Journal_Spatial Air Temperature Variations and Urban Land

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1. Introduction

It is a well-known fact that the air temperature mayvary 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 airtemperature differences up to 9 C have been reportedrecently (Spronken-Smith & Oke 1998, Upmanis et al.1998, Svensson et al. 2002). These differences are clearlyof a magnitude that is important for application inurban planning. Studies in North America and Sweden,for example, show a change of up to 14kWh in daily netenergy load (Matsuura 1995) or a 20% change in yearlyenergy consumption (Svensson & Eliasson 2002) due toair temperature differences. Also human comfort in thecity is influenced by intra-urban air temperature differ-ences as shown, for example, by Jendritzky & Grtz(1997), Matzarakis et al. (1999) and Friedrich et al.(2001). Air temperature differences have an indirectinfluence on the air quality in urban areas throughchanges in stability and the development of local windsystems (e.g. Eliasson & Holmer 1990, Kuttler &Romberg 1992, Eliasson & Upmanis 2000). There is nodoubt that the application of this knowledge is ofimportance for urban planning and the integration ofclimate and planning has been a subject of concern inthe international literature for a long time (e.g. Morgan1960, Oke 1984, Givoni 1998). Nevertheless mostauthors agree that climatic aspects have low impact inthe urban planning process (e.g. Oke 1984, Pielke &

Uliasz 1998, Mills 1999, Eliasson 2000). One reason forthis is that the data provided by climate researchers donot always meet the demands of urban planners and

architects (Mills 1999). Climatologists have a tendencyto focus on nocturnal clear and calm weather condi-tions while planners and architects are mostly inter-ested in the average daytime conditions, when humansare most active. Shortcomings are thus related to the

spatial and temporal availability of data. Short-termstudies during extreme weather conditions are, forexample, more common than long-term studies cover-ing all weather conditions. Many studies also base theirresults on data from a few point measurements whilestudies of spatial variation are less frequent. If meteo-rological data are to be of general use in planning appli-cations, the differences have to be statistically signifi-cant for average weather conditions and not only aresult of carefully chosen data periods.

The results presented in this paper are part of a projectthat aims to integrate climate knowledge in the urbanplanning process through the development of a GIS-based empirical model (Svensson et al. 2002). This par-ticular paper focuses on a statistical analysis of spatialair temperature variations and its relation to land use inan urban area (Gteborg, Sweden). Measurementsshow intra-urban air temperature differences of up to9 C in the urban district and the main purpose is todetermine if these variations are statistically significant.First, the relative importance of surface cover for airtemperature variations in the area was tested with amultiple regression analysis. Secondly, an analyse ofvariance test were carried out to study whether

observed air temperature differences within differentland use/land cover categories were statistically signifi-cant during both day and night, under different

Meteorol. Appl. 10, 135149 (2003) DOI:10.1017/S1350482703002056

Spatial air temperature variations and urban landuse a statistical approach

I Eliasson & M K Svensson Laboratory of Climatology, Physical Geography, Earth Sciences

Centre, University of Gteborg, Box 460, SE-405 30, Gteborg, SwedenEmail: [email protected]

Using a statistical approach this paper focuses on an analysis of spatial air temperature variations and theirrelation to urban land use. The work is based on data collected during an 18-month period at 30 sites in Gteborg,Sweden. Measurements show intra-urban air temperature differences of up to 9 C in the urban district, and themain purpose of this study is to determine if these variations are statistically significant. A stepwise multipleregression 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 Gteborg, and a separate site description analysis. The site description analysis includes a test ofthree methods using aerial or fish-eye photos for characterisation of surface cover in the urban district. The resultsshow statistically significant temperature variations between different land use/land cover categories on a diurnalbasis and for all weather conditions. The importance of the results for urban planning application is discussed.

135


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weather conditions and seasons. This included a sitedescription analysis to describe and characterise thesurface cover at the measurement sites. Three differentmethods for characterisation of surface cover weretested and evaluated in this paper and the results, com-bined with land use information, were used to definethe land use/land cover categories used in the analysis

of variance test.

2. Study area

The Gteborg urban district (72,200 ha and 500,000inhabitants) is situated on the Swedish west coast inScandinavia (5742N, 1158E). Four broad, flat val-leys dominate the area and most of the built-up land islocated in the valley bottoms around 010 m. a. s. l.(Figure 1). The coastline is irregular and borders anarchipelago with scattered small islands. The climate inGteborg is a typical maritime west coast climate with,

for its latitude, relatively warm winters and cool sum-mers. The monthly mean temperature for Gteborg is0.4 C in winter (DecemberFebruary) and 16.3 C insummer (JuneAugust) for the 19611990 climaticperiod (Vder och Vatten 2000).

3. Data

3.1. The database

The present analysis is based on air temperature mea-

surements over 18 months (September 1998March

2000) at 30 locations in Gteborg, Sweden (Figure 1).The data show considerable variations within the urbandistrict under different seasons and weather conditions,and on a diurnal basis (Svensson et al. 2002, Svensson &Eliasson 2002, Svensson 2002). Table 1 shows the rangeof temperature deviations at each station from the meanof all stations during each hour within the urban area

during the measurement period. Data are divided intotime of day, weather and season.

Day and night: As mentioned above there is a focus onnocturnal data in the literature and consequently a lackof information on daytime conditions. Thus the mainanalysis in this study is the comparison between dayand night conditions. The temperature deviation, fromthe mean off all stations, at every station (on a seasonal,monthly and daily basis) has been analysed for two sit-uations, representing daytime and night time. Thechoice of the daytime and night-time period are basedon a previous analysis (Svensson 2002) and information

found in the literature. Owing to Gteborgs high lati-tude, the daylight lengths vary greatly throughout theyear. In the middle of June the day is approximately 16hours long and in December about 6 hours long (andvice versa for the night). In order to avoid seasonalinfluence, daytime is thus represented by solar noon(1200 hours local winter time, UTC+0100 h) as this isthe time when the sun is near its highest position in thesky. This time also corresponds to lunch-time whenpeople have the opportunity to be outdoors. This isespecially important during winter when the onlychance to get the some daylight (and counteract winter

depression) is to take a walk during the lunch break.

