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Spatiotemporal statistical analysis of the Urban Heat Island effect in a Mediterranean region
Sustainable Cities and Society 46 (2019) 101427
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Spatiotemporal statistical analysis of the Urban Heat Island effect in a Mediterranean region
T
Daniel Jato-Espino GITECO Research Group, Universidad de Cantabria, Av. de los Castros 44, 39005, Santander, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Albedo Land cover Statistical analysis Urban Heat Island
The Urban Heat Island (UHI) effect has become one of the major threats for the welfare of developed cities over the last years, as a result of increased urbanisation. For this reason, this research developed a statistical methodology to model the UHI effect, taking the Valencian Community (eastern Spain) as a case study. Inferential statistics were used to explore the spatiotemporal patterns of the UHI effect, whilst descriptive statistics were applied to model its relationships to elevation, land cover and solar radiation variables. The results achieved demonstrated the existence of significant differences between urban and peri-urban areas, as well as an increase in the intensity of the UHI effect over time. The main cause of these results was found in the low reflectance of built-up surfaces, suggesting the implementation of solar reflective and green infrastructure as a solution to palliate the UHI effect.
1. Introduction Increased urbanisation is favouring a variety of environmental threats, among which the Urban Heat Island (UHI) effect has emerged as one of the most prominent ones (Dwivedi & Khire, 2018). This phenomenon is related to the difference in temperature between rural and urban areas, whereby the daily thermal amplitude of the latter is reduced as a result of diurnal solar heat absorption that is progressively released during the night (Kolokotroni, Giannitsaris, & Watkins, 2006). The UHI effect is usually associated with two different but related processes. Firstly and most importantly, land use and land cover alterations stemming from urbanisation, which promote the use of materials with high heat absorption and retention capacity, such as asphalt (Liu et al., 2015). And secondly, thermal emissions derived from anthropogenic activities, especially in relation to transport and industrial processes that contribute to boosting global warming (Taha, 1997). One of the main consequences of the UHI effect is economic, since its existence might lead to a rise in energy consumption devoted to cooling buildings in densely populated urban areas (Kolokotsa et al., 2018). Furthermore, continued exposure to high temperatures may cause health disorders, including exhaustion due to dehydration, fainting, heat strokes, cerebrovascular disorders or respiratory problems (Martínez Navarro, Simón-Soria, & López-Abente, 2004). Due to all these considerations, the UHI effect has become a widely investigated topic over the years, especially during the last decade. Stathopoulou and Cartalis (2007) highlighted asphalt and concrete
surfaces, and bare soil associated with dumping and construction sites as the main drivers for the UHI effect throughout Greek cities. Kolokotsa, Psomas, and Karapidakis, (2009) also focused on Greece (Hania), studying the impact of several meteorological variables on the UHI effect, which was found to be especially influenced by wind speed and direction. Priyadarsini, Hien, and Wai David, (2008) investigated the main factors causing the UHI effect in Singapore (Asia), highlighting the relevance of the materials and colours used in building façades on air temperature. Hart and Sailor (2009) developed a regression model to determine the factors having the greatest influence on the UHI effect in Portland (U.S.), which were found to be canopy cover and roadway density. Buyantuyev and Wu (2010) examined the UHI effect with respect to the land cover configuration of Phoenix (U.S.), confirming its strong dependence on both vegetation and pavements. Takebayashi and Moriyama (2012) explored potential locations to implement UHI mitigation measures by assessing surface temperature and solar radiation in street canyons, priority areas being related to buildings with large roofs and roads. Ivajnšič, Kaligarič, and Žiberna, (2014) determined that the UHI effect in the city centre of Ljutomer (Slovenia) was explained by variables such as distance to urban area, building volume, land cover or topography. Similarly, Yao et al. (2018) found that the variations in the UHI effect were caused by reduced vegetation, increased population and the Albedo coefficient. The review of previous studies highlighted a research gap regarding the statistical modelling of the spatiotemporal patterns of the UHI effect
E-mail address: [email protected]. https://doi.org/10.1016/j.scs.2019.101427 Received 4 October 2018; Received in revised form 7 January 2019; Accepted 7 January 2019 Available online 08 January 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.
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and its interaction with solar radiation, land cover reflectance and elevation. Hence, this investigation aimed at filling this gap by designing and applying a methodology based on inferential and descriptive statistics. The main characteristics of this methodology are provided in Section 2, whilst Section 3 compiles and discusses the results achieved through the application of the proposed approach to the case study of the Valencian Community. Finally, the main conclusions drawn from this research are summarised in Section 4. 2. Methodology The methodology used to evaluate the Urban Heat Island (UHI) effect in the Valencian Community is outlined in Fig. 1. It consisted of a combination of tests using inferential and descriptive statistics. Consequently, the development and application of this approach were supported by the use of the statistical package Minitab (Minitab Inc., 2017), which was coupled with the desktop version of ArcGIS (ESRI, 2017) when requiring the generation of spatial inputs via Geographic Information Systems (GIS). Inferential statistics were used to analyse either the existence or absence of significant differences between the values of thermal amplitude (ΔT ) recorded in both urban and peri-urban areas at different locations and times. ΔT , which was determined as formulated in Eq. (1), was taken as an indicator of the UHI effect, since it accounts for the difference between maximum (Tmax ) and minimum (Tmin ) daily temperature (Scott, Waugh, & Zaitchik, 2018). In other words, this parameter was expected to identify the magnitude of heat dissipation during
Fig. 1. Flowchart of the proposed approach to model the Urban Heat Island (UHI) effect.
Fig. 2. Location of the meteorological stations and buffer areas considered in modelling the Urban Heat Island (UHI) effect in the Valencian Community. 2
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Table 1 Albedo coefficients corresponding to the land cover types considered in the Spanish Land Use and Land Cover Information System (SIOSE). Group
Land cover type (SIOSE code)
Albedo
Artificial cover
Buildings (EDF) Artificial green areas and urban woodland (ZAU) Artificial water bodies (LAA); Road, parking or pedestrian area without vegetation (VAP) Other constructions (OCT) Non-built soil (SNE); Extraction or discharge zones (ZEV) Herbaceous: rice (CHA); Herbaceous: other than rice (CHL); Woody: citrus fruit (LFC); Woody: non-citrus fruit (LFN); Woody: vineyard (LVI); Woody: olive grove (LOL); Woody: other (LOC) Meadows (PRD) Pastureland (PST) Hardwood: deciduous (FDC); Hardwood: perennials (FDP) Coniferous (CNF) Brushwood (MTR) Beaches, dunes and sand (PDA) Bare soil (SDN); Burned areas (ZQM); Valleys (RMB); Rocky: sea cliffs (ACM); Rocky: rocky outcrops (ARR); Rocky: screes (CCH); Rocky: volcanic rocks (CLC) Glaciers and permanent snow (GNP) Inland wetlands: swamps (HPA); Inland wetlands: peatbogs (HTU); Inland wetlands: salt flats (HAS); Marine wetlands: marshes (HMA); Marine wetlands: salt marshes (HSM) Inland waters: water courses (ACU); Inland waters: lakes, ponds and reservoirs (ALG); Inland waters: reservoirs (AEM); Marine waters: coastal lagoons (ALC); Marine waters: estuaries (AES) Marine waters: seas and oceans (AMO)
0.15 0.23 0.10 0.15 0.17 0.18
Crops
Pastureland Woodland Brushwood Barren vegetation
Humid cover Water cover
0.60 0.10 0.10 0.07
region with daily temperature data were used to model the UHI effect. Three of these stations were located in the capital cities of the abovementioned provinces, resulting in a variety of degrees of urbanisation (ALI, CAS and VAL). Moreover, two additional stations situated in the airports of Alicante and Valencia (ALIAER and VALAER) were also considered as representative of peri-urban sites. Given the collateral impacts associated with the UHI effect during the summer, which include increasing energy consumption, concentrations of pollutants and health impacts (Giannopoulou et al., 2011), the data acquired from these stations was limited to the period from June 21 to September 23. This was also consistent with Jonsson (2004), who highlighted the increased intensity of the UHI effect during summer in arid and semi-arid regions like the Valencian Community. In order to facilitate the modelling of the UHI effect, up to 4 different buffer areas were defined as illustrated in Fig. 2. The radii corresponding to these buffer areas (250 m, 500 m, 750 m and 1000 m) were previously suggested in similar investigations to study the association between the UHI effect and the land cover of urban sites (van Hove et al., 2015). As specified before, this research went one step further and also took into consideration variables related to the elevation, radiation, hillshade and reflectance of the study area. A LiDAR cloud produced during 2012 with a resolution of 0.5 points per m2, available at the Spanish Geographic Institute (IGN) (CNIG, 2018), served to characterise the first three variables. The reflectance of the buffer areas, represented by their Albedo coefficients, was coupled with their land cover configurations, which were delineated from the Spanish Land Use and Land Cover Information System (SIOSE, 2015). The SIOSE project is provided at a 1:25,000 scale in four different years (2005, 2009, 2011 and 2015) and includes the following eight groups: artificial cover, crops, pastureland, woodland, brushwood, barren vegetation, humid cover and water cover. As shown in Table 1, these groups are in turn broken down into several categories. Based on previous studies about the reflectance of urban surfaces (Coakley, 2003; Wei et al., 2001), each of these categories was related to a value of Albedo coefficient, which is defined as the amount of solar energy scattered by their corresponding land cover types to space (Taha, 1997). As an illustrative example, Fig. 3 depicts the land cover configurations associated with the 1000 m buffer area of VALAER over the years. Although the classes into which the buffer area was divided could consist of a single land cover type, such as ‘OCT’ or ‘PST’, the most common scenarios involved combinations of different surfaces. For
the night at monitored stations in both urban and peri-urban areas by considering the thermal evolution from the warmest hours of the day (maximum temperature) to the coldest ones (minimum temperature). Hence, the higher the value of ΔT , the lower the heat dissipated during the night and, therefore, the less likely that the UHI effect is an issue. Unlike other indicators commonly used to measure the UHI effect, which are based on complex parameters such as temperature gradient, air density or specific heat (Lee, Lee, & Wang, 2012), the proposed approach only considered air temperature, which is a variable globally available.
