Spatial and Temporal Variations in the Surface Energy Balance in ...

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Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia ANDREW M. COUTTS, JASON BERINGER,

AND

NIGEL J. TAPPER

School of Geography and Environmental Science, Monash University, Melbourne, Victoria, Australia (Manuscript received 23 September 2005, in final form 28 April 2006) ABSTRACT Variations in urban surface characteristics are known to alter the local climate through modification of land surface processes that influence the surface energy balance and boundary layer and lead to distinct urban climates. In Melbourne, Australia, urban densities are planned to increase under a new strategic urban plan. Using the eddy covariance technique, this study aimed to determine the impact of increasing housing density on the surface energy balance and to investigate the relationship to Melbourne’s local climate. Across four sites of increasing housing density and varying land surface characteristics (three urban and one rural), it was found that the partitioning of available energy was similar at all three urban sites. Bowen ratios were consistently greater than 1 throughout the year at the urban sites (often as high as 5) and were higher than the rural site (less than 1) because of reduced evapotranspiration. The greatest difference among sites was seen in urban heat storage, which was influenced by urban canopy complexity, albedo, and thermal admittance. Resulting daily surface temperatures were therefore different among the urban sites, yet differences in above-canopy daytime air temperatures were small because of similar energy partitioning and efficient mixing. However, greater nocturnal temperatures were observed with increasing density as a result of variations in heat storage release that are in part due to urban canyon morphology. Knowledge of the surface energy balance is imperative for urban planning schemes because there is a possibility for manipulation of land surface characteristics for improved urban climates.

1. Introduction In 2003, 47% of the world’s 6.3 billion inhabitants were living in urban areas; in more developed countries, 75% of the population (1.2 billion) lived in urban areas (Population Reference Bureau 2003). As increasing numbers of people make their residencies in the heart of the industrial and commercial world, urbanization will continue to grow. Alterations to the natural environment, resulting from the physical structure of the city and its artificial energy and pollution emissions, interact to form distinct urban climates (Bridgman et al. 1995). These urban climates can often be undesirable, causing increases in air pollution and aiding the formation of urban heat islands (UHI). Urban warming can have substantial implications for air quality and human health (Stone and Rodgers 2001). Factors generating the UHI are believed to include emissions of atmospheric pollutants that increase longwave radiation

Corresponding author address: Andrew M. Coutts, School of Geography and Environmental Science, Monash University, Wellington Rd., Clayton, Victoria 3800, Australia. E-mail: [email protected] DOI: 10.1175/JAM2462.1 © 2007 American Meteorological Society

JAM2462

from the sky and/or increased absorption of shortwave radiation (depending on the pollutant), anthropogenic heating, reduced horizontal airflow due to increased friction, absorption and retention of energy from solar radiation due to canyon geometry, reduced longwave loss due to limited sky-view factor, and reduced evapotranspiration from vegetation removal, which is a natural cooling mechanism (Tapper 1984; Oke 1982; Stone and Rodgers 2001). Urban structure, intensity of development, and type of building material can also influence UHI intensity, which suggests that UHI may be more a product of urban design rather than, as commonly assumed, the density of development (Stone and Rodgers 2001). As a consequence, different urban land use types such as city centers, parklands, and various suburban residential areas produce distinct alterations in net radiation, heat storage, and sensible and latent heating, resulting in variable local climates (Fehrenbach et al. 2001). Melbourne, Australia, with a population of over 3.6 million, features UHI consistently throughout the year (Morris et al. 2001). In 1992, an automobile transect across the Melbourne region during the evening found a peak warming of 7.1°C in the central business district

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(CBD), with smaller peaks in industrial areas and the medium-density terrace housing in the inner northern suburbs (Torok et al. 2001). The Victorian state government, which oversees planning in Melbourne, released a document in 2002 titled Melbourne 2030 that is a long-term strategic plan to manage growth and change across metropolitan Melbourne and its surrounding region (Department of Sustainability and Environment 2002). Based on population trends, Melbourne’s population is estimated to increase by 1 million people over the next 30 yr, leading to an increase of around 620 000 households. Melbourne 2030 aims to achieve a livable, attractive, and prosperous city through planning toward a more compact city by increasing housing in established urban areas, particularly around activity centers (built-up centers for highquality development, activity, and living for the community) and establishing an urban growth boundary (Department of Sustainability and Environment 2002). Understanding the link between urbanization and urban climate, together with the impact of changes in land surface properties, such as the amount of impervious and vegetational cover, is very important. Thoughtful design of residential areas can purposefully alter the thermal environment and manipulate microclimates through alterations in the surface energy balance (Bonan 2000) and can aid in avoiding negative impacts and compounding extreme events such as heat waves. A number of studies have been conducted in urban areas over the last few decades that investigate the urban surface energy balance, particularly in suburban areas, though few have employed the use of multiple sites, especially within a single city. Comparisons among discrete urban land use types (residential, commercial, industrial) often show large differences in flux partitioning (Oke et al. 1999; Grimmond and Oke 1999). Substantial differences in the surface energy balance between urban and rural landscapes have also been well documented as rural vegetated surfaces are replaced by concrete, asphalt, and other impervious surfaces (Cleugh and Oke 1986). A study in Vancouver, British Columbia, Canada, that investigated the temporal variability of flux partitioning found that the partitioning of energy over spatial scales of tens to hundreds of meters varied because of the heterogeneity of the urban surface (Schmid et al. 1991). This was evident in the variability of turbulent flux measurements across different source areas, with differences as large as the average urban–rural flux contrast. Trends in flux partitioning in response to changes in surface cover can aid in reducing negative climatological impacts, which is evident in effects such as increased vegetation cover. In suburban areas, general patterns of flux partition-

