scholarly journals Assessment of Spatio-Temporal and diurnal Urban Heat Island Intensities in Delhi Urban agglomeration using a high resolution Weather Research and Forecasting Model

2021 ◽  
Author(s):  
Kshama Gupta ◽  
Pushplata Garg ◽  
Arijit Roy
Keyword(s):  
High Resolution ◽  
Urban Heat Island ◽  
Urban Agglomeration ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Heat Island ◽  
Urban Heat ◽  
Spatio Temporal ◽  
Weather Research

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Spatio -temporal variations in urban heat island and its interaction with heat wave

2019 ◽  
Vol 185 ◽  
pp. 50-57 ◽  
Author(s):  
Shahnilla Haider Rizvi ◽  
Khan Alam ◽  
Muhammad Jawed Iqbal
Keyword(s):  
Urban Heat Island ◽  
Heat Wave ◽  
Temporal Variations ◽  
Heat Island ◽  
Urban Heat ◽  
Spatio Temporal

scholarly journals Influence of Urban Scale and Urban Expansion on the Urban Heat Island Effect in Metropolitan Areas: Case Study of Beijing–Tianjin–Hebei Urban Agglomeration

2020 ◽  
Vol 12 (21) ◽  
pp. 3491 ◽  
Author(s):  
Mingxing Chen ◽  
Yuan Zhou ◽  
Maogui Hu ◽  
Yaliu Zhou
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Urban Expansion ◽  
Urban Agglomeration ◽  
Heat Island ◽  
Average Surface ◽  
Island Effect ◽  
Urban Heat ◽  
The Relationship

Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban expansion and the degree of the urban heat island (UHI) effect, as well as a discussion on mitigation and adaptation of the UHI effect from the perspective of planning. This paper analyzes the regional urbanization process, average surface temperature variation characteristics, surface urban heat island (SUHI), which reflects the intensity of UHI, and the relationship between urban expansion, urban scale, and the UHI in the Beijing–Tianjin–Hebei (BTH) urban agglomeration using multi-source analysis of data from 2000, 2005, 2010, and 2015. The results show that the UHI effect in the study area was significant. The average surface temperature of central areas was the highest, and decreased from central areas to suburbs in the order of central areas > expanding areas > rural residential areas. From the perspective of spatial distribution, in Beijing, the southern part of the study area, the junction of Tianjin, Langfang, and Cangzhou are areas with intense SUHI. The scale and pace of expansion of urban land in Beijing were more than in other cities, the influencing range of SUHI in Beijing increased obviously, and the SUHI of central areas was most intense. The results indicate that due to the larger urban scale of the BTH urban agglomeration, it will face a greater UHI effect. The UHI effect was also more significant in areas of dense distribution in cities within the urban agglomeration. Based on results and existing research, planning suggestions are proposed for central areas with regard to expanding urban areas and suburbs to alleviate the urban heat island effect and improve the resilience of cities to climate change.

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