Spatio temporal dynamics of the urban heat island in singapore 3

90 While all stations posted a minimum UHIraw that is less than 0◦ C, for minimum UHImax , 12 stations had positive values. The fact that less stations have negative minimums is expected as UHImax does not consider daytime values. All of the minimum values of UHIraw in urban stations occur during daytime, which is likely due to shading effects from buildings. Three stations posted minimum UHImax values greater than 1.5◦ C (S44, S45 and S46), although little can be established from this as all three stations had less than eight valid nights of data and thus may not be representative. Table 4.6: Mean, minimum and maximum values of UHImax and UHIraw . Summary across all stations Mean of station minimums Mean of station means Mean of station maximums Overall minimum Overall maximum UHImax -0.40 2.71 4.83 -2.44 6.46 UHIraw -2.98 1.81 5.09 -4.44 6.70 UHIM AX intensity and time of occurrence The maximum UHIraw value measured across all stations and time intervals is 6.70◦ C, occurring on 30th July 2009 at 03:10 local time at S22 (Table 4.7). The maximum of UHImax was registered at 6.46◦ C at 22:20 local time on 24th April 2009, at S22. The latter value is deemed to be the UHIM AX due to its stricter filtering requirements. Although non-ideal conditions usually dampen rather than increase UHI intensities, unequal antecedent conditions (Φa ) can result in higher than expected intensities (as discussed earlier). 91 Table 4.7: Maximum UHI intensities and their time of occurrence for all stations and their available dataset across the entire study period (Feb 2008 to Jun 2011), Refer to Appendix B for the UCZ classes. Stn Description S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S17 S18 S19 S20 S21 S22 S23 S24 S25 S27 S28 S29 S30 S31 S32 S33 S34 S36 S37 S38 S39 S40 S41 S42 S43 Coastal Industrial Forest Park Low-rise residential High-rise residential High-rise commercial High-rise residential Coastal Forested/agricultural Clearing Industrial Low-rise residential High-rise residential Low-rise residential High-rise residential Low-rise commercial Low-rise residential Open Low-rise residential High-rise commercial Rural Low-rise residential Low-rise industrial Rural Rural Urban park Large park Low-rise residential Low-rise residential Low-rise residential Reservoir Open grass High-rise residential High-rise residential Open grass patch Low-rise residential Med-rise commercial Low-rise residential Low-rise commercial (mock-up) Med-rise commercial Low-rise residential High-rise commercial S44 S45 S46 Max UHIraw 4.75 5.22 2.26 3.50 5.42 4.76 6.44 5.93 5.36 4.04 5.00 5.69 6.19 6.10 5.95 6.30 5.35 5.72 4.86 5.17 6.70 2.88 6.20 6.32 0.88 3.20 5.27 4.90 5.78 4.51 5.07 3.81 5.30 5.62 6.03 2.86 6.43 6.01 6.20 4.04 Date and time of occurrence 2008-05-05 22:50 2009-04-25 00:10 2008-07-31 05:10 2011-05-10 01:10 2008-09-25 20:20 2008-05-19 06:30 2009-07-31 07:30 2009-04-24 22:20 2008-05-19 07:30 2009-08-03 08:00 2008-05-19 07:20 2009-07-30 01:50 2009-04-24 21:50 2009-07-31 07:30 2009-07-31 05:20 2009-04-24 22:40 2008-04-01 21:10 2009-04-24 23:00 2008-07-03 07:50 2009-04-24 23:10 2009-07-30 03:10 2008-04-03 08:50 2009-07-31 08:00 2009-07-31 07:30 2008-05-19 22:00 2008-09-23 09:10 2009-04-24 23:10 2009-08-03 05:20 2009-07-31 07:50 2009-08-03 08:00 2008-06-12 21:50 2009-08-03 08:20 2009-08-03 07:50 2009-07-31 05:00 2009-07-31 03:40 2009-01-19 08:50 2009-07-31 07:30 2009-07-31 05:40 2009-07-31 07:00 2009-08-03 08:20 Max UHImax 4.55 2.10 5.03 6.36 5.28 3.76 4.95 5.55 6.06 5.30 4.74 6.59 2.56 6.14 6.30 2.09 4.48 5.75 4.43 5.03 3.75 5.16 1.94 5.80 6.18 3.27 Date and time of occurrence 2009-07-31 06:00 2008-05-17 23:40 2008-05-19 03:30 2009-07-31 06:20 2009-04-24 22:20 2008-05-18 04:10 2008-05-19 06:50 2009-07-31 05:40 2009-04-24 22:20 2008-10-11 02:00 2008-05-19 06:40 2009-07-31 06:50 2008-03-28 01:50 2009-07-31 06:30 2009-07-31 05:20 2011-04-17 22:10 2009-07-30 23:00 2009-07-31 06:20 2009-04-24 23:50 2008-10-11 01:20 2009-04-24 20:50 2009-07-31 04:30 2009-02-19 06:50 2009-02-19 06:50 2009-07-31 06:50 2009-07-30 22:20 LCZ 