115 13:00 hrs 15:00 hrs 17:00 hrs 19:00 hrs 21:00 hrs 23:00 hrs Captions on next page. 116 01:00 hrs 03:00 hrs 05:00 hrs 07 :00 hrs 09:00 hrs 11 :00 hrs Figure 4.19: Bi-hourly ensemble UHIraw maps interpolated using data from all stations for the entire observation period (February 2008 to Jun 2011). 117 Between 21:00 to 23:00 hrs, the highest intensities are found in the CBD, with a warm belt stretching across the south-western coast of Singapore and a secondary peak found over the western industrial estates. These findings are consistent with the study conducted three decades ago by the Singapore Meteorological Services (1986), which took measurements at 22:00 hrs (Figure 4.20). A notable feature not present in the 1986 study is the existence of warm spots in the north and north-east, which are expected due to rapid development of high-rise residential estates in the past few decades. The cool islands in the central catchment area and rural north-west also appear to have diminished in influence, possibly caused by the development of the Bukit Timah Expressway (beginning in 1983) and the Kranji Expressway (beginning in the early 90s), along with new residential and industrial estates along the expressway, found between the two cool zones. Another factor that may have contributed to the northward migration of the cool center in the rural north-west is the development residential and industrial areas in the Jurong West Extension in the early 1990s. The 1997 study by Goh and Chang (1999) found that the residential estates in Jurong West have the highest heat island intensities among the 17 towns sampled in Singapore. The growth of another secondary heat island in the east is discussed in Goh and Chang (1998), a period during which new developments in the east were completed. In the present study, large parts of the east have high UHI intensities with the exception of a small cool spot over an airfield, noticeably different from three decades back. 118 Figure 4.20: Isothermal maps of Singapore during the NE (top) and SW (bottom) monsoons produced with data collected over nine days between 1979 and 1981. Source: Singapore Meteorological Services (1986). 119 4.5.2 Spatial variation of ensemble mean monthly UHI across a seasonal cycle The spatial pattern of UHIraw also experiences seasonal variations (see Section 4.3.2). In January, during the wet and cool NE monsoon season, mean UHIraw gradients are small and both heat island and cool islands are not very developed (Figure 4.21). The highest mean intensities during this month are ∼2◦ C. In February, UHI intensities are generally lower, with a more pronounced cool island ( 0.6). On the other 123 hand, their predictive strengths for daytime mean UHIraw are notably weaker than BUP100 and BUP500. Maximum UHIraw is best explained by VP500 but the other LULC variables have relatively high R2 values too. However, none of these variables have strong relationships with minimum UHIraw , although BUP100 is weakly correlated with it (p < 0.05). Table 4.10: Urban variables and their relationships with dependent variables. Observations from 35 stations for the entire observation period (Feb 2008 to Jul 2011) are used. NM UHIraw DM UHIraw Max UHIraw Min UHIraw NM UHImax Max UHImax BUP100 y=x 0.622*** √ y= x 0.499*** y=x 0.513*** y=log(x) 0.149* BUP100 y=x 0.576*** y=x 0.475*** BUP500 y=x 0.579*** y=x 0.385*** y=x 0.547*** y=x 0.022 BUP500 y=x 0.509*** √ y= x 0.469*** VP100 y=x 0.627*** y=x 0.290*** y=x 0.492*** y=x2 +x 0.074 VP100 y=x 0.611*** y=x 0.480*** VP500 y=x 0.677*** √ y= x 0.244** y=x 0.608*** y=x2 +x 0.077 VP500 y=x 0.632*** y=x 0.551*** *** = p < 0.001; ** = p < 0.01; * = p < 0.05; + HW y=log(x) 0.351** √ y= x 0.243* y=log(x) 0.321** y=x 0.123 HW y=log(x) 0.310** y=log(x) 0.360** ZH y=log(x) 0.111 y=x 0.213** y=log(x) 0.085 y=x 0.177* ZH y=log(x) 0.088 y=log(x) 0.080 SVF y=x2 +x 0.290 y=x2 +x 0.478** y=x2 +x 0.170 y=log(x) 