Predicting air temperature simultaneously for multiple locations in an urban environment: A bottom up approach |
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| Institution: | 1. School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150000, China;2. Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China;3. State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resource and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China;4. Shenzhen Tourism College of Jinan University, Shenzhen 518053, China;5. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150000, China;1. Department of Geography, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong;2. Australian Research Council Centre of Excellence for Climate System Science, Climate Change Research Centre, The University of New South Wales, Sydney, Australia;1. Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel;2. Tel Hai College, Tel Hai, Israel;1. Faculty of Engineering, University of Nottingham Malaysia Campus, Selangor, Malaysia;2. Faculty of Architecture Planning and Surveying, Universiti Teknologi MARA, Selangor, Malaysia;3. National Center for Atmospheric Research NCAR Boulder, CO, USA |
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| Abstract: | Cities are characterized by high heterogeneity that results in varied microclimate effects. The current study introduces a new bottom–up approach linking the urban Canyon Air Temperature (CAT) model with spatially distributed inputs extracted from a GIS data-base and remote sensing products to predict intra-urban temperature variability simultaneously for multiple locations in an urban environment. To provide proof of concept, the model was applied for the city of Bat-Yam, Israel. Simulation shows a maximum nighttime urban heat island (UHI) intensity of 2–2.25 °C, relative to a rural reference point, during both summer and winter, with significant spatial variability related to the height-to-width ratio of urban street canyons and to the surface land cover. The CAT simulation also highlighted the important influence of the local wind regime on the development and persistence of the nocturnal UHI. We conclude that linking CAT to a GIS data-base supports simulations at the city scale that reflect the local intra-urban variability. The model can be used to investigate both macro and micro scale spatio-temporal characteristics of the UHI in various urban development scenarios, which may be applied to generate appropriate geographically-explicit mitigation and adaptation measures. |
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| Keywords: | Computer modelling Urban heat island Urban planning Spatial variability GIS |
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