Spatial air temperature variations and urban land use a statistical approach

Transcription

1 Meteorol. Appl. 10, (2003) DOI: /S Spatial air temperature variations and urban land use a statistical approach I Eliasson & M K Svensson Laboratory of Climatology, Physical Geography, Earth Sciences Centre, University of Göteborg, Box 460, SE , Göteborg, Sweden ingegard.eliasson@gvc.gu.se Using a statistical approach this paper focuses on an analysis of spatial air temperature variations and their relation to urban land use. The work is based on data collected during an 18-month period at 30 sites in Göteborg, Sweden. Measurements show intra-urban air temperature differences of up to 9 C in the urban district, and the main purpose of this study is to determine if these variations are statistically significant. A stepwise multiple regression analysis confirmed that surface cover is important for governing air temperature differences in the area. Information on land use and surface cover was gathered from a continuously updated land use database, the Master Plan of Göteborg, and a separate site description analysis. The site description analysis includes a test of three methods using aerial or fish-eye photos for characterisation of surface cover in the urban district. The results show statistically significant temperature variations between different land use/land cover categories on a diurnal basis and for all weather conditions. The importance of the results for urban planning application is discussed. 1. Introduction It is a well-known fact that the air temperature may vary greatly between different locations in urban areas. In a review of urban heat island intensities Oke (1981) reports magnitudes up to 12 C, and intra-urban air temperature differences up to 9 C have been reported recently (Spronken-Smith & Oke 1998, Upmanis et al. 1998, Svensson et al. 2002). These differences are clearly of a magnitude that is important for application in urban planning. Studies in North America and Sweden, for example, show a change of up to 14kWh in daily net energy load (Matsuura 1995) or a 20% change in yearly energy consumption (Svensson & Eliasson 2002) due to air temperature differences. Also human comfort in the city is influenced by intra-urban air temperature differences as shown, for example, by Jendritzky & Grätz (1997), Matzarakis et al. (1999) and Friedrich et al. (2001). Air temperature differences have an indirect influence on the air quality in urban areas through changes in stability and the development of local wind systems (e.g. Eliasson & Holmer 1990, Kuttler & Romberg 1992, Eliasson & Upmanis 2000). There is no doubt that the application of this knowledge is of importance for urban planning and the integration of climate and planning has been a subject of concern in the international literature for a long time (e.g. Morgan 1960, Oke 1984, Givoni 1998). Nevertheless most authors agree that climatic aspects have low impact in the urban planning process (e.g. Oke 1984, Pielke & Uliasz 1998, Mills 1999, Eliasson 2000). One reason for this is that the data provided by climate researchers do not always meet the demands of urban planners and architects (Mills 1999). Climatologists have a tendency to focus on nocturnal clear and calm weather conditions while planners and architects are mostly interested in the average daytime conditions, when humans are most active. Shortcomings are thus related to the spatial and temporal availability of data. Short-term studies during extreme weather conditions are, for example, more common than long-term studies covering all weather conditions. Many studies also base their results on data from a few point measurements while studies of spatial variation are less frequent. If meteorological data are to be of general use in planning applications, the differences have to be statistically significant for average weather conditions and not only a result of carefully chosen data periods. The results presented in this paper are part of a project that aims to integrate climate knowledge in the urban planning process through the development of a GISbased empirical model (Svensson et al. 2002). This particular paper focuses on a statistical analysis of spatial air temperature variations and its relation to land use in an urban area (Göteborg, Sweden). Measurements show intra-urban air temperature differences of up to 9 C in the urban district and the main purpose is to determine if these variations are statistically significant. First, the relative importance of surface cover for air temperature variations in the area was tested with a multiple regression analysis. Secondly, an analyse of variance test were carried out to study whether observed air temperature differences within different land use/land cover categories were statistically significant during both day and night, under different 135

2 I Eliasson and M K Svensson weather conditions and seasons. This included a site description analysis to describe and characterise the surface cover at the measurement sites. Three different methods for characterisation of surface cover were tested and evaluated in this paper and the results, combined with land use information, were used to define the land use/land cover categories used in the analysis of variance test. 2. Study area The Göteborg urban district (72,200 ha and 500,000 inhabitants) is situated on the Swedish west coast in Scandinavia (57 42 N, E). Four broad, flat valleys dominate the area and most of the built-up land is located in the valley bottoms around 0 10 m. a. s. l. (Figure 1). The coastline is irregular and borders an archipelago with scattered small islands. The climate in Göteborg is a typical maritime west coast climate with, for its latitude, relatively warm winters and cool summers. The monthly mean temperature for Göteborg is 0.4 C in winter (December February) and 16.3 C in summer (June August) for the climatic period (Väder och Vatten 2000). 