5.67 5.36 5.80 2011-05-09 21:10 2011-05-10 23:00 2011-05-09 21:40 - - 12 3 12 BG 3B A B 3 4D 1 1 GB A9 BF 2 3 12 3 1E 3 3 5D 6 12 BA 5 8 BA BA B1 BG 31 6D 3 DG D8 1D 1 D 2 3 21 6A 92 Table 4.7 includes both “rural” and “urban” sites in an effort to capture heat island differences across the entire spectrum of land use. A few points to note include (i) UHImax filtering see less unexpected time of maximum UHI values than UHIraw filtering; (ii) stations found in rural and vegetated areas tend to have maximum UHIraw occurring close to sunrise; and (iii) most of the maximum values for both UHIraw and UHImax are found during the April-May (Pre-SW monsoon) and July-Sept (SW monsoon) period. Oke (1981) states that UHIM AX typically occurs 3 to 5 hours after sunset, which in Singapore’s case, would be 22:00 to 00:00 hrs. This is consistent with the maximum of UHImax , which occurred at 22:20 hrs local time. The peak occurrence of UHI in Singapore, however, varies between stations and time of year. It is noted that some stations and months see UHI values that peak around sunrise. However, care has to be taken when interpreting values close to sunrise and sunset as rapid changes to meteorological conditions mean that artefacts may arise (Oke, 2006). Detailed discussion of temporal variations in time of maximum UHI occurrence is covered in Section 4.3.2. The UHIM AX value in this study (6.46◦ C) is approximately half a degree less than that reported by Chow and Roth (2006), which was 7.07◦ C. Apart from the slight difference in magnitude, the time of occurrence (present study: 22:20 hrs, Chow and Roth: 22:00 hrs), time of year (24th April, 17th May) and location of occurrence (both in Orchard Road) are almost identical. Unsurprisingly, past studies in Singapore have all identified the location of UHIM AX to be within the CBD (Nieuwolt, 1966; Singapore Meteorological Services, 1986; Goh and Chang, 1998; Wong and Chen, 2005; Chow and Roth, 2006; Priyadarsini et al., 2008). Of interest is the general increase in reported UHIM AX intensities with time, from 3 93 to 5◦ C in the earlier studies to approximately 7◦ C in more recent studies, a trend echoed in the neighbouring country of Malaysia (Elsayed, 2011; Roth and Chow, 2012). 4.3 Temporal variability of the urban thermal environment There are several temporal scales at which climatological and meteorological variability operate. The focus of this section on distinctive temporal patterns and therefore the diurnal, seasonal and inter-annual scales will be of greatest interest; they represent cyclical patterns as opposed to, for example, a weekly scale. UHI intensities referred to in this section are UHIraw values unless otherwise stated. 4.3.1 Diurnal variability of air temperature An hourly box-and-whiskers plot of ensemble mean air temperature (across all stations and the entire study period) illustrates that the range of measured air temperature across all the stations changes throughout the course of a day. The interquartile range (75th percentile - 25th percentile) begins to increase as the sun sets and is largest in the night (19:00 - 06:00 hrs), exceeding 1◦ C during each hour (Figure 4.7). The same applies for the full range (max - min), even after taking outliers into consideration. During daytime (07:00 - 18:00 hrs), the interquartile range decreases as the sun rises and most hours have a sub-1◦ C range, especially around solar noon (13:00 hrs). This finding is in agreement with earlier discussions on the effects of urban influence on the diurnal air temperature cycle. Temporal variations are most dis- 95 (a) ✂✄☎✆✝✞✟ ✂✄☎✞✟✠ 30 Air temperature (°C) Location ✁   28       Rural (S16)       24           Low-rise Residential (S15) High-rise Residential (S08)         Commercial (S22)       Commercial (S07)     26 Forest (S03)     Urban park S29   00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Local Time Figure 4.8: Ensemble mean hourly air temperatures for selected stations across all weather conditions and for the entire study period (Feb 2008 to Jul 2011). The selected stations are representative of sites of common