Temporal And Spatial Changes Of Surface Urban Heat Island In China And Its Prediction Under The Shared Socioeconomic Pathways

Human activities are key factors contributing to the urban heat island effect.Most of the current studies have focused on the spatial and temporal evolution of the urban heat island effect and its driving mechanisms.However,the impacts of population,economic development and urban expansion on the urban heat island effect at a large scale for cities are still unclear which cause the difficulties in the process of prediction.In order to deal with the urban heat island and the corresponding environmental problems,this study proposed a prediction model by coupling socio-economic development scenarios with traditional urban heat island effect research methods;calculated the intensity of surface urban heat island（SUHI）on a day-to-day level at the city scale in China from 2011 to 2020 based on multi-source geographical data;and analysed the spatial and temporal evolution characteristics of the SUHI and its driving mechanisms.On the basis of constructing a relationship model between driving factors and the SUHI,this study combined the results of this relationship model with the Shared Socioeconomic Pathways（SSPs）proposed by the Intergovernmental Panel on Climate Change（IPCC）.Thus,this study predicted the changes in the SUHI caused by human factors from 2020-2100.The main findings of this study are as follows:（1）The SUHI of 331 cities in China varied significantly on a monthly and seasonal basis,with less interannual variation from 2011 to 2020.Firstly,as for monthly changes,it found that the highest mean SUHI value（1.87℃）occurred in August,and the lowest（0.20℃）occurred in January.Secondly,as for seasonal changes,the mean SUHI values changed from strong to weak in the order of summer（1.68℃）>spring（0.93℃）>autumn（0.82℃）>winter（0.24℃）.Finally,as for annual changes,the mean SUHI value presented an increase from 0.99℃in 2011 to 1.07℃in 2020.In general,the temporal variation of SUHI in China was relatively obvious,with obvious seasonal characteristics and interannual fluctuation and growth trend.（1）The SUHI of 331 cities in China varied significantly on a monthly and seasonal basis,with less interannual variation from 2011 to 2020.Firstly,as for monthly changes,it found that the highest mean SUHI value（1.87℃）occurred in August,and the lowest（0.20℃）occurred in January.Secondly,as for seasonal changes,the mean SUHI values changed from strong to weak in the order of summer（1.68℃）>spring（0.93℃）>autumn（0.82℃）>winter（0.24℃）.Finally,as for annual changes,the mean SUHI value presented an increase from 0.99℃in 2011 to 1.07℃in 2020.In general,the temporal variation of SUHI in China was relatively obvious,with obvious seasonal characteristics and interannual fluctuation and growth trend.（2）The spatial distribution of the SUHI in 331 cities in China was highly heterogeneous（Moran’s I index ranges from 0.25 to 0.31 in each year,P<0.01）,and varied greatly in monthly and seasonal periods.In spring and summer,the cities with strong heat islands（SUHI>3℃）were mostly located east of the"Hu Line";on the other hand,in winter,high values of SUHI were mainly distributed in the northeast,southeast coast and some cities in the southwest.In terms of interannual variation,the 10-year average annual SUHI showed that China’s urban heat island was generally higher in the south than in the north,higher in the east than in the west,strong in the southeast coast and weak in the northwest.（3）The results of the driver analysis showed that the SUHI of each city in China was influenced by both natural and anthropogenic factors.As the results showed,the precipitation,ΔNDVI,ΔDEM,urban area,population size,and GDP usually presented significant impact,among whichΔNDVI has the greatest influence on SUHI,with a significant negative correlation（P<0.01,coefficient of-9.26）.Meanwhile,urban area,population size,and GDP have positively enhanced the SUHI,with GDP exhibiting the most significant effect(P<0.01,coefficient of 8.1×10^-5).（4）The SUHI in China in 2020-2100 under different SSPs may show different degrees of increase due to the joint influence of climate change and human socioeconomic development.In terms of temporal changes,SUHI under the five paths shows a slow growth trend that less than the historical growth rate of 0.010℃/a in2011-2020,and the growth rate in the first 40 years is significantly higher than that in the last 40 years.For example,the mean SUHI increases up to 0.007℃/a during 2020-2060 under SSP5,while the increase decreases to 0.006℃/a during 2060-2100.As for spatial characteristics,the SUHI in most cities in China under each pathway are enhanced and the overall spatial pattern is similar to the history.As the results show,by the end of this century,the range of the SUHI under the SSP5 path that undergoes drastic changes（ΔSUHI>0.50℃）is much larger than these of other paths.And in provinces,such as Jiangsu and Zhejiang,there is an obvious aggregation of the SUHI.The overall SUHI intensity under the SSP3 regional competition path is the smallest compared with these of the other four paths,and this mitigation is more evident in northern regions（e.g.Shandong,Hebei and Henan provinces）.（5）The socioeconomic development trends of typical cities under different SSPs vary significantly,which ultimately leads to differences in the SUHI across cities.In general,the volatility of future SUHI development trends is more pronounced in large cities than in small cities under different paths.The results of the study show that at the end of this century,the SUHI of seven typical cities may reach 4.32-7.97℃（Shanghai urban agglomeration）,3.33-5.07℃（Tianjin city）,1.03-2.33℃（Zhengzhou city）,2.61-4.05℃（Nanning city）,2.91-3.31℃（Kunming city）,1.12-1.57℃（Urumqi city）,and0.63～0.74℃（Hulunbeier City）,respectively.In sum,this study established a heat island effect prediction model based on the historical relationship model,analyzed the possible evolution trend of heat island effect under different SSPs in the future,enriched the method of heat island effect prediction,and the prediction results can provide theoretical reference for improving urban ecological environment and achieving sustainable urban development.