0.180* SVF y=x2 +x 0.189 y=x2 +x 0.112 = p < 0.1; The equations of the best performing LULC variables with strongly significant relationships (p < 0.001) are as follows: Nocturnal mean UHIraw = −0.033 VP500 + 3.826 (4.2) � BUP100 − 0.238 (4.3) Daytime mean UHIraw = 0.130 Maximum UHIraw = −0.039 VP500 + 6.562 (4.4) ● ● 2 ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 1 ● ● ● 0 ● 0 20 40 BUP100 60 80 100 ● 4 ●● ● ● ● ● ●● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● 1 ● ● 0 ● ● 0 20 40 60 80 VP100 100 ● 1.5 ● 1.0 ● ● 0.5 ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● −0.5 −1.0 ● 0 Daytime mean UHIraw(°C) ● ● ● ● 2 4 6 8 sqrt(BUP100) ● ●● ● ● 1.0 ●● ● ● ● ● ● 0.5 ● ● ● ● ● ● 0.0 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● −0.5 −1.0 ● 0 20 40 60 VP100 80 ● 4 100 ●● ●● ● 3 ● 2 ● ● ● ● ● ● ● 1 ● ● ● 0 ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 20 40 BUP500 60 80 ● 4 ● ● ● ● 3 ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● 1 ● ● 0 20 40 60 VP500 ● ● 80 ● ● 1.5 ● 1.0 ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● 0.5 ● ● ● ● ● ● ● ● −0.5 −1.0 ● 10 ● 1.5 Nocturnal mean UHIraw(°C) 3 ● ● ● ● ●● ● ● Daytime mean UHIraw(°C) ● Nocturnal mean UHIraw(°C) ● 4 0 Daytime mean UHIraw(°C) Daytime mean UHIraw(°C) Nocturnal mean UHIraw(°C) Nocturnal mean UHIraw(°C) 124 20 40 BUP500 60 80 ● 1.5 ● ● ● 1.0 ● 0.5 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● ● ● ●● ● ● ● ●● −0.5 −1.0 ● 2 4 6 sqrt(VP500) 8 Figure 4.22: LULC variables and their relationships with nocturnal mean UHIraw (top) and daytime mean UHIraw (bottom). Observations from 35 stations across the entire observation period (Feb 2008 to Jun 2011) are used. Shaded region represents the 95% confidence bands. 125 The similar slopes of the linear equations for Equations 4.2 and 4.4 (-0.033 and -0.039 respectively) suggest that nocturnal mean UHIraw and maximum UHIraw are influenced at similar rates by the ratio of vegetated surfaces in a 500 metre radius from each station. For every 10% increase in vegetated surface ratio, nocturnal mean and maximum UHI decreases by 0.3 to 0.4◦ C. The base value difference (when VP500 = 0) of just under 3◦ C is the main differentiating factor between Equations 4.2 and 4.4. As for daytime mean UHIraw , when BUP100 = 0, the base value is -0.405◦ C. For every 25% increase in BUP100 , the daytime mean UHIraw increases by 0.73◦ C. ●● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ●● ●● ● ● 4 ● ● ● ● 3 ●● ● 2 0 20 40 BUP100 60 80 6 5 ● ● ●● ● ● ● ● ●● ● ● ● 4 ● ● ● ● 3 ●● ● 2 1 40 60 VP100 80 100 ● ● ● 4 ● ● ● ● 3 ●● ● 2 ● 0 20 6 5 40 BUP500 60 80 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● 4 ● ● ● ● 3 ● ● ● 2 1 ● 20 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 7 ● ● ● ●● ● ● ● ● ● ● ● ● 0 ● 5 100 ● ● ● ● ●● 6 1 ● 7 ● ● Maximum UHIraw(°C) 6 1 Maximum UHIraw(°C) 7 ● ● ● Maximum UHIraw(°C) Maximum UHIraw(°C) 7 ● 20 40 VP500 60 80 Figure 4.23: LULC variables and their relationships with maximum UHIraw . Observations from 35 stations across the entire observation period (Feb 2008 to Jun 2011) are used. Shaded region represents the 95% confidence bands. 126 Canyon geometry The canyon geometry factors, H/W, zH /W and SVF (see Section 3.5.2), have been calculated for the surroundings of each station (Appendices B and D). Among the three canyon geometry variables, H/W ratio has the highest predictive strength (p < 0.01) for nocturnal mean UHIraw and maximum UHIraw (Table 4.10). In general, zH /W ratio has the lowest predictive strength for these two dependent variables. SVF has the strongest relationship with daytime mean UHIraw (p < 0.01). Interestingly, zH /W and SVF have weakly significant relationships with 4.5 ● 4.0 ●● 3.5 3.0 ● ● ● ● ● ● ● ● ● −2 Maximum UHIraw(°C) ● ● ● ● 2.5 2.0 ● ● ● ● −1 0 1 log(H/W) 2 Daytime mean UHIraw(°C) Nocturnal mean UHIraw(°C) minimum UHIraw (p < 0.05). ● 1.5 ● ● ● 1.0 ● ● ● 0.5 ●● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.5 −1.0 ● 0.2 0.4 SVF 0.6 0.8 7.0 ● 6.5 ● ● ● ● ● 6.0 ● ● ● ● 5.5 ● ● ● ● ● ● ● ● 5.0 ● 4.5 ● −2 −1 0 log(H/W) 1 2 Figure 4.24: Canyon geometry variables and their relationships with UHI variables. Observations from 35 stations for the entire observation period (Feb 2008 to Jul 2011) are used. Note that log relationships require predictor values > 0 not present in some stations for the H/W variable, which are thus omitted in the regression of this variable. Shaded region represents the 95% confidence bands. 