land use types, such as forest (S03), rural (S16), commercial (S22 & S07), low-rise residential (S15), high-rise residential (S08) and urban park (S29). residential areas, which may be shaded. One striking behaviour is that the rural station has air temperatures not unlike the urban sites during solar noon (13:00 hrs). Relationship between air temperature, cooling rate and UHI intensity The hourly cooling rate can be mathematically defined as the rate of change of air temperature between each hourly interval (Equation 4.1). −(Ti − Ti−1 ) (4.1) where i = current time-step and i − 1 = previous time-step The relationship between air temperature, cooling rate and UHI intensity is shown in Figure 4.9 for three sites. Cooling begins for all three stations at 14:00 hrs. The maximum cooling rates are reached around 18:00 and 19:00 hrs. The cooling rates begin to fall after sunset when K* becomes zero but remain positive during this entire period, stabilizing around midnight. Negative cooling rates (i.e. warming 96 rates) occur after sunrise. The highest warming rates are observed before solar noon for all three stations, at 09:00 hrs at the rural and low-rise residential stations and 11:00 hrs at the commercial station. This is possibly due to greater solar access from a nearly overhead sun and hence warming potential is greatest closer to noon in the high-rise commercial area. As discussed previously, a distinct difference between urban and non-urban sites is the magnitude of their rates of cooling. This can be seen in the much higher rates of cooling for the rural site between 14:00 to 23:00 hrs compared to the two other urban sites (Figure 4.9). The openness of the rural site with low re-absorption of L↑ and lower daytime heat storage flux, unlike in urban areas, contribute to the higher cooling rates. Furthermore, reduced rural µ during drier periods contributes to increased cooling potential. The cooling rate differential gives rise to the establishment of the UHI. When the cooling rate of the rural station exceeds that of the urban stations (approximately 14:00 - 23:00 hrs), the effective UHI intensity (note that ∆Tu−r used here is unfiltered for weather effects as discussed in Section 2.1) increases, in this case peaking at around 01:00 and 02:00 hrs. Conversely, the UHI intensity will begin falling after sunrise when warming rate becomes larger in rural areas than urban areas. In the case of the commercial station (S07), UHI intensities are negative from 12:00 to 14:00 hrs, creating an urban cool island. This is attributed to shading from direct K ↓ by tall buildings during daytime. The low-rise residential station (S15) also experiences lower UHI intensities during the day but the intensities remain positive due to a lack of shading. 98 4.3.2 Seasonal change in UHI characteristics The focus of this section is to explore the seasonal variation of UHIraw while specific relationships between synoptic weather elements and heat island intensity will be covered in a later section (Section 4.7). As discussed in Section 3.1, the weather conditions in Singapore follow a monsoonal pattern (refer to Tables 3.1 and Figure 3.2). Figure 4.10 shows the monthly mean values for the entire study period. There appears to be a usual trend, which sees the pre-south-west (PSW) intermonsoon and south-west (SW) monsoon periods having the highest heat island intensities. The pre-north-east (PNE) usually experiences a drop in UHI intensity before the lowest values occur during the north-east monsoon (NE). Nocturnal mean UHIraw (°C) 29.0 4 28.5 3 28.0 2 27.5 1 27.0 0 Air temperature at Changi Met Station (°C ) 5 Monsoon period NE PNE PSW SW 26.5 −1 2008 2009 2010 2011 Time Figure 4.10: Boxplot of mean monthly nocturnal (19:00 - 07:00 hrs) UHIraw for all stations together with air temperature measured at Changi Meteorological Station (red dashed line). Only stations with more than 24 months of data are used in order to prevent skewed results. Note that there is no data for September 2009, October 2009 and June 2011. The warmer months have higher nocturnal mean UHIraw intensities while