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ing have been shown to be similar across many cities, and features such as precipitation and irrigation are important in driving the suburban climate (Grimmond and Oke 1995). Sensible heating and heat storage are generally the dominant fluxes during summer, and latent heating is small, largely influenced by water availability from precipitation, irrigation, and vegetation cover (Spronken-Smith 2002). Results from Basel, Switzerland, from a network of seven sites indicated that, as green space increased, latent heat fluxes became more dominant while the sensible heat and storage fluxes decreased. Also, the diurnal flux partitioning showed variations among the sites, with positive sensible heat fluxes during the night at the dense urban sites. The amount of energy going into storage varied throughout the day, particularly at the urban sites (Christen and Vogt 2004). Grimmond et al. (1996) also found that a higher vegetation cover increased latent heat flux while decreasing the partitioning of energy into sensible heat flux. However, the storage heat flux partitioning increased and the absolute magnitude of fluxes increased as a result of increased net radiation, resulting in slightly greater temperatures above the canopy (Grimmond et al. 1996). Given the potential changes in the size and density of Melbourne, we initiated a study to investigate the potential impacts of the implementation of the Melbourne 2030 planning strategy on local climate by examining the surface energy balance across a range of urban residential densities. We hypothesize that a more compact city, incorporating increased housing density similar to current designs and the further development of activity centers, will intensify the Melbourne UHI through changes in land surface cover—in particular, reduced vegetation cover. This may in turn conflict with the Melbourne 2030 vision statement of a more livable city. This study is one of only a few worldwide to measure surface fluxes simultaneously across multiple urban sites. A new and unique dataset was produced, allowing direct site comparisons and an increased understanding of the impact of changes in urban land use on local climate. Such knowledge should be used to generate appropriate planning tools/information and therefore should lead to informed sustainable planning decisions based on multiple benefits, such as increased density and improved climate.

2. Site characteristics a. Site selection and locations To investigate the differences in urban surface energy balance and climate across a range of housing den-

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FIG. 1. Site locations of Melbourne CBD and three urban sites (Armadale: HIGH, Preston: MEDIUM, and Surrey Hills: LOW) and one rural site (Lyndhurst: RURAL) on a Landsat image of the region (Department of Sustainability and Environment 2000).

sities, four surface energy balance sites were selected throughout the Melbourne region (37°49⬘S, 144°53⬘E), including three urban sites of varying housing density and one rural control site (pastoral land) (Fig. 1). Each of the urban sites was located in predominantly residential areas that are the likely focus of development through Melbourne 2030’s compact-city objective. The first urban site (“HIGH”) was a highly developed medium-density site with row (detached closed set) housing and flats, located to the southeast of Melbourne in Armadale [urban climate zone (UCZ) 3; Oke 2004]. The second urban site (“MEDIUM”) was located north of the Melbourne CBD in Preston and consisted of moderately developed low-density housing (UCZ 5). This site continues to be maintained as a long-term

urban flux site (Table 1). The third urban site (“LOW”) was also a moderately developed suburban residential housing site, but of lower density, located east of Melbourne in the well-vegetated suburb of Surrey Hills (UCZ 5). The rural site (“RURAL”) was located to the southeast of Melbourne in Lyndhurst, just outside the main metropolitan region, almost directly on the Melbourne 2030 urban growth boundary. The rural site was a semirural cleared grassed area (nonirrigated) used for farming purposes (horse grazing) (UCZ 7). The sites were operating for varying periods as indicated in Table 1. MEDIUM was operational for the entire observational period selected for the study (August 2003– August 2004). However, from March to May 2004, all four sites were operating simultaneously.

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TABLE 1. The study sites across differing housing density in Melbourne and the measurement details including housing density, the measurement system used, albedo ␣, height of instrumentation zm, roughness length z0, maximum height of roughness elements zH, mean building height zB, mean height-to-width ratio (H:W), mean wall-to-plan area ratio (W:P), the UCZ (Oke 2004), and the period of operation. HIGH

MEDIUM

LOW

RURAL

System

Eddy covariance

Eddy covariance

Eddy covariance

Bowen ratio

␣ zm (m) z0 (m) zH (m) zB (m) H:W W:P UCZ Operational

0.19 40 0.62 16 8.8 0.56 0.59 3 Dec 2003–May 2004

0.15 40 0.4 12 6.4 0.42 0.4 5 Aug 2003–Aug 2004

0.17 38 0.68 16 7.2 0.41 0.4 5 Mar–Jul 2004

b. Housing density, structure, and population Housing densities at each of the sites were graded using visual observations from both the ground and from the tower, aerial photography, and census data (Australian Bureau of Statistics 2001). Housing and population data for each of the urban study sites were collected from the census districts within a 1.5-km radius from each tower, with population and total dwellings largely determining the density of the sites (Table 2). HIGH had both a lower number of separate houses than MEDIUM and LOW and a higher number of flats, units, or apartments, which matched visual observations that HIGH was a much denser site. The region located near the HIGH site in Armadale is listed as a future major activity center in the Melbourne 2030 plan (Department of Sustainability and Environment 2002).

c. Surface cover fractions Because of the heterogeneity of the urban surface, we determined the nature of the surface types that may influence the partitioning of energy and hence local climate, which in turn gave further insight into the density of development. Two methods were employed to determine the surface characteristics, both of which used aerial photography covering an area of 500-m radius around each tower. Manual surface classifications were conducted by visually identifying the surface cover at intersections on a 10-m grid overlay. This method accurately determined the surface type but did not result in a high-resolution spatial coverage and required considerable time to conduct. A second method of classification was employed that used a geographical information system (GIS) and aerial photography. Following Kunapo et al. (2005), a method called expert classification was used with the multispectral classification decision rule using GIS software tools to determine

Arms at 3 and 5.2

7 Nov 2003–May 2004

the surface cover surrounding each tower (Fig. 2). Although this method gives good spatial coverage, the surface can sometimes be misinterpreted, because individual pixels from the aerial photos can have similar spectral signatures. In summary, it is clear that HIGH had the largest impervious cover and LOW had the highest vegetation cover and highest pervious cover (Table 3). Furthermore, the expert classification method also highlighted the differences in the rooftop color, which is important in influencing site albedos (Table 1). The classification gave a good approximation of the degree of urbanization and density at each of the sites, but the percentages of these surface covers contributing to the observed turbulent fluxes will vary over time with changes in the flux footprint (Schmid 1994). The spatial analysis using GIS methods as performed here will be linked to the turbulent fluxes in a forthcoming paper, which will yield a dynamic characterization of the surface with varying fetch. Other site characteristics important in influencing surface energy balTABLE 2. Information from the 2001 census on the average density per square kilometer of population, various type and number of dwellings at each of the urban sites, and overall density of dwellings from the census districts located within a 1.5-km radius from each tower.