I Eliasson and M K Svensson

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Figure 1. Map of the Gteborg urban district, Sweden, showing the extension of the built-up area, the altitude and the locationof the 30 measurement sites (Tiny-logger), as well as the location of the meteorological station, Sve Airport.


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Night is represented by data taken three hours aftersunset as several earlier studies (e.g. Oke 1987,Upmanis et al. 1998, Svensson et al. 2002) show that themaximum temperature deviation occurs at that time.Another advantage of this choice is that the seasonalinfluence (night length variation) is avoided.

Weather groups: Meteorological parameters are impor-tant as shown by the many studies that examine thedevelopment of the urban heat island (e.g. Sundborg1951, Lindqvist 1970, Oke 1973, Park 1986, Kidder &Essenwanger 1995). The common findings from theseand other studies are that wind speed and cloud coverare the main influencing meteorological variables (seesummary in Upmanis & Chen 1999, Table 6). Manystudies focus their analysis on clear and calm weatherconditions as this type favours the largest variations intemperature. However, this weather type is not veryfrequent at most middle and high latitudes. In order toget information on all weather types in the Gteborgdistrict the analysis includes a division of data into dif-ferent weather groups. The division was based on windand cloud data from a permanent meteorological sta-tion situated in the district (Sve airport, Figure 1).Wind speed was divided into two groups being 3.3 ms1 and >3.3 m s1. The wind speed limit was chosenfrom the Beaufort scale, which quantifies wind in termsof the effect on humans (Lee 1987). Two on theBeaufort scale is 1.63.3 m s1 and is the limit for weakwinds (SMHI 1989, Lindqvist 1991). Cloudiness wasdivided into three classes: clear (02 octas), partly

cloudy (35 octas) and cloudy (68 octas). Table 2shows the frequency of available data in each weathergroup during the measuring period.

Seasons: The incoming solar radiation varies greatly atthe study area due to the high latitude and that resultsin four distinct seasons. Indications that the seasonaldifferences in energy balance influences the processesfavouring intra-urban temperature differences havebeen shown in earlier studies (Eliasson 1994). One part

of the analysis therefore comprises the seasonal aspectsuch that Spring is March to May, summer is June toAugust, autumn September to November and winterDecember to February.

3.2. Instrumentation

The temperature stations (Tiny-logger, Gemini Dataloggers) were located in different types of land useareas (Figure 1). The main purpose was that the num-ber of stations in a specific land use should correlate tothe area size of that specific category. Sensors werelocated at sites that were representative for the land usebut in an open location to avoid disturbance of site-specific obstacles such as house facades. Figure 2 showstypical examples of measurement sites for the five landuse/land cover categories used in the analysis of vari-ance test. The fish-eye photographs were captured atthe exact location of the sensor, at 2 m height above theground.

The sensor is a 10k NTC thermistor (encapsulated)with a time constant (63%) in air of 11 minutes. Thesensor accuracy is 0.2 C from 0 C to 70 C and the

resolution of the system is 0.03 C at 25 C, accordingto the manufacturer. The Tiny-loggers were intercom-pared for instrumental differences in a climate chamber

Air temperature variations and urban land use

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Table 1. Statistics describing the air temperature differences (C) within the area, i.e. the temperature deviation ateach 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 missingdue to technical problems or non-occurrence during the 18-month measuring period.

Day Night1200 hours 3 hours after sunset

N Min Max Range Mean Median N Min Max Range Mean Median

Autumn A 54 2.04 4.77 6.81 0.00 0.24 336 4.17 4.86 9.03 0.00 0.01B 140 3.00 3.19 6.19 0.00 0.12 54 5.16 4.24 9.40 0.00 0.29C 400 1.76 2.91 4.67 0.00 0.04 438 2.82 3.36 6.18 0.00 0.03D 842 1.85 1.56 3.41 0.00 0.03 540 1.69 1.28 2.97 0.00 0.01

Winter A 110 6.09 3.32 9.41 0.00 0.01 84 4.66 4.21 8.87 0.00 0.03B 29 1.02 1.23 2.25 0.00 0.01 111 5.89 2.83 8.72 0.00 0.03C 472 2.08 2.27 4.35 0.00 0.01 532 2.34 2.35 4.69 0.00 0.02D 1382 1.74 1.44 3.18 0.00 0.05 924 2.16 1.69 3.85 0.00 0.03

Spring A 122 5.90 4.98 10.88 0.00 0.10 483 4.81 5.73 10.54 0.00 0.18B 152 2.25 4.46 6.71 0.00 0.01 0 0.00 C 184 2.38 3.30 5.68 0.00 0.01 459 2.82 2.39 5.21 0.00 0.02D 1289 4.10 2.68 6.78 0.00 0.04 890 1.87 2.62 4.49 0.00 0.04

Summer A 0 0.00 332 4.17 4.86 9.03 0.00 0.03

B 0 0.00 54 5.16 4.24 9.40 0.00 0.29C 118 2.27 1.76 4.03 0.00 0.02 438 2.82 3.36 6.18 0.00 0.03D 470 1.99 2.65 4.64 0.00 0.02 540 1.69 1.28 2.97 0.00 0.01


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before the field measurements started and after one

year of measurements. The climate chamber is capableof maintaining a constant temperature to an accuracy of0.1 C. The instruments were tested for temperaturesranging from +20 C to 18 C. The inter-comparisonshowed that all Tiny-loggers ran together in a narrowinterval with a maximum range of 0.5 C. The largestdifferences, i.e. 0.5 C, occurred only in temperaturesbelow 10 C. To avoid systematic differences betweenstations the Tiny-loggers were moved between the dif-ferent sites approximately every second month as datawere collected. The Tiny-logger instruments were shel-tered with radiation shields constructed of black andsilver coloured plastic pipes with radii of 90 mm. Thesewere mounted at a height of approximately 2 m. Theradiation shields are constructed as chimneys so that,as the air in the black part of the radiation shield iswarmed and rises, the ventilation and the amount of airflowing through increases (Svensson 2002).

