Abstract
Abstract. Mitigating urban heat islands has become an important objective for many cities experiencing heat waves. Despite notable progress, the spatial relationship between land use and/or land cover patterns and the distribution of air temperature remains poorly understood. This article presents a reusable computational workflow to simulate the spatial distribution of air temperature in urban areas from their land use and/or land cover data. The approach employs the InVEST urban cooling model, which estimates the cooling capacity of the urban fabric based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the parameters of the model to best fit air temperature observations from monitoring stations. In a case study in Lausanne, Switzerland, spatial estimates of air temperature obtained with the calibrated model show that the urban cooling model outperforms spatial regressions based on satellite data. This represents two major advances in urban heat island modeling. First, unlike in black-box approaches, the calibrated parameters of the urban cooling model can be interpreted in terms of the physical mechanisms that they represent; therefore, they can help promote an understanding of how urban heat islands emerge in a particular context. Second, the urban cooling model requires only land use and/or land cover and reference temperature data and can, therefore, be used to evaluate synthetic scenarios such as master plans, urbanization prospects and climate scenarios. The proposed approach provides valuable insights into the emergence of urban heat islands which can serve to inform urban planning and assist the design of heat mitigation policies.
Highlights
Since the industrial revolution, the Earth has seen a global increase in temperature which has been especially prominent in urban areas (Oke, 1973; Arnfield, 2003; Clinton and Gong, 2013)
The following most significant variable is the normalized difference water index (NDWI) spatially averaged over a 200 m radius (p = 0.071) and without spatial averaging (p = 0.231), and the land surface temperatures (LSTs) spatially averaged over a 400 m radius (p = 0.277)
The present article presents a spatially explicit approach to simulate urban heat island” (UHI) with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model, which is based on three biophysical mechanisms, namely tree shade, evapotranspiration and albedo
Summary
The Earth has seen a global increase in temperature which has been especially prominent in urban areas (Oke, 1973; Arnfield, 2003; Clinton and Gong, 2013). By modifying the energy and water balance processes and influencing the movement of air, urban surfaces alter local climatic characteristics, often resulting in warmer temperatures than their rural surroundings (Oke, 1982). This phenomenon is known as the “urban heat island” (UHI) effect.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.