ΔT = Tmax − Tmin
0.19 0.19 0.16 0.10 0.16 0.30 0.17
(1)
Descriptive statistics were applied to model the degree of association between a series of variables related to the orography and land cover configuration of the terrain, in order to identify which of them contributed the most to estimate ΔT . These variables included the Albedo coefficient and land cover type of the surface, as well as the elevation of the ground, which in turn were used to determine solar radiation and hillshade-related parameters. Hence, the proposed framework enabled modelling the UHI effect at single locations using these explanatory variables, such that urban areas resulted in lower values of ΔT , and vice versa for peri-urban locations. This course of action differentiated this approach from former methods, which require combined calculations considering both urban and peri-urban or rural areas. 2.1. Study area The Valencian Community is located in the east of Spain, has a surface of 23,259 km2 and is home to 4,935,182 inhabitants distributed throughout three provinces, as shown in Fig. 2: Alicante, Castellón and Valencia (INE, 2018). About 50% of the surface area and people of the whole region are concentrated in Valencia, which along with Castellón is characterised by a temperate Mediterranean climate. This corresponds to the category C according to the Köppen classification, contrasting with the drier conditions of the province of Alicante, which belongs to group B (Chazarra et al., 2011). Overall, the Valencian Community is characterised by its susceptibility to undergoing the UHI effect, due to the presence of large crop areas in the surroundings of the main cities and their proximity to the sea, which might boost the differences between the values of ΔT in peri-urban and urban sites (ABC, 2017; eltiempo, 2016). For this reason, the five meteorological stations available in the 3
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accordance with the availability of meteorological and land cover data. Hence, the combination of locations (5) and years (4) resulted in 20 datasets including 95 observations (from June 21 to September 23) of daily maximum and minimum temperature across the Valencian Community. These samples were examined using either parametric or non-parametric tests, depending on whether they were normally distributed and homoscedastic or not. The potential application of parametric tests was preceded by checking the normality and homoscedasticity of the datasets under analysis. Normality was evaluated using the Ryan-Joiner test (Zolgharnein & Shahmoradi, 2010), which is based on determining the correlation between the data and their normal scores. Homoscedasticity was checked through Levene’s test (Gastwirth, Gel, & Miao, 2009), consisting of assessing the deviation of the observations from the median of the sample to which they belong. In these cases, H0 implied that the datasets were normal and homoscedastic (p-value > 0.05). In those situations in which both assumptions were met, parametric tests were applied to compare the samples under analysis. To this end, the data were first grouped in spatial (5 samples per group) and temporal (4 samples per group) terms. Then, a one-way analysis of variance (ANOVA) (Fisher, 1919) was used to explore the similarity between all the samples belonging to each group. Once the existence or absence of general differences was determined, the Student’s t-test (Gosset, 1908) was used to carry out comparisons between each pair of samples. In contrast, non-parametric tests were used when the samples proved not to meet the assumptions of normality and homoscedasticity. Again, an overall comparison among all the samples corresponding to either a location or a year was carried out through the Kruskal-Wallis test (Kruskal & Wallis, 1952). In those cases where this test provided evidence of the presence of significant differences, pairwise comparisons were made using the Mann-Whitney U test (Mann & Whitney, 1947).
Fig. 3. Land cover classification of the buffer area corresponding to the station located in the airport of Valencia (VALAER) (a) 2005 (b) 2009 (c) 2011 (d) 2015.
2.3. Descriptive statistics Table 2 Variables and summary statistics proposed to model the Urban Heat Island (UHI) effect. Variable
MIN
MAX
RANGE
MEAN
STD
SUM
Albedo coefficient (Dimensionless) Digital Elevation Model (DEM) (m) Hillshade [0, 255] Global Solar Radiation (Wh/m2) Diffuse Solar Radiation (Wh/m2) Direct Solar Radiation (Wh/m2) Direct Solar Radiation Duration (h)
X1.1 X2.1 X3.1 X 4.1 X5.1 X6.1 X7.1
X1.2 X2.2 X3.2 X 4.2 X5.2 X6.2 X7.2
X1.3 X2.3 X3.3 X 4.3 X5.3 X6.3 X7.3
X1.4 X2.4 X3.4 X 4.4 X5.4 X6.4 X7.4
X1.5 X2.5 X3.5 X 4.5 X5.5 X6.5 X7.5
X1.6 X2.6 X3.6 X 4.6 X5.6 X6.6 X7.6
In contrast with inferential statistics, which enable the conclusions extracted from specific data to be generalised for broader conditions, descriptive statistics only focus on interpreting the particularities of the data under analysis. The descriptive statistical technique required to model the UHI effect in the Valencia Community was Multiple Regression Analysis (MRA), which was based on a series of summary statistics of the variables expected to contribute to producing variations in this phenomena, as listed in Table 2. Six summary statistics were calculated over the buffer areas represented in Fig. 2: minimum (MIN), maximum (MAX), range, mean, standard deviation (STD) and sum. This task was accomplished with the support of geoprocessing tools available in ArcGIS, starting by establishing a new attribute in the land cover map obtained from the SIOSE project through the ‘Add Field’ and ‘Calculate Field’ tools. Then, the LiDAR data was cleaned to create a Digital Elevation Model (DEM) of the study area. This was carried out through the ‘Raster Calculator’ tool, which enabled the outliers found in the elevation map derived from the LiDAR point cloud to be replaced by mean values from surrounding cells. The clean DEM was used as input in the ‘Area Solar Radiation’ tool, whose application yielded the four radiation-related variables included in Table 2. The parameters established to run this tool involved 200 cells resolution and 0.5-hour interval. Moreover, since the LiDAR data was from 2012, the time interval was set at 2011 (whole year), in order to carry out the MRA during the period between 2011 and 2012. The ‘Hillshade’ tool was also implemented from the DEM, resulting in a relief highlighting illuminated and shaded zones within the study area. All these maps were confined to the buffer areas represented in Fig. 2 through the ‘Clip’ tool. Finally, the ‘Zonal Statistics as Table’ tool was used to obtain the values indicated in Table 2. These summary statistics generated were used as predictors in the
instance, the class’ 25SNE_20EDF_20VAP_20ZAU_15OCT’ in Fig. 3 represented the following mix of categories: 25% non-built soil (SNE), 20% buildings (EDF), 20% road, parking or pedestrian area without vegetation (VAP), 20% artificial green areas and urban woodland (ZAU) and 15% other constructions (OCT). 2.2. Inferential statistics Inferential statistics involve the extrapolation of conclusions drawn from the analysis of sample data (Moore, 1996). This branch of statistics is applied to evaluate the probability of rejecting the null hypothesis (H0 ) with respect to the alternative hypothesis (H1) due to chance. This was undertaken through the p-value, which indicates the probability of wrongly rejecting H0 when it is true. If the p-value is below a significance level α of 0.05 (Fisher, 1925), the probability of error is below. In this context, H1 referred to the existence of significant differences between the values of ΔT registered at different locations (ALI, ALIAER, CAS, VAL and VALAER) and years (2005, 2009, 2011 and 2015), in 4
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Fig. 4. Time series of daily thermal amplitude (ΔT ) in the meteorological stations (a) ALI (b) ALIAER (c) CAS (d) VAL (e) VALAER.
availability of LiDAR data, the relationships between the Albedo coefficient and ΔT over time were further explored through the Pearson Correlation Coefficient (CC ). This numerical measure enabled the determination of whether the warming sequestration potential of the land cover configurations of the buffer areas was statistically significant for the existence of the UHI effect or not. In accordance with the land cover data, this analysis included the summer season of the following years: 2005, 2009, 2011 and 2015.