3. Data 3.1. The database The present analysis is based on air temperature measurements over 18 months (September 1998 March 2000) at 30 locations in Göteborg, Sweden (Figure 1). The data show considerable variations within the urban district under different seasons and weather conditions, and on a diurnal basis (Svensson et al. 2002, Svensson & Eliasson 2002, Svensson 2002). Table 1 shows the range of temperature deviations at each station from the mean of all stations during each hour within the urban area during the measurement period. Data are divided into time of day, weather and season. Day and night: As mentioned above there is a focus on nocturnal data in the literature and consequently a lack of information on daytime conditions. Thus the main analysis in this study is the comparison between day and night conditions. The temperature deviation, from the mean off all stations, at every station (on a seasonal, monthly and daily basis) has been analysed for two situations, representing daytime and night time. The choice of the daytime and night-time period are based on a previous analysis (Svensson 2002) and information found in the literature. Owing to Göteborg s high latitude, the daylight lengths vary greatly throughout the year. In the middle of June the day is approximately 16 hours long and in December about 6 hours long (and vice versa for the night). In order to avoid seasonal influence, daytime is thus represented by solar noon (1200 hours local winter time, UTC+0100 h) as this is the time when the sun is near its highest position in the sky. This time also corresponds to lunch-time when people have the opportunity to be outdoors. This is especially important during winter when the only chance to get the some daylight (and counteract winter depression) is to take a walk during the lunch break. Figure 1. Map of the Göteborg urban district, Sweden, showing the extension of the built-up area, the altitude and the location of the 30 measurement sites (Tiny-logger), as well as the location of the meteorological station, Säve Airport. 136

3 Air temperature variations and urban land use Table 1. Statistics describing the air temperature differences ( C) within the area, i.e. the temperature deviation at each station from the mean of all stations during each hour. The data are divided into different weather groups: A (clear, calm), B (clear, windy), C (cloudy, calm) and D (cloudy, windy). A dash ( ) indicates data that are missing due to technical problems or non-occurrence during the 18-month measuring period. Day Night 1200 hours 3 hours after sunset N Min Max Range Mean Median N Min Max Range Mean Median Autumn A B C D Winter A B C D Spring A B C D Summer A B C D Night is represented by data taken three hours after sunset as several earlier studies (e.g. Oke 1987, Upmanis et al. 1998, Svensson et al. 2002) show that the maximum temperature deviation occurs at that time. Another advantage of this choice is that the seasonal influence (night length variation) is avoided. Weather groups: Meteorological parameters are important as shown by the many studies that examine the development of the urban heat island (e.g. Sundborg 1951, Lindqvist 1970, Oke 1973, Park 1986, Kidder & Essenwanger 1995). The common findings from these and other studies are that wind speed and cloud cover are the main influencing meteorological variables (see summary in Upmanis & Chen 1999, Table 6). Many studies focus their analysis on clear and calm weather conditions as this type favours the largest variations in temperature. However, this weather type is not very frequent at most middle and high latitudes. In order to get information on all weather types in the Göteborg district the analysis includes a division of data into different weather groups. The division was based on wind and cloud data from a permanent meteorological station situated in the district (Säve airport, Figure 1). Wind speed was divided into two groups being 3.3 m s 1 and >3.3 m s 1. The wind speed limit was chosen from the Beaufort scale, which quantifies wind in terms of the effect on humans (Lee 1987). Two on the Beaufort scale is m s 1 and is the limit for weak winds (SMHI 1989, Lindqvist 1991). Cloudiness was divided into three classes: clear (0 2 octas), partly cloudy (3 5 octas) and cloudy (6 8 octas). Table 2 shows the frequency of available data in each weather group during the measuring period. Seasons: The incoming solar radiation varies greatly at the study area due to the high latitude and that results in four distinct seasons. Indications that the seasonal differences in energy balance influences the processes favouring intra-urban temperature differences have been shown in earlier studies (Eliasson 1994). One part of the analysis therefore comprises the seasonal aspect such that Spring is March to May, summer is June to August, autumn September to November and winter December to February Instrumentation The temperature stations (Tiny-logger, Gemini Data loggers) were located in different types of land use areas (Figure 1). The main purpose was that the number of stations in a specific land use should correlate to the area size of that specific category. Sensors were located at sites that were representative for the land use but in an open location to avoid disturbance of sitespecific obstacles such as house facades. Figure 2 shows typical examples of measurement sites for the five land use/land cover categories used in the analysis of variance test. The fish-eye photographs were captured at the exact location of the sensor, at 2 m height above the ground. The sensor is a 10k NTC thermistor (encapsulated) with a time constant (63%) in air of 11 minutes. The sensor accuracy is ± 0.2 C from 0 C to 70 C and the resolution of the system is 0.03 C at 25 C, according to the manufacturer. The Tiny-loggers were intercompared for instrumental differences in a climate chamber 137