127 The equations of the best performing canyon geometry variables with strong relationships (p < 0.01) are as follows: Nocturnal mean UHIraw = 0.293 log10 H/W + 3.426 (4.5) Daytime mean UHIraw = 1.299 SVF − 1.885 (SVF)2 + 0.6 (4.6) Maximum UHIraw = 0.309 log10 H/W + 6.076 (4.7) As with LULC variables, the variable H/W best predicts both nocturnal mean UHIraw and maximum UHIraw . The coefficient for base-10 logarithm of H/W is similar between the two variables (0.293 and 0.309) with the main difference being the base values when log10 H/W = 0. The base values here are also similar to those from LULC suggesting the effects of land use, land cover and urban geometry may be linked and difficult to separate. All in all, the canyon geometry variables do not account for UHI variance as well as the LULC variables. A logarithmic relationship between height-width ratio and UHI intensity is also identified by Oke (1981) when comparing H/W in city centres against their respective UHIM AX . However, little or no studies have verified this relationship and a past study on Singapore by Goh and Chang (1999) also questions the logarithmic relationship. However, the present study reports a relatively better performance of H/W in predicting night time UHI values in this study (R2 = 0.351) as compared to the study by Goh and Chang (R2 = 0.285) which uses a median H/W across the estate to predict heat island intensity in a straight-line function. As the H/W ratio used in the present study focuses on the immediate proximity of the sensor, it may be indicative of the relative importance of microscale variables. 128 Urban metabolism In the previous discussion on QF , two possible means of QF influencing UHI are identified. There is the spatial variation of QF dependent on the land use and function, as well as the temporal variation due temporal differences in patterns of anthropogenic activity (e.g. Sailor, 2011; Quah and Roth, 2012). As comprehensive data for anthropogenic flux for each station is not available, surrogate variables are used to identify any significant effects. Spatial variation of QF is difficult to detach from existing urban variables such as built-up ratio and is omitted. With regards to temporal variability, significant variations in QF within a week (weekdays vs weekends) are identified by (Quah and Roth, 2012). If QF plays an important role in influencing UHI in the study area, comparing weekday (Mon to Fri) and weekend (Sat and Sun) observations would yield identifiable differences as most Singaporeans have a 5-day working week. ● ● 6 ● ● ● ● ●●● ●● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● 3 ●● ● 2 ● ● ● ● ● ● ● ● ● 1 ● ● Weekday max UHIraw(°C) Weekday mean UHIraw(°C) 4 ● ● ● ● ● 5 ● ● ●● 4 ● ● ● ● ● ● ● 3 ● ● ● 2 ● 1 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 1 2 3 Weekend mean UHIraw(°C) 4 0 1 2 3 4 Weekend max UHIraw(°C) 5 6 Figure 4.25: Scatter plots of mean UHIraw (left) and maximum UHIraw (right) during weekdays and weekends and 1:1 lines. Data are taken from all stations for the entire observation period (Feb 2008 to Jul 2011). Figure 4.25 shows the relationship between weekend and weekday mean 129 Table 4.11: Distribution of stations and a comparison of their maximum UHIraw values for weekdays and weekends. Data are taken from all stations across the entire observation period (Feb 2008 to Jul 2011). WE = weekends, WD = weekdays. Type Commercial WD UHIraw < WE UHIraw Low-rise residential High-rise residential Mixed Industrial Rural/park Coastal S05, S32 WD UHIraw > WE UHIraw S07, S18, S22, S24, S29, S31, S33, S42, S44, S45, S46 S15, S19, S21 S06 S08, S14, S17, S37, S38 S20, S40 S25 S03, S10, S11, S28 S09 S13, S33, S41, S43 S02, S12, S36 S04, S23, S27, S30, S34, S39 S01 UHIraw and maximum UHIraw intensities. Paired t-test tests (N = 43) show that while weekday and weekend mean UHIraw intensities do not have a significant relationship (t = −2.03; p > 0.01), weekday and weekend maximum UHIraw intensities have significantly different means (t = 5.50; p < 0.001). It is postulated that commercial areas see higher UHIraw intensities during the weekdays due to increased QF from anthropogenic activities (Chow and Roth, 2006; Quah and Roth, 2012). For this purpose, Table 4.11 was drawn up. All of the commercial sites had higher weekday than weekend maximum UHIraw intensities, consistent with the hypothesis. Industrial stations are also expected to have lower QF during weekends and three of these stations (S02, S12 and S36) had higher maximum weekday UHIraw intensities than weekend maximum UHIraw intensities. Only one industrial station (S25) had the opposite relationship. For residential stations (both low-rise and high-rise), three stations (S05, S06 and S32) had higher weekend maximum UHIraw intensities than weekday maximum UHIraw intensities. However, eight other residential stations had the opposite relationship. There is almost an equal number of stations sited in rural areas, parks and coastal areas in both categories. 130 4.7 Weather effects on monthly UHI Earlier sections studied the variation of air temperature and consequently UHI, given ”mean” conditions across the entire study period and filtering for weather conditions. This section investigates the influence of synoptic weather conditions on mean monthly UHI intensities across all stations. As we are interested in the longer-term effects of weather, the non-filtered definition of UHI, ΔTu−r , will be used. Due to a lack of synoptic data with high spatio-temporal resolution, monthly data from Changi Meteorological Station will be used as a surrogate. On a day-to-day basis, the effects of extreme weather conditions have a more pronounced effect but it is difficult to isolate the effects of synoptic conditions on UHI. For example, in an ideal situation, the same amount of rain has to fall at the same intensity over both the urban and rural sites, ceteris paribus. Any asynchronous occurrence of discrete weather conditions, such as rainfall events, will bring about artificial increases or decreases in UHI. When averaging across entire months, relationships between weather conditions and UHI become more clear Air temperature Regression plots of monthly air temperature at Changi Meteorological Station against monthly mean and monthly mean maximum UHI (Figure 4.26) show significant positive relationships (p < 0.001) with R2 values above 0.5. Based on the regression equations, mean and maximum monthly UHI increase by 0.274◦ C and 0.411◦ C, respectively, with every degree increase in air temperature. Air temperature, however, may not be the direct factor that influences the UHI but correlated along with other influential factors such as solar radiation and drier conditions, 131 similar to the differences between winter and summer UHI in temperate countries (Oke, 1982). Rainfall Interestingly, there are no significant relationships (p > 0.1; R2 < 0.1) between total monthly rainfall, and either mean or maximum UHI intensities. A second monthly variable, number of rain days, was also tested against mean and maximum UHI intensities and similarly yielded no significant relationships. A possible explanation is that soil moisture (and hence thermal admittance) is a more important variable and