Population No. census districts included Separate house Semidetached, row, or terrace house; townhouse; etc. Flat, unit, or apartment Other dwelling (e.g., caravan, dwelling attached to shop) Not stated Unoccupied private dwellings Dwelling density

HIGH

MEDIUM

LOW

3167 51 613 220

2939 38 872 104

2748 32 699 248

512 12

173 10

90 4

12 128 1495

6 83 1248

3 70 1113

FIG. 2. An example of the expert classification method used in determining surface cover fractions of a segment of an area within a 0.5-km radius surrounding the HIGH site.

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TABLE 3. Surface cover fractions for 500-m radius using both the expert classification method and the manual method and using an average of the two methods. Other refers to bare ground or swimming pools.

Expert classification Building area Concrete Road Vegetation (excluding grass) Grass Other Manual classification Building area Concrete Roads Vegetation (excluding grass) Grass Other Average Building area Concrete Roads Vegetation (excluding grass) Grass Other Total impervious Total pervious

HIGH

MEDIUM

LOW

0.44 0.06 0.11 0.21 0.17 0.01

0.44 0.03 0.13 0.29 0.11 0.00

0.36 0.07 0.09 0.27 0.17 0.02

0.47 0.11 0.12 0.19 0.10 0.01

0.45 0.06 0.13 0.16 0.19 0.01

0.42 0.04 0.08 0.31 0.13 0.03

0.46 0.09 0.12 0.20 0.14 0.01 0.67 0.35

0.45 0.05 0.13 0.23 0.15 0.01 0.62 0.38

0.39 0.06 0.09 0.29 0.15 0.03 0.53 0.47

ance partitioning are given in Table 1, including albedo, roughness lengths, height-to-width (H:W) ratio, and wall-to-plan (W:P) area ratio.

3. Surface energy balance measurements a. Techniques and instrumentation To study the surface energy balance in urban areas, a number of methodological considerations must be accounted for during measurement. In this study, the eddy covariance approach was used to examine the exchange rates of heat and moisture between the surface and the atmosphere at the urban sites (Baldocchi et al. 1988). The surface energy balance in urban areas is given by (Oke 1988) Q* ⫹ QF ⫽ QH ⫹ QE ⫹ ⌬QS ⫹ ⌬QA, where Q* is net radiation, QF is the anthropogenic heat flux, QH is sensible heat flux, QE is latent heat flux, ⌬QS is the storage heat flux, and ⌬QA is the advective heat flux. In urban studies, exchanges of energy and momentum are viewed as being between the atmosphere and the top of a volume of air encompassing the buildings and trees and other elements of the urban area being studied (Oke 1988). The three urban sites were established on preexisting tall telecommunications towers around Melbourne.

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This method constrained site selection, but careful consideration ensured that instrumentation was mounted at a height of zm ⬎ 2zH so that measurements would be taken in the constant flux layer above the height of the roughness sublayer z to avoid the influence of indi* vidual roughness elements (Rotach 1999; KastnerKlein and Rotach 2004) (Table 1). The measurement height also ensured that fluxes were representative of the local scale (102–104 m). In addition, sites were selected so that the land uses below and surrounding the towers were as homogeneous as possible so that the likely radiation and flux source area types would be similar (Schmid 1994). Each urban site recorded net radiation Q*, sensible heat flux QH, and latent heat flux QE, as well as temperature and humidity at the measurement heights given in Table 1. Instrumentation included a 3D sonic anemometer (CSAT3; Campbell Scientific, Inc.) to measure the three-dimensional wind velocities and either a krypton hygrometer (KH20; Campbell Scientific) or an infrared gas analyzer (LI7500; Li-Cor Biosciences, Inc.) to measure the turbulent fluctuation of water vapor. Data were recorded using a datalogger (CR23X; Campbell Scientific) at 10 Hz, and data were block averaged using 30-min intervals. Temperature and relative humidity were measured using a temperature and relative humidity sensor at 1 Hz and were averaged over 30 min (HMP45C; Campbell Scientific). The urban storage heat flux ⌬QS at the urban sites was not measured directly because of the range of ground surfaces (soil, concrete, asphalt) and was therefore approximated as a residual from the energy balance equation. Caution must be used when evaluating ⌬QS as a residual, because errors within the measurements accumulate in this term. In addition, recent work has shown that the convective fluxes of QH and QE may be underestimated by as much as 20% (Wilson et al. 2002), and so the residual value of ⌬QS should be seen as the upper limit of energy stored within the urban canopy. The net advective flux ⌬QA is difficult to determine and in this study, as in many others, is assumed to be negligible and has been ignored (Grimmond 1992; Grimmond and Oke 1995; Spronken-Smith 2002). The radiation balance was also measured at the three urban sites, allowing the albedo and surface radiative temperature to be calculated. Radiation instruments included a net radiometer (Q7.1; Radiation and Energy Balance Systems, Inc.), an albedometer (CM 7B; Kipp and Zonen, Inc.), and a net pyrgeometer (CG2 or 2 ⫻ CG4; Kipp and Zonen). The Bowen-ratio technique (Bowen 1926) was employed at the rural site using a commercially available system (BR023; Campbell Scientific). Temperature and