MRA models built for estimating the UHI effect. In other words, MRA enabled the determination of the degree of association between the values of ΔT recorded by the weather stations included in Fig. 2 and the predictors proposed in Table 2. These relationships were expressed as shown in Eq. (2).
Y = β0 + β1.1 * X1.1 + …+β3.4 * X3.4 …+β7.6 * X7.6 + E
(2)
where Y is ΔT , Xi . j are the summary statistics of the terrain-related variables, βi . j are the coefficients associated with the predictors β0 is the constant of the MRA model and E represents its residuals. The quality of the MRA models was evaluated according to two goodness-of-fit measures, namely the standard error of the regression (S ) and the coefficient of determination. The latter was considered through three dif2 2 ferent versions: standard (R2 ), adjusted (Radj. ) and predicted (Rpr. ). The legitimacy of the results obtained was determined through the analysis of the residuals, which were examined in terms of their normality, homoscedasticity, linearity and multicollinearity. As described in the case of parametric inferential statistics, the first two verifications were undertaken with the support of the Ryan-Joiner and Levene’s tests. Linearity was checked by comparing the p-value of the MRA model with the significance level established (0.05), whilst the presence of multicollinearity was associated with values of Variance Inflation Factor (VIF) above 5 (Menard, 2002). The assumption of independence of residuals was disregarded, since the datasets under analysis were not arranged in the form of time series, so that the detection of autocorrelation was pointless in this case. Since the application of MRA was restricted to 2011–2012 by the
3. Results and discussion This section presents and examines the results achieved through the implementation of the framework schematised in Fig. 1 to model the Urban Heat Island (UHI) effect over the Valencian Community. As a previous step to the application of the methodology described above, Fig. 4 depicts the time series of daily maximum temperature, minimum temperature and thermal amplitude (ΔT ) in the meteorological stations considered (Fig. 2). The visual inspection of these charts suggested that the values of ΔT associated with peri-urban sites (ALIAER and VALAER) where higher than those corresponding to urban locations (ALI, CAS and VAL). To confirm the validity of this perception, inferential and descriptive statistics were sequentially applied to these data in following subsections. 3.1. Inferential statistics Inferential statistics were used to compare the variations of ΔT over 5
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As a result, these two stations were evaluated through a one-way ANOVA test, whilst the remaining locations (ALIAER, CAS and VAL) required the application of the Kruskal-Wallis test. The outputs of these tests revealed that the differences among the groups were only significant for ALIAER, CAS and VALAER. These stations corresponded to less urbanised and less populated sites than those located in the main cities of the region (ALI and VAL), which might explain their susceptibility to undergoing urban developments and, therefore, decreases in ΔT throughout the years. A more in-depth analysis through the Student’s t and Mann-Whitney U tests demonstrated that the values of ΔT decreased with time, involving a statistically significant increase of the UHI effect in all the stations with respect to the first year considered (2005). Hence, the general trend in the results suggested a reduction in the values of ΔT over time, although these changes were statistically significant only in some cases (Table 3). The same procedure as described above was followed to evaluate the variations of ΔT depending on the location of the stations. According to the p-values previously obtained for the R-J test, Levene’s test was only carried out for 2011. Since the p-value was below the significance level (0.025), every comparison was approached using non-parametric tests. The results of the Kruskal-Wallis test were below the significance level for all the groups (Table 4), involving significant differences among the values of ΔT corresponding to the five stations under analysis for every year. This guaranteed the suitability of the stations considered, since they demonstrated to provide a representative sample of sites with different characteristics in terms of thermal amplitude. The pairwise comparisons made with the support of the MannWhitney U test indicated a difference between urban and peri-urban areas, whereby the stations located in the airports (ALIAER and VALAER) reached values of ΔT significantly higher than those obtained for the city centres (ALI, CAS and VAL). The reduced difference between diurnal and nocturnal temperatures was especially remarkable in Valencia (VAL), which is the most crowded and built-up city in the region. Moreover, CAS was found to result in narrower spread of values of ΔT in comparison with ALI, even though the latter has twice the population of the former. This might be related to the particularities of the climate in Alicante, which is less wet than in Castellón.
Table 3 Temporal analysis of the Urban Heat Island (UHI) effect in the Valencian Community. Station
Year
Central tendency
Comparison (p-value)
ALI
2005
9.282*
ALIAER
2009 2011 2015 2005
9.519* 9.866* 9.109* 9.500**
CAS
2009 2011 2015 2005
9.200** 8.400** 8.650** 9.500**
VAL
2009 2011 2015 2005
**
8.600 9.200** 9.000** 7.700**
VALAER
2009 2011 2015 2005
7.400** 7.600** 7.000** 11.432*
2009 2011 2015
10.324* 9.971* 9.624*
vs 2009 (0.63) vs 2011 vs 2015 – vs 2009 (0.01) vs 2011 vs 2015 – vs 2009 (0.10) vs 2011 vs 2015 – vs 2009 (0.02) vs 2011 vs 2015 – vs 2009 (0.00) vs 2011 vs 2015 –
(0.47); vs 2011 (0.09); vs 2015 (0.30); vs 2015 (0.24) (0.04) (0.37); vs 2011 (0.01); vs 2015 (0.04); vs 2015 (0.04) (0.88) (0.00); vs 2011 (0.10); vs 2015 (0.29); vs 2015 (0.21) (0.95) (0.52); vs 2011 (0.19); vs 2015 (0.45); vs 2015 (0.08) (0.32) (0.02); vs 2011 (0.00); vs 2015 (0.43); vs 2015 (0.12) (0.40)
* Mean. ** Median. Table 4 Spatial analysis of the Urban Heat Island (UHI) effect in the Valencian Community. Year
Station
Median
Comparison (p-value)
2005
ALI
9.10
2009
ALIAER CAS VAL VALAER ALI
9.50 9.50 7.70 11.20 9.40
2011
ALIAER CAS VAL VALAER ALI
9.20 8.60 7.40 10.00 9.65
2015
ALIAER CAS VAL VALAER ALI
8.40 9.20 7.60 10.00 9.10
ALIAER CAS VAL VALAER
8.65 9.00 7.00 9.55
vs ALIAER (0.21); vs CAS (0.31); vs VAL (0.00); vs VALAER (0.00) vs CAS (0.68); vs VAL (0.00); vs VALAER (0.00) vs VAL (0.00); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.63); vs CAS (0.01); vs VAL (0.00); vs VALAER (0.11) vs CAS (0.04); vs VAL (0.00); vs VALAER (0.06) vs VAL (0.01); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.00); vs CAS (0.01); vs VAL (0.00); vs VALAER (0.63) vs CAS (0.42); vs VAL (0.01); vs VALAER (0.00) vs VAL (0.00); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.19); vs CAS (0.83); vs VAL (0.00); vs VALAER (0.24) vs CAS (0.21); vs VAL (0.00); vs VALAER (0.02) vs VAL (0.00); vs VALAER (0.16) vs VALAER (0.00) –
3.2. Descriptive statistics The use of descriptive statistics was oriented to exploring the relationships between ΔT and a series of spatial factors. Hence, this step started by producing the variables stemming from the Digital Elevation Model (DEM) and the SIOSE land cover map with the support of ArcGIS. The application of diverse geoprocessing tools resulted in a series of maps indicating the elevation, solar radiation and shadow patterns in the Valencian Community, whilst the values of Albedo were obtained by multiplying the coefficients associated with the solar reflecting capacity of the land cover types in the region, as schematised in Table 1 and Fig. 3. These maps were then clipped to the largest buffer areas (1000 m) corresponding to each station, yielding the results represented in Fig. 5. The values of elevation obtained demonstrated that the station located in Valencia (VAL) corresponded to the area having the lowest altitude, with most of its cells only a few meters above sea level. In contrast, the buffer area associated with the station in Alicante (ALI) contained cells with values of elevation ranging between 50 and 122 m. These elevation maps were directly responsible for the solar radiation and hillshade results, since the presence of buildings provoked the existence of hotspots in terms of radiation, illumination and shadows. The reflecting power of the buffer areas in 2011 was uneven, with both the highest and lowest values of Albedo corresponding to peri-urban locations (ALIAER and VALAER, respectively). Next was to compute the summary statistics indicated in Table 2 from the values determined in Fig. 5. The breakdown of these zonal
the Valencian Community (ALI, ALIAER, CAS, VAL and VALAER) throughout the years (2005, 2009, 2011 and 2015). First, the values of ΔT recorded in the stations were analysed temporally, so that data associated with each of them were divided into four groups according to the years considered. Table 3 summarises the results achieved in this regard. The Ryan-Joiner (R-J) test indicated that only the datasets corresponding to two stations (ALI and VALAER) were normally distributed (p-values > 0.05). In consequence, Levene’s test was only pertinent for these two stations, yielding values above the significance level in both cases (0.697 and 0.240, respectively). 6
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Fig. 5. Values of the variables stemming from the application of geoprocessing tools (a) Elevation (m) (b) Global Solar Radiation (Wh/m2) (c) Hillshade [0, 255] (d) Albedo coefficient [0, 1]. Table 5 Summary of the Multiple Regression Analysis (MRA) for estimating the Urban Heat Island (UHI) effect in the Valencian Community. Term
R = 250 m
R = 500 m
R = 750 m
R = 1000 m
Coef.
