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Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2020-04-25 , DOI: 10.1016/j.jag.2020.102131
Long Li , Yong Zha , Jiahua Zhang

Urban heat island (UHI) effect is among the most typical characteristics of urban climate. The analysis of surface UHI (SUHI) mechanisms has received the most extensive attention in the world. Here, we quantify the diurnal and seasonal SUHI intensity (SUHII) in global 419 major cities during the period 2003-2013. A geographically weighted regression (GWR) was established to assess the relationships between SUHII and several driving factors, and it further was compared to the ordinary least square (OLS) and stepwise multiple linear regression (SMLR) models. We show that GWR model has higher determination coefficient (R2) than OLS and SMLR models (Time: summer daytime, summer night, winter daytime and winter nighttime; GWR: 0.805, 0.458, 0.699 and 0.582; OLS: 0.732, 0.347, 0.473 and 0.320; SMLR: 0.732, 0.341, 0.468 and 0.316), indicating the spatially non-stationarity in the relationships. During the day, both vegetation activity and tree cover fraction have stronger cooling effect on SUHI in the summer of Asia. At night, there are stronger albedo effects on SUHI in the summer of Eastern Asia and Western North America and in the winter of Eastern Asia. Furthermore, temperature has stronger effect on daytime SUHI in Africa, Europe and South America in summer, and precipitation has stronger effect on nighttime SUHI in Africa and Europe in summer. Our results emphasize the spatial variation of the relationships between SUHII and relevant driving factors across global major cities, further indicating that the spatially non-stationary effect of driving factors on SUHII need to be considered in the future.



中文翻译:

潜在驱动因素对全球主要城市地表热岛的空间非平稳影响

城市热岛效应是城市气候最典型的特征之一。表面UHI(SUHI)机制的分析受到了世界上最广泛的关注。在此,我们对2003-2013年期间全球419个主要城市的日照和季节性SUHI强度(SUHII)进行了量化。建立了地理加权回归(GWR)来评估SUHII与几个驱动因素之间的关系,并将其进一步与普通最小二乘(OLS)模型和逐步多元线性回归(SMLR)模型进行了比较。我们证明GWR模型具有更高的确定系数(R 2)而非OLS和SMLR模型(时间:夏季白天,夏季夜晚,冬季白天和冬季夜间; GWR:0.805、0.458、0.699和0.582; OLS:0.732、0.347、0.473和0.320; SMLR:0.732、0.341、0.468和0.316 ),表示关系中的空间非平稳性。白天,在亚洲夏季,植被活动和树木覆盖率对SUHI的降温作用都较强。晚上,东亚夏季和北美洲西部以及东亚冬季对SUHI的反照率作用更大。此外,夏季,温度对非洲,欧洲和南美的白天SUHI影响更大,夏季,降水对非洲和欧洲的夜间SUHI影响更大。

更新日期:2020-04-25
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