4 I Eliasson and M K Svensson Table 2. The frequency of available data in each weather group during the measurement period. (a) Daytime data collected at 1200 h, and (b) night time data from 3 h after sunset. Sunset varies with month. Data that are missing due to technical problems or not occurring during the 18 months measuring period are shown by a dash ( ). a) Day Wind (m s 1 ) 0 2 (octas) 3 5 (octas) 6 8 (octas) Total Autumn > Winter > Spring > Summer > Total b) Night Wind (m s 1 ) 0 2 (octas) 3 5 (octas) 6 8 (octas) Total Autumn > Winter > Spring > Summer > Total before the field measurements started and after one year of measurements. The climate chamber is capable of maintaining a constant temperature to an accuracy of 0.1 C. The instruments were tested for temperatures ranging from +20 C to 18 C. The inter-comparison showed that all Tiny-loggers ran together in a narrow interval with a maximum range of 0.5 C. The largest differences, i.e. 0.5 C, occurred only in temperatures below 10 C. To avoid systematic differences between stations the Tiny-loggers were moved between the different sites approximately every second month as data were collected. The Tiny-logger instruments were sheltered with radiation shields constructed of black and silver coloured plastic pipes with radii of 90 mm. These were mounted at a height of approximately 2 m. The radiation shields are constructed as chimneys so that, as the air in the black part of the radiation shield is warmed and rises, the ventilation and the amount of air flowing through increases (Svensson 2002). Wind speed/direction and cloud amount from one permanent meteorological station was also used (Säve airport, Figure 1). Wind is measured at 10 m height with a Vaisala wind anemometer (accuracy 0.1 m s 1, threshold 0.4 m s 1 ) and a Vaisala wind vane (accuracy 3, 0.3 m s 1 ). Cloud cover is measured in octas (0/8 to 8/8) with mobile cloud cover equipment (CMBE) Land use Land use information has been extracted from the Master Plan, an official policy document available from the City Planning Authority in Göteborg. The Master Plan is used at the comprehensive planning level to show the present land use in the municipality. It includes a land use classification that shows the function of different parts of the municipality. The present digitised Master Plan includes 12 land use classes (urban dense, multi-family, single houses, working premises etc, industries etc, larger institutions, recreation, impermeable, cemeteries, agricultural, other green, water). The Master Plan classification, in combination with site descriptions of the temperature stations, has been used to define the land use/land cover categories used in the analysis of variance test Statistics Two statistical methods were applied, one stepwise multiple regression analysis and an analysis of variance test. Temperature anomalies, calculated as the temperature deviation at every station from the mean of all stations during each hour, were used in the statistical analysis (Shudo et al. 1997). The multiple regression analysis was performed to determine the relative effect of surface cover on the temperature pattern. The choice of independent variables was based on a literature survey in order to cover the most important processes.

5 Air temperature variations and urban land use Figure 2. Fish-eye photographs showing typical examples of the five land use/land cover categories used in the statistical analysis and described in Table 3: (a) urban dense, (b) multi-family, (c) single houses, (d e) other built-up, and (f) green. As mentioned above, most studies regard the weather conditions as most important and this parameter was incorporated in the present analysis through the division of data into weather groups. Altitude is an important parameter governing temperature differences on both local and regional scales (e.g. Laughlin 1982, Thornes 1989, Ninyerola et al. 2000, Postgård 2000). The distance to large water bodies is another parameter that has been found to be equal in importance to altitude in several studies (Carrega 1995, Tveito & Førland 1999). Earlier studies in Sweden confirm the influence of the sea on the urban temperature pattern (Lindqvist & Mattsson 1989, Svensson et al. 2002). Surface cover has been shown to be very important for urban temperature variations (e.g. Katayama 1992, Alcoforado 1994, 1998, Heisler et al. 1994, Shudo et al. 1997, Vogt et al. 1997, Upmanis et al. 1998). 4. Characterisation of surface cover and land use/land cover categories 4.1. Site description analysis The site description analysis includes a test of three methods based on aerial and fish-eye photographs from which the percentage of different surface coverings at each temperature station was calculated. From aerial photos (scale 1:15 000) surface characteristics 139