Wind speed/direction and cloud amount from onepermanent meteorological station was also used (Sveairport, Figure 1). Wind is measured at 10 m heightwith a Vaisala wind anemometer (accuracy 0.1 m s1,threshold 0.4 m s1) and a Vaisala wind vane (accuracy3, 0.3 m s1). Cloud cover is measured in octas (0/8 to

8/8) with mobile cloud cover equipment (CMBE).

3.3. Land use

Land use information has been extracted from the MasterPlan, an official policy document available from the CityPlanning Authority in Gteborg. The Master Plan is usedat the comprehensive planning level to show the presentland use in the municipality. It includes a land use classi-fication that shows the function of different parts of themunicipality. The present digitised Master Plan includes12 land use classes (urban dense, multi-family, singlehouses, working premises etc, industries etc, larger insti-tutions, recreation, impermeable, cemeteries, agricultural,other green, water). The Master Plan classification, incombination with site descriptions of the temperaturestations, has been used to define the land use/land covercategories used in the analysis of variance test.

3.4. Statistics

Two statistical methods were applied, one stepwisemultiple regression analysis and an analysis of variancetest. Temperature anomalies, calculated as the tempera-ture deviation at every station from the mean of allstations during each hour, were used in the statisticalanalysis (Shudo et al. 1997). The multiple regressionanalysis was performed to determine the relative effect

of surface cover on the temperature pattern. The choiceof independent variables was based on a literaturesurvey in order to cover the most important processes.


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Table 2. The frequency of available data in each weather group during the measurement period.(a) Daytime data collected at 1200 h, and (b) nighttime 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 areshown by a dash ().

a)

Day Wind (m s1) 02 (octas) 35 (octas) 68 (octas) Total

Autumn

3.3 2 7 14 23> 3.3 5 21 32 58Winter 3.3 4 10 13 27

> 3.3 1 12 53 66Spring 3.3 3 3 4 10

> 3.3 6 21 44 71Summer 3.3 6 3 9

> 3.3 22 17 39

Total 21 102 180

b)

Night Wind (m s1) 02 (octas) 35 (octas) 68 (octas) Total

Autumn 3.3 11 18 16 45> 3.3 3 14 20 37

Winter 3.3 3 16 13 32> 3.3 4 18 39 61

Spring 3.3 16 11 7 34> 3.3 8 36 44

Summer 3.3 7 13 8 28> 3.3 9 12 21

Total 44 107 151


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As mentioned above, most studies regard the weatherconditions as most important and this parameter wasincorporated in the present analysis through the divi-sion of data into weather groups.

Altitude is an important parameter governing tempera-ture differences on both local and regional scales (e.g.Laughlin 1982, Thornes 1989, Ninyerola et al. 2000,Postgrd 2000).

The distance to large water bodies is another parameterthat has been found to be equal in importance to alti-tude in several studies (Carrega 1995, Tveito & Frland

1999). Earlier studies in Sweden confirm the influenceof the sea on the urban temperature pattern (Lindqvist& Mattsson 1989, Svensson et al. 2002).

Surface coverhas been shown to be very important forurban 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 landuse/land cover categories

4.1. Site description analysis

The site description analysis includes a test of threemethods based on aerial and fish-eye photographs fromwhich the percentage of different surface coverings at

each temperature station was calculated.

From aerial photos (scale 1:15 000) surface characteristics


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Figure 2.Fish-eye photographs showing typical examples of the five land use/land cover categories used in the statistical analy-sis and described in Table 3: (a) urban dense, (b) multi-family, (c) single houses, (de) other built-up, and (f) green.


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were determined within a circle. A 100 m radius, earlierused by Alcoforado (1994, 1998), represents the influ-ence from the nearest surroundings (~1 block). In orderto test if a larger fetch area would improve the analysisa comparison was made with data from a circle with aradius of 500 m (~5 blocks). The method assumes thatthe surface is spatially homogeneous within the circle

and/or that over time the variation of wind directionwill create spatial averaging (Grimmond & Souch1994). Percentages of the following five surface cate-gories were determined: built-up, impermeable, vegeta-tion, water bodies and urban vegetation. The last cate-gory includes both vegetation and buildings that aredifficult to separate from each other. This close mix ofvegetation and buildings is common in Sweden.

Fish-eye photographs were used to determine the sky-view factor (SVF) as well as the percentage of vegetationand 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 witha Nikon 8 mm fish-eye lens at the height of the temper-ature sensor (2 m above ground). The digital imageswere processed by the raster based and commerciallyavailable software IDRISI (Clark University 1999). Thesky-view factor was finally calculated according to aGIS 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 fromthe sea and distance from the city centre for each tem-perature station were determined from the topographicalmap (1:50 000).

4.2. Definition of land use/land cover categories

from the Master Plan

An analysis of variance was made in order to test if the12 land use classes found in the Master Plan could be dif-ferentiated on the basis of temperature data. The resultsshowed that the air temperature deviations in land useclasses urban dense, multi-family and single houses,could be differentiated on a statistical basis. For the otherland use classes no statistical differences were found. Thesite description analysis (Table 3) confirms results fromthe analysis of variance that those land use classes whichcould not be statistically differentiated from othersbased on temperature differences showed a uniform sur-face covering. Based on results from the statistical and

site description analysis the original land use classes werethus grouped into five, more uniform categories (Table3). The Master Plan classes for cemeteries (9), agriculture(10) and other green (11), which all have the same pro-portion of surface characteristics, were grouped into thegreen category (new category no. 16, Table 3). The newcategory other built-up (no. 15, Table 3) consists of theold Master Plan land use classes: working premises, etc.(4), industries, etc. (5), and recreation (8). The five cate-gories resulting from this procedure (Table 3) have thusbeen defined on the basis of the surfaces function andform and are hereafter referred to as land use/land cover

categories (Figures 2 and 3).