p-value
Coef.
p-value
Coef.
p-value
Coef.
p-value
y β0
– 8.94
0.086 0.003
– −78.50
0.039 0.064
– −73.30
0.030 0.064
– −7.22
0.011 0.039 0.009
β1.4
–
–
–
–
–
–
94.58
β2.4
2.23E-02
0.099
–
–
–
–
2.18E-02
0.017
β2.3
–
–
−5.64E-02
0.020
−8.32E-02
0.019
–
–
β4.3
–
–
1.85E-04
0.051
1.78E-04
0.047
–
–
β4.5 R-J Levene VIF S
−1.20E-05
0.071
–
–
–
–
–
–
0.974 0.07 1.00 0.372 0.914
> 0.100 0.815 – – –
0.938 0.83 1.46 0.251 0.961
> 0.100 0.430 – – –
0.955 0.99 1.03 0.222 0.970
> 0.100 0.393 – – –
0.992 8.40 1.01 0.131 0.989
> 0.100 0.063 – – –
2 R adj.
0.828
–
0.922
–
0.939
–
0.979
–
2 Rpr.
0.000
–
0.076
–
0.786
–
0.931
–
R2
Table 5. Overall, the results of the MRA models highlighted that their degree of accuracy was directly proportional to the radius of the buffer areas. 2 This was especially evident through the inspection of Rpred. , which was
statistics according to the four buffer areas (250 m, 500 m, 750 m and 1000 m) depicted in Fig. 2 provided the inputs to use as predictors in the Multiple Regression Analysis (MRA) for estimating ΔT . Hence, the application of Eq. (2) resulted in the regression models summarised in 7
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(ALIAER and VALAER). In addition, there was a third group including two outliers represented by VALAER in 2011 and 2015. The values of CC achieved for the two representative groups were high (0.869 and 0.985, respectively) and statistically significant (pvalues < 0.05). In general, these results suggested a decrease in the values of ΔT with time in all the stations, coinciding with a reduction in their Albedo coefficients favoured by the growing presence of built-up surfaces. The only exception to this trend was found in the station located in Alicante (ALI), where the values of ΔT were sorted as follows: ALI_2015 < ALI_2005 < ALI_2009 < ALI_2011. The rank reversal between 2005 and 2011was caused by the imbalanced evolution of the buffer area associated with this station. This might be related to the housing bubble experienced in Spain between 1997 and 2007 (Cano Fuentes, Etxezarreta Etxarri, Dol, & Hoekstra, 2013), especially in Alicante, which is responsible for the majority of housing transactions in the Valencian Community (Informacion.es., 2018). Therefore, the stagnation of this phenomenon in subsequent years may explain why the values of ΔT in 2005 were lower than those in 2009 and 2011. However, the persisting urbanisation trend in terms of roads, parking and pedestrian areas without vegetation can justify the fact that the lowest values of ΔT in ALI were achieved in 2015, despite the progressive inclusion of greenspace observed throughout the years.
Fig. 6. Observed and predicted values of daily thermal amplitude (ΔT ) in the meteorological stations.
4. Conclusions This research developed and implemented a methodology to assess the Urban Heat Island (UHI) effect in spatial and temporal terms. The proposed approach combined inferential and descriptive statistical tests. The former enabled the determination of the absence or existence of differences in the values of daily thermal amplitude (ΔT ) over the stations located in the Valencian Community, which was the region selected as a case study. Descriptive statistics were used to model the relationships between ΔT and a series of variables expected to contribute to causing the UHI effect. Overall, the statistical analysis of ΔT in the Valencian Community confirmed the theoretical assumptions on which the UHI effect is based. Daily ΔT was found to decrease over time and be lower in densely urbanised areas than in the airports of the main cities in the region. The main factors influencing ΔT were the Albedo coefficient and elevation in the buffer areas around the stations, which relate to the reflectivity of the land surfaces and the existence of buildings altering the natural elevation of the ground, respectively. A more detailed exploration of the values of Albedo and ΔT observed in the stations over time highlighted an inverse relationship between the two variables, suggesting a reduction in the differences between diurnal and nocturnal temperature over the years as a result of an increased presence of built-up surfaces. In consequence, the attenuation of the UHI effect should take advantage of the adoption of strategic actions of urban regeneration seeking to replace built-up surfaces made of traditional materials by solar reflective pavements and roofs and/or green infrastructure. Moreover, urban designs should be reformulated to avoid concentrations of tall buildings that favour the creation of micro-climates by capturing heat during the sunshine hours. Although the results achieved in this study served to draw the aforementioned conclusions, further efforts should be devoted to extending this type of analysis to a greater number of stations and other regions with different climate and demographic characteristics.
Fig. 7. Relationship between the Albedo coefficient and the values of daily thermal amplitude (ΔT ) in the meteorological stations over time.
null for the narrowest radii (250 and 500 m). Moreover, the p-values of some terms in the models with R < 1000 m were above the significance level, which further invalidated them. In contrast, the values 2 of Rpred. and S were excellent for the buffer area of 1000 m, which justified their acceptance for subsequent steps. Furthermore, the residuals of this model fulfilled the assumptions of linearity, normality and homoscedasticity, since the p-values of the regression term ( y ) and both the R-J and Levene’s test were below and above the significance level, respectively. Multicollinearity was not found to be an issue either, since the Variance Inflation Factor (VIF) was 1.01 (< 5). As proof of the accuracy of this MRA model, Fig. 6 provides a comparison of the values of ΔT observed and predicted in each station. The variables involved in the model built for the buffer area of 1000 m were the mean values of Albedo and elevation. Their relationships to ΔT were positive, as shown in Table 5, and logical in both cases. On the one hand, high values of Albedo imply less reflection of incoming radiation, which contributes to storing heat during the day that is progressively released at night. On the other hand, higher values of elevation are normally related to longer distances to the coast, which limits the smoothing effect of water masses on thermal amplitude. As a support to the insights derived from the MRA model, the maps shown in Fig. 5(a) and (d) can help point out the location of the most influential zones within the buffer areas in terms of these statistically significant variables, facilitating the adoption of actions at strategic sites to mitigating the UHI effect, especially through the progressive replacement of built-up surfaces by greenspace. Since the Albedo coefficient was found to be one of the factors favouring the existence of the UHI effect over the Valencian Community (Table 5), its relationship to ΔT was further modelled with the support of the Pearson CC . To this end, first a scatterplot was drawn, which proved that the relationship between the mean Albedo coefficients in the buffer areas of 1000 m and the values of ΔT was clearly fragmented into three groups (Fig. 7). One of the two main groups was formed by the datasets corresponding to the city centres (ALI, CAS and VAL), whilst the other contained the samples associated with the airports
Acknowledgments This paper was possible thanks to the research project SUPRISSUReS (Ref. BIA2015-65240-C2-1-R MINECO/FEDER, UE), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). 8
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References
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Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs
Spatiotemporal statistical analysis of the Urban Heat Island effect in a Mediterranean region
T
Daniel Jato-Espino GITECO Research Group, Universidad de Cantabria, Av. de los Castros 44, 39005, Santander, Spain
A R T I C LE I N FO
A B S T R A C T
Keywords: Albedo Land cover Statistical analysis Urban Heat Island
The Urban Heat Island (UHI) effect has become one of the major threats for the welfare of developed cities over the last years, as a result of increased urbanisation. For this reason, this research developed a statistical methodology to model the UHI effect, taking the Valencian Community (eastern Spain) as a case study. Inferential statistics were used to explore the spatiotemporal patterns of the UHI effect, whilst descriptive statistics were applied to model its relationships to elevation, land cover and solar radiation variables. The results achieved demonstrated the existence of significant differences between urban and peri-urban areas, as well as an increase in the intensity of the UHI effect over time. The main cause of these results was found in the low reflectance of built-up surfaces, suggesting the implementation of solar reflective and green infrastructure as a solution to palliate the UHI effect.