6 I Eliasson and M K Svensson were determined within a circle. A 100 m radius, earlier used by Alcoforado (1994, 1998), represents the influence from the nearest surroundings (~1 block). In order to test if a larger fetch area would improve the analysis a comparison was made with data from a circle with a radius of 500 m (~5 blocks). The method assumes that the surface is spatially homogeneous within the circle and/or that over time the variation of wind direction will create spatial averaging (Grimmond & Souch 1994). Percentages of the following five surface categories were determined: built-up, impermeable, vegetation, water bodies and urban vegetation. The last category includes both vegetation and buildings that are difficult to separate from each other. This close mix of vegetation and buildings is common in Sweden. Fish-eye photographs were used to determine the skyview factor (SVF) as well as the percentage of vegetation and impervious surfaces for each temperature station (Heisler et al. 1994). Impervious surfaces were chosen as a group which included all types of artificial surfaces (pavement, buildings, etc.) in the fish-eye photographs. The fish-eye photos were captured at every station with a Nikon 8 mm fish-eye lens at the height of the temperature sensor (2 m above ground). The digital images were processed by the raster based and commercially available software IDRISI (Clark University 1999). The sky-view factor was finally calculated according to a GIS based method developed by Holmer et al. (2001). A tool for measuring area units (Leica digital Planimeter, Placom) was used for calculation of the percentages of different surface coverings in both aerial and fish-eye photographs. Information about altitude, distance from the sea and distance from the city centre for each temperature station were determined from the topographical map (1:50 000) Definition of land use/land cover categories from the Master Plan An analysis of variance was made in order to test if the 12 land use classes found in the Master Plan could be differentiated on the basis of temperature data. The results showed that the air temperature deviations in land use classes urban dense, multi-family and single houses, could be differentiated on a statistical basis. For the other land use classes no statistical differences were found. The site description analysis (Table 3) confirms results from the analysis of variance that those land use classes which could not be statistically differentiated from others based on temperature differences showed a uniform surface covering. Based on results from the statistical and site description analysis the original land use classes were thus grouped into five, more uniform categories (Table 3). The Master Plan classes for cemeteries (9), agriculture (10) and other green (11), which all have the same proportion of surface characteristics, were grouped into the green category (new category no. 16, Table 3). The new category other built-up (no. 15, Table 3) consists of the old Master Plan land use classes: working premises, etc. (4), industries, etc. (5), and recreation (8). The five categories resulting from this procedure (Table 3) have thus been defined on the basis of the surfaces function and form and are hereafter referred to as land use/land cover categories (Figures 2 and 3). Figure 3. The distribution of the five land use/land cover categories used in the statistical analysis. The inner city of Göteborg, 2% of the total area, is characterised by the category urban dense which has the least vegetation cover and consequently the largest part (>70 %) of built-up and impervious surfaces. The amount of greenery increases with distance from the city centre through areas with multi-family (9%) buildings and single houses (9%). The category other built-up makes up 11% of the total area, and 69% of the urban district is classified as green areas. 140

7 Air temperature variations and urban land use Table 3. Results from the objective site description analysis. The table shows percentage of surface cover calculated from fish-eye photographs and aerial photographs (100 m or 500 m radius) for each land use class. Sky-view factor (SVF) is calculated from fish-eye photographs. Fish-eye photographs Aerial photographs (100 m circle) Aerial photographs (500 m circle) Station Original Description New Description SVF Per cent Per cent Per cent Per cent Per cent Per cent Per cent Per cent Per cent Per cent Per cent No land use category vegetation impervious vegetation urban built-up impermeable vegetation urban water built-up impermeable class vegetation vegetation 16 1 urban dense 1 urban dense urban dense 1 urban dense multi-family 2 multi-family multi-family 2 multi-family multi-family 2 multi-family multi-family 2 multi-family multi-family 2 multi-family multi-family 2 multi-family multi-family 2 multi-family single houses 3 single houses single houses 3 single houses single houses 3 single houses single houses 3 single houses single houses 3 single houses single houses 3 single houses single houses 3 single houses single houses 3 single houses industries etc 15 other built-up industries etc 15 other built-up working etc 15 other built-up recreation 15 other built-up recreation 15 other built-up cemeteries 16 green cemeteries 16 green agricultural 16 green other green 16 green other green 16 green other green 16 green other green 16 green other green 16 green

8 I Eliasson and M K Svensson 5. Multiple regression analysis Multiple regression analysis was performed to determine the relative effect of parameters related to land use as well as other parameters on the temperature pattern. The analysis was performed on three data sets: monthly, seasonal and single days. The independent variables were altitude, distance from sea, distance from city centre, SVF at 2 m height and measures of surface covering determined from fish-eye or aerial photographs (Table 4) Diurnal variations on a monthly basis In order to verify/detect possible diurnal patterns in explaining factors and to examine if the coefficient of determination (R 2 ) varied during the day, multiple regression analyses were developed for each hour on a monthly basis. The mean air temperature deviation for each hour during clear/calm and cloudy/windy situations respectively was used in the analyses. Results based on data from May 1999, a month that included all weather types, are shown in Table 5. The results presented in Table 5 are based on data from the three classification methods (fish-eye or aerial photos). As shown, the results were not affected much by the choice of classification method. However, the