Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands

Lai, Jiameng; Zhan, Wenfeng; Huang, Fan; Voogt, James A.; Bechtel, Benjamin; Allen, Michael; Peng, Shushi; Hong, Falu; Liu, Yongchao; Du, Peijun

doi:10.1016/j.rse.2018.08.021

Cited by 91 publications

(59 citation statements)

References 72 publications

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“…Although some researchers argue that it is not appropriate [38], the AB method is simple and versatile. Moreover, two additional reasons support our use of the AB method in the SUHI intensity calculations: (i) for Chinese cities, the rural areas within the administrative borders typically correspond well to the associated size of the urban areas [27] (ii) and some studies have confirmed that the administrative division has a significant impact on the spatial form of an urban area [49]. The above two points support the rationality of using the AB method to calculate the SUHI intensity.…”

Section: Discussionmentioning

confidence: 52%

“…Although the SUHI intensity is primarily controlled by diverse rural extents, the methods for selecting appropriate rural extents for future research remain largely unclear. Few previous studies on the uncertainty of SUHI intensity have focused on the differences in the driving factors of the calculations from different methods, with most of the comparison generally focusing on the spatiotemporal distribution [27,46]. However, the most important purpose of UHI research is to explore the drivers of the UHI phenomenon and to propose further mitigation suggestions.…”

Section: Discussionmentioning

confidence: 99%

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Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

et al. 2020

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Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

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Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

et al. 2020

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“…SUHI data are based on clear-sky conditions only so that the analysis is not representative of the full range of urban conditions experienced by cities, especially where cloudy days are frequent. Note also that seasonal variations in day length can introduce additional uncertainties in the estimated values of mean daily SUHIs as they are calculated by averaging daytime and nighttime observations (80).…”

Section: Methodsmentioning

confidence: 99%

Seasonal hysteresis of surface urban heat islands

Manoli

Fatichi

Bou‐Zeid

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences (ΔTs) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of ΔTs. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce ΔTs during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation.

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“…In addition, the impervious surface percentage should be considered when defining rural regions [29]. Fourth, although the ecological context has an obvious impact on SUHII, including water and heat combinations, vegetation characteristics, landforms, and human activities, most previous studies at large scale have not considered this factor [25,28,34,35,37,45] but simply use administrative boundary data to determine regions of different contexts [27,42] or identify these regions too coarsely [20,26], with the exception of a few studies [29,30,46,47]. Finally, certain research areas remain unstudied or have conflicting findings [22].…”

Section: Introductionmentioning

confidence: 99%

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

Wang

Liu

et al. 2019

Advances in Meteorology

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The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

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Identification of typical diurnal patterns for clear-sky climatology of surface urban heat islands

Cited by 91 publications

References 72 publications

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

Seasonal hysteresis of surface urban heat islands

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

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