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Figure 3. The distribution of the five land use/land cover categories used in the statistical analysis. The inner city of Gteborg,2% of the total area, is characterised by the category urban dense which has the least vegetation cover and consequently thelargest part (>70 %) of built-up and impervious surfaces. The amount of greenery increases with distance from the city centrethrough areas with multi-family (9%) buildings and single houses (9%). The category other built-up makes up 11% of thetotal area, and 69% of the urban district is classified as green areas.


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141

Tab

le3

.Resu

lts

fromt

heo

bjectivesitedescriptionana

lysis.

Thetableshowspe

rcentageo

fsurfacecoverca

lcu

lated

fromfi

sh-eyephotographsan

daerialphotographs(100

mo

r50

0mr

adius)

foreach

lan

duseclass.Sky-view

factor(SVF)isca

lcu

lated

fromfi

sh-eyephotographs.

Fish-eyep

hotograp

hs

Aerialphotograp

hs(100mcirc

le)

Aerialp

hot

ograp

hs(500mcirc

le)

Station

Original

Description

New

Description

SVF

Percent

Percent

Percent

Percent

Percent

Percent

Percent

Percent

Percent

Percent

Percent

No

lan

duse

category

vegetation

impervious

vegetation

urban

built-up

imperm

eable

vegetation

urban

water

built-up

impermea

ble

class

vegetation

vegetation

16

1

urban

dense

1

urba

ndense

0.9

8.6

22.0

29.4

17.7

23.5

29

.4

14.7

39.3

44.1

2.0

17

1

urban

dense

1

urba

ndense

0.4

0.0

77.3

11.8

11.8

76.5

10.7

22.0

58.8

8.5

10

2

mu

lti-family

2

multi-family

0.9

2.6

26.9

100.0

64.6

35.4

12

2

mu

lti-family

2

multi-family

0.8

9.7

31.3

11.8

88.2

34.7

47.2

18.1

18

2

mu

lti-family

2

multi-family

0.7

26.7

27.9

100.0

16.1

83.9

22

2

mu

lti-family

2

multi-family

0.8

23.8

23.3

41.2

58.8

55.7

42.9

1.4

23

2

mu

lti-family

2

multi-family

1.0

8.6

3.1

28

2

mu

lti-family

2

multi-family

0.9

14.1

5.1

47.1

52.9

64.1

20.9

15.0

31

2

mu

lti-family

2

multi-family

0.9

15.1

4.0

1

3

sing

lehouses

3

single

houses

0.9

17.5

8.8

64.7

35.3

83.3

16.7

6

3

sing

lehouses

3

single

houses

1.0

14.5

7.2

29.4

70.6

16.4

83.6

7

3

sing

lehouses

3

single

houses

0.9

32.2

6.3

100.0

14.1

77.1

5.7

3.1

8

3

sing

lehouses

3

single

houses

1.0

10.2

5.3

100.0

15.8

63.3

20.9

9

3

sing

lehouses

3

single

houses

0.9

11.7

8.0

100.0

17.8

82.2

15

3

sing

lehouses

3

single

houses

1.0

10.6

6.6

100.0

82.5

17.5

25

3

sing

lehouses

3

single

houses

100.0

17.8

82.2

29

3

sing

lehouses

3

single

houses

0.7

52.7

23.7

100.0

54.0

30.8

15.3

2

5

industriesetc

15

other

built-up

0.8

22.7

8.0

17.7

82

.4

70.9

29.1

11

5

industriesetc

15

other

built-up

0.7

8.2

25.6

41.2

11.8

47

.1

13.6

24.0

31.6

5.9

24.9

26

4

workingetc

15

other

built-up

1.0

8.9

10.9

35.3

64

.7

20.1

44.4

35.6

19

8

recreation

15

other

built-up

1.0

5.3

9.8

35.3

64

.7

9.9

55.9

19.2

15.0

20

8

recreation

15

other

built-up

0.7

22.3

35.3

52.9

47.1

27.1

57.1

15.8

14

9

cemeteries

16

green

0.9

21.5

0.0

100.0

98.8

1.1

24

9

cemeteries

16

green

0.9

35.9

2.0

3

10

agricu

ltura

l

16

green

1.0

6.6

1.5

76.5

23.5

83.9

16.1

4

11

othergreen

16

green

0.9

15.0

1.5

64.7

35

.3

68.4

31.6

5

11

othergreen

16

green

0.9

4.6

5.3

100.0

95.8

4.2

13

11

othergreen

16

green

0.9

24.3

0.0

100.0

68.6

7.6

23.7

27

11

othergreen

16

green

0.9

18.0

0.0

100.0

72.0

28.0

30

11

othergreen

16

green

0.9

32.2

0.0

100.0

95.2

4.8


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5. Multiple regression analysis

Multiple regression analysis was performed to deter-mine the relative effect of parameters related to land useas 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 fromcity centre, SVF at 2 m height and measures of surfacecovering determined from fish-eye or aerial pho-tographs (Table 4).

5.1. Diurnal variations on a monthly basis

In order to verify/detect possible diurnal patterns inexplaining factors and to examine if the coefficient ofdetermination (R2) varied during the day, multipleregression analyses were developed for each hour on amonthly basis. The mean air temperature deviation for

each hour during clear/calm and cloudy/windy situa-tions respectively was used in the analyses. Resultsbased on data from May 1999, a month that included allweather types, are shown in Table 5. The results pre-sented in Table 5 are based on data from the three clas-sification methods (fish-eye or aerial photos). Asshown, the results were not affected much by thechoice of classification method. However, the classifi-cation made from aerial photographs (500m) usuallygave the highest R2 coefficients especially duringcloudy/windy situations.

Table 5 shows that the highest coefficients were foundduring the night and the lowest in the middle of theday, especially during cloudy/windy conditions, exceptduring winter. Lower air stability during daytime andcloudy, windy conditions is a possible explanation. Ingeneral, the explanation factor was greater duringcloudy and windy situations with a maximum coeffi-cient of determination of 0.72 at 0100 hours. The gen-eral pattern during windy and cloudy conditions is,first, that altitude explains most of the variance in tem-perature and, second, that different surface coveringsusually per cent built-up area (or with the fish-eyephoto classification, per cent impervious) explains therest (see Table 5). During clear and calm situations dis-tance from sea explains most of the temperature vari-ance, with SVF or surface cover (per cent vegetation orbuilt-up) explaining the rest. The best linear fit duringclear and calm situations occurs at 1400 hours(R2=0.70).