1. Introduction Increased urbanisation is favouring a variety of environmental threats, among which the Urban Heat Island (UHI) effect has emerged as one of the most prominent ones (Dwivedi & Khire, 2018). This phenomenon is related to the difference in temperature between rural and urban areas, whereby the daily thermal amplitude of the latter is reduced as a result of diurnal solar heat absorption that is progressively released during the night (Kolokotroni, Giannitsaris, & Watkins, 2006). The UHI effect is usually associated with two different but related processes. Firstly and most importantly, land use and land cover alterations stemming from urbanisation, which promote the use of materials with high heat absorption and retention capacity, such as asphalt (Liu et al., 2015). And secondly, thermal emissions derived from anthropogenic activities, especially in relation to transport and industrial processes that contribute to boosting global warming (Taha, 1997). One of the main consequences of the UHI effect is economic, since its existence might lead to a rise in energy consumption devoted to cooling buildings in densely populated urban areas (Kolokotsa et al., 2018). Furthermore, continued exposure to high temperatures may cause health disorders, including exhaustion due to dehydration, fainting, heat strokes, cerebrovascular disorders or respiratory problems (Martínez Navarro, Simón-Soria, & López-Abente, 2004). Due to all these considerations, the UHI effect has become a widely investigated topic over the years, especially during the last decade. Stathopoulou and Cartalis (2007) highlighted asphalt and concrete
surfaces, and bare soil associated with dumping and construction sites as the main drivers for the UHI effect throughout Greek cities. Kolokotsa, Psomas, and Karapidakis, (2009) also focused on Greece (Hania), studying the impact of several meteorological variables on the UHI effect, which was found to be especially influenced by wind speed and direction. Priyadarsini, Hien, and Wai David, (2008) investigated the main factors causing the UHI effect in Singapore (Asia), highlighting the relevance of the materials and colours used in building façades on air temperature. Hart and Sailor (2009) developed a regression model to determine the factors having the greatest influence on the UHI effect in Portland (U.S.), which were found to be canopy cover and roadway density. Buyantuyev and Wu (2010) examined the UHI effect with respect to the land cover configuration of Phoenix (U.S.), confirming its strong dependence on both vegetation and pavements. Takebayashi and Moriyama (2012) explored potential locations to implement UHI mitigation measures by assessing surface temperature and solar radiation in street canyons, priority areas being related to buildings with large roofs and roads. Ivajnšič, Kaligarič, and Žiberna, (2014) determined that the UHI effect in the city centre of Ljutomer (Slovenia) was explained by variables such as distance to urban area, building volume, land cover or topography. Similarly, Yao et al. (2018) found that the variations in the UHI effect were caused by reduced vegetation, increased population and the Albedo coefficient. The review of previous studies highlighted a research gap regarding the statistical modelling of the spatiotemporal patterns of the UHI effect
E-mail address: [email protected]. https://doi.org/10.1016/j.scs.2019.101427 Received 4 October 2018; Received in revised form 7 January 2019; Accepted 7 January 2019 Available online 08 January 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.
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and its interaction with solar radiation, land cover reflectance and elevation. Hence, this investigation aimed at filling this gap by designing and applying a methodology based on inferential and descriptive statistics. The main characteristics of this methodology are provided in Section 2, whilst Section 3 compiles and discusses the results achieved through the application of the proposed approach to the case study of the Valencian Community. Finally, the main conclusions drawn from this research are summarised in Section 4. 2. Methodology The methodology used to evaluate the Urban Heat Island (UHI) effect in the Valencian Community is outlined in Fig. 1. It consisted of a combination of tests using inferential and descriptive statistics. Consequently, the development and application of this approach were supported by the use of the statistical package Minitab (Minitab Inc., 2017), which was coupled with the desktop version of ArcGIS (ESRI, 2017) when requiring the generation of spatial inputs via Geographic Information Systems (GIS). Inferential statistics were used to analyse either the existence or absence of significant differences between the values of thermal amplitude (ΔT ) recorded in both urban and peri-urban areas at different locations and times. ΔT , which was determined as formulated in Eq. (1), was taken as an indicator of the UHI effect, since it accounts for the difference between maximum (Tmax ) and minimum (Tmin ) daily temperature (Scott, Waugh, & Zaitchik, 2018). In other words, this parameter was expected to identify the magnitude of heat dissipation during
Fig. 1. Flowchart of the proposed approach to model the Urban Heat Island (UHI) effect.
Fig. 2. Location of the meteorological stations and buffer areas considered in modelling the Urban Heat Island (UHI) effect in the Valencian Community. 2
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Table 1 Albedo coefficients corresponding to the land cover types considered in the Spanish Land Use and Land Cover Information System (SIOSE). Group
Land cover type (SIOSE code)
Albedo
Artificial cover
Buildings (EDF) Artificial green areas and urban woodland (ZAU) Artificial water bodies (LAA); Road, parking or pedestrian area without vegetation (VAP) Other constructions (OCT) Non-built soil (SNE); Extraction or discharge zones (ZEV) Herbaceous: rice (CHA); Herbaceous: other than rice (CHL); Woody: citrus fruit (LFC); Woody: non-citrus fruit (LFN); Woody: vineyard (LVI); Woody: olive grove (LOL); Woody: other (LOC) Meadows (PRD) Pastureland (PST) Hardwood: deciduous (FDC); Hardwood: perennials (FDP) Coniferous (CNF) Brushwood (MTR) Beaches, dunes and sand (PDA) Bare soil (SDN); Burned areas (ZQM); Valleys (RMB); Rocky: sea cliffs (ACM); Rocky: rocky outcrops (ARR); Rocky: screes (CCH); Rocky: volcanic rocks (CLC) Glaciers and permanent snow (GNP) Inland wetlands: swamps (HPA); Inland wetlands: peatbogs (HTU); Inland wetlands: salt flats (HAS); Marine wetlands: marshes (HMA); Marine wetlands: salt marshes (HSM) Inland waters: water courses (ACU); Inland waters: lakes, ponds and reservoirs (ALG); Inland waters: reservoirs (AEM); Marine waters: coastal lagoons (ALC); Marine waters: estuaries (AES) Marine waters: seas and oceans (AMO)
0.15 0.23 0.10 0.15 0.17 0.18
Crops
Pastureland Woodland Brushwood Barren vegetation
Humid cover Water cover
0.60 0.10 0.10 0.07
region with daily temperature data were used to model the UHI effect. Three of these stations were located in the capital cities of the abovementioned provinces, resulting in a variety of degrees of urbanisation (ALI, CAS and VAL). Moreover, two additional stations situated in the airports of Alicante and Valencia (ALIAER and VALAER) were also considered as representative of peri-urban sites. Given the collateral impacts associated with the UHI effect during the summer, which include increasing energy consumption, concentrations of pollutants and health impacts (Giannopoulou et al., 2011), the data acquired from these stations was limited to the period from June 21 to September 23. This was also consistent with Jonsson (2004), who highlighted the increased intensity of the UHI effect during summer in arid and semi-arid regions like the Valencian Community. In order to facilitate the modelling of the UHI effect, up to 4 different buffer areas were defined as illustrated in Fig. 2. The radii corresponding to these buffer areas (250 m, 500 m, 750 m and 1000 m) were previously suggested in similar investigations to study the association between the UHI effect and the land cover of urban sites (van Hove et al., 2015). As specified before, this research went one step further and also took into consideration variables related to the elevation, radiation, hillshade and reflectance of the study area. A LiDAR cloud produced during 2012 with a resolution of 0.5 points per m2, available at the Spanish Geographic Institute (IGN) (CNIG, 2018), served to characterise the first three variables. The reflectance of the buffer areas, represented by their Albedo coefficients, was coupled with their land cover configurations, which were delineated from the Spanish Land Use and Land Cover Information System (SIOSE, 2015). The SIOSE project is provided at a 1:25,000 scale in four different years (2005, 2009, 2011 and 2015) and includes the following eight groups: artificial cover, crops, pastureland, woodland, brushwood, barren vegetation, humid cover and water cover. As shown in Table 1, these groups are in turn broken down into several categories. Based on previous studies about the reflectance of urban surfaces (Coakley, 2003; Wei et al., 2001), each of these categories was related to a value of Albedo coefficient, which is defined as the amount of solar energy scattered by their corresponding land cover types to space (Taha, 1997). As an illustrative example, Fig. 3 depicts the land cover configurations associated with the 1000 m buffer area of VALAER over the years. Although the classes into which the buffer area was divided could consist of a single land cover type, such as ‘OCT’ or ‘PST’, the most common scenarios involved combinations of different surfaces. For
the night at monitored stations in both urban and peri-urban areas by considering the thermal evolution from the warmest hours of the day (maximum temperature) to the coldest ones (minimum temperature). Hence, the higher the value of ΔT , the lower the heat dissipated during the night and, therefore, the less likely that the UHI effect is an issue. Unlike other indicators commonly used to measure the UHI effect, which are based on complex parameters such as temperature gradient, air density or specific heat (Lee, Lee, & Wang, 2012), the proposed approach only considered air temperature, which is a variable globally available.