classification made from aerial photographs (500m) usually gave the highest R 2 coefficients especially during cloudy/windy situations. Table 5 shows that the highest coefficients were found during the night and the lowest in the middle of the day, especially during cloudy/windy conditions, except during winter. Lower air stability during daytime and cloudy, windy conditions is a possible explanation. In general, the explanation factor was greater during cloudy and windy situations with a maximum coefficient of determination of 0.72 at 0100 hours. The general pattern during windy and cloudy conditions is, first, that altitude explains most of the variance in temperature and, second, that different surface coverings usually per cent built-up area (or with the fish-eye photo classification, per cent impervious) explains the rest (see Table 5). During clear and calm situations distance from sea explains most of the temperature variance, with SVF or surface cover (per cent vegetation or built-up) explaining the rest. The best linear fit during clear and calm situations occurs at 1400 hours (R 2 =0.70). In summary the multiple regression analysis based on data from each hour during a month shows that distance from sea, sky-view factor and surface cover (per cent built-up or impervious) generally explains most of the variance within the area during clear, calm situations. Altitude, surface cover (usually per cent built-up or impervious) and distance from sea explain most of the variance during cloudy, windy situations. In stable 142 Table 4. Independent variables used in the stepwise multiple regression analysis. Surface characteristics listed in the table are derived from aerial photographs (scale 1:15 000). The percentage of each surface cover has been calculated using a circle of radius 100 m and 500 m from the temperature station. Surface characterisations from the fish-eye photographs are limited to the percentage of impervious and vegetated surfaces respectively (marked by *). Parameters Altitude (m. a. s. l.) Distance from sea (km) Distance from city centre (km) Sky view factor at sensor height (m) *Vegetated surface (%) *Impermeable/impervious surface (%) Built-up surface (%) Built-up surface with vegetation (%) Water bodies (%) conditions (clear and calm) the closest environment is more important. For example, distance from sea, SVF and type of surface covering have more influence on the temperature pattern Seasonal variation Seasonal temperature variation was analysed with the 18 months of data divided into four weather classes: A) clear and calm situations ( 2 octas and 3.3 m s 1 ), B) clear and windy situations ( 2 octas and >3.3 m s 1 ), C) cloudy and calm situations ( 6 octas and 3.3 m s 1 ) and D) cloudy and windy situations ( 6 octas and >3.3 m s 1 ). The results are presented in Table 6 and indicate that temperature variations are more dependent on weather than season. The best correlation was found for clear, calm conditions (A) independent of season. One major exception to this is during winter daytime when the best correlation (independent of characterisation method) is found during cloudy, windy situations (D). The likely reason is the small amount of data obtained for this situation (Table 2). Stronger relationships are generally found for night-time data. During clear, calm (A) and clear, windy (B) conditions distance from sea and surface cover are important. With cloudy, calm (C) weather, altitude is most important during daytime and at night surface cover. During cloudy, windy (D) conditions altitude explains most of the variance during both day and night and secondly surface cover except during daytime in summer and spring when distance from the sea is more important. The correlation is, however, very low in summer and spring during cloudy, windy situations (D).

9 Air temperature variations and urban land use Table 5. Stepwise multiple regression analysis to determine the relative effect of land use/land cover and other parameters on the temperature pattern for each hour during one month (May 1999). The analysis is based on data from fish-eye photographs (SVF) and aerial photographs with a radius of 100 m and 500 m respectively. In the table the coefficient of determination (R 2 ) is shown for the three different classification methods used and for two weather groups (clear, calm; and cloudy, windy). Bold letters show the feature explaining the variance, i) first factor and ii) second factor. Bold letters are used for the following parameters; per cent impervious (I), built-up (B), vegetation (V), urban vegetation (UV), water (W) and SVF (S), altitude (A), distance from sea (DS) and distance from city centre (DC). For example, during clear, calm conditions, at 0100 h, the R 2 value is 0.59 and per cent impervious explains 36 % of the correlation and distance from sea explains the rest up to 51 % (i.e. 15 %). Clear and Calm Cloudy and Windy ( 2 octas and 3.3 m s 1 ) (>6 octas and >3.3 m s 1 ) Hour R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 (SVF) i ii (100m) i ii (500m) i ii (SVF) i ii (100m) i ii (500m) i ii I36 DS DS27 S DS27 S A49 I A48 B A48 B I DS26 S DS26 S A35 I A38 B A38 B I DS24 S DS24 S A47 I A44 B A44 B DS26 S DS28 S DS28 S A21 I A22 B A22 B DS28 S DS29 S DS29 S A42 I A40 B A40 B DS27 DC DS27 DC DS27 DC A46 I A45 B A45 V S S16 DS S16 DS A48 I A46 B A46 V S26 V S25 B S25 UV A43 I A41 B A41 B A A4 DS B A38 V A33 B A33 B S B15 S W21 B A15 DS A13 B A13 B DS18 DC DS19 DC DS19 W A12 DS A10 DS A10 W DS28 DC DS31 UV DS31 UV A7 DS A6 W A6 W DS34 DC DS36 UV DS36 DC A8 DS A8 DS A8 DS DS63 A DS65 UV DS65 W A10 DS A8 DS A8 V A12 DS A9 DS A10 DS DS23 S DS23 S DS23 S A10 DS A8 DC A8 V DS22 S DS24 S DS24 S A19 DC A27 B A27 B S15 V S13 B S13 B A20 DC A22 B A22 V A6 DS V16 DS A1 DS A29 I A29 B A29 B A V38 UV V33 A A34 I A34 B A34 B I39 S DC