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vapor pressure gradients were measured across a distance of 2.2 m. Net radiation and ground heat flux QG were determined directly using a net radiometer (Q7.1) and ground heat flux plates (HFT3; Radiation and Energy Balance Systems) and soil temperature thermocouples (TCAV; Campbell Scientific). Wind speed and direction were also measured at the site (Wind Sentry; R. M. Young Co.). Fetch was greater than 1 km in each direction over nonirrigated pasture. Although an intercomparison of the eddy covariance and Bowen-ratio systems was not conducted for this study they have previously been found to be within 10%–15% (Beringer and Tapper 2000). Eddy covariance measurements were corrected for oxygen absorption (Tanner et al. 1993) and density effects (Webb et al. 1980), and data underwent strict quality-control measures. Discarded and missing data were gap filled using a neural-network procedure. The radial basis function (RBF) neural network was used, in which gaps were filled by drawing on available data such as temperature and solar radiation from nearby stations or the Monash University weather station (www.arts.monash.edu.au/ges/research/climate/ weather). The quality-controlled available data for each station were used to train the RBF network for the corresponding site’s dataset, along with the site-specific available energy balance data for the station. The RBF network tests the results and outputs an estimate value for each 0.5-h period that was either missing or screened out. The results showed an acceptable performance in generating the missing data (data not shown).

b. Anthropogenic heat release QF Anthropogenic heat QF is an added source of heat to the energy balance that is unique to urban environments and aids the development of the UHI (Oke 1988). Therefore, QF needs to be accounted for; however, it cannot be measured directly with the eddy correlation technique. An estimate of QF for this study was calculated following Sailor and Lu (2004) and utilized sources of somewhat readily available data. The majority of anthropogenic heat comes from three main sources: vehicles (QV), the building sector (QB), and human metabolism (QM), as represented by the equation QF ⫽ QV ⫹ QB ⫹ QM. We employed a simplified version of the Sailor and Lu (2004) model, because the same detailed databases were not available for Melbourne. Variations in QV, QB, and QM among the urban sites were determined primarily as a function of population density, computed for each site (section 2a).

For the estimation of heat release from vehicles, data were collected from the Australian Bureau of Statistics (2003) from a vehicle survey during November 2002– October 2003. During this period, 31 538 ⫻ 106 km were traveled in the year in Melbourne (7693.6 km2). Given a population of 3 471 625 from the 2001 census (Australian Bureau of Statistics 2001), an estimated value of 24.89 km was traveled per person per day. Hourly values of QV were then determined using the following equation (Sailor and Lu 2004): QV 共h兲 ⫽ pcDVD ⫻ Ft 共h兲␳pop ⫻ EV, where pcDVD is per capita daily vehicle distance, Ft (h) is the hourly fraction of total daily travel, ␳pop is the population density, and EV is the energy release per vehicle, in this case estimated at 3866.7 J m⫺1 for Melbourne vehicles. We did not have detailed traffic counts to construct temporal traffic profiles, and so we determined the hourly fraction of daily traffic Ft (h) using an average for U.S. cities (Hallenbeck et al. 1997), which was assumed to be similar in Australia. Heat release from buildings QB in the residential areas of Melbourne is due primarily to the consumption of electricity QBE and natural gas QBNG, where QB ⫽ QBE ⫹ QBNG. For electricity, half-hourly demand data (MW h) were obtained from the National Electricity Market Management Company for 2001. Only the percentage of electricity used directly in heating was accounted for, with space heating at 4.3%, space cooling at 2.8%, water heating at 26.4%, and cooking at 9.6%, giving a total of 43.1% of electricity that was converted to heat (National Appliance and Equipment Energy Efficiency Committee 1998). The remaining 56.9% was used in refrigeration, lighting, and appliances, where heat is generated but is a small by-product. For natural gas, only daily consumption data were available from the Victorian Energy Networks Corporation for 2001. Data were converted to per capita consumption using an assumed thermal combustion efficiency of 80%. To estimate the diurnal heating profile, the diurnal variability in natural gas consumption was modeled as a function of the daily range in temperature (Sailor and Lu 2004) using mean maximum and minimum temperatures obtained from the Bureau of Meteorology and a linear interpolation between the minimum and maximum temperatures, occurring at approximately 0700 and 1700 LST. This method has been shown to be an accurate estimate for the diurnal natural gas heating profile (Sailor and Lu 2004). The human metabolic rate was approximated at 75 W for the nighttime hours (2300–

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FIG. 3. (left) Diurnal profile (LST) of the contributing sources of anthropogenic heat (where QV ⫽ vehicles, QBE ⫽ building electricity, QBNG ⫽ building natural gas, and QM ⫽ human metabolism) at MEDIUM and the resultant total of QF for July during the Southern Hemisphere winter, and (right) the average monthly variation in QF at each of the urban sites.

0500 LST), 175 W for daytime hours (0700–2100 LST), and 125 W for transitional periods (0600 and 2200 LST) (Sailor and Lu 2004). The QM was then calculated for each site from the population density. Figure 3 presents the diurnal (LST) course of the different contributions of estimated QF for each component at MEDIUM for July (Fig. 3, left panel) and the annual variation in QF for the three urban sites (Fig. 3, right panel). This method gives a good approximation of QF for each of the study sites, but it is recognized that there are a number of assumptions made in the method. In particular, the methods assume that spatial population densities are constant across all time scales, traffic profiles are similar for weekends and weekdays, and there is no lag between electricity consumption and resultant heat release. Nevertheless, the results give an excellent picture of the relative contribution of QF to the total energy balance diurnally and seasonally and show that anthropogenic heating is higher in winter, primarily as a result of increased natural gas consumption. In this study, QF is similar to the range of values found in previous studies for suburban areas (Klysik 1996; Grimmond 1992; Khan and Simpson 2001; Kalma and Newcombe 1976).