In summary the multiple regression analysis based ondata from each hour during a month shows that dis-tance from sea, sky-view factorand surface cover (percent built-up or impervious) generally explains most ofthe variance within the area during clear, calm situa-

tions.Altitude, surface cover(usually per cent built-upor impervious) and distance from sea explain most ofthe variance during cloudy, windy situations. In stable

conditions (clear and calm) the closest environment ismore important. For example, distance from sea, SVFand type of surface covering have more influence onthe temperature pattern.

5.2. Seasonal variation

Seasonal temperature variation was analysed with the18 months of data divided into four weather classes:A) clear and calm situations (2 octas and 3.3 m s1),B) clear and windy situations (2 octas and >3.3 m s1),C) cloudy and calm situations (6 octas and 3.3 m s1)and D) cloudy and windy situations (6 octas and>3.3 m s1).

The results are presented in Table 6 and indicate thattemperature variations are more dependent on weatherthan season. The best correlation was found for clear,calm conditions (A) independent of season. One majorexception to this is during winter daytime when thebest correlation (independent of characterisationmethod) is found during cloudy, windy situations (D).The likely reason is the small amount of data obtainedfor this situation (Table 2). Stronger relationships aregenerally found for night-time data. During clear, calm(A) and clear, windy (B) conditions distance from seaand surface coverare important. With cloudy, calm (C)weather, altitude is most important during daytime andat night surface cover. During cloudy, windy (D) con-ditions altitude explains most of the variance duringboth day and night and secondly surface cover exceptduring daytime in summer and spring when distance

from the sea is more important. The correlation is,however, very low in summer and spring duringcloudy, windy situations (D).


142

Table 4. Independent variables used in the stepwisemultiple regression analysis. Surface characteristicslisted in the table are derived from aerial photographs(scale 1:15 000). The percentage of each surface coverhas been calculated using a circle of radius 100 m and500 m from the temperature station. Surfacecharacterisations from the fish-eye photographs are

limited to the percentage of impervious and vegetatedsurfaces 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 (%)


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5.3. Single days

Earlier studies have shown a strong relationshipbetween air temperature and surface cover duringsingle days or groups of days with specific weatherconditions (e.g. Katayama 1992, Alcoforado 1998).Days representing specific weather conditions weretherefore selected from the 18-month database. Theselection was based on the weather conditions recordedat Sve airport (Figure 1). Air temperature data for aspecific hour (1200 h or 3 h after sunset) during a singleday together with data on surface covering determinedfrom the aerial photographs and fish-eye photographswere used in a multiple regression analysis. Generallythese results showed strong statistical relationships andhigh coefficients of determination. Table 7 shows theresults for the 500 m radius aerial photographs classifi-cation. Stronger correlations are more frequent at nighttime than during the day although the highest coeffi-cient of determination (R2=0.86) among these single

occasions is found during the day.

6. Analysis of variance test

The multiple regression analysis verified that surfacecover and SVF are important for governing air temper-ature variations. In order to test if air temperaturecould be differentiated on the basis of the aggregatedland use/land cover categories (Table 3), an analysis ofvariance was performed. Indirectly this was also a testof the temperature stations ability to represent specificcategories in the land use/land cover database (MasterPlan). The analysis of variance test was made separatelyfor each weather group. A normal distribution of thetemperature data was assumed and the physical proper-ties in each category were assumed to be similar whenperforming the analysis of variance test. The nullhypothesis tested was no significant differences intemperature exist between the categories and a 5 percent 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 (02, 35 or 68 octas)and two wind groups (3.3 m s1 or >3.3 m s1). Theresults, presented in Table 8, show that statistically


143

Table 5. Stepwise multiple regression analysis to determine the relative effect of land use/land cover and otherparameters on the temperature pattern for each hour during one month (May 1999). The analysis is based on datafrom fish-eye photographs (SVF) and aerial photographs with a radius of 100 m and 500 m respectively. In thetable the coefficient of determination (R2) is shown for the three different classification methods used and for twoweather groups (clear, calm; and cloudy, windy). Bold letters show the feature explaining the variance, i) firstfactor 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) anddistance from city centre (DC). For example, during clear, calm conditions, at 0100 h, the R2 value is 0.59 and percent 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 s1) (>6 octas and >3.3 m s1)

Hour R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2 R2

(SVF) i ii (100m) i ii (500m) i ii (SVF) i ii (100m) i ii (500m) i ii

0100 0.59 I36 DS51 0.55 DS27 S48 0.56 DS27 S48 0.64 A49 I64 0.66 A48 B64 0.72 A48 B620200 0.55 I32 0.52 DS26 S46 0.53 DS26 S46 0.46 A35 I46 0.56 A38 B53 0.60 A38 B510300 0.60 I30 0.51 DS24 S44 0.51 DS24 S44 0.60 A47 I60 0.63 A44 B61 0.66 A44 B590400 0.54 DS26 S46 0.51 DS28 S45 0.52 DS28 S45 0.27 A21 I25 0.32 A22 B31 0.33 A22 B290500 0.46 DS28 S41 0.43 DS29 S39 0.43 DS29 S39 0 52 A42 I51 0.55 A40 B53 0.58 A40 B510600 0.31 DS27 DC30 0.33 DS27 DC33 0.36 DS27 DC33 0.54 A46 I54 0.58 A45 B56 0.63 A45 V550700 0.38 S15 0.33 S16 DS25 0.30 S16 DS25 0.52 A48 I52 0.55 A46 B54 0.62 A46 V53

