Magnitude of urban heat islands largely explained by climate and population

Manoli, Gabriele; Fatichi, Simone; Schläpfer, Markus; Yu, Kailiang; Crowther, Thomas W.; Meili, Naika; Burlando, Paolo; Katul, Gabriel G.; Bou‐Zeid, Elie

doi:10.1038/s41586-019-1512-9

Cited by 683 publications

(452 citation statements)

References 65 publications

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“…With rapid development and the intensification of urbanization, the scale of cities has expanded rapidly and urban buildings tend to be denser, which brings more pressure on the population, transportation, and industrial production, and causes severe damage to the ecological environment of the entire city. Urban Heat Islands (UHIs) are attracting more and more attention, especially in summer, as scorching hot temperatures have severely affected people's lives and work [1][2][3]. To date, researchers all over the world have carried out much research on UHIs.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

et al. 2019

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The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 × 0.5 km to 2 × 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model. Appl. Sci. 2019, 9, 5224 2 of 20 absorbing heat faster and the specific heat capacity being smaller gives rise to limited heat release. In this way, the average LST in the area gradually increases with the spatial distribution of the UHI area to become more continuous and concentrated, forming a high-temperature area centered on artificial buildings in the city [1,9,10]. However, our comprehensive literature research found that past studies focused on the analysis of seasonal, daily, and annual variations of the UHI, which revealed the temporal and spatial variation of the UHI, the distribution characteristics of the horizontal and vertical directions, etc., while there has been less quantitative analysis of the single factors of UHIs, especially discussion of the ubiquitous relationship between impervious surfaces and UHI in the city.As an integral part of the urban impervious surface percentage, the urban building density (UBD) (percentage of built area) can directly reflect the degree of intensification and land-use efficiency in cities to some extent [11]. It is also an essential comprehensive social indicator that can function as an urban planning layout measure [12,13], resource utilization efficiency measure [14,15], and assessment of the urban ecological environment and livability [16][17][18][19]. Some researchers have pointed out that UBD is a crucial indicator to reduce the UHI effect [20] and proved a positive correlation between UBD and LST or UHI [21][22][23]. Additionally, researchers have tried to use a variety of urban design factors to study the relationships of UHIs, including UBD information. For example, Yang studied the summer heat island intensity of three high-rise residential districts in Shanghai, based on the three indices of building layout, density, and greeni...

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Section: Introductionmentioning

confidence: 99%

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

et al. 2019

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“…These influences have not been well studied, and neither have the region-specific behavior of urban trees with respect to ecosystem services. Although urban trees are often used to mitigate urban heat island effects, their ability to provide city scale cooling may be limited in wet, tropical climates where high humidity reduces the cooling effect of evapotranspiration [121]. Shading still provides localized cooling and countless other benefits to pedestrians, but it is important to keep context/setting in mind and realize that effective tools to solve a specific problem in one region may not be as effective in another region.…”

Section: Potential Negative Consequencesmentioning

confidence: 99%

It Is Not Easy Being Green: Recognizing Unintended Consequences of Green Stormwater Infrastructure

Taguchi

Weiss

Gulliver

et al. 2020

Water

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Green infrastructure designed to address urban drainage and water quality issues is often deployed without full knowledge of potential unintended social, ecological, and human health consequences. Though understood in their respective fields of study, these diverse impacts are seldom discussed together in a format understood by a broader audience. This paper takes a first step in addressing that gap by exploring tradeoffs associated with green infrastructure practices that manage urban stormwater including urban trees, stormwater ponds, filtration, infiltration, rain gardens, and green roofs. Each green infrastructure practice type performs best under specific conditions and when targeting specific goals, but regular inspections, maintenance, and monitoring are necessary for any green stormwater infrastructure (GSI) practice to succeed. We review how each of the above practices is intended to function and how they could malfunction in order to improve how green stormwater infrastructure is designed, constructed, monitored, and maintained. Our proposed decision-making framework, using both biophysical (biological and physical) science and social science, could lead to GSI projects that are effective, cost efficient, and just.

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“…(Pa·K -1 ) is the total aerodynamic resistance to vapor transport at the soil surface (including the surface resistance and aerodynamic resistance for vapor transport); and is the air relative humidity. In Equations (4) and (5), the parameters , ∆, , , , , and can be directly obtained or calculated from meteorological observations. The other parameters ( , , , * , , * , , , , , Considering the heterogeneity of pixel components, the urban RS-PM model used a re-derived Penman-Monteith algorithm that incorporates the surface energy balance dual-source parallel model.…”

Section: Et Estimation Using the Urban Rs-pm Modelmentioning

confidence: 99%

“…Widespread changes in surface and the emission of anthropogenic heat in urban areas not only change regional surface radiation and heat conduction characteristics but also have a significant impact on the regional energy balance, water cycle, and atmospheric conditions. In addition, urban population density [3][4][5], urban energy consumption [6], urban climatic and meteorological conditions [5,7], and urban atmospheric pollution [8] have been widely shown to be the main factors influencing the formation and enhancement of UHI effect. energy balance; and (3) the component surface characteristics parameters are added in the inversion of component net radiation, which has simulated the differences of the net radiations for various surfaces.…”

Section: Introductionmentioning

confidence: 99%

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

et al. 2020

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As an important energy absorption process in the Earth's surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman-Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014-2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May-October) but not during the colder months (November-March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W· m −2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.As the UHI effect has a negative environmental impact in urban areas, several studies have explored the factors that mediate the UHI effect with a view to mitigating its effects [9][10][11]. One common conclusion of this previous research is that vegetation and water have cooling and humidifying functions, which are the main factors acting to alleviate the UHI effect [12][13][14]. In general, water bodies in cities are often isolated and tend to be located in urban parks. Therefore, vegetation arguably plays a more important role in alleviating the thermal environment and in improving proximate human thermal comfort [15]. Urban vegetation has various types and functions, including vegetation in recreational parks and gardens, allotments, and street vegetation. One approach to alleviate the heat island effect is to use vegetation as shading on construction land to prevent direct solar heating [3,14,16]. It has been proven, for example, that the area and density of vegetation [3,10,14] can be used as indices for the degree of UHI mitigation, as can other indices such as the normalized difference vegetation index (NDVI) [17][18][19], which have also shown significant negative correlations with the UHI intensity. In addition, a series of remote-sensing-based studies have demonstrated that vegetation characteristics including size [14,20], shape [21], species [22], and spatial patterning [23,24] have significant impacts on the urban cooling effect.Another important mechanism through whic...

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Magnitude of urban heat islands largely explained by climate and population

Cited by 683 publications

References 65 publications

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

It Is Not Easy Being Green: Recognizing Unintended Consequences of Green Stormwater Infrastructure

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

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