ΔT = Tmax − Tmin
0.19 0.19 0.16 0.10 0.16 0.30 0.17
(1)
Descriptive statistics were applied to model the degree of association between a series of variables related to the orography and land cover configuration of the terrain, in order to identify which of them contributed the most to estimate ΔT . These variables included the Albedo coefficient and land cover type of the surface, as well as the elevation of the ground, which in turn were used to determine solar radiation and hillshade-related parameters. Hence, the proposed framework enabled modelling the UHI effect at single locations using these explanatory variables, such that urban areas resulted in lower values of ΔT , and vice versa for peri-urban locations. This course of action differentiated this approach from former methods, which require combined calculations considering both urban and peri-urban or rural areas. 2.1. Study area The Valencian Community is located in the east of Spain, has a surface of 23,259 km2 and is home to 4,935,182 inhabitants distributed throughout three provinces, as shown in Fig. 2: Alicante, Castellón and Valencia (INE, 2018). About 50% of the surface area and people of the whole region are concentrated in Valencia, which along with Castellón is characterised by a temperate Mediterranean climate. This corresponds to the category C according to the Köppen classification, contrasting with the drier conditions of the province of Alicante, which belongs to group B (Chazarra et al., 2011). Overall, the Valencian Community is characterised by its susceptibility to undergoing the UHI effect, due to the presence of large crop areas in the surroundings of the main cities and their proximity to the sea, which might boost the differences between the values of ΔT in peri-urban and urban sites (ABC, 2017; eltiempo, 2016). For this reason, the five meteorological stations available in the 3
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accordance with the availability of meteorological and land cover data. Hence, the combination of locations (5) and years (4) resulted in 20 datasets including 95 observations (from June 21 to September 23) of daily maximum and minimum temperature across the Valencian Community. These samples were examined using either parametric or non-parametric tests, depending on whether they were normally distributed and homoscedastic or not. The potential application of parametric tests was preceded by checking the normality and homoscedasticity of the datasets under analysis. Normality was evaluated using the Ryan-Joiner test (Zolgharnein & Shahmoradi, 2010), which is based on determining the correlation between the data and their normal scores. Homoscedasticity was checked through Levene’s test (Gastwirth, Gel, & Miao, 2009), consisting of assessing the deviation of the observations from the median of the sample to which they belong. In these cases, H0 implied that the datasets were normal and homoscedastic (p-value > 0.05). In those situations in which both assumptions were met, parametric tests were applied to compare the samples under analysis. To this end, the data were first grouped in spatial (5 samples per group) and temporal (4 samples per group) terms. Then, a one-way analysis of variance (ANOVA) (Fisher, 1919) was used to explore the similarity between all the samples belonging to each group. Once the existence or absence of general differences was determined, the Student’s t-test (Gosset, 1908) was used to carry out comparisons between each pair of samples. In contrast, non-parametric tests were used when the samples proved not to meet the assumptions of normality and homoscedasticity. Again, an overall comparison among all the samples corresponding to either a location or a year was carried out through the Kruskal-Wallis test (Kruskal & Wallis, 1952). In those cases where this test provided evidence of the presence of significant differences, pairwise comparisons were made using the Mann-Whitney U test (Mann & Whitney, 1947).
Fig. 3. Land cover classification of the buffer area corresponding to the station located in the airport of Valencia (VALAER) (a) 2005 (b) 2009 (c) 2011 (d) 2015.
2.3. Descriptive statistics Table 2 Variables and summary statistics proposed to model the Urban Heat Island (UHI) effect. Variable
MIN
MAX
RANGE
MEAN
STD
SUM
Albedo coefficient (Dimensionless) Digital Elevation Model (DEM) (m) Hillshade [0, 255] Global Solar Radiation (Wh/m2) Diffuse Solar Radiation (Wh/m2) Direct Solar Radiation (Wh/m2) Direct Solar Radiation Duration (h)
X1.1 X2.1 X3.1 X 4.1 X5.1 X6.1 X7.1
X1.2 X2.2 X3.2 X 4.2 X5.2 X6.2 X7.2
X1.3 X2.3 X3.3 X 4.3 X5.3 X6.3 X7.3
X1.4 X2.4 X3.4 X 4.4 X5.4 X6.4 X7.4
X1.5 X2.5 X3.5 X 4.5 X5.5 X6.5 X7.5
X1.6 X2.6 X3.6 X 4.6 X5.6 X6.6 X7.6
In contrast with inferential statistics, which enable the conclusions extracted from specific data to be generalised for broader conditions, descriptive statistics only focus on interpreting the particularities of the data under analysis. The descriptive statistical technique required to model the UHI effect in the Valencia Community was Multiple Regression Analysis (MRA), which was based on a series of summary statistics of the variables expected to contribute to producing variations in this phenomena, as listed in Table 2. Six summary statistics were calculated over the buffer areas represented in Fig. 2: minimum (MIN), maximum (MAX), range, mean, standard deviation (STD) and sum. This task was accomplished with the support of geoprocessing tools available in ArcGIS, starting by establishing a new attribute in the land cover map obtained from the SIOSE project through the ‘Add Field’ and ‘Calculate Field’ tools. Then, the LiDAR data was cleaned to create a Digital Elevation Model (DEM) of the study area. This was carried out through the ‘Raster Calculator’ tool, which enabled the outliers found in the elevation map derived from the LiDAR point cloud to be replaced by mean values from surrounding cells. The clean DEM was used as input in the ‘Area Solar Radiation’ tool, whose application yielded the four radiation-related variables included in Table 2. The parameters established to run this tool involved 200 cells resolution and 0.5-hour interval. Moreover, since the LiDAR data was from 2012, the time interval was set at 2011 (whole year), in order to carry out the MRA during the period between 2011 and 2012. The ‘Hillshade’ tool was also implemented from the DEM, resulting in a relief highlighting illuminated and shaded zones within the study area. All these maps were confined to the buffer areas represented in Fig. 2 through the ‘Clip’ tool. Finally, the ‘Zonal Statistics as Table’ tool was used to obtain the values indicated in Table 2. These summary statistics generated were used as predictors in the
instance, the class’ 25SNE_20EDF_20VAP_20ZAU_15OCT’ in Fig. 3 represented the following mix of categories: 25% non-built soil (SNE), 20% buildings (EDF), 20% road, parking or pedestrian area without vegetation (VAP), 20% artificial green areas and urban woodland (ZAU) and 15% other constructions (OCT). 2.2. Inferential statistics Inferential statistics involve the extrapolation of conclusions drawn from the analysis of sample data (Moore, 1996). This branch of statistics is applied to evaluate the probability of rejecting the null hypothesis (H0 ) with respect to the alternative hypothesis (H1) due to chance. This was undertaken through the p-value, which indicates the probability of wrongly rejecting H0 when it is true. If the p-value is below a significance level α of 0.05 (Fisher, 1925), the probability of error is below. In this context, H1 referred to the existence of significant differences between the values of ΔT registered at different locations (ALI, ALIAER, CAS, VAL and VALAER) and years (2005, 2009, 2011 and 2015), in 4
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Fig. 4. Time series of daily thermal amplitude (ΔT ) in the meteorological stations (a) ALI (b) ALIAER (c) CAS (d) VAL (e) VALAER.
availability of LiDAR data, the relationships between the Albedo coefficient and ΔT over time were further explored through the Pearson Correlation Coefficient (CC ). This numerical measure enabled the determination of whether the warming sequestration potential of the land cover configurations of the buffer areas was statistically significant for the existence of the UHI effect or not. In accordance with the land cover data, this analysis included the summer season of the following years: 2005, 2009, 2011 and 2015.