4. Results and discussion a. Temporal variations in the surface energy balance The magnitude of Q* was highest during the austral summer when solar radiation input was at its greatest and the magnitudes of QE, QH, and ⌬QS followed the monthly course of available Q* throughout the obser-

vational period (Fig. 4). The QH was the dominant flux during the summer period as a result of strong surface heating, the effect of which decreased as winter approached. The magnitude of QE was low at the urban sites throughout the year in comparison with previous studies because of low moisture availability, resulting from less irrigation because of domestic water use restrictions in Melbourne during the study period and several years of drought (Nicholls 2004). Despite poor summertime moisture availability, the summer months showed greater QE because of higher Q* and greater rates of transpiration. The daytime evaporative fraction QE/Q* (Fig. 5) in this study (0.3 in July 2004 and 0.19 in January 2004) was smaller than that seen in suburban Vancouver (0.46 and 0.3 for winter and early summer, respectively) because of differences in irrigation (Grimmond 1992). This resulted in high Bowen ratios at the urban sites, with mean monthly summer values of greater than 2 and daily values often in excess of 5 (Fig. 5). As a result, summertime climates across Melbourne were often very warm and dry. This has the potential to produce conditions that are unfavorable for urban dwellers, likely leading to the development of intense UHI and putting at risk those inhabitants who are particularly vulnerable to extreme heat (Schär et al. 2004). During winter, QE/Q* generally increased and was influenced by the increasing moisture at the sites from rain-bearing weather systems. Reduced Q* limited the magnitude of surface heating and the sensible heat fraction (QH/Q*), reducing Bowen ratios (Fig. 5). Surface heating was reduced, but the role of anthropogenic heating became more pronounced during winter (Fig. 3). Greater heat release largely from natural gas com-

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FIG. 4. Comparison of mean (⫾std error) monthly daytime (1000–1600 LST) surface energy balance components (Q*, ⌬QS , QE , and QH) across each of the sites.

bustion for heating resulted in a greater contribution toward measured Q H at the urban sites, varying throughout the day but particularly significant during the early morning, evening, and throughout the night. The mean contribution in winter of anthropogenic heat to QH was 30% during the day at MEDIUM and was much higher at other times. High temperatures were not a cause for concern during winter, but results indicated that the contribution of QF was likely to be the main driver of winter UHI. The daytime partitioning of energy into ⌬QS was similar throughout the year, with a slight increase during winter (Fig. 5). Despite a reduced Q*, the ability for heat conduction through the surface (roads, buildings) was still significant because the surface properties and form of the urban surfaces allowed continued heat conduction into the ground, as did the storage of energy within the urban canopy layer, while more moisture also increased soil thermal conductivity. The reason for the slight increase may also have been due to the syn-

optic conditions, with a more stable atmosphere (as seen in reduced friction velocity u values in winter) * resulting in smaller turbulent fluxes. Because ⌬QS was calculated as a residual of the energy balance, this condition resulted in a slight increase. Spronken-Smith (2002) also found ⌬QS to increase in winter in Christchurch, New Zealand, where often a strong nighttime surface inversion persisted into the morning and suppressed turbulence and hence limited QE and QH. This was in contrast to the results of Grimmond (1992) in Vancouver, where, using the Objective Hysteresis Model (Grimmond et al. 1991), ⌬QS /Q* was observed to increase as summer approached, resulting from increased dry surface conditions, with less intense atmospheric heating and QH fraction, leading to greater heat storage.

b. Urban–rural contrasts Large differences were observed in the surface energy balance between urban and rural landscapes, typical of previous urban–rural comparisons (Cleugh and

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FIG. 5. Mean (⫾std error) monthly daytime (1000–1600 LST) partitioning of energy into the energy balance components and the Bowen ratio for each of the sites.

Oke 1986; Christen and Vogt 2004). The main contrast was the greater QE at RURAL as a result of the greater vegetative cover and increased water storage in the soil (Fig. 4). The differences increased in the wetter months as the soil moisture increased in contrast to the impervious surfaces in the urban area that dispersed water away from the region through the storm water network. Large differences were also seen in the heat storage with a twofold–threefold increase in ⌬QS at the urban sites in comparison with RURAL. The magnitude of ⌬QS was influenced by the amount of impervious surface cover because common urban materials typically have a higher thermal conductivity and heat capacity than natural materials and the 3D urban canopy also stores significant amounts of energy. However, QH at RURAL was as high as at the urban sites in the summer months, in part because of the low QG. RURAL itself was very dry during this period as a result of the drought conditions. Therefore, early summertime Bowen ratios were not much lower than at the

urban sites, yet once Q* began to decrease into the winter months and precipitation increased, QE became much larger and QH considerably decreased relative to the urban landscapes. In addition, Q* was lower at RURAL because of the absence of radiation trapping within urban canyons. Urban–rural contrasts were not significant during the summer because RURAL was located in a nonirrigated pasture land, yet more forested vegetation may have yielded stronger contrasts. Nevertheless, two major driving forces were still significant for UHI formation: 1) the large difference in ⌬QS (QG) and subsequent nighttime heat release and 2) the added source of QF in the urban landscape.

c. Housing density and variations among urban sites If one looks more closely at the differences among the surface energy balances across the range of housing densities, one can assess more-detailed information about important site characteristics and their influence

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on energy partitioning that could potentially help urban planners in improving local climates. The Q* varied slightly between the sites as a result of differences in the radiation balance and was influenced largely by the albedo and H:W ratio (Table 1). LOW had an albedo of 0.17 and had a Q* similar to RURAL. This was due to a lower impervious cover and H:W ratio, which would have limited the amount of radiation trapping. MEDIUM had a similar H:W ratio yet had a lower albedo (0.15) because of the roof color and increase in building area (observed from the expert classification). However, despite increased H:W and W:P (refer to Table 1 for definitions) ratios at HIGH, the albedo was highest (0.19). This was likely due to a large amount of light-colored concrete and corrugated iron used as rooftop materials at the site. Hence, MEDIUM generally showed the highest Q* throughout the year. Despite the greater albedo at HIGH, Q* was similar to MEDIUM. Analysis of the radiation budget measured by the radiation instruments showed a 5%–6% higher net longwave radiation at HIGH relative to MEDIUM, consistent with an increase in H:W and also perhaps urban factors such as air pollution (higher emissivities) and increased sky temperatures (Arnfield 2003). The major difference in energy partitioning among sites of varying density was seen in contrasting ⌬QS values (Fig. 4). In general, increasing urban density and surface cover resulted in higher heat storage. At HIGH, heat storage was greater than that seen at LOW, despite a higher albedo, because of the greater trapping and storage of energy in the urban canopy. At LOW, the combination of a lower building and impervious surface cover and less complex canopy resulted in much reduced ⌬QS, with values approaching those observed at the rural site. The reason that ⌬QS was the highest at MEDIUM was the low albedo from darker rooftop surfaces, which effectively increased the amount of solar radiation absorbed by urban buildings and structures (Taha 1997) despite similar H:W and W:P ratios to LOW. Therefore, although the increase in density and built-up surfaces of urban areas contributed to increased ⌬QS from a greater urban surface area for both absorption and trapping of energy within the urban canyons, the albedo was also highly important in influencing the heat storage. The differences among the convective fluxes across the urban sites were smaller than anticipated (Fig. 4). There appeared to be no clear relationship between QE/Q* (Fig. 5) and urban characteristics of vegetation cover or housing density (Tables 2 and 3) as might have been expected. The relative partitioning into QE among the urban sites was similar and was generally small across all sites because of low moisture availability.

