 2015

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Complexities and uncertainties surrounding urbanization and climate change complicate water resource sustainability. Although research has examined various aspects of complex water systems, including uncertainties, relatively few attempts have been made to synthesize research findings in particular contexts. We fill this gap by examining the complexities, uncertainties, and decision processes for water sustainability and urban adaptation to climate change in the case study region of Phoenix, Arizona. In doing so, we integrate over a decade of research conducted by Arizona State University's Decision Center for a Desert City (DCDC). DCDC is a boundary organization that conducts research in collaboration with policy makers, with the goal of informing decision-making under uncertainty. Our results highlight: the counterintuitive, non-linear, and competing relationships in human-environment dynamics; the myriad uncertainties in climatic, scientific, political, and other domains of knowledge and practice; and, the social learning that has occurred across science and policy spheres. Finally, we reflect on how our interdisciplinary research and boundary organization has evolved over time to enhance adaptive and sustainable governance in the face of complex system dynamics. OPEN ACCESSSustainability 2015, 7 14762

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Land cover, climate, and the summer surface energy balance in Phoenix, AZ, and Portland, OR

Cited by 41 publications

References 42 publications

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

Improving Heat-Related Health Outcomes in an Urban Environment with Science-Based Policy

Decision-Making under Uncertainty for Water Sustainability and Urban Climate Change Adaptation

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