the wet monsoon (NE) sees lower intensities. High precipitation rates during the cooler months lead to an increase in the rural thermal admittance (µ). This is despite the filtering out of intervals with rainfall as surface moisture levels may still be high 99 due to build up of soil moisture during the rainy season. As discussed in Section 2.1, this contributes to the “dampening” of UHI intensities (Runnalls and Oke, 2000; Chow and Roth, 2006). Warmer months also tend to have less cloud cover and lower wind speeds, which may contribute to higher UHIraw intensities. Specific relationships between the weather variables and UHI are covered in Section 4.7. It is clear that the diurnal characteristics of the heat island undergo changes across the year, in terms of magnitude, agreement across stations (i.e. SD), onset, peak and decline (Figures 4.11, 4.12 and 4.13). The plots include measurements from all weather conditions. To ensure consistency, stations with less than 80% observed month-hours (observations at a given hour of a given month in any year, i.e. 12 months×24 hours= 288 month-hours) during the study period are removed (Table 4.8). Table 4.8: Omitted stations and percentages of month-hour observed. Stn % month-hour S06 67.0 S09 33.7 S18 72.2 S20 75.0 S27 8.7 S33 69.1 S39 25.4 S42 60.4 S44 32.6 S45 32.3 S46 32.3 In terms of magnitude, the months in the middle of the year, from April to October, typically have a higher number of stations having mean UHIraw intensities that exceed 4◦ C during the night (Figure 4.11). In contrast, months during the north-east monsoon (December to March) see lower heat island intensities (< 4◦ C). January and February see the UHI intensities increase at a somewhat slow rate starting at just before sunset, typically reaching the peak just before sunrise (Table 4.9 and Figure 4.13). This creates diurnal UHI curves that are skewed towards sunrise, which is unlike usual descriptions of UHI peaking a few hours after sunset. These two months are characterised by relatively strong winds throughout 103 The thick cloud cover and relatively high levels of surface moisture and µ towards the end of the rainy season are also important to the shape of the UHI curve during these months. The difference in cooling rate between urban and rural areas determines the slope of the curve at the start of the night. Months with high precipitation rate and wind speeds may see urban areas cooling at a faster rate than usual after sunset, results in slow UHI development (i.e. gentler slopes around sunset during December, January and February). As the sun rises, the UHI intensities drop rapidly in a few hours before stabilizing at noon. Table 4.9: Time of maximum UHIraw hourly ensemble for each month of the year. month of year hour of UHImax monsoon 01 07 NE 02 07 NE 03 05 NE 04 05 06 00 00 00 PSW PSW SW 07 04 SW 08 07 SW 09 01 SW 10 23 PNE 11 23 PNE 12 03 NE A transition occurs around March and the shape of the diurnal curves from April to June are rather symmetrical with peaks for most stations occurring around midnight, which is five hours after sunset. Synoptic air temperatures are higher, wind speeds and precipitation rates are generally lower during this period. The lower rural µ due to reduced soil moisture creates larger relative difference in cooling potential between rural and urban areas, creating a more “ideal” heat island formation conditions. During July and August, the typical peak heat island occurrence moves later and takes place closer to sunrise than sunset, although the diurnal curves are still rather symmetrical compared to January and February. In September and October, the peak intensities of UHI occur earlier in the night, prior to midnight. This may be the opposite of the above-mentioned effect. Lower wind speeds and relatively drier conditions reduces the cooling rate of urban areas relative to rural areas resulting in quicker development of UHI (i.e. steeper 104 UHI curve at the start of the night). As the north-east monsoon returns in December, the diurnal UHIraw curves are dampened starting from November. In terms of agreement