V37 B A40 I A40 B A40 B I41 DS DC28 DS V34 B A25 I A26 B A26 B I41 DS B B A31 I A31 B A31 B I39 DS S27 DS B A36 I A36 B A Single days Earlier studies have shown a strong relationship between air temperature and surface cover during single days or groups of days with specific weather conditions (e.g. Katayama 1992, Alcoforado 1998). Days representing specific weather conditions were therefore selected from the 18-month database. The selection was based on the weather conditions recorded at Säve airport (Figure 1). Air temperature data for a specific hour (1200 h or 3 h after sunset) during a single day together with data on surface covering determined from the aerial photographs and fish-eye photographs were used in a multiple regression analysis. Generally these results showed strong statistical relationships and high coefficients of determination. Table 7 shows the results for the 500 m radius aerial photographs classification. Stronger correlations are more frequent at night time than during the day although the highest coefficient of determination (R 2 =0.86) among these single occasions is found during the day. 6. Analysis of variance test The multiple regression analysis verified that surface cover and SVF are important for governing air temperature variations. In order to test if air temperature could be differentiated on the basis of the aggregated land use/land cover categories (Table 3), an analysis of variance was performed. Indirectly this was also a test of the temperature stations ability to represent specific categories in the land use/land cover database (Master Plan). The analysis of variance test was made separately for each weather group. A normal distribution of the temperature data was assumed and the physical properties in each category were assumed to be similar when performing the analysis of variance test. The null hypothesis tested was no significant differences in temperature exist between the categories and a 5 per cent level of significance was chosen prior to the test. Data were analysed for days (12 h) and nights (3 h after sunset) with three cloud groups (0 2, 3 5 or 6 8 octas) and two wind groups ( 3.3 m s 1 or >3.3 m s 1 ). The results, presented in Table 8, show that statistically 143

10 I Eliasson and M K Svensson Table 6. Stepwise multiple regression analysis using seasonal data. The analysis is based on data from fish-eye photographs (SVF) and aerial photographs with radii of 100 m and 500 m respectively. Data (temperature deviation for each measuring station) are divided into the following weather groups: clear, calm (A), clear, windy (B), cloudy, calm (C) and cloudy, windy (D) See Table 5 for more information on the table. Bold letters show the feature explaining the variance, i) first factor and ii) second factor. Bold letters are used for the following parameters; per cent impervious (I), built-up (B), vegetation (V), urban vegetation (UV), water (W) and SVF (S), altitude (A), distance from sea (DS) and distance from city centre (DC). For N values see Table 1. Day (1200 h) N R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 R 2 (SVF) i ii (100 m) i ii (500 m) i ii Autumn A S17 I S S17 B DS25 V DS25 UV DS25 C V17 A A14 S V16 B22 D A23 V A23 B A23 V25 Summer A 110 B 29 C A13 I A13 DC A13 DC20 D A6 DS A6 DS A6 DS12 Spring A DS47 V DC47 UV B DS28 S DS28 S C A A D A20 DS A20 DS Winter A A4 V A4 B I8 B12 B DC DC DC17 I29 C A9 V A9 B V10 B14 D A33 I A33 B A33 B39 Night (3 hours after sunset) N R 2 R 2 R 2 (SVF) i ii (100 m) i ii (500 m) i ii Autumn A I31 DS DS26 B A26 S39 B DS DS DS49 C I23 DS B21 DS DS20 B32 D A34 I A34 B A34 B41 Summer A I31 DS DS26 B DS26 S39 B DS DS DS49 C I23 DS I21 DS DS20 B32 D A34 I A34 B A34 B41 Spring A I37 DS B26 DS B28 DS44 B 0 C I26 A DC21 B DC21 A28 D A33 I A33 B A33 B44 Winter A DS35 I DS35 B DS35 B47 B DS DS31 V DS30 V33 C I29 A B24 A B25 A39 D A26 I A26 B A26 B35 significant temperature differences between the land use/land cover categories, in general, are more frequent during the night than during the day. 144 At night urban dense, other built-up and green areas have, with few exceptions, different temperature patterns compared to the other categories (Table 8). During windy, clear nocturnal situations few differences exist between the categories and only urban dense and single houses are different from multi-family and green. This weather group (windy, clear) is, however, not common during the measurement period (Table 2). Considering daytime in general, the analyses showed that significant temperature differences between the

11 Air temperature variations and urban land use Table 7. The determination coefficients (R 2 ) from the stepwise multiple regression analysis performed on air temperature data from a specific hour during a single day together with data on surface covering determined from aerial photographs with a radius of 500 m. Weather conditions: A) clear, calm; B) clear, windy; C) cloudy, calm; and D) cloudy, windy. categories are more frequent during cloudy conditions, independent of wind speed. As seen in Table 8 the temperature recorded during daytime situations with 6 8 octas is different in all categories a spatial temperature pattern exists that corresponds to the land use/land cover map (Figure 3). The opposite is seen during clear and calm daytime situations where no statistically significant differences in temperature are recorded in the area (Table 8). The amount of data used in these situations is, however, much smaller since clear days do not occur as frequently during the measurement period (Table 2). The results from the analysis verified that weather characteristics are important for prediction of air temperature differences between the categories. The results confirm that statistically significant temperature differences exist between densely built-up areas, large open areas and green areas during windy and cloudy situations both day and night. The categories urban dense, other built-up and green have a statistically significant different temperature pattern during both day and night while during the day multi-family and single houses are also different on the 5% level (Table 8). A seasonal parameter was finally added in the analysis of variance test to see if there were any variations between the different seasons. The analyses did not show any major differences from the results presented on a yearly basis. 