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LOW generally showed the highest QE/Q* (except in March) because of the higher pervious surface cover and vegetation. However, a greater QE/Q* was observed at HIGH relative to MEDIUM despite a slightly lower vegetation cover. Differences could be attributed to differences in irrigation rates or vegetation types. Vegetation types at HIGH and LOW were more exotic, including many deciduous trees, relative to MEDIUM, where vegetation was dominantly native evergreen species, which were likely to transpire at a lower rate but throughout the year require less irrigation. The partitioning of available energy was similar at each of the sites as illustrated by the Bowen ratios (Fig. 5). In general, MEDIUM had a higher Bowen ratio (although the magnitude of QE and QH was not the highest). In comparison with North American suburban sites at similar latitudes (Grimmond and Oke 1995), the values for mean daytime Bowen ratios were high, especially during summer, leading to warm and dry climates across all urban surfaces. The role of vegetation in influencing QE in the urban environment was minimal across the small range of vegetation covers measured in this study (0.21–0.29). This led to QH being the dominant flux, indicating that manipulation of urban form and structure in climate control may be important in Melbourne. However, varying vegetation cover outside the range measured here could also influence local climate. In either case, the amelioration of the hot/dry climate by vegetation is likely limited by water availability. Initiatives such as rainwater tanks could allow irrigation and improve climate. It is evident from this study that the nature of surface properties (albedo and thermal admittance) and canopy structure (H:W and W:P ratio) were particularly important in controlling the available energy for the convective fluxes. We were not able to measure over a truly high density inner urban site for logistical and methodological reasons, and therefore we may expect the differences to be much greater over the full range of urban densities in Melbourne.

d. Diurnal variations in the surface energy balance There was generally high day-to-day variation in the magnitude of the fluxes and timing of peak fluxes because of variations in source area, synoptic conditions, and water availability associated with precipitation events or irrigation. Averaged over longer time scales, general trends in flux partitioning can be seen and differences among urban areas emerge (Fig. 6). The contributions to the fluxes averaged over longer periods become increasingly dominated by the prevailing wind directions. The Melbourne region and hence all the sites examined here are subject to a land–sea breeze

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FIG. 6. Mean (⫾std error) diurnal surface energy balance for March 2004 at each of the sites (LST). Anthropogenic heating is not included because the contribution is small (see Fig. 3).

year-round, and so seasonal variations in source areas are not that evident. The discussion that follows focuses on two months (March and May) during which all four stations were operating. Though these particular months reside in the Southern Hemisphere autumn, a good picture of the seasonal differences in radiative and energy flux partitioning between drier and wetter climate regimes can be seen. The diurnal partitioning of energy during March 2004 at the end of summer (mean daily maximum temperature of 23.8°C) was characterized by small QE at the urban sites (Fig. 6) due to the summertime water balance deficit and to the impervious nature of the surface and associated runoff. The QH and ⌬QS were the dominant fluxes, showing a hysteresis pattern in ⌬QS with a peak that preceded the peak in Q* by 1–2 h (Grimmond et al. 1991). An asymmetrical pattern in QH was also evident as a greater proportion of energy was partitioned into ⌬QS in the morning until the maximum heat storage capacity was reached, at which point QH became dominant. Christen and

Vogt (2004) suggested that in the morning ⌬QS was a result of urban materials and sunlit area whereas in the afternoon complete aspect ratio ␭C (the complete three-dimensional built-up surface area) was the dominant control. This may explain the early peak ⌬QS at LOW as a lower built-up surface area (H:W) reduced the ability for heat to be absorbed as the day progressed and led to the higher values of QH in March (Figs. 4 and 5). Results also showed that QH remained positive into the early evening, despite Q* becoming negative, being supported by a strong negative ⌬QS. This feature increased with increasing density because of the features of impervious surface cover and canopy H:W ratio and W:P ratio (Table 1), which restricted longwave cooling and maintained high surface temperatures into the evening (Table 4). Positive QE fluxes were also observed for most of the night at the urban sites, and the magnitude was found to be the highest at LOW with the largest vegetation cover (Table 3). This was likely to be

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TABLE 4. Measurements of daytime and nighttime surface and above-canopy air temperatures T for 1-week-long periods during March and May 2004. Values were calculated over a period during which all instruments were fully operational. Mean and standard error are shown. Surface temperatures were calculated from outgoing longwave radiation. March 2004 (yeardays 62–68)

Daytime (1000–1600 LST) HIGH MEDIUM LOW Nighttime (2200–0400 LST) HIGH MEDIUM LOW

May 2004 (yeardays 146–152)

