H. (2021). Climate change projections for sustainable and healthy cities. Buildings and Cities, 2(1), pp. 812-836.

<p>The Surface Urban Heat Island (SUHI), known as the difference in land surface temperature (LST) created by the presence of a city, is impacted by both the climate and morphology of the city in question. Subsequently, a changing climate would be expected to result in consequences for characteristics of the SUHI. Modelling the future climate of cities remains a challenge as resolution of global climate models is too coarse to capture the scale of a city, and regional climate models are computationally expensive. In order to address these issues, statistical models can be used. Using a dataset of cities selected based on similar characteristics such as population, variation of elevation within the city and surrounding area, and proximity to water bodies, satellite data is used to quantify the SUHI magnitude. A statistical model is fitted to current observations using predictive variables based on climate. The model shows promising performance for the majority of cities in the dataset and results are discussed.  </p>

<p>As centres of human activity, cities contain over half the world’s population and this proportion is projected to increase to around 70 percent in 2050. The urban heat island (UHI) is a well observed phenomenon, where temperature in a city is warmer than the surrounding rural area.</p><p>The UHI is influenced by both the climate and the morphology of the city. Focusing on cities in the tropics and subtropics and those with a population of less than 1 million, this research explores the relationship between the UHI effect and climate. Cities in different climate zones are selected based on similar characteristics such as population, variation of elevation within the city and surrounding area, and proximity to water bodies. Satellite data, with global coverage, is used to quantify the SUHI of the chosen cities. Peak SUHI was calculated using the Gaussian Surface Approximation methodology and the mean SUHI defined as the mean land surface temperature of urban pixels minus the mean of the surrounding rural area.  </p><p>Statistical techniques including Multiple Linear Regression, Random Forest Regression and Gaussian Process Regression are used to find relationships between SUHI and variables such as vegetation greenness (EVI), evaporative fraction, precipitation, incoming solar radiation, and city area. </p>