0800 0.31 S26 V31 0.28 S25 B28 0.33 S25 UV29 0.43 A43 I44 0.47 A41 B44 0.50 A41 B440900 0.18 A6 0.18 A4 DS9 0.16 B5 0.40 A38 V40 0.28 A33 B35 0.39 A33 B351000 0.23 S16 0.33 B15 S22 0.48 W21 B33 0.20 A15 DS17 0.19 A13 B16 0.22 A13 B161100 0.25 DS18 DC25 0.38 DS19 DC29 0.42 DS19 W33 0.15 A12 DS15 0.13 A10 DS13 0.16 A10 W141200 0.43 DS28 DC37 0.54 DS31 UV42 0.52 DS31 UV42 0.10 A7 DS10 0.10 A6 W9 0.13 A6 W91300 0.34 DS34 DC36 0.44 DS36 UV44 0.36 DS36 DC39 0.12 A8 DS12 0.13 A8 DS11 0.14 A8 DS111400 0.63 DS63 A65 0.70 DS65 UV70 0.65 DS65 W66 0.11 A10 DS11 0.09 A8 DS9 0.11 A8 V91500 0.14 A12 DS14 0.10 A9 DS11 0.10 A10 DS111600 0.34 DS23 S34 0.32 DS23 S32 0.40 DS23 S32 0.11 A10 DS11 0.09 A8 DC9 0.11 A8 V291700 0.36 DS22 S36 0.45 DS24 S36 0.44 DS24 S36 0.20 A19 DC20 0.29 A27 B29 0.29 A27 B241800 0.15 S15 V17 0.21 S13 B21 0.16 S13 B16 0.20 A20 DC20 0.24 A22 B24 0.24 A22 V361900 0.24 A6 DS15 0.34 V16 DS23 0.20 A1 DS20 0.34 A29 I33 0.39 A29 B38 0.40 A29 B452000 0.37 A21 0.55 V38 UV45 0.45 V33 A42 0.45 A34 I45 0.51 A34 B48 0.53 A34 B532100 0.51 I39 S46 0.58 DC31 0.55 V37 B47 0.53 A40 I52 0.59 A40 B55 0.59 A40 B342200 0.57 I41 DS54 0.59 DC28 DS46 0.59 V34 B46 0.30 A25 I30 0.37 A26 B36 0.38 A26 B45

2300 0.56 I41 DS53 0.55 B28 0.56 B30 0.47 A31 I44 0.53 A31 B48 0.55 A31 B490000 0.57 I39 DS52 0.54 S27 DS48 0.56 B28 0.50 A36 I47 0.55 A36 B49 0.61 A36


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significant temperature differences between the landuse/land cover categories, in general, are more frequentduring the night than during the day.

At night urban dense, other built-up and green areas

have, with few exceptions, different temperature pat-terns compared to the other categories (Table 8).During windy, clear nocturnal situations few differ-

ences exist between the categories and only urbandense and single houses are different from multi-familyand green. This weather group (windy, clear) is, how-ever, not common during the measurement period(Table 2).

Considering daytime in general, the analyses showedthat significant temperature differences between the


144

Table 6. Stepwise multiple regression analysis using seasonal data. The analysis is based on data from fish-eyephotographs (SVF) and aerial photographs with radii of 100 m and 500 m respectively. Data (temperaturedeviation 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 thefeature explaining the variance, i) first factor and ii) second factor. Bold letters are used for the followingparameters; 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 R2 R2 R2 R2 R2 R2 R2 R2 R2

(SVF) i ii (100 m) i ii (500 m) i ii

Autumn A 54 0.54 S17 I40 0.17 S17 0.17 S17 B 140 0.49 DS25 V38 0.27 DS25 UV27 0.25 DS25 C 400 0.31 V17 A25 0.27 A14 S22 0.28 V16 B22D 842 0.29 A23 V28 0.27 A23 B26 0.28 A23 V25

Summer A 110 B 29 C 472 0.30 A13 I22 0.23 A13 DC20 0.24 A13 DC20D 1382 0.17 A6 DS12 0.12 A6 DS12 0.12 A6 DS12

Spring A 122 0.54 DS47 V50 0.50 DC47 UV50 0.52B 152 0.42 DS28 S33 0.37 DS28 S33 0.38C 184 0.09 A9 0.09 A9 0.09D 1289 0.26 A20 DS24 0.26 A20 DS24 0.27

Winter A 0 0.14 A4 V10 0.09 A4 B9 0.15 I8 B12B 0 0.17 DC17 0.17 DC17 0.29 DC17 I29C 118 0.15 A9 V12 0.16 A9 B14 0.16 V10 B14D 470 0.40 A33 I37 0.41 A33 B39 0.42 A33 B39

Night (3 hours after sunset)N R2 R2 R2

(SVF) i ii (100 m) i ii (500 m) i ii

Autumn A 336 0.54 I31 DS45 0.53 DS26 B42 0.51 A26 S39B 54 0.49 DS49 0.49 DS49 0.49 DS49 C 438 0.35 I23 DS34 0.37 B21 DS35 0.35 DS20 B32D 540 0.47 A34 I42 0.48 A34 B44 0.46 A34 B41

Summer A 84 0.54 I31 DS45 0.53 DS26 B42 0.51 DS26 S39B 111 0.49 DS49 0.49 DS49 0.49 DS49 C 532 0.35 I23 DS34 0.37 I21 DS35 0.35 DS20 B32D 924 0.47 A34 I42 0.50 A34 B44 0.46 A34 B41

Spring A 483 0.59 I37 DS51 0.57 B26 DS44 0.57 B28 DS44B 0 C 459 0.34 I26 A31 0.36 DC21 B29 0.35 DC21 A28D 890 0.47 A33 I46 0.50 A33 B46 0.49 A33 B44

Winter A 332 0.50 DS35 I48 0.52 DS35 B49 0.51 DS35 B47B 54 0.30 DS30 0.37 DS31 V34 0.33 DS30 V33C 438 0.42 I29 A39 0.46 B24 A41 0.44 B25 A39D 540 0.38 A26 I36 0.38 A26 B35 0.38 A26 B35


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categories are more frequent during cloudy conditions,independent of wind speed. As seen in Table 8 the tem-perature recorded during daytime situations with 68octas is different in all categories a spatial temperaturepattern exists that corresponds to the land use/landcover map (Figure 3). The opposite is seen during clearand calm daytime situations where no statistically sig-nificant differences in temperature are recorded in thearea (Table 8). The amount of data used in these situa-tions is, however, much smaller since clear days do notoccur as frequently during the measurement period

(Table 2).The results from the analysis verified that weathercharacteristics are important for prediction of air tem-perature differences between the categories. The resultsconfirm that statistically significant temperature differ-ences exist between densely built-up areas, large openareas and green areas during windy and cloudy situa-tions both day and night. The categories urban dense,other built-up andgreen have a statistically significantdifferent temperature pattern during both day andnight while during the day multi-family and singlehouses are also different on the 5% level (Table 8).