Tsfc

Tair

Tsfc

Tair

31.2 ⫾ 0.6 31.7 ⫾ 0.6 30.7 ⫾ 0.6

22.4 ⫾ 0.6 22.1 ⫾ 0.6 22.8 ⫾ 0.6

17.4 ⫾ 0.2 15.4 ⫾ 0.2 14.2 ⫾ 0.2

12.6 ⫾ 0.2 12.0 ⫾ 0.2 12.3 ⫾ 0.2

19.9 ⫾ 0.3 19.5 ⫾ 0.3 18.7 ⫾ 0.3

17.5 ⫾ 0.4 16.7 ⫾ 0.4 17.2 ⫾ 0.4

12.6 ⫾ 0.3 10.3 ⫾ 0.2 9.5 ⫾ 0.2

10.3 ⫾ 0.2 9.3 ⫾ 0.2 9.9 ⫾ 0.2

supported by energy release from storage that evaporated moisture that had been made available by restricted irrigation with watering systems between 2000 and 1000 LST. Hence, the influence of housing density and vegetation cover on climate may be more evident at night. The contrast in energy partitioning was again greatest between the rural and urban sites. The QE was the dominant flux at RURAL with small substrate storage (QG) relative to urban ⌬QS . At RURAL, the peak in QE preceded the peak in Q* as moisture available at the surface was initially evaporated. These patterns in partitioning between both the urban and rural sites and the urban sites themselves contribute to the spatial variations in the Melbourne UHI intensity and its peak in the early morning as previously observed by Torok et al. (2001) and Morris et al. (2001). During the early winter month of May 2004 (Fig. 7) (mean daily maximum temperature of 16.6°C), patterns in diurnal partitioning were similar to those in March, though variations in absolute fluxes among the sites were smaller because of reduced Q*. However, the importance of QF increased as winter approached, becoming a significant contribution to QH particularly during the night, morning, and evening, causing greater atmospheric heating than may otherwise have been. The QE became more dominant during the day because of greater moisture availability and continued as a positive flux at night while QH became negative as a result of the reduced surface temperatures and turbulence. This would not necessarily result in a reduced UHI intensity because differences in partitioning between urban and rural sites were still significant and enhanced by a greater urban QF . The QG at RURAL was positive for a shorter period of time relative to the urban sites because of a differential effect at a lower solar zenith angle, whereby the three-dimensional structure of the urban regions permits higher radiation trapping and greater ⌬QS. The QH and QE were unexpectedly also slightly positive at the rural site during the night, pos-

sibly as a result of generally cloudy skies and fog that would have reduced surface cooling.

e. Local climate development and management The radiation and energy exchanges above the urban canopy can subsequently lead to distinct boundary layer development and local climates. Urban planners who may wish to incorporate climate knowledge into metropolitan planning schemes such as Melbourne 2030 will be largely concerned with thermal comfort (mainly temperature related) and addressing ways in which to alleviate heat stress that may be exacerbated by the UHI. The ultimate influence of differences in the surface energy budget translates into local climate, and we have calculated mean above-canopy air temperatures and derived surface radiative temperatures to examine any differences (from outgoing longwave radiation and assuming an emissivity of 0.95 for all sites) (Table 4). During March, daytime radiative surface temperatures increased with increasing ⌬QS because of more energy absorbed by the surface, which in turn restricted the absolute QH. Therefore, above-canopy air temperatures would be reduced by lower QH, but we observed little difference in air temperatures among sites because of the effective mixing of the atmosphere over the rough urban landscape. This process often leads to the sometimes-observed urban cool islands, though the heat island is still consistently seen in the urban boundary layer (Oke 1995). Of interest is that air temperature was anticorrelated with surface temperature, and this illustrates the dual impact of increasing urban density: 1) increased density allows greater admittance of energy into the substrate and hence higher surface temperatures and 2) increased density will generate increased surface roughness and enhanced vertical mixing of heat and will thereby reduce the above-canopy air temperatures. It is important to note that although the above-canopy air temperatures show little difference the greatest differences are expected to be found

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FIG. 7. As in Fig. 6, but for May 2004.

at street level, where inhabitants will feel the difference (Svensson and Eliasson 2002). Therefore, an increase in density would result in decreased thermal comfort and could have more serious health implications if coupled with heat waves and extreme temperature events. During the night, density-related patterns were more pronounced, with surface temperatures increasing with increasing densities. This was driven by the greater release of ⌬QS and maintenance of positive QH into the evening (Fig. 6), which increased with increasing density as found by Christen and Vogt (2004). The slower rate of energy release at HIGH was likely to be associated with urban canyon differences of greater H:W and W:P ratios and a lower sky-view factor, as well as the flux of QF . This pattern extends through to May and also then extends into the day. Increasing withincanopy air temperatures would be expected with increasing density because of changes in the morphology of the buildings. For example, Kusaka and Kimura (2004) found that higher-density sites decreased the sky-view factor, resulting in higher surface tempera-

tures at night, and aided the development of the UHI. Sakakibara (1996) found that the surface temperature of an urban street floor cooled more slowly than a parking lot, because of the geometry of the urban area. In our study, surface radiative temperatures (Table 4) were modified primarily by the partitioning of energy into storage and the subsequent release into the atmosphere at night (Fig. 5) and therefore supported higher surface temperatures at HIGH. Introducing larger amounts of vegetation cover into suburban neighborhoods and increasing the albedo of the surface to improve thermal comfort have often been proposed in previous studies (Rosenfeld et al. 1998). An increase in albedo and vegetation cover can generally reduce ground-level temperatures and would be expected to improve thermal comfort as seen at LOW in this study. However, during winter, the morphology of the surface (H:W and W:P) was a more important feature that requires further investigation, in particular addressing the modification of local climate at street level. Across the sites we examined in Mel-

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bourne, QE did not respond significantly to changes in vegetation cover, although had unlimited irrigation rates been allowed differences may have become more pronounced and relationships may have become apparent between vegetation cover and QE, as seen in some North American studies.