MRA models built for estimating the UHI effect. In other words, MRA enabled the determination of the degree of association between the values of ΔT recorded by the weather stations included in Fig. 2 and the predictors proposed in Table 2. These relationships were expressed as shown in Eq. (2).
Y = β0 + β1.1 * X1.1 + …+β3.4 * X3.4 …+β7.6 * X7.6 + E
(2)
where Y is ΔT , Xi . j are the summary statistics of the terrain-related variables, βi . j are the coefficients associated with the predictors β0 is the constant of the MRA model and E represents its residuals. The quality of the MRA models was evaluated according to two goodness-of-fit measures, namely the standard error of the regression (S ) and the coefficient of determination. The latter was considered through three dif2 2 ferent versions: standard (R2 ), adjusted (Radj. ) and predicted (Rpr. ). The legitimacy of the results obtained was determined through the analysis of the residuals, which were examined in terms of their normality, homoscedasticity, linearity and multicollinearity. As described in the case of parametric inferential statistics, the first two verifications were undertaken with the support of the Ryan-Joiner and Levene’s tests. Linearity was checked by comparing the p-value of the MRA model with the significance level established (0.05), whilst the presence of multicollinearity was associated with values of Variance Inflation Factor (VIF) above 5 (Menard, 2002). The assumption of independence of residuals was disregarded, since the datasets under analysis were not arranged in the form of time series, so that the detection of autocorrelation was pointless in this case. Since the application of MRA was restricted to 2011–2012 by the
3. Results and discussion This section presents and examines the results achieved through the implementation of the framework schematised in Fig. 1 to model the Urban Heat Island (UHI) effect over the Valencian Community. As a previous step to the application of the methodology described above, Fig. 4 depicts the time series of daily maximum temperature, minimum temperature and thermal amplitude (ΔT ) in the meteorological stations considered (Fig. 2). The visual inspection of these charts suggested that the values of ΔT associated with peri-urban sites (ALIAER and VALAER) where higher than those corresponding to urban locations (ALI, CAS and VAL). To confirm the validity of this perception, inferential and descriptive statistics were sequentially applied to these data in following subsections. 3.1. Inferential statistics Inferential statistics were used to compare the variations of ΔT over 5
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As a result, these two stations were evaluated through a one-way ANOVA test, whilst the remaining locations (ALIAER, CAS and VAL) required the application of the Kruskal-Wallis test. The outputs of these tests revealed that the differences among the groups were only significant for ALIAER, CAS and VALAER. These stations corresponded to less urbanised and less populated sites than those located in the main cities of the region (ALI and VAL), which might explain their susceptibility to undergoing urban developments and, therefore, decreases in ΔT throughout the years. A more in-depth analysis through the Student’s t and Mann-Whitney U tests demonstrated that the values of ΔT decreased with time, involving a statistically significant increase of the UHI effect in all the stations with respect to the first year considered (2005). Hence, the general trend in the results suggested a reduction in the values of ΔT over time, although these changes were statistically significant only in some cases (Table 3). The same procedure as described above was followed to evaluate the variations of ΔT depending on the location of the stations. According to the p-values previously obtained for the R-J test, Levene’s test was only carried out for 2011. Since the p-value was below the significance level (0.025), every comparison was approached using non-parametric tests. The results of the Kruskal-Wallis test were below the significance level for all the groups (Table 4), involving significant differences among the values of ΔT corresponding to the five stations under analysis for every year. This guaranteed the suitability of the stations considered, since they demonstrated to provide a representative sample of sites with different characteristics in terms of thermal amplitude. The pairwise comparisons made with the support of the MannWhitney U test indicated a difference between urban and peri-urban areas, whereby the stations located in the airports (ALIAER and VALAER) reached values of ΔT significantly higher than those obtained for the city centres (ALI, CAS and VAL). The reduced difference between diurnal and nocturnal temperatures was especially remarkable in Valencia (VAL), which is the most crowded and built-up city in the region. Moreover, CAS was found to result in narrower spread of values of ΔT in comparison with ALI, even though the latter has twice the population of the former. This might be related to the particularities of the climate in Alicante, which is less wet than in Castellón.
Table 3 Temporal analysis of the Urban Heat Island (UHI) effect in the Valencian Community. Station
Year
Central tendency
Comparison (p-value)
ALI
2005
9.282*
ALIAER
2009 2011 2015 2005
9.519* 9.866* 9.109* 9.500**
CAS
2009 2011 2015 2005
9.200** 8.400** 8.650** 9.500**
VAL
2009 2011 2015 2005
**
8.600 9.200** 9.000** 7.700**
VALAER
2009 2011 2015 2005
7.400** 7.600** 7.000** 11.432*
2009 2011 2015
10.324* 9.971* 9.624*
vs 2009 (0.63) vs 2011 vs 2015 – vs 2009 (0.01) vs 2011 vs 2015 – vs 2009 (0.10) vs 2011 vs 2015 – vs 2009 (0.02) vs 2011 vs 2015 – vs 2009 (0.00) vs 2011 vs 2015 –
(0.47); vs 2011 (0.09); vs 2015 (0.30); vs 2015 (0.24) (0.04) (0.37); vs 2011 (0.01); vs 2015 (0.04); vs 2015 (0.04) (0.88) (0.00); vs 2011 (0.10); vs 2015 (0.29); vs 2015 (0.21) (0.95) (0.52); vs 2011 (0.19); vs 2015 (0.45); vs 2015 (0.08) (0.32) (0.02); vs 2011 (0.00); vs 2015 (0.43); vs 2015 (0.12) (0.40)
* Mean. ** Median. Table 4 Spatial analysis of the Urban Heat Island (UHI) effect in the Valencian Community. Year
Station
Median
Comparison (p-value)
2005
ALI
9.10
2009
ALIAER CAS VAL VALAER ALI
9.50 9.50 7.70 11.20 9.40
2011
ALIAER CAS VAL VALAER ALI
9.20 8.60 7.40 10.00 9.65
2015
ALIAER CAS VAL VALAER ALI
8.40 9.20 7.60 10.00 9.10
ALIAER CAS VAL VALAER
8.65 9.00 7.00 9.55
vs ALIAER (0.21); vs CAS (0.31); vs VAL (0.00); vs VALAER (0.00) vs CAS (0.68); vs VAL (0.00); vs VALAER (0.00) vs VAL (0.00); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.63); vs CAS (0.01); vs VAL (0.00); vs VALAER (0.11) vs CAS (0.04); vs VAL (0.00); vs VALAER (0.06) vs VAL (0.01); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.00); vs CAS (0.01); vs VAL (0.00); vs VALAER (0.63) vs CAS (0.42); vs VAL (0.01); vs VALAER (0.00) vs VAL (0.00); vs VALAER (0.00) vs VALAER (0.00) – vs ALIAER (0.19); vs CAS (0.83); vs VAL (0.00); vs VALAER (0.24) vs CAS (0.21); vs VAL (0.00); vs VALAER (0.02) vs VAL (0.00); vs VALAER (0.16) vs VALAER (0.00) –
3.2. Descriptive statistics The use of descriptive statistics was oriented to exploring the relationships between ΔT and a series of spatial factors. Hence, this step started by producing the variables stemming from the Digital Elevation Model (DEM) and the SIOSE land cover map with the support of ArcGIS. The application of diverse geoprocessing tools resulted in a series of maps indicating the elevation, solar radiation and shadow patterns in the Valencian Community, whilst the values of Albedo were obtained by multiplying the coefficients associated with the solar reflecting capacity of the land cover types in the region, as schematised in Table 1 and Fig. 3. These maps were then clipped to the largest buffer areas (1000 m) corresponding to each station, yielding the results represented in Fig. 5. The values of elevation obtained demonstrated that the station located in Valencia (VAL) corresponded to the area having the lowest altitude, with most of its cells only a few meters above sea level. In contrast, the buffer area associated with the station in Alicante (ALI) contained cells with values of elevation ranging between 50 and 122 m. These elevation maps were directly responsible for the solar radiation and hillshade results, since the presence of buildings provoked the existence of hotspots in terms of radiation, illumination and shadows. The reflecting power of the buffer areas in 2011 was uneven, with both the highest and lowest values of Albedo corresponding to peri-urban locations (ALIAER and VALAER, respectively). Next was to compute the summary statistics indicated in Table 2 from the values determined in Fig. 5. The breakdown of these zonal
the Valencian Community (ALI, ALIAER, CAS, VAL and VALAER) throughout the years (2005, 2009, 2011 and 2015). First, the values of ΔT recorded in the stations were analysed temporally, so that data associated with each of them were divided into four groups according to the years considered. Table 3 summarises the results achieved in this regard. The Ryan-Joiner (R-J) test indicated that only the datasets corresponding to two stations (ALI and VALAER) were normally distributed (p-values > 0.05). In consequence, Levene’s test was only pertinent for these two stations, yielding values above the significance level in both cases (0.697 and 0.240, respectively). 6
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Fig. 5. Values of the variables stemming from the application of geoprocessing tools (a) Elevation (m) (b) Global Solar Radiation (Wh/m2) (c) Hillshade [0, 255] (d) Albedo coefficient [0, 1]. Table 5 Summary of the Multiple Regression Analysis (MRA) for estimating the Urban Heat Island (UHI) effect in the Valencian Community. Term
R = 250 m
R = 500 m
R = 750 m
R = 1000 m
Coef.