A seasonal parameter was finally added in the analysisof variance test to see if there were any variationsbetween the different seasons. The analyses did notshow any major differences from the results presentedon a yearly basis.

7. Discussion

7.1. Parameters important for temperature

differentiation

The stepwise multiple regression analysis showed thatparameters related to surface cover are important for

governing temperature variations in the Gteborgurban area. Other important parameters were altitude,distance from sea and sky-view factor. The effect of dif-ferent surface coverings on the energy balance and con-sequently on the temperature pattern is more pro-nounced during clear and calm weather with high airstability. Windy and cloudy conditions smooth out

these differences, making other parameters, such as alti-tude and distance from sea (day) or surface cover(night), more important.

Several studies, performed in corresponding climaticzones, have pointed out the role of altitude duringcloudy weather conditions (e.g. Laughlin 1982,Thornes 1989, Postgrd 2000). A high correlationbetween altitude and air temperature for cloudy andwindy situations has also been reported by Bogrenet al. (2000) who presents data from 32 temperaturestations.

Land use/land cover parameters have also proved to bean 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 morn-ing car traverses in summer in Fukuoka, Japan showedthat artificial covering explained 63% of the air tem-perature variation (Katayama 1992), a value compara-ble with the general results presented in this paper.Alcoforado (1998) found that a parameter describingdistance to main streets and the product between thesky-view factor and percentage of built-up areaexplained 74% of the temperature differences in Lisbon

(Portugal) for the average of five nights with high-pres-sure conditions. These results are comparable with thecoefficient of determination of 0.78 that was calculatedin this study for a single day with similar weather con-ditions (Table 7).

Distance to city centre does not, however, explain alarge part of the variations in Gteborg. This is proba-bly due to the typical north European green city struc-ture found in Gteborg. Even though the site descrip-tion analysis revealed a progressive transition of landcharacteristics, with increasing greenery from theurban dense areas through multi-family and singlehouse areas (Table 3, Figure 3) the differences are prob-ably not large enough to create an effect that could beexplained by the parameter distance to city centre, i.e.other parameters related to land use and distance to seaare more important. The choice of representative mea-surement points is, of course, also important.

The results showed that the correlation was slightlylower when using longer data periods compared to sin-gle occasions or groups of days with the same weathertype (Alcoforado 1994). This points to the difficultiesof making generalisations based on specific data peri-

ods. Single measurements, during specific weather situ-ations, are often assumed to represent average condi-tions. The results in this paper are based on a large


145

Table 7. The determination coefficients (R2) from thestepwise multiple regression analysis performed on airtemperature data from a specific hour during a singleday together with data on surface covering determinedfrom aerial photographs with a radius of 500 m.Weather conditions: A) clear, calm; B) clear, windy; C)cloudy, calm; and D) cloudy, windy.

Weather Julian R2 Cloud cover Wind speedgroup day (octas) (m s1)

Day A 92 0.65 1 1.0(noon) B 79 0.86 2 6.2

C 73 0.50 8 0.0D 66 0.72 8 15.5

Night A 29 0.76 2 0.5(3 h after B 11 0.78 1 4.1sunset) C 80 0.76 8 1.0

D 129 0.82 8 5.2


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amount of data and the lower correlations are an effectof the large variation within each weather group sinceextreme events are mixed with more average days in the18-month data period.

Covariation between the chosen independent variablesmay cause problems in the analysis. The emphasis inthe present study was on the correlation between air

temperature and possible explaining factors, butcovariation still needs to be mentioned. Further inlandthe elevation increases and intuitively this indicatesproblems. A parameter that included a possible covari-ation between altitude and distance from coast was

tested as an independent variable and included in theregression analysis with little or no result (Eliasson &Svensson, unpublished results). The reason is found in


146

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 tableshows the five land use/land cover categories and these categories are also present vertically in the left column. Forexample, in the upper-left corner of the table the asterisks show that the temperature in category 1, urban dense, issignificantly different from that of the other categories at night. The temperature in category 2, multi-family, isdifferent from that in category 15 and 16 (other built-up and green) and so on. N is the amount of data used inthe test..

Land use/land cover category

Night Day

1 2 3 15 16 1 2 3 15 16

Calm and clear 1 * * * * * 3.3 m s1 02 octas 2 * *

3 *Night N=1102 15 *Day N=285 16

Calm and partly cloudy 1 * * * * * * * * 3.3 m s1 35 octas 2 *

3 *Night N=1857 15 * * *Day N=902 16

Calm and cloudy 1 * * * * * * * * 3.3 m s1 68 octas 2 * * *

3 * *Night N=1692 15 * * * *Day N=1173 16

Windy and clear 1 * * *> 3.3 ms1 02 octas 2 *

3 * * *Night N=164 15Day N=320 16 *

Windy and partly cloudy 1 * * * * *> 3.3 m s1 35 octas 2 * * *

3 * * * *Night N=1186 15 * * *Day N=2023 16

Windy and cloudy 1 * * * * * * * *>3.3 m s1 68 octas 2 * * *

3 * *Night N=2651 15 * * * * * *Day N=3982 16

Urban

dense

Mu

lti-family

Sing

lehouses

Other

built-up

Green

Urban

dense

Mu

lti-family

Sing

lehouses

Other

built-up

Green


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the network of measuring stations and in the landscapeitself. The large broad valleys (5001000 m wide) inGteborg make it possible to perform measurementson similar altitudes even further inland and the measur-ing network includes stations at different altitudes inboth inland and coastal positions (Figure 1). However,for further work a principal component analysis (PCA)

would rule out any possibilities of correlation betweenthe chosen independent variables.