7. Discussion Weather Julian R 2 Cloud cover Wind speed group day (octas) (m s 1 ) Day A (noon) B C D Night A (3 h after B sunset) C D Parameters important for temperature differentiation The stepwise multiple regression analysis showed that parameters related to surface cover are important for governing temperature variations in the Göteborg urban area. Other important parameters were altitude, distance from sea and sky-view factor. The effect of different surface coverings on the energy balance and consequently on the temperature pattern is more pronounced during clear and calm weather with high air stability. Windy and cloudy conditions smooth out these differences, making other parameters, such as altitude and distance from sea (day) or surface cover (night), more important. Several studies, performed in corresponding climatic zones, have pointed out the role of altitude during cloudy weather conditions (e.g. Laughlin 1982, Thornes 1989, Postgård 2000). A high correlation between altitude and air temperature for cloudy and windy situations has also been reported by Bogren et al. (2000) who presents data from 32 temperature stations. Land use/land cover parameters have also proved to be an important parameter in several studies (e.g. Katayama 1992, Alcoforado 1994,1998, Heisler et al. 1994, Shudo et al. 1997, Vogt et al. 1997). Early morning car traverses in summer in Fukuoka, Japan showed that artificial covering explained 63% of the air temperature variation (Katayama 1992), a value comparable with the general results presented in this paper. Alcoforado (1998) found that a parameter describing distance to main streets and the product between the sky-view factor and percentage of built-up area explained 74% of the temperature differences in Lisbon (Portugal) for the average of five nights with high-pressure conditions. These results are comparable with the coefficient of determination of 0.78 that was calculated in this study for a single day with similar weather conditions (Table 7). Distance to city centre does not, however, explain a large part of the variations in Göteborg. This is probably due to the typical north European green city structure found in Göteborg. Even though the site description analysis revealed a progressive transition of land characteristics, with increasing greenery from the urban dense areas through multi-family and single house areas (Table 3, Figure 3) the differences are probably not large enough to create an effect that could be explained by the parameter distance to city centre, i.e. other parameters related to land use and distance to sea are more important. The choice of representative measurement points is, of course, also important. The results showed that the correlation was slightly lower when using longer data periods compared to single occasions or groups of days with the same weather type (Alcoforado 1994). This points to the difficulties of making generalisations based on specific data periods. Single measurements, during specific weather situations, are often assumed to represent average conditions. The results in this paper are based on a large 145

12 I Eliasson and M K Svensson Table 8. Result from the analysis of variance test with data divided into time of day and weather group. Significant air temperature differences on the 5% level are indicated with an asterisk (*). The head of the table shows the five land use/land cover categories and these categories are also present vertically in the left column. For example, in the upper-left corner of the table the asterisks show that the temperature in category 1, urban dense, is significantly different from that of the other categories at night. The temperature in category 2, multi-family, is different from that in category 15 and 16 (other built-up and green) and so on. N is the amount of data used in the test.. Land use/land cover category Night Day Urban dense Multi-family Single houses Other built-up Green Urban dense Multi-family Single houses Other built-up Green Calm and clear 1 * * * * * 3.3 m s octas 2 * * 3 * Night N= * Day N= Calm and partly cloudy 1 * * * * * * * * 3.3 m s octas 2 * 3 * Night N= * * * Day N= Calm and cloudy 1 * * * * * * * * 3.3 m s octas 2 * * * 3 * * Night N= * * * * Day N= Windy and clear 1 * * * > 3.3 ms octas 2 * 3 * * * Night N= Day N= * Windy and partly cloudy 1 * * * * * > 3.3 m s octas 2 * * * 3 * * * * Night N= * * * Day N= Windy and cloudy 1 * * * * * * * * >3.3 m s octas 2 * * * 3 * * Night N= * * * * * * Day N= amount of data and the lower correlations are an effect of the large variation within each weather group since extreme events are mixed with more average days in the 18-month data period. Covariation between the chosen independent variables may cause problems in the analysis. The emphasis in the present study was on the correlation between air 146 temperature and possible explaining factors, but covariation still needs to be mentioned. Further inland the elevation increases and intuitively this indicates problems. A parameter that included a possible covariation between altitude and distance from coast was tested as an independent variable and included in the regression analysis with little or no result (Eliasson & Svensson, unpublished results). The reason is found in

