Quantifying diurnal and seasonal variation of surface urban heat island intensity and its associated determinants across different climatic zones over Indian cities

ABSTRACT

Surface urban heat island (SUHI) is a major anthropogenic alteration of Earth’s surface and can influence the local thermal environment by altering the surface energy flux balances. Researchers have paid much attention to SUHI studies in the last decades; still, its geospatial variability over a larger area is poorly understood. Detailed research is required to understand the mechanism and dynamics of SUHI along with its different driving variables. Hence in this study, we quantified the diurnal, seasonal, and interannual variation of SUHI intensity (SUHII) over 150 major Indian cities situated over different climatic zones using MODIS data from 2003 to 2018. The results reveal urban cool islands occurrence over the hot desert, hot steppe, and tropical monsoon climatic zone during daytime in both summer (−0.25 to −0.17°C) and winter (−0.33 to 0.17°C) season. In contrast, nighttime SUHII shows clear evidence of positive urban heat island irrespective of climatic region and seasonal variation of 0.48–1°C in summer and 0.46–1.32°C in winter is seen. The Mann–Kendall and Sen’s slope estimator tests are used to detect the trend of the SUHII during the study period, which suggests a higher percentage of cities showing an increasing trend of SUHII for urban heat islands than the cities of the urban cool island. Pearson’s correlation and stepwise multiple linear regression model determine the possible SUHII controlling variable over different climatic zones. During the daytime, the SUHII’s distribution is controlled by vegetation, evapotranspiration, and thermal inertia in the summer/winter season. Whereas, it is linked tightly to built-up intensity, white sky albedo, and thermal inertia in both seasons during nighttime. Overall, we found that the stepwise multiple linear regression model can explain the SUHII variance more in the daytime (>0.8) than in nighttime (>0.7, except for tropical cities) and more in understanding the SUHII behaviors for cool cities as compared to hot cities. Moreover, this study also quantifies the significant control of thermal inertia and soil moisture in understating urban heat and cool island behavior over different climatic zones.

KEYWORDS:

• Surface urban heat island
• climatic zone
• driving variables
• Indian cities
• thermal inertia
• stepwise multiple linear regression model

Highlights

• The diurnal, seasonal, and interannual variation of SUHII is assessed over 150 major Indian cities situated over different climatic regions.

• Seven different variables are explored together to understand the variability of SUHII using stepwise multiple linear regression model.

• The daytime SUHII is significant control by vegetation and evapotranspiration while built-up intensity, albedo, and nighttime lights control nighttime SUHII.

• Thermal inertia shows negative correlation with daytime SUHII and positive with nighttime.

• Soil moisture is found to be essential for understating UHI and UCI phenomenon.

Acknowledgements

The authors are grateful to the editor and the anonymous reviewers for their valuable and constructive comments and suggestions to improve the quality of the manuscript. The authors would like to acknowledge the Department of Earth Sciences, Indian Institute of Technology, Roorkee, India, for providing necessary infrastructure facilities. The authors also acknowledge MHRD and DST NRDMS (Grant number- NRDMS/01/179/2015(C)), New Delhi, for providing the necessary financial support.

Conflicts of interest

The author(s) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data and codes availability statement

The data and codes that support the findings of this study are available on request from the corresponding author.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the Natural Resources Data Management System [NRDMS/01/179/2015(C))].