## 3 Results
### Spatial Variability of Annual and Seasonal SUHI
#### 3.3.2 Daytime SUHI  
The daytime observation of the annual SUHI anomaly over Indian cities and surrounding cities was found to be positive in some cities and negative in the rest. Colombo (1.36 \({}^{\circ}\)C) showed the highest annual daytime SUHI effect followed by Lahore (1.22 \({}^{\circ}\)C), Dhaka (0.95 \({}^{\circ}\)C), Kolkata (0.95 \({}^{\circ}\)C), Faisalabad (0.92 \({}^{\circ}\)C), Yangon (0.83 \({}^{\circ}\)C), and Lucknow (0.80 \({}^{\circ}\)C). Pune, Hyderabad, Bengaluru, Karachi, and Jaipur were found to have a negative annual daytime SUHI effect of \(-\)0.13, \(-\)0.26, \(-\)0.33, \(-\)0.48, \(-\)0.92 \({}^{\circ}\)C, respectively (Table 7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**SI.** & **City** & **Ann.** & **AMJ** & **DJF** & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline
1. & Delhi & 0.09 & \(-\)0.77 & 0.58 & 0.66 & 1.36 & 1.57 & \(-\)0.38 & \(-\)1.07 & \(-\)0.86 & \(-\)0.47 & 0.42 & 0.85 & \(-\)0.11 & \(-\)0.67 & \(-\)0.28 \\ \hline
2. & Mumbai & 0.16 & \(-\)0.67 & 0.49 & 0.47 & 0.28 & \(-\)0.05 & \(-\)0.42 & \(-\)0.81 & \(-\)0.78 & \(-\)0.40 & 0.30 & 0.74 & 1.38 & 1.22 & 0.73 \\ \hline
3. & Dhaka & 0.95 & 0.96 & 0.80 & 0.70 & 1.10 & 1.11 & 1.21 & 1.08 & 0.60 & 0.90 & 1.14 & 1.00 & 1.00 & 0.85 & 0.61 \\ \hline
4. & Karachi & \(-\)0.48 & \(-\)0.49 & \(-\)0.53 & \(-\)0.53 & \(-\)0.47 & \(-\)0.53 & \(-\)0.37 & \(-\)0.47 & \(-\)0.63 & \(-\)0.83 & \(-\)0.37 & \(-\)0.12 & \(-\)0.38 & \(-\)0.53 & \(-\)0.59 \\ \hline
5. & Kolkata & 0.95 & 0.99 & 0.73 & 0.63 & 0.94 & 1.17 & 1.22 & 0.88 & 0.88 & 0.03 & 0.73 & 1.38 & 1.50 & 1.05 & 0.63 \\ \hline
6. & Lahore & 1.22 & 1.01 & 0.84 & 0.92 & 1.69 & 2.61 & 1.95 & 0.36 & 0.72 & 1.09 & 1.61 & 2.02 & 1.54 & 0.28 & \(-\)0.08 \\ \hline
7. & Bengaluru & \(-\)0.33 & \(-\)0.65 & \(-\)0.71 & \(-\)0.74 & \(-\)1.15 & \(-\)1.27 & \(-\)0.98 & \(-\)0.39 & \(-\)0.58 & \(-\)0.46 & \(-\)0.08 & 0.54 & 0.90 & 0.49 & \(-\)0.23 \\ \hline
8. & Chennai & 0.40 & \(-\)0.18 & 0.63 & 0.62 & 0.32 & 0.26 & \(-\)0.11 & \(-\)0.32 & \(-\)0.11 & 0.25 & 0.67 & 0.78 & 0.69 & 0.82 & 0.94 \\ \hline
9. & Hyderabad & \(-\)0.26 & \(-\)0.93 & \(-\)0.84 & \(-\)0.91 & \(-\)0.97 & \(-\)0.99 & \(-\)0.94 & \(-\)1.04 & \(-\)0.80 & 0.07 & 0.94 & 1.29 & 0.97 & \(-\)0.04 & \(-\)0.64 \\ \hline
10. & Ahmedabad & 0.32 & \(-\)0.56 & 0.53 & 0.58 & 1.05 & 0.88 & 0.18 & \(-\)0.59 & \(-\)1.27 & \(-\)1.56 & 0.49 & 2.04 & 1.55 & 0.29 & \(-\)0.03 \\ \hline \end{tabular}
\end{table}
Table 7: Daytime surface thermal anomaly showing urban heat island (SUHIMean) effect (average LSTurban = average LSTrural).  
Figure 10: Monthly time series showing (**a,b**) average nighttime temperature, and (**c,d**) nighttime surface urban heat island (SUHI) effect over selected major Indian cities for the period 2000–2023.  
The monthly time series plot for the daytime SUHI over different cities is plotted in Figure 11, which shows the spatial variability in the seasonal characteristics of daytime SUHI. It can be clearly delineated from Figure 11a,b that the average daytime temperature peaked during pre-monsoon to onset of monsoon months (March-April-May-June) months and post-monsoon, with a prominent dip in temperatures during monsoon months due to heavy rainfall and also during winter months.