Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France)

Lemonsu, Aude; Viguié, Vincent; Daniel, M.; Masson, Valéry

doi:10.1016/j.uclim.2015.10.007

Cited by 250 publications

(134 citation statements)

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“…Numerous studies have found that urban sprawl [1,2] (i.e., low density outward expansion of cities) reduces vegetation cover and, thus, contributes to the urban heat island (UHI) effect-a phenomenon where urban areas experience high temperatures compared to their non-urban surroundings. Most of these studies, however, are based on cross-sectional evidence, which means that they investigated the link between land cover type and land surface temperature, based on data from a single point in time to substantiate their claims.…”

Section: Introductionmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

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Section: Introductionmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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“…To represent the atmosphere, the Community Atmosphere Model version 3 (CAM3; [31]) was used. In addition to this, the system components are: the Community Land Surface Model version 3 (CLM3, [32,33]) to represent the surface, the Community Sea Ice Model version 5 (CSIM5, [34]) to represent the sea ice, and the Parallel Ocean Program version 1.4.3 (POP; [35]) to represent the ocean. The standard version of CAM3 is based on a spectral and Eulerian dynamic core with triangular spectral truncation at wave numbers 31, 42 and 85.…”

Section: Numerical Modeling and Configurationmentioning

confidence: 99%

Dynamical Downscaling of Future Climate Change Scenarios in Urban Heat Island and Its Neighborhood in a Brazilian Subtropical Area

Morais

Guerrero

Martins

et al. 2017

Proceedings of the 2nd International Electronic Conference on Atmospheric Sciences

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According to IPCC reports, a greater frequency, intensity and duration of heat waves in urban areas are expected. This is related to the dynamical evolution of the cities, due the changes of the natural surface modifies the roughness pattern reduce the wind intensity, modify the available humidity in the soil and the radiative properties. These topics characterize the formation of the urban heat island (UHI). A dynamical downscaling of A2 and B1 future scenarios was made for Londrina, a medium-size city of Southern Brazil, using Weather Research Forecasting model, to investigate the impact of this projections on the UHI formation and intensity, as well for rural area, which consistent an important agricultural landscape. For this, an evaluation of the model and the scenarios were done to investigate the current trends. The results shows a tendency of following the worst proposed scenarios (A2), and a drier rural area for the sustainable projection, which has directly influence on the urban heat island intensity and formation, and in the agriculture of the region.

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“…Thus, urban areas are usually warmer than nearby rural areas, a phenomenon recognized as the Urban Heat Island (UHI) effect [3,4]. Studies have shown that the environmental impacts of urban areas can extend well beyond administrative boundaries [5,6], and urban heat islands can increase the health risk of vulnerable populations to heat waves [7]. Intensity of the UHI effect depends on many factors, including building density, height, and arrangement; thermal and reflective properties of construction, paving, and roofing materials structures; size and arrangement of green spaces within the city; local and regional wind fields; season and time of year; and recent weather [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown that the environmental impacts of urban areas can extend well beyond administrative boundaries [5,6], and urban heat islands can increase the health risk of vulnerable populations to heat waves [7]. Intensity of the UHI effect depends on many factors, including building density, height, and arrangement; thermal and reflective properties of construction, paving, and roofing materials structures; size and arrangement of green spaces within the city; local and regional wind fields; season and time of year; and recent weather [7,8]. The UHI has been primarily characterized by near surface air temperature measurements and by remote sensing of the land surface temperature using thermal infrared sensors [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Urban–Rural Contrasts in Central-Eastern European Cities Using a MODIS 4 Micron Time Series

Tomaszewska

Henebry

2016

Remote Sensing

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Abstract:A primary impact of urbanization on the local climate is evident in the phenomenon recognized as the Urban Heat Island (UHI) effect. This urban thermal anomaly can increase the health risks of vulnerable populations to heat waves. The surface UHI results from emittance in the longer wavelengths of the thermal infrared; however, there are also urban anomalies that are detectable from radiance in the shorter wavelengths (3-5 micron) of the Middle Infrared (MIR). Radiance in the MIR can penetrate urban haze which frequently obscures urban areas by scattering visible and near infrared radiation. We analyzed seasonal and spatial variations in MIR for three Central European cities from 2003 through 2012 using Moderate Resolution Imaging Spectrometer (MODIS) band 23 (~4 micron) to evaluate whether MIR radiance could be used to characterize heat anomalies associated with urban areas. We examined the seasonality of MIR radiance over urban areas and nearby croplands and found that the urban MIR anomalies varied due to time of year: cropland MIR could be larger than urban MIR when there was more exposed soil at planting and harvest times. Further, we compared monthly mean MIR with the Normalized Difference Vegetation Index (NDVI) to analyze contrasts between urban and rural areas. We found that the seasonal dynamic range of the MIR could exceed that of the NDVI. We explored the linkage between meteorological data and MIR radiance and found a range of responses from strong to weak dependence of MIR radiance on maximum temperature and accumulated precipitation. Our results extend the understanding of the anomalous characteristics of urban areas within a rural matrix.

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Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France)

Cited by 250 publications

References 50 publications

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Dynamical Downscaling of Future Climate Change Scenarios in Urban Heat Island and Its Neighborhood in a Brazilian Subtropical Area

Urban–Rural Contrasts in Central-Eastern European Cities Using a MODIS 4 Micron Time Series

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