7.2. Air temperature variations between land

use/land cover categories.

It is a well-known fact that clear and calm weatherfavours large temperature differences between areaswith different land uses. Both observations and the sta-tistical analysis confirmed this, but, more interestingly,the results showed that this was true also for cloudy

conditions (68 octas) for both day and night. Theseresults are especially interesting for planning consider-ations as cloudy and windy weather is a commonweather type in Sweden (Table 2; see also Postgrd2000). Large air temperature differences during cloudyweather have been observed and reported also byLaughlin (1982), Thornes (1989) and Bogren et al.(2000).

Schudo et al. (1997) reports that the effect of land useon temperature in Hokkaido, Japan, is greater duringwinter than in summer. By contrast, studies in Phoenix,

Arizona, show large temperature variations betweendifferent surface coverings during summer (Brazel &Johnson 1980, Martin et al. 2000) as well as autumn andspring (Selover 2000). Despite indications of a strongerrelationship for winter data the influence of seasoncould not be statistically confirmed in the presentanalysis. However, significant differences in tempera-ture between land use/land cover categories were foundfor both daytime and night-time data. Brazel &Johnson (1980) also report daytime fluctuations in tem-perature between different land use but could not showany statistical significance for their data.

For practical application it is important to rememberthat the analysis of variance test only gives informationabout the statistical significance of the differences andnot the actual differences. For example, measurementsshow large temperature differences between differentland use/land cover categories during clear and calmconditions (Table 1) but none of these differences isstatistically significant according to the analysis of vari-ance test (Table 8). This discrepancy can be explainedby the fact that clear and calm weather occurs at a lowfrequency 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 s1). Cloudyand windy situations, on the other hand, are muchmore frequent (daytime 49% and night-time 35%) and

the analysis showed that temperatures were statisticallydifferent for several of the land use/land cover cate-gories during these weather conditions. Both the aver-age and extreme condition might be important in prac-tical applications, and in order to be able to judgewhether differences are significant in any sense it isimportant to base the analysis on a combination of sta-

tistics and actual temperature observations.

7.3. Land use/land cover information

The basic requirements for a land use/land cover data-base that is to be used for urban climate purposes is (i)a suitable resolution of the categories and (ii) up-to-date information. Previous studies show that availabledatabases often lack these two requirements. Existingland use classes usually need to be aggregated intocoarser categories and the decision as to which classesto choose is difficult and the aggregation may induce

several errors (Shudo et al. 1997, Burian & Brown2002). Another problem is that available databases arenot always updated, such as the widely used AmericanUSGS database that is based on satellite data from the1970s (Brown et al. 2000). The results presented in thispaper showed that the Master Plan could be used as aland use/land cover database for an analysis of spatialurban air temperature patterns. The advantages of theMaster Plan are that the limited number of categories iswell defined and that the planning authorities continu-ously update the database. The site description analysisshowed that a minor regrouping of the original Master

Plan land use classes was necessary. The Master Planprimarily shows function, and the surface characteris-tics, important for temperature patterns, were found tobe similar for several categories. The regrouping, basedon a thorough examination of the characteristics at eachstation, resulted in the creation of two new categories:15 (other built-up) and 16 (green). The site descriptionanalysis and statistical analysis of temperature differ-ences performed on the five new land use/land covercategories showed that category 16 was uniform whilecategory 15 still showed variations of both surfacecharacteristics and temperature pattern after theregrouping. This scatter is considered to be a result ofthe wide range of different land uses that is included incategory 15 in Table 3. Figure 4 shows the area per-centage of Master Plan land use classes in category 15.The class Industries makes up more than 50% of thiscategory, followed by Recreation (25%). However,both these classes and the other three land use classes(Figure 4) are characterised by a large variation of sur-face cover ranging from open asphalt surfaces withlower building complexes to densely green areas orhigh-rise buildings (Table 3 and Figure 2d, 2e).Category 15 was basically created as a consequence ofthe first variance analysis, which showed few statisti-

cally significant temperature differences between thedifferent land use classes. However, only five stationsrepresent this category and, as it makes up 11% of the


147


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total Master Plan area, an increased number of temper-ature stations within this category would probably resultin a different division of the original land use classes. Theresults thus show the difficulties of classifying differenthomogeneous land use/land cover categories but also the

possibilities of successful aggregation.

8. Conclusion

This study has shown that land use and surface coverhave an important influence on the spatial air tempera-ture variation within an urban district during both dayand night and in all weather situations. Results showedthat the air temperature deviation was statistically dif-ferent for all land use/land cover categories duringcloudy conditions. As observations showed tempera-

ture ranges of up to 6.8 C (Table 1) during cloudy con-ditions and as this weather type is most common witha frequency of 59% and 50% during day and nightrespectively (Table 2), these results are of interest forurban planning. A future aim is to apply these results inurban planning through a combination of statistics andthe GIS model (Svensson et al. 2002). The site descrip-tion analysis confirms that the Gteborg urban districthas a classical European land use/land cover divisioncharacterised by an open and green structure. Theresults based on the five land use/land cover categories urban dense, multi-family, single houses, other-builtup andgreen can thus be easily compared and trans-ferred to other North European cities through the sitedescription methodology presented.

Acknowledgements

This study received financial support from the SwedishEnvironmental Protection Agency and the SwedishSociety for Anthropology and Geography. The authorsare grateful to Professor Sven Lindqvist and AssociateProfessor Bjrn Holmer for valuable comments on themanuscript and statistics. The Comprehensive Plan-

ning Department in the Municipality of Gteborgmade the Master Plan available for this study and MrsAgnetha Malm drew one of the maps. Colleagues and

family are also acknowledged for hosting Tiny-loggerequipment.

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