has a lagged relationship with rainfall events. Wind speed Monthly mean wind speeds have significant negative relationships with monthly mean and maximum UHI intensities (p < 0.001 and p < 0.01 respectively), with coefficients of determination at 0.298 and 0.414 respectively. With every ms−1 increase in wind speed, monthly mean and maximum UHI intensities are expected to decrease by 0.284 and 0.485 ◦ C respectively. This finding is consistent with previous research showing the influence of wind on UHI intensities (e.g. Oke, 1998) and with the study on Singapore by Chow and Roth (2006). 132 Mean UHI intensity (°C) ● 2.0 ● ● ● ● 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1.0 ● ● 26.5 27.0 27.5 28.0 Air temperature at Changi Met Station(°C) 28.5 29.0 Mean UHI intensity (°C) ● 2.0 ● ● ● 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● 1.0 ● ● 0 100 200 300 400 Monthly rain at Changi Met Station(mm) 500 Mean UHI intensity (°C) ● 2.0 ● ● 1.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 1.0 ● ● 1.5 2.0 2.5 Wind speed at Changi Met Station(m/s) 3.0 Figure 4.26: Regression of monthly mean UHI intensity against (top) air temperature, (middle) total monthly rainfall, and (bottom) mean wind speed. Note that UHI intensity here is ΔTu−r . Observations from 35 stations for the entire observation period (Feb 2008 to Jul 2011) are used. Shaded region represents the 95% confidence bands. Max UHI intensity (°C) 133 ● 3.0 ● ● ● ● ● ● ● 2.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2.0 ● ● ● ● 26.5 27.0 27.5 28.0 Air temperature at Changi Met Station(°C) 28.5 29.0 Max UHI intensity (°C) ● 3.0 ● ● ● ● ● ●● ● ● ● ● ● ● 2.5 ● ● ● ● ● ● ● ● ● ● ● 2.0 ● ● ● ● 0 100 200 300 400 Monthly rain at Changi Met Station(mm) 500 Max UHI intensity (°C) ● 3.0 ● ● 2.5 ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2.0 ● ● ● ● 1.5 2.0 2.5 Wind speed at Changi Met Station(m/s) 3.0 Figure 4.27: Regression of monthly maximum UHI intensity against (top) air temperature, (middle) total monthly rainfall, and (bottom) mean wind speed. Note that UHI intensity here is ΔTu−r . Observations from 35 stations for the entire observation period (Feb 2008 to Jul 2011) are used. Shaded region represents the 95% confidence bands. 134 4.8 Landscape effects on UHI As the study area is relatively small in area and located near the equator, landscape effects studied are limited to lapse rate due to elevation and proximity from water bodies. Table 4.12 shows the matrix of coefficients of determination (R2 ) between the landscape factors (elevation, distance from sea and distance from large water body) and dependent variables of UHIraw . Table 4.12: Landscape factors and the strength of their relationship (coefficients of determination) with dependent variables. A large water body is defined as a lake or reservoir with an area larger than 1 km2 including the sea. Elevation Min UHIraw Max UHIraw Max UHIraw(t) hrly ens. NM UHIraw DM UHIraw 0.043 0.016 0.049 0.005 0.183** Distance from sea 0.018 0.089+ 0.016 Distance from large water body 0.025 0.000 0.058 0.056 0.007 0.000 0.106* *** = p < 0.001; ** = p < 0.01; * = p < 0.05; + = p < 0.1; NM = nocturnal mean; DM = daytime mean Minimum UHIraw does not appear to be influenced by any landscape effects, with R2 values not exceeding 0.05. The time of occurrence of maximum hourly ensemble UHIraw and nocturnal mean UHIraw also do not show any significant relationships with landscape variables. A weakly significant (p < 0.1) relationship is found between maximum UHIraw and distance from sea. However, this relationship account for only ∼9% of the variance. The strongest relationships are found between elevation and daytime mean UHIraw (p < 0.01; R2 = 0.183) and distance from large water body and daytime mean UHIraw (p < 0.05; R2 = 0.106)(Figure 4.28). UHIraw reducing with elevation is intuitive due to adiabatic cooling. For the 135 (a) ● ● 1.5 ● ● ● Daytime mean UHIraw(°C) ● 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.0 ● ● ● ● −0.5 y = 1.1 + −0.027 ⋅ x, r 2 = 0.183 ● −1.0 ● ● 10 20 30 40 Elevation (m) 50 (b) ● ● 1.5 ● ● Daytime mean UHIraw(°C) ● ● ● ● ● 1.