across stations, the months during the north-east monsoon have the smallest interquartile range through the day (Figure 4.12). This suggests that conditions such as strong winds, wet surfaces and low air temperatures reduce the variability between different stations (and result in a later peak of UHI as discussed above). Hotter and/or drier months with lower wind speeds (such as May, June and October) have a larger interquartile range with somewhat earlier peaks. The ‘dampening’ of UHI intensities in the NE monsoon relative to the SW monsoon is in agreement with previous studies by Singapore Meteorological Services (1986), Goh and Chang (1998) and Chow and Roth (2006). The reduction of UHI intensities attributed to a decrease in soil moisture content due seasonal rainfall changes has also been also reported in other tropical cities such as Mexico and Gabarone (Jauregui, 1997 and Jonsson, 2004, respectively). The relatively high variability in rural µ values is a likely driving force behind UHI variability in the tropics (Roth, 2007). 4.3.3 Inter-annual trending and cycles of UHI intensities Beyond the seasonal variability of UHI intensities, variations may also occur at the inter-annual scale (Pigeon and Masson, 2009). Inter-annual trends in UHI can be identified by first removing the seasonal trends. As such decomposition is very sensitive to noise and outliers, this analysis will only be conducted at the station level for stations with good data continuity. Seasonal-trend decomposition procedure based on loess (STL) (Cleveland 105 et al., 1990) is used to decompose the UHI series into a seasonal signal, a trend signal and a remainder (or residual). This procedure is often used for decomposing climate data (e.g. Dufresne and Bony, 2008; Capilla, 2008). Plots for S07, S15, S22 and S24, demonstrate seasonal double-maxima patterns (except S15 which has a single peak)(Figures 4.14 and 4.15). The range of values for monthly mean UHI for the stations are all approximately 1.2◦ C, with the seasonal trend accounting for variances greater than 0.8◦ C for stations S07, S22 and S24. For station S15, the seasonal trend has a range of ∼0.6◦ C. After removing the seasonality, a second background signal emerges with a range of >0.2◦ C for S07, S22 and S24 and 0.30) and the ACF for the same hour on the day before is ∼0.30 for both stations. This suggests that ∆Tu−r values within four hours before a specific hour are more likely to be related than the value at the same hour on the day before. Given that diurnal trends have already been removed, the remaining significant self-correlations suggest that UHI values exhibit strong lagged relationships in the span of a few hours which may be related to soil surface moisture and rural µ values. Over a longer time span, auto-correlations are significant (DurbinWatson test: p < 0.001) around the same time of day up to approximately 25 days before each hourly interval, after which ACF values diminish. 109 2-day ACF S15 ACF 0.4 0.2 0.4 0.0 0.0 0.2 ACF 0.6 0.6 0.8 0.8 1.0 1.0 S22 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 Lag 1.5 2.0 Lag ACF 0.4 0.2 0.4 0.0 0.0 0.2 ACF 0.6 0.6 0.8 0.8 1.0 1.0 50-day ACF 0 10 20 30 40 50 0 10 20 30 Lag 40 50 Lag 0.6 ACF 0.2 0.4 0.4 0.2 0.0 0.0 ACF 0.6 0.8 0.8 1.0 1.0 335-day ACF 0 50 100 150 200 Lag 250 300 0 50 100 150 200 250 300 Lag Figure 4.16: 2-day (first row), 50-day (second row) and 335-day (third row) ACF plots for S15 and S22. Bars beyond the blue dotted lines are statistically significant. Each lag interval is one day. Note that the first value is 1.0 as it is the correlation at lag 0 (i.e. self-correlation). 