5. Conclusions Investigating current partitioning of energy into the surface energy balance and the resulting climates across a range of housing densities allows a broad estimation of likely climatic impacts resulting from increasing urbanization in Melbourne as directed in the Melbourne 2030 vision. The results from this study suggest that a move toward a more compact city with built-up activity centers would result in a larger heat storage fraction because of changes in the surface characteristics through reduced albedo and less vegetated cover, but more so through increased built-up surface area (e.g., H:W ratio). This would raise urban surface and withincanopy temperatures, leading to unfavorable conditions, in particular for those with increased vulnerability to excess temperatures, thereby contradicting the goals of Melbourne 2030. The low evapotranspiration at all sites as a result of water restrictions led to high Bowen ratios during the summer months. A move toward a more compact city will extend the seasonal exposure to unfavorable climatic conditions, with warmer temperatures expected in the shoulder months on either side of summer. In addition, diurnal exposure will also increase with warmer temperatures continuing into the evening, because of increasing built-up surface area (including walls) and increased storage. As a result of these findings, improvements in climate at activity centers could be made through strategies such as reducing the available energy by incorporating the use of lighter-colored building and roofing materials to increase albedo. The highest observed heat storage at MEDIUM was driven by a lower albedo despite a low H:W ratio, demonstrating that higher albedos can be a significant option for reducing energy storage. Also, the integration of rooftop gardens at activity centers to increase the evaporative fraction and the establishment of within-canopy vegetation would help to reduce surface temperatures and Bowen ratios. However, this option may be limited to the nighttime hours in its effect because Melbourne now employs permanent water restrictions, and so natural evaporative cooling from vegetation would be small without daytime irrigation. Initiatives such as onsite water-saving methods (rainwater tanks) may allow greater provision of irrigation and increase daytime evapotranspiration. En-

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ergy-saving strategies would be beneficial for reducing summer anthropogenic heat flux; however, the increased relative contribution of QF in the winter is not likely to be seen as a negative impact. Although a more compact city with high-density activity centers is the plan for Melbourne and the increase in total built-up surfaces cannot be changed, these suggested strategies could easily be incorporated into the planning framework to improve local climates. The investigation of local-scale fluxes through the eddy covariance technique gave a good indication of the exchanges between the top of the canopy and the atmosphere and of boundary layer climate, but the within-canopy temperatures and climate are of particular interest for thermal comfort levels. To identify further the driving forces of urban climates through the surface energy balance, temperature analysis within the urban canopy would be beneficial as well as further characterization of the urban canyon. The patterns of temperature and wind within the urban street canyons will be different from those above the canopy, and this area of research needs further work. It is clear from the difference between the urban and rural sites that the potential range of surface energy partitioning and air temperatures is large and that urban dwellers will experience a range of climates across different areas of the city. In this study, sites were chosen to represent areas of low-, medium-, and high-density housing. Because of the difficulty of finding a site in a truly high density area, we likely underestimate the impact of a high-density site. The impact of increased density also assumes that densification follows current urban configurations, and the impact could be amplified or reduced if alternate designs are considered. We were also unable to quantify the energy balance over the Melbourne CBD, which would likely display a distinct climate itself. Coupled land surface–atmosphere modeling of the Melbourne region, incorporating the features of the Melbourne 2030 plan and knowledge acquired from the observational results presented in this study, would be useful to determine particularly vulnerable areas around Melbourne and to examine further the potential impact of the Melbourne 2030 key directions across the entire city. Indeed, if the modeling results were in good agreement with above-canopy flux observations, screen-level temperatures could be modeled with a large degree of confidence and climate impacts associated with increased urbanization and planning management directions could be assessed and recommendations reported to the appropriate authorities. If it is possible to improve the urban planning directions by employing climatic strategies that work with the key

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directions of Melbourne 2030, the Victorian state government can help to achieve its vision of Melbourne as one of the most livable, attractive, and prosperous areas in the world for residents, business, and visitors. Acknowledgments. Thanks are given to Peter Wallace of P. G. Wallace Communications and Geoff Syms of Comgroup Australia for assistance and permission of the use of their communications towers and to Miduuri and her family for use of their land for the rural station. Thanks also are given to field assistants Christopher Barker and Jamie Spellman and rigger Phil Peacock for their valuable contribution in the setting up and maintenance of the research towers. The loan of instrumentation by Lindsay Hutley (Charles Darwin University) and Russell Jaycock (James Cook University) is also greatly appreciated. Thanks are also given to Dr. Helen Cleugh for input during the early stages of project development. REFERENCES Arnfield, A. J., 2003: Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol., 23, 1–26. Australian Bureau of Statistics, 2001: 2001 Census of Population and Housing. CDATA 2001 Australia, MapInfo CD-ROM. ——, 2003: Survey of motor vehicle use: Data cubes, Australia, 01 Nov 2002 to 31 Oct 2003. Catalog No. 9210.0.55.001. Baldocchi, D. D., B. B. Hicks, and T. P. Meyers, 1988: Measuring biosphere–atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69, 1331– 1340. Beringer, J., and N. J. Tapper, 2000: The influence of subtropical cold fronts on the surface energy balance of a semi-arid site. J. Arid Environ., 44, 437–450. Bonan, G. B., 2000: The microclimates of a suburban Colorado (USA) landscape and implications for planning and design. Landscape Urban Plann., 49, 97–114. Bowen, I. S., 1926: The ratio of heat losses by conduction and by evaporation from any water surface. Phys. Rev., 27, 779–787. Bridgman, H., R. Warner, and J. Dodson, 1995: Urban Biophysical Environments. Oxford University Press, 152 pp. Christen, A., and R. Vogt, 2004: Energy and radiation balance of a central European city. Int. J. Climatol., 24, 1395–1421. Cleugh, H. A., and T. R. Oke, 1986: Suburban–rural energy balance comparisons in summer for Vancouver, B.C. Bound.Layer Meteor., 36, 351–369. Department of Sustainability and Environment, 2000: Landsat mosaic. TM Mosaic 2000 Colour, 30 metre pixels, Vicmap Australia. ——, 2002: Melbourne 2030: Planning for Sustainable Growth. State Government of Victoria, 206 pp. [Available online at http://www.dse.vic.gov.au/melbourne2030online/downloads/ 2030_complete.pdf.] Fehrenbach, U., D. Scherer, and E. Parlow, 2001: Automated classification of planning objectives for the consideration of climate and air quality in urban and regional planning for the

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