13 Air temperature variations and urban land use the network of measuring stations and in the landscape itself. The large broad valleys ( m wide) in Göteborg make it possible to perform measurements on similar altitudes even further inland and the measuring network includes stations at different altitudes in both inland and coastal positions (Figure 1). However, for further work a principal component analysis (PCA) would rule out any possibilities of correlation between the chosen independent variables. the analysis showed that temperatures were statistically different for several of the land use/land cover categories during these weather conditions. Both the average and extreme condition might be important in practical applications, and in order to be able to judge whether differences are significant in any sense it is important to base the analysis on a combination of statistics and actual temperature observations Land use/land cover information 7.2. Air temperature variations between land use/land cover categories. It is a well-known fact that clear and calm weather favours large temperature differences between areas with different land uses. Both observations and the statistical analysis confirmed this, but, more interestingly, the results showed that this was true also for cloudy conditions (6 8 octas) for both day and night. These results are especially interesting for planning considerations as cloudy and windy weather is a common weather type in Sweden (Table 2; see also Postgård 2000). Large air temperature differences during cloudy weather have been observed and reported also by Laughlin (1982), Thornes (1989) and Bogren et al. (2000). Schudo et al. (1997) reports that the effect of land use on temperature in Hokkaido, Japan, is greater during winter than in summer. By contrast, studies in Phoenix, Arizona, show large temperature variations between different surface coverings during summer (Brazel & Johnson 1980, Martin et al. 2000) as well as autumn and spring (Selover 2000). Despite indications of a stronger relationship for winter data the influence of season could not be statistically confirmed in the present analysis. However, significant differences in temperature between land use/land cover categories were found for both daytime and night-time data. Brazel & Johnson (1980) also report daytime fluctuations in temperature between different land use but could not show any statistical significance for their data. For practical application it is important to remember that the analysis of variance test only gives information about the statistical significance of the differences and not the actual differences. For example, measurements show large temperature differences between different land use/land cover categories during clear and calm conditions (Table 1) but none of these differences is statistically significant according to the analysis of variance test (Table 8). This discrepancy can be explained by the fact that clear and calm weather occurs at a low frequency daytime 3%, night-time 12% (Table 2) and that the range within this weather group is large (e.g. for wind, completely calm to 3.3 m s 1 ). Cloudy and windy situations, on the other hand, are much more frequent (daytime 49% and night-time 35%) and The basic requirements for a land use/land cover database that is to be used for urban climate purposes is (i) a suitable resolution of the categories and (ii) up-todate information. Previous studies show that available databases often lack these two requirements. Existing land use classes usually need to be aggregated into coarser categories and the decision as to which classes to choose is difficult and the aggregation may induce several errors (Shudo et al. 1997, Burian & Brown 2002). Another problem is that available databases are not always updated, such as the widely used American USGS database that is based on satellite data from the 1970s (Brown et al. 2000). The results presented in this paper showed that the Master Plan could be used as a land use/land cover database for an analysis of spatial urban air temperature patterns. The advantages of the Master Plan are that the limited number of categories is well defined and that the planning authorities continuously update the database. The site description analysis showed that a minor regrouping of the original Master Plan land use classes was necessary. The Master Plan primarily shows function, and the surface characteristics, important for temperature patterns, were found to be similar for several categories. The regrouping, based on a thorough examination of the characteristics at each station, resulted in the creation of two new categories: 15 (other built-up) and 16 (green). The site description analysis and statistical analysis of temperature differences performed on the five new land use/land cover categories showed that category 16 was uniform while category 15 still showed variations of both surface characteristics and temperature pattern after the regrouping. This scatter is considered to be a result of the wide range of different land uses that is included in category 15 in Table 3. Figure 4 shows the area percentage of Master Plan land use classes in category 15. The class Industries makes up more than 50% of this category, followed by Recreation (25%). However, both these classes and the other three land use classes (Figure 4) are characterised by a large variation of surface cover ranging from open asphalt surfaces with lower building complexes to densely green areas or high-rise buildings (Table 3 and Figure 2d, 2e). Category 15 was basically created as a consequence of the first variance analysis, which showed few statistically significant temperature differences between the different land use classes. However, only five stations represent this category and, as it makes up 11% of the 147