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 ● ●● ● ● ● 0.0 ● ● ● ● −0.5 y = 0.22 + 0.00018 ⋅ x, r 2 = 0.106 ● −1.0 ● 0 ● 1000 2000 3000 Distance from large water body (m) 4000 Figure 4.28: Relationship between daytime mean UHIraw and (a) elevation, and (b) distance from large water body. Observations from all stations across the entire observation period (Feb 2008 to Jul 2011) are used. Shaded region represents the 95% confidence bands. latter, the relationship is also consistent with the moderating effect of the cooler large water bodies in the day as daytime mean UHIraw decrease closer to the water bodies. 136 Chapter 5 Summary and Conclusions The precise definition of UHI terms used in this study, the description of instrumentation metadata and the accounting for various confounding factors such as weather and thermal inertia are in-line with the criteria laid out by Stewart (2011). This achieves the initial goal of having a scientifically robust UHI study. Comparison of certain summary values suggests that stricter filtering (i.e. UHImax ) has a higher accuracy when dealing with extreme values. • Definition of UHI intensity following Lowry (1977) and including the known confounding factors of landscape effect (L), weather factor (Φw ), moisture factor (Φm ) and antecedent conditions (Φa ) : ΔT = ((Tu − Lu ) − (Tr − Lr ))Φw Φm Φa . • The removal of nights with antecedent weather conditions (Φa ), results in lower UHImax than UHIraw suggesting some form of influence by unequal antecedent conditions in UHIraw . Thus, UHImax filtering is more accurate for reporting extreme values. • Stricter UHImax filtering see less unexpected time of maximum UHI values than using UHIraw filtering 137 The goal of completing an extensive monitoring exercise was achieved as observations span a period of 41 months and across 44 stations. This is the most extensive study both spatially and temporally in Singapore to date. Results corroborate with earlier studies and also provide new findings. From the findings of this study we can also conclude that the UHI in Singapore exhibits strong diurnal trends linked with time of day and strong seasonal trends linked with monsoonal seasons. Inter-annual trending, however, does not appear to be significant. • The previous longest study was conducted by Chow and Roth (2006), for a period of 13 months. This is the first study to have a few years of data, allowing for inter-annual analysis. • Across all weather conditions without filtering, maximum air temperature was 36.59◦ C at 15:00 hrs on March 10, 2010 (S11). Minimum air temperature was 20.08◦ C at 06:20 hrs on 19 January 2009 (S23). • Maximum recorded air temperature has no distinct relationship with either mean air temperature (R2 = .0196) or minimum air temperature (R2 = .001). However, mean air temperature and minimum air temperature have a statistically significant relationship (p < 0.01; R2 = .762). • The highest UHIM AX (with UHImax filter) values was 6.46◦ C, measured at 22:20 hrs local time on 24th April 2009, at S22. Time of occurrence (3 hours after sunset) and location (commercial core) are consistent with previous studies (Singapore Meteorological Services, 1986; Chow and Roth, 2006) although magnitude is half a degree lower than what Chow and Roth (2006) found. • Stations found in rural and vegetated areas tend to have maximum UHIraw occurring close to sunrise. 