Time-Series Analysis Reveals Intensified Urban Heat Island Effects but without Significant Urban Warming

Abstract

Numerous studies have shown an increased surface urban heat island intensity (SUHII) in many cities with urban expansion. Few studies, however, have investigated whether such intensification is mainly caused by urban warming, the cooling of surrounding nonurban regions, or the different rates of warming/cooling between urban and nonurban areas. This study aims to fill that gap using Beijing, China, as a case study. We first examined the temporal trends of SUHII in Beijing and then compared the magnitude of the land surface temperature (LST) trend in urban and nonurban areas. We further detected the temporal trend of LST (TrendLST) at the pixel level and explored its linkage to the temporal trends of EVI (TrendEVI) and NDBI (TrendNDBI). We used MODIS data from 2000 to 2015. We found that (1) SUHII significantly increased from 4.35 °C to 6.02 °C, showing an intensified surface urban heat island (SUHI) effect, with an annual increase rate of 0.13 °C in summer during the daytime and 0.04 °C in summer at night. In addition, the intensification of SUHII was more prominent in new urban areas (NUA). (2) The intensified SUHII, however, was largely caused by substantial cooling effects in nonurban areas (NoUA), not substantial warming in urban areas. (3) Spatially, there were large spatial variations in significant warming and cooling spots over the entire study area, which were related to TrendNDBI and TrendEVI. TrendNDBI significantly affected TrendLST in a positive way, while the TrendEVI had a significant positive effect (p = 0.023) on TrendLST only when EVI had an increasing trend. Our study underscores the importance of quantifying and comparing the changes in LST in both urban and nonurban areas when investigating changes in SUHII using time-series trend analysis. Such analysis can provide insights into promoting city-based urban heat mitigation strategies which focused on both urban and nonurban areas.

Global long-term mapping of surface temperature shows intensified intra-city urban heat island extremes

“…1 and estimates at pixel level were first aggregated to macro-region by taking the spatial median over urban pixels in FUA of the considered macro-region. The trend's slope was then estimated on the macro-region annual 1 and values using a Theil-Sen estimator 25 .…”

“…Large-scale UHI studies have focused primarily on coarse-grained representations of UHIs, where the thermal behaviour is lumped spatially over urban centres 23,24 and temporally over seasonal 5 or annual 3 time scales. As a consequence, these fail to capture UHI peaks that are localised in space and time, and that are better represented in fine-scale studies [25][26][27] . Characterising temporal fluctuations is of foremost importance to assess the real threat posed to human health by urban heat, as phenomena such as heat waves can often last only few days [28][29][30] .…”

Global long-term mapping of surface temperature shows intensified intra-city urban heat island extremes

“…Large-scale UHI studies have focused primarily on coarse-grained representations of UHIs, where the thermal behaviour is lumped spatially over urban centres 23,24 and temporally over seasonal 5 or annual 3 time scales. As a consequence, these fail to capture UHI peaks that are localised in space and time, and that are better represented in fine-scale studies [25][26][27] . Characterising temporal fluctuations is of foremost importance to assess the real threat posed to human health by urban heat, as phenomena such as heat waves can often last only few days [28][29][30] .…”

Spatial correlation length of summer extreme heat stress over eastern China

“…Many studies indicate that intra‐seasonal oscillation, the South Asian High, the East Asian summer monsoon, sea‐surface temperatures, and other large‐scale circulation anomalies play an important role in changes in extreme heat stress in eastern China (e.g., Chen and Lu, 2015; Wang et al ., 2017; Chen et al ., 2018; Sparrow et al ., 2018). In addition, human activities like rapid urbanization and greening in rural areas also amplify and affect the trend, severity, and spatial heterogeneity of temperature variability (e.g., Oleson et al ., 2015; Yang et al ., 2015; Ho et al ., 2016; Wang et al ., 2016b; Wang et al ., 2019b). Luo and Lau (2018) pointed out that urbanization in eastern China contributed about 30% of the increase in mean heat stress and the frequency of extreme heat stress days.…”