共享社会经济路径下中国城市热岛时空演变及预测研究

doi:10.12006/j.issn.1673-1719.2022.266

摘要/Abstract

摘要：

人类活动已被认为是造成城市热岛效应的关键因素。然而市域尺度下人为因素对热岛效应强度的影响尚不明确。基于多源地理数据计算了2012—2018年中国市域尺度的日间地表城市热岛效应强度（SUHI)，分析了市域尺度SUHI的时空演变规律和驱动机制，并构建驱动因子与SUHI的关系模型；结合IPCC提出的共享社会经济路径（SSPs）来预测不同情景下的人类活动强度，并模拟2020—2100年的SUHI。结果表明：2012—2018年市域尺度上，年均SUHI空间分布呈现出南方整体高于北方，东南沿海最强、西北地区最弱的特征，且具有明显的正向空间自相关性，2012—2018年间SUHI有略微的增强（均值增幅达0.08℃)。SUHI受人为因素与自然因素共同影响，其中GDP及城乡植被差异（ΔNDVI）对SUHI影响显著。2020—2100年5种SSPs情景下中国大部分城市的SUHI显著增强，其中SSP5情景下SUHI变化最剧烈（均值增幅达0.67℃)。此外，大体量城市未来的SUHI在不同发展情景下差异明显。从驱动因素来看，大多数城市的GDP是SUHI增长的主要人为驱动因素。对典型城市的对比分析发现，在21世纪末上海城市群、天津市、乌鲁木齐市及南宁市的SUHI波动范围可能分别达到4.83~8.98℃、3.24~4.95℃、1.11~1.55℃、2.61~4.05℃。研究结果揭示了不同社会经济发展路径下SUHI可能的时空变化趋势，对实现城市生态环境的健康发展具有重要意义。

关键词: 地表城市热岛, 时空格局, 驱动因子, 共享社会经济路径（SSPs)

Abstract:

Human activities have been thought as the key factors for the urban heat island effect. However, the impacts of anthropogenic factors on the intensity of the urban heat island effect at the city scale are still unclear. The intensity of daytime surface urban heat island (SUHI) at the city scale in China from 2012 to 2018 was calculated based on multi-source geographic data. Then the spatial and temporal patterns at the city scale were analyzed, following which the relationship between the driving factors and SUHI was discovered. Coupled with the shared socioeconomic pathways (SSPs) proposed by IPCC, the intensity of human activities under different scenarios were used to project the SUHI from 2020 to 2100. The spatial distribution of the annual mean SUHI at the city scale in 2012-2018 showed an obvious positive spatial autocorrelation. The southern China experienced higher SUHI than the northern China in general, and the strongest SUHI occurred in the southeast coast, with the weakest SUHI occurring in the northwest during 2012-2018. The mean value of SUHI slightly increased from 2012 to 2018 by 0.08℃ and was influenced by both anthropogenic and natural factors, among which GDP and ΔNDVI presented the largest contributions. From 2020 to 2100, the SUHI in most Chinese cities under the five SSPs scenarios will increase significantly, with the mean value of SUHI under the SSP5 scenario increasing by 0.67℃. In addition, the future SUHI in large cities varies largely under different scenarios. In terms of driving factors, the GDP will be the main anthropogenic driver of SUHI growth for most cities. Meanwhile, the comparative analysis for typical cities suggests that the fluctuations of SUHI in Shanghai urban agglomeration, Tianjin city, Urumqi and Nanning city may reach 4.83-8.98℃, 3.24-4.95℃, 1.11-1.55℃ and 2.61-4.05℃ at the end of the 21st century, respectively. The results of this study reveal the possible temporal and spatial changing trend of SUHI under the SSPs in future. The conclusions are important for achieving healthy development of urban ecological environment.

Key words: Surface urban heat island, Spatial and temporal pattern, Driving factors, The shared socioeconomic pathways (SSPs)

高运翔, 栗珂珂, 张文婷, 王天巍, 李珊. 共享社会经济路径下中国城市热岛时空演变及预测研究[J]. 气候变化研究进展, 2023, 19(4): 431-445.

GAO Yun-Xiang, LI Ke-Ke, ZHANG Wen-Ting, WANG Tian-Wei, LI Shan. Spatio-temporal evolution and projection of urban heat island in China under the shared socioeconomic pathways[J]. Climate Change Research, 2023, 19(4): 431-445.

图/表 13

表1 研究数据信息汇总

Table 1 Information of research data

图1 中国2012—2018年地表城市热岛效应强度（SUHI）的7年均值(a)、7年差值（2018年相对于2012年）(b)的空间分布及箱式图(c) 注：此图未含港澳台数据，下同；IQR表示四分位间距。

Fig. 1 Spatial distribution of annual average surface urban heat island intensity (SUHI) in China from 2012 to 2018. (a) 7-year mean, (b) 7-year difference, (c) box plot. (Data of Hong Kong, Macao and Taiwan are not included. IQR stands for interquartile range, the same below)

表2 全局空间自相关结果参数

Table 2 Result of global Moran’s index

表3 ULR模型结果表

Table 3 Results of ULR model

图2 GWR模型R2 (a)与残差(b)空间分布图

Fig. 2 Spatial distribution of R2 (a) and residual (b) in the GWR model

图3 2018年SUHI实际值与模拟值对比图注：图中横坐标“城市”按照SUHI实际值从小到大排列。

Fig. 3 Comparison of actual and simulated SUHI values in 2018

表4 不同SSPs情景下中国2020—2100年及2012—2018年SUHI增速

Table 4 Growth rate of SUHI in China during 2020-2100 and 2012-2018 under different SSPs

图4 不同SSPs情景下中国2020—2100年SUHI均值变化图

Fig. 4 Plot of variation of mean value of SUHI in China from 2020 to 2100 under different SSPs

图5 SSP1、SSP5情景下中国2020—2100年SUHI均值和差值空间分布图

Fig. 5 Spatial distribution of mean and difference of SUHI in China from 2020 to 2100 under SSP1 and SSP5

图6 不同SSPs情景下上海城市群2020—2100年SUHI、人口及GDP时序变化图

Fig. 6 Time series changes of SUHI, population and GDP in Shanghai urban agglomeration from 2020 to 2100 under different SSPs

图7 不同SSPs情景下天津市2020—2100年SUHI、人口及GDP时序变化图

Fig. 7 Time series changes of SUHI, population and GDP in Tianjin from 2020 to 2100 under different SSPs

图8 不同SSPs情景下乌鲁木齐市2020—2100年SUHI、人口及GDP时序变化图

Fig. 8 Time series changes of SUHI, population and GDP in Urumqi from 2020 to 2100 under different SSPs

图9 不同SSPs情景下南宁市2020—2100年SUHI、人口及GDP时序变化图

Fig. 9 Time series changes of SUHI, population and GDP in Nanning from 2020 to 2100 under different SSPs

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