The urban heat island (UHI) concept describes heat trapping that elevates urban temperatures relative to rural temperatures, at least in temperate/humid regions. In drylands, urban irrigation can instead produce an urban cool island (UCI) effect. However, the UHI/UCI characterization suffers from uncertainty in choosing representative urban/rural endmembers, an artificial dichotomy between UHIs and UCIs, and lack of consistent terminology for other patterns of thermal variation at nested scales. We use the case of a historically well-enforced urban growth boundary (UGB) around Portland (Oregon, USA): to explore the representativeness of the surface temperature UHI (SUHI) as derived from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data, to test common assumptions of characteristically “warm” or “cool” land covers (LCs), and to name other common urban thermal features of interest. We find that the UGB contains heat as well as sprawl, inducing a sharp surface temperature contrast across the urban/rural boundary. The contrast ranges widely depending on the end-members chosen, across a spectrum from positive (SUHI) to negative (SUCI) values. We propose a new, inclusive “urban thermal deviation” (UTD) term to span the spectrum of possible UHI-zero-UCI conditions. We also distinguish at finer scales “microthermal extremes” (MTEs), discrete areas tending in the same thermal direction as their LC or surroundings but to extreme (hot or cold) values, and microthermal anomalies (MTAs), that run counter to thermal expectations or tendencies for their LC or surroundings. The distinction is important because MTEs suggest a need for moderation in the local thermal landscape, whereas MTAs may suggest solutions.
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Forest ethnography: An approach to study the environmental history and political ecology of urban forests: Submission to “The Structure-Function Continuum: Understanding urban transformation through socio-ecological flows,” a special issue of Urban Ecosystems
- Award ID(s):
- 1637661
- NSF-PAR ID:
- 10086702
- Date Published:
- Journal Name:
- Urban Ecosystems
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1083-8155
- Page Range / eLocation ID:
- 49 to 63
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.
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Abstract The impact of climate extremes upon human settlements is expected to accelerate. There are distinct global trends for a continued rise in urban dwellers and associated infrastructure. This growth is occurring amidst the increasing risk of extreme heat, rainfall, and flooding. Therefore, it is critical that the urban development and architectural communities recognize climate impacts are expected to be experienced globally, but the cities and urban regions they help create are far more vulnerable to these extremes than nonurban regions. Designing resilient human settlements responding to climate change needs an integrated framework. The critical elements at play are climate extremes, economic growth, human mobility, and livability. Heightened public awareness of extreme weather crises and demands for a more moral climate landscape has promoted the discussion of urban climate change ethics. With the growing urgency for considering environmental justice, we need to consider a transparent, data-driven geospatial design approach that strives to balance environmental justice, climate, and economic development needs. Communities can greatly manage their vulnerabilities under climate extremes and enhance their resilience through appropriate design and planning towards long-term stability. A holistic picture of urban climate science is thus needed to be adopted by urban designers and planners as a principle to guide urban development strategy and environmental regulation in the context of a growingly interdependent world.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11252-018-0744-z
