Mitigating Extreme Summer Heat Waves with the Optimal Water-Cooling Island Effect Based on Remote Sensing Data from Shanghai, China

Hongyu Du and Fengqi Zhou
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Hongyu Du: Institute of Ecology and Sustainable Development, Shanghai Academy of Social Sciences, No. 7, Lane 622, Huaihaizhong Road, Huangpu District, Shanghai 200020, China
Fengqi Zhou: Institute of Ecology and Sustainable Development, Shanghai Academy of Social Sciences, No. 7, Lane 622, Huaihaizhong Road, Huangpu District, Shanghai 200020, China

IJERPH, 2022, vol. 19, issue 15, 1-14

Abstract: Due to the progress in global warming, the frequency, duration and intensity of climate extremes are increasing. As one of these extremes, heat waves influence the well-being of human beings and increase societies’ energy consumption. The Water-Cooling Island (WCI) effect of urban water bodies (UWBs) is important in urban heat wave mitigation. In this paper, the impact of WCI, especially the landscape pattern of the surrounding area, was explored. The results indicate that water bodies with a larger total area and simpler shape have a longer cooling effect. In the areas surrounding UWBs, a lower percentage or discrete distribution of impervious surfaces or green land provide a longer cooling effect. The amplitude of WCI is mainly decided by the impervious surface in the surrounding areas. A lower percentage or discrete distribution of impervious surfaces or green land leads to a smaller-amplitude WCI. The gradient is impacted by the shape of the UWB and surrounding green land. A complex shape and discrete distribution of green land lead to a higher gradient of WCI. The linear regress model was significant in terms of WCI range and gradient, while the model of WCI amplitude was not significant. This indicates that WCI is directly decided by impact factors through gradient and range. The conclusions provide a methodology for WCI prediction and optimization, which is important when mitigating summer heat waves.

Keywords: water cooling island effect (WCI); landscape pattern; remote sensing (RS); regress analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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