138 • Most maximum values for both UHIraw and UHImax are found during the April-May (Pre-SW monsoon) and July-Sept (SW monsoon) period. • The daytime mean UHIraw intensities are similar across the study area. Small cool islands are found in the rural north-west and the central catchment area. Small heat islands are found in urban areas across the island. The core of the city does not have a distinctive daytime UHI formation, possibly due to shading by tall buildings. • The largest daytime mean UHIraw intensity is ∼1.5◦ C and is located in the middle of Singapore, in a large and dense low-rise residential area (S05 and S19). • Night-time mean UHIraw is most pronounced over the city centre and other warm areas occur in industrial and residential zones. The night-time mean UHIraw intensities exceed 3.5◦ C in several parts of Singapore. Steep isolines suggest that the thermal gradient is significantly larger at night, expected due to the different rates of cooling. • The diurnal characteristics of the heat island changes across the year, in terms of magnitude, standard deviation across stations, onset, peak and decline. • Pre-SW inter-monsoon and SW monsoon periods have the highest heat island intensities. Pre-NE inter-monsoon experiences a drop in UHI intensity and the lowest values occur during the NE monsoon, which also experiences later time of peak occurrence. • Although seasonal trends are distinct, inter-annual trends do not appear to have much influence on UHI patterns. A longer study may yield better results. • A temporal autocorrelation analysis suggests that ΔTu−r values within four hours are highly correlated. Over a longer period, auto-correlations around 139 the same hour of day remain largely significant (Durbin-Watson test: p < 0.001) for about 25 days. Causal factors of UHI have been established in the study. This achieves the final objective of this research. Several urban parameters are found to be significant in influencing UHI-based dependent variables. Unlike synoptic weather conditions such as air temperature and wind, landscape effects do not appear to be influential in determining UHI magnitudes and behaviour in Singapore. • The LULC variables have significant relationships with nocturnal mean UHIraw , daytime mean UHIraw and maximum UHIraw . VP100 and VP500 explain much of the nocturnal mean UHIraw (R2 > 0.6). BUP100 and BUP500 explain much of the daytime mean UHIraw . None of the LULC variables have strong relationships with minimum UHIraw . • H/W ratio has the highest predictive strength (where p < 0.01) for nocturnal mean UHIraw and maximum UHIraw (log relationships). zH /W ratio has the lowest predictive strength. SVF has strongest relationship with daytime mean UHIraw (p < 0.01). zH /W and SVF have weakly significant relationships with minimum UHIraw (p < 0.05). • Base values of regression equations of canyon geometry are similar to LULC suggesting the effects of land-use, land cover and urban geometry are linked and difficult to separate. Canyon geometry variables do not account for UHI variance as well as the LULC variables. • Weekly variability of QF has more influence over maximum UHIraw intensities (p < 0.001) than mean UHIraw intensities. All commercial sites had higher weekday maximum UHIraw intensities than weekend maximum UHIraw intensities. The same result applies for most industrial stations. [...]... performance of H/W in predicting night time UHI values in this study (R2 = 0.351) as compared to the study by Goh and Chang (R2 = 0.285) which uses a median H/W across the estate to predict heat island intensity in a straight-line function As the H/W ratio used in the present study focuses on the immediate proximity of the sensor, it may be indicative of the relative importance of microscale variables 128 Urban. .. closer to the water bodies 136 Chapter 5 Summary and Conclusions The precise definition of UHI terms used in this study, the description of instrumentation metadata and the accounting for various confounding factors such as weather and thermal inertia are in- line with the criteria laid out by Stewart (2011) This achieves the initial goal of having a scientifically robust UHI study Comparison of certain