p-value
Coef.
p-value
Coef.
p-value
Coef.
p-value
y β0
– 8.94
0.086 0.003
– −78.50
0.039 0.064
– −73.30
0.030 0.064
– −7.22
0.011 0.039 0.009
β1.4
–
–
–
–
–
–
94.58
β2.4
2.23E-02
0.099
–
–
–
–
2.18E-02
0.017
β2.3
–
–
−5.64E-02
0.020
−8.32E-02
0.019
–
–
β4.3
–
–
1.85E-04
0.051
1.78E-04
0.047
–
–
β4.5 R-J Levene VIF S
−1.20E-05
0.071
–
–
–
–
–
–
0.974 0.07 1.00 0.372 0.914
> 0.100 0.815 – – –
0.938 0.83 1.46 0.251 0.961
> 0.100 0.430 – – –
0.955 0.99 1.03 0.222 0.970
> 0.100 0.393 – – –
0.992 8.40 1.01 0.131 0.989
> 0.100 0.063 – – –
2 R adj.
0.828
–
0.922
–
0.939
–
0.979
–
2 Rpr.
0.000
–
0.076
–
0.786
–
0.931
–
R2
Table 5. Overall, the results of the MRA models highlighted that their degree of accuracy was directly proportional to the radius of the buffer areas. 2 This was especially evident through the inspection of Rpred. , which was
statistics according to the four buffer areas (250 m, 500 m, 750 m and 1000 m) depicted in Fig. 2 provided the inputs to use as predictors in the Multiple Regression Analysis (MRA) for estimating ΔT . Hence, the application of Eq. (2) resulted in the regression models summarised in 7
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(ALIAER and VALAER). In addition, there was a third group including two outliers represented by VALAER in 2011 and 2015. The values of CC achieved for the two representative groups were high (0.869 and 0.985, respectively) and statistically significant (pvalues < 0.05). In general, these results suggested a decrease in the values of ΔT with time in all the stations, coinciding with a reduction in their Albedo coefficients favoured by the growing presence of built-up surfaces. The only exception to this trend was found in the station located in Alicante (ALI), where the values of ΔT were sorted as follows: ALI_2015 < ALI_2005 < ALI_2009 < ALI_2011. The rank reversal between 2005 and 2011was caused by the imbalanced evolution of the buffer area associated with this station. This might be related to the housing bubble experienced in Spain between 1997 and 2007 (Cano Fuentes, Etxezarreta Etxarri, Dol, & Hoekstra, 2013), especially in Alicante, which is responsible for the majority of housing transactions in the Valencian Community (Informacion.es., 2018). Therefore, the stagnation of this phenomenon in subsequent years may explain why the values of ΔT in 2005 were lower than those in 2009 and 2011. However, the persisting urbanisation trend in terms of roads, parking and pedestrian areas without vegetation can justify the fact that the lowest values of ΔT in ALI were achieved in 2015, despite the progressive inclusion of greenspace observed throughout the years.
Fig. 6. Observed and predicted values of daily thermal amplitude (ΔT ) in the meteorological stations.
4. Conclusions This research developed and implemented a methodology to assess the Urban Heat Island (UHI) effect in spatial and temporal terms. The proposed approach combined inferential and descriptive statistical tests. The former enabled the determination of the absence or existence of differences in the values of daily thermal amplitude (ΔT ) over the stations located in the Valencian Community, which was the region selected as a case study. Descriptive statistics were used to model the relationships between ΔT and a series of variables expected to contribute to causing the UHI effect. Overall, the statistical analysis of ΔT in the Valencian Community confirmed the theoretical assumptions on which the UHI effect is based. Daily ΔT was found to decrease over time and be lower in densely urbanised areas than in the airports of the main cities in the region. The main factors influencing ΔT were the Albedo coefficient and elevation in the buffer areas around the stations, which relate to the reflectivity of the land surfaces and the existence of buildings altering the natural elevation of the ground, respectively. A more detailed exploration of the values of Albedo and ΔT observed in the stations over time highlighted an inverse relationship between the two variables, suggesting a reduction in the differences between diurnal and nocturnal temperature over the years as a result of an increased presence of built-up surfaces. In consequence, the attenuation of the UHI effect should take advantage of the adoption of strategic actions of urban regeneration seeking to replace built-up surfaces made of traditional materials by solar reflective pavements and roofs and/or green infrastructure. Moreover, urban designs should be reformulated to avoid concentrations of tall buildings that favour the creation of micro-climates by capturing heat during the sunshine hours. Although the results achieved in this study served to draw the aforementioned conclusions, further efforts should be devoted to extending this type of analysis to a greater number of stations and other regions with different climate and demographic characteristics.
Fig. 7. Relationship between the Albedo coefficient and the values of daily thermal amplitude (ΔT ) in the meteorological stations over time.
null for the narrowest radii (250 and 500 m). Moreover, the p-values of some terms in the models with R < 1000 m were above the significance level, which further invalidated them. In contrast, the values 2 of Rpred. and S were excellent for the buffer area of 1000 m, which justified their acceptance for subsequent steps. Furthermore, the residuals of this model fulfilled the assumptions of linearity, normality and homoscedasticity, since the p-values of the regression term ( y ) and both the R-J and Levene’s test were below and above the significance level, respectively. Multicollinearity was not found to be an issue either, since the Variance Inflation Factor (VIF) was 1.01 (< 5). As proof of the accuracy of this MRA model, Fig. 6 provides a comparison of the values of ΔT observed and predicted in each station. The variables involved in the model built for the buffer area of 1000 m were the mean values of Albedo and elevation. Their relationships to ΔT were positive, as shown in Table 5, and logical in both cases. On the one hand, high values of Albedo imply less reflection of incoming radiation, which contributes to storing heat during the day that is progressively released at night. On the other hand, higher values of elevation are normally related to longer distances to the coast, which limits the smoothing effect of water masses on thermal amplitude. As a support to the insights derived from the MRA model, the maps shown in Fig. 5(a) and (d) can help point out the location of the most influential zones within the buffer areas in terms of these statistically significant variables, facilitating the adoption of actions at strategic sites to mitigating the UHI effect, especially through the progressive replacement of built-up surfaces by greenspace. Since the Albedo coefficient was found to be one of the factors favouring the existence of the UHI effect over the Valencian Community (Table 5), its relationship to ΔT was further modelled with the support of the Pearson CC . To this end, first a scatterplot was drawn, which proved that the relationship between the mean Albedo coefficients in the buffer areas of 1000 m and the values of ΔT was clearly fragmented into three groups (Fig. 7). One of the two main groups was formed by the datasets corresponding to the city centres (ALI, CAS and VAL), whilst the other contained the samples associated with the airports
Acknowledgments This paper was possible thanks to the research project SUPRISSUReS (Ref. BIA2015-65240-C2-1-R MINECO/FEDER, UE), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). 8
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