110 4.4 Spatial variability of the thermal environment Thermal conditions vary not only temporally but also spatially. Spatial variability can be attributed to two factors. The first is a difference in location within Singapore, i.e. geographical differences. A second spatial consideration is that of land use and urban morphology. As with the section on temporal variability, specific relationships between landscape or urban factors will be discussed later. Multidimensional methods such as multiple linear regression and geographically-weighted regression are known to provide better spatialization of UHI (Szymanowski and Kryza, 2009). However, these methods require good predictors for multidimensional model building which is beyond the scope of this study. For interpolation methods that require only the observation data, ordinary kriging and IDW are typical methods. While ordinary kriging is often preferred, the low number of sample points makes semivariogram estimation difficult and thus IDW is the method chosen. The difference between the mean daytime and night-time spatial patterns of heat (and cool) islands is shown in Figure 4.17. For inclusiveness, values from all stations are used to create the maps. The inverse distance-weighted (IDW) spatial interpolation method, with distance decay parameterised as the second power, is used to generate the maps from the station data (and for all other interpolated maps from here on). The isotherms have a fixed interval of 0.5◦ C and the colour scale kept identical where possible. Care must be taken when interpreting these maps as they do not consider any form of weighting and assume distance to be the sole influencing factor. For example, a small cool park would not show up on the map unless it is represented by a station. For comparison purposes, Figure 3.6 shows the extent of urbanisation in the study area. 113 The daytime mean UHIraw intensities are rather similar across large parts of the island and the isotherms are generally far apart from each other. Small cool islands (UHIraw [...]... islands remain weak, creating high negative gradients By 17:00 hrs, the cool islands can be seen to diminish and island- wide variation in UHIraw is at its minimum As the sun begins to set at 19:00 hrs the UHIraw values begin to increase across the island A heat island develops in the city area in the south but pockets of warm islands also develop in other parts while the footprint of cool islands shrink At... area Small heat islands of low intensity can be found in residential and industrial areas across the island Interestingly, the core of the city does not have a distinctive heat island formation, possibly due to the amount of tall buildings providing shade at the street level The largest mean daytime UHIraw intensity is just above 1.5◦ C and is located in the middle of the island in a large and dense low-rise... 1 03 The thick cloud cover and relatively high levels of surface moisture and µ towards the end of the rainy season are also important to the shape of the UHI curve during these months The difference in cooling rate between urban and rural areas determines the slope of the curve at the start of the night Months with high precipitation rate and wind speeds may see urban areas cooling at a faster... across the island are quite consistent as deduced from the limited number of isolines (Figure 4.18) The lowest minimum value of UHIraw is found at the station in the central catchment forest High minimum values of UHIraw are found at the rural north-west as the reference site is located there The maximum values of UHIraw are above 5 ◦ C for most of the island with the lowest values found in the rural... on the map unless it is represented by a station For comparison purposes, Figure 3. 6 shows the extent of urbanisation in the study area 1 13 The daytime mean UHIraw intensities are rather similar across large parts of the island and the isotherms are generally far apart from each other Small cool islands (UHIraw ... hrs the UHIraw values begin to increase across the island A heat island develops in the city area in the south but pockets of warm islands also develop in other parts while the footprint of cool... towards the end of the rainy season are also important to the shape of the UHI curve during these months The difference in cooling rate between urban and rural areas determines the slope of the curve... across the island Interestingly, the core of the city does not have a distinctive heat island formation, possibly due to the amount of tall buildings providing shade at the street level The largest