summary... in industrial and residential zones The night-time mean UHIraw intensities exceed 3.5◦ C in several parts of Singapore Steep isolines suggest that the thermal gradient is significantly larger at night, expected due to the different rates of cooling • The diurnal characteristics of the heat island changes across the year, in terms of magnitude, standard deviation across stations, onset, peak and decline... found in urban areas across the island The core of the city does not have a distinctive daytime UHI formation, possibly due to shading by tall buildings • The largest daytime mean UHIraw intensity is ∼1.5◦ C and is located in the middle of Singapore, in a large and dense low-rise residential area (S05 and S19) • Night-time mean UHIraw is most pronounced over the city centre and other warm areas occur in. .. UHI intensities (p < 0.001 and p < 0.01 respectively), with coefficients of determination at 0.298 and 0 .41 4 respectively With every ms−1 increase in wind speed, monthly mean and maximum UHI intensities are expected to decrease by 0.2 84 and 0 .48 5 ◦ C respectively This finding is consistent with previous research showing the in uence of wind on UHI intensities (e.g Oke, 1998) and with the study on Singapore. .. suggesting some form of in uence by unequal antecedent conditions in UHIraw Thus, UHImax filtering is more accurate for reporting extreme values • Stricter UHImax filtering see less unexpected time of maximum UHI values than using UHIraw filtering 137 The goal of completing an extensive monitoring exercise was achieved as observations span a period of 41 months and across 44 stations This is the most... 0) of just under 3◦ C is the main differentiating factor between Equations 4. 2 and 4. 4 As for daytime mean UHIraw , when BUP100 = 0, the base value is -0 .40 5◦ C For every 25% increase in BUP100 , the daytime mean UHIraw increases by 0.73◦ C ●● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ●● ●● ● ● 4 ● ● ● ● 3 ●● ● 2 0 20 40 BUP100 60 80 6 5 ● ● ●● ● ● ● ● ●● ● ● ● 4 ● ● ● ● 3 ●● ● 2 1 40 60 VP100 80 100 ● ● ● 4 ●... study both spatially and temporally in Singapore to date Results corroborate with earlier studies and also provide new findings From the findings of this study we can also conclude that the UHI in Singapore exhibits strong diurnal trends linked with time of day and strong seasonal trends linked with monsoonal seasons Inter-annual trending, however, does not appear to be significant • The previous longest... section investigates the in uence of synoptic weather conditions on mean monthly UHI intensities across all stations As we are interested in the longer-term effects of weather, the non-filtered definition of UHI, ΔTu−r , will be used Due to a lack of synoptic data with high spatio- temporal resolution, monthly data from Changi Meteorological Station will be used as a surrogate On a day-to-day basis, the effects... effects of extreme weather conditions have a more pronounced effect but it is difficult to isolate the effects of synoptic conditions on UHI For example, in an ideal situation, the same amount of rain has to fall at the same intensity over both the urban and rural sites, ceteris paribus Any asynchronous occurrence of discrete weather conditions, such as rainfall events, will bring about artificial increases ... parts of the island At the end of the NE monsoon in March and April, heat islands begin to grow stronger (∼2.5◦ C) and thermal gradients are increasing Values of mean UHIraw continue to increase,... (Figure 4. 20) In the NE monsoon, strong heat islands are absent, with warm belts over the urban areas The cool islands are distinctly larger during the NE than the SW monsoon periods Strong heat islands... Extension in the early 1990s The 1997 study by Goh and Chang (1999) found that the residential estates in Jurong West have the highest heat island intensities among the 17 towns sampled in Singapore The