## 4 Results and Discussion
### Yearly Trend of SUHI Footprint and Magnitude  
A comparative analysis of the footprint area Ar61% and magnitude a0 as a function of the 14-year interval (2003-2016) was performed to provide a specific-city and intercity overview of the SUHI trend. These parameters were compared with the urban area evolution across the years, computed from LULC Landsat maps (Figure 2) to analyze how the urban growth affects the SUHI pattern for both daytime and nighttime. Since previous results showed a weak seasonality dependence, Ar61% and a0 were averaged over summer and winter maps for each year. The upper panels of Figure 11 show the yearly trend of the urban area (black line) computed from LULC Landsat data inside the boundary map of Figure 3, compared with the daytime (red line) and nighttime (blue line) Ar61% for BG, CM, and NR. Lower panels report the daytime (green line) and nighttime (magenta line) a0 values. The curves were obtained as shape-preserving interpolants over the 14-year points. The same is reported in Figure 12 for UR, KK, and UT. Figure 11: BG, CM, and NR: yearly trend (2003–2016) of the urban area computed from land use and land cover (LULC) Landsat maps (black line, upper panel), daytime Ar61% (red line, upper panel), nighttime Ar61% (blue line, upper panel), daytime a0 (green line, lower panel), and nighttime a0 (magenta line, lower panel). For NR, daytime a0 is the maximum SUHI value, since daytime Gaussian fitting is not reliable. Figure 10: Inercity comparison of central location (\(\chi_{0},y_{0}\)) (km) during summer (**upper panels**) and winter (**bottom panels**). Central location is averaged over the 14 years, considering the diurnal variation at 10:00, 14:00, 22:00, and 02:00. The black bar is the standard deviation across the years. For each city, the urban growth during the years is either weakly correlated or not correlated with the SUHI footprint trend, whereas no correlation is found with the a0 trend. Particularly, daytime A61% values for BG slightly increases from 660 km\({}^{2}\) to around 800 km\({}^{2}\) between 2003 and 2016, exhibiting a correlation coefficient r = 0.9 with the urban area trend. Daytime A61% values for CM increase from 42 km\({}^{2}\) to 54 km\({}^{2}\), but with r = 0.6 with respect to the urban increase. Instead, nighttime A61% for BG, CM, and NR are unrelated to city growth. For the three smaller cities (i.e., UR, KK and UT), the urban expansion does not affect the daytime and nighttime SUHI footprint. Figures 11 and 12 show how the magnitude a0 is decorrelated with the city sprawl across the years. As expected, maximum SUHI intensities are mainly driven by specific yearly thermal conditions regardless of urban growth. These results confirm how the spatiotemporal urban heating patterns depend strongly on the background climate, such as precipitation, air temperature, solar radiation, and wind speed [18]. BG shows the highest annual a0 during daytime, with values ranging between 4 \({}^{\circ}\)C and 6 \({}^{\circ}\)C. During nighttime, a0 is rather similar for all the six cities, between 1 \({}^{\circ}\)C and 2 \({}^{\circ}\)C, even if CM shows mean values around 2.4 \({}^{\circ}\)C between 2003 and 2010. The day-night magnitude difference may be ascribed to different factors, such as the different solar radiation absorption between urban and rural areas, since urban surfaces have a larger heat capacity and can absorb and store large amounts of thermal radiation with a higher rate than the rural surroundings [42]. Furthermore, anthropogenic heat released from energy consumption is an important contributor to urban warming and it is positively correlated with the urban heat island intensity [43,44]. Heat release from energy consumption (e.g., air conditioning systems, industrial heat generation, and energy use in transportation) usually increases during daytime due to intense human and economic activities [45].