Urban heat island: Aerodynamics or imperviousness?

Li, Dan; Liao, Weilin; Rigden, A. J.; Li, Xiaoping; Wang, Dagang; Malyshev, Sergey; Shevliakova, Elena

doi:10.1126/sciadv.aau4299

Cited by 237 publications

(172 citation statements)

References 31 publications

(54 reference statements)

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“…3.1. Spatial anomaly of temperature and moisture Similar to other cities, Madison is largely influenced by the local effect of urbanization and surface heterogeneity (Li et al 2019), where a remarkable intraurban spatial variability of temperature and absolute humidity are observed with a strong seasonality (e.g., figure 1 for July and figure S3 is available online at stacks.iop. org/ERL/15/034041/mmedia for January).…”

Section: Resultsmentioning

confidence: 86%

Greenspace, bluespace, and their interactive influence on urban thermal environments

2020

Environ. Res. Lett.

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Urban land use land cover (LULC) change raises ambient temperature and modifies atmospheric moisture, which increases heat-related health risks in cities. Greenspace and bluespace commonly coexist in urban landscapes and are nature-based heat mitigation strategies. Yet, their interactive effects on urban thermal environments are rarely assessed and it remains unclear how extreme heat events (EHEs) affect their ability to regulate human thermal comfort. Using multi-year observations from a dense urban observational network in Madison, WI, we found that green and blue spaces jointly modify the intraurban spatiotemporal variability of temperature and humidity, and the resultant effects on thermal comfort show diurnal and seasonal asymmetry. Greenspace is more effective at cooling throughout the year, particularly at night. Accelerated cooling efficiency is found in areas with dominant greenspace coverage and little co-influence from bluespace. The thermal comfort benefit due to greenspaces can be offset by bluespaces because of intensified nighttime warming and humidifying effects during the warm months, although a weak daytime cooling of bluespace is observed. EHEs enhance bluespace cooling, but the overall joint thermal regulation remains the same due to the enhanced moisture effect. Our findings suggest that diverse outcomes of green and blue spaces cross multiple temporal scales should be holistically assessed in urban planning. The analysis framework based on generalized additive models is robust and transferable to other cities and applications to disentangle the nonlinear co-influences of different drivers of urban environmental phenomena.

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Section: Resultsmentioning

confidence: 86%

Greenspace, bluespace, and their interactive influence on urban thermal environments

2020

Environ. Res. Lett.

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“…Thus, it is critical to understand clearly the urbanization effects on winds and their feedback to the urban environment. Secondly, because reduced evaporative cooling in cities is among the dominant drivers of the urban heat island (UHI) phenomenon (Zhao et al 2014, Li et al 2019a, the reduction in wind speed by urbanization and associated reduction in evaporation will further enhance UHI (McVicar et al 2012). In conclusion, our results emphasize the importance of considering the wind-related urbanization effects in addition to the traditional hotspottemperature, for better understanding and predicting of the environment change in urban areas.…”

Section: Discussionmentioning

confidence: 73%

“…Both observational and modeling studies show that urbanization can be significantly related to the reduced surface wind speed in Beijing , Hou et al 2013. The potential impact of urbanization on surface wind speed has also been identified in large cities in China (Wu et al 2016, 2019a, Peng et al 2018. The declining trends in surface wind speed show large regional variations in China with relatively smaller decreases in southern regions compared to northern regions (Shi et al 2015), indicating that background regional climate changes may partly counteract the urbanization effect.…”

Section: Introductionmentioning

confidence: 98%

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Chen

Jeong

Park

et al. 2020

Environ. Res. Lett.

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Wind stilling has been observed in many regions across the Northern Hemisphere; however, the related mechanisms are not well understood. Analyses of the wind speed variations in South Korea during 1993-2015 in this study reveal that the annual-mean surface wind speeds at rural stations have increased by up to 0.41 m s −1 decade −1 , while those at urban stations have decreased by up to −0.63 m s −1 decade −1 . The local wind speed variations are found to be negatively correlated with the population density at the corresponding observation sites. Gustiness analyses show the increase in local surface roughness due to urbanization can explain the observed negative wind speed trends at urban stations as the urbanization effect overwhelms the positive wind speed trend due to climate change. The observed negative wind speed trend in urban areas are not found in the regional climate model simulations in the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA) as these models do not take into account the impact of urbanization on wind variations during the period. This study suggests that urbanization can play an important role in the recent wind stilling in rapidly developing regions such as South Korea. Our results suggest that future climate projections in CORDEX-EA may overestimate wind speeds in urban areas, and that future regional climate projections need to consider the effects of urbanization for a more accurate projection of wind speeds.

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“…The rapid increase in land surface temperature (LST) and increased UHI are primarily resulting from expanding urban developed land which encroaches upon cultivated land, grassland, and forest land [2,3]. During the process of urbanization, constant changes in underlying surface features and land use cover result in decreased latent heat flux and the increased sensible heat flux, which are the root causes of an increased UHI effect [4]. At present, the urban heat problem is no longer a simple climate and environmental issue, but a major risk to the sustainable development of the urban environment [5].…”

Section: Introductionmentioning

confidence: 99%

“…At present, research on urban heat environment risk focuses on mapping heat risk and vulnerability in various environments [5][6][7], especially in the context of global climate change and heat waves [8][9][10]. Researchers have endeavored to employ theoretical and technical methods, such as statistical [11][12][13][14][15][16][17][18][19], energy-balance [3,4,20], numerical [21][22][23], analytical [24], and physical models [25]. For this process, researchers usually grade and evaluate air temperature data from meteorological stations and the LST data observed via remote sensing from the perspective of climate vulnerability or human exposure [26], and developed some vulnerability and risk indexes, such as manual indicator removal [27], as well as more complicated techniques, such as Monte Carlo simulation and variance-based global sensitivity analysis [28].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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Urban heat island: Aerodynamics or imperviousness?

Cited by 237 publications

References 31 publications

Greenspace, bluespace, and their interactive influence on urban thermal environments

Greenspace, bluespace, and their interactive influence on urban thermal environments

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

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