a climatic responsive urban planning model

International Journal of Sustainable Building Technology and Urban Development / December 2011 323

Title No. SUSB-2011/026 http://dx.doi.org/10.5390/SUSB.2011.2.4.323

SUSB Journal Technical Paper

A Climatic Responsive Urban Planning Model for High Density City: Singapore’s

Commercial District

Wong Nyuk Hien, Steve Kardinal Jusuf, Rosita Samsudin, Anseina Eliza, and Marcel Ignatius*

AbstractLocal climate condition and urban morphology affect air temperature generated within urban canopy layer which related to urbanheat island (UHI) intensity and later impacts on outdoor thermal comfort and urban energy usage. Climatic responsive urbanplanning by careful consideration on urban morphology parameters of urban corridor width, building height, urban surface materials,sky view factor (SVF) and vegetation help to improve urban environment quality. This study mainly focuses on commercialdistrict and observes impacts of various urban structures configurations towards air temperature by interpolating climatic andurban morphology predictors. The urban structures indeed show relation with level of air temperature generated althoughvegetation also contributes in reducing air temperature through its evapotranspiration process. Therefore the understanding ofrelation between urban morphology with thermal performance and UHI benefits in future urban planning and development.

Keywords: Urban morphology, Temperature map, Urban heat island (UHI), Singapore’s commercial district

1. INTRODUCTIONCities are growing towards megacities with higher density

urban planning, narrower urban corridors and more high-rise urban structures. This urban transformation causesday-time and night-time urban heat island (UHI) whichleads to declining of urban environment quality. Earlierstudies show strong relation between urban morphologyand increasing air temperature within cities center. Urbanstructures absorb solar heat during day-time and release itduring night-time. Densely built area tends to trap the heatwhen it is released from urban structures into urbanenvironment, increases urban air temperature compared tosurrounding rural areas and causes UHI effect. UHI affectsstreet level thermal comfort, health, environment qualityand may cause increase of urban energy demand.

In a built environment at micro-scale, buildings andvegetation influences the incident solar radiation receivedby urban surface. This is determined by the openness of anurban surface which is called as sky view factor (SVF) asmentioned by Cleugh in his study [1]. SVF explains thepercentage of a point’s field of view that is occupied by thesky as opposed to the buildings, trees or any other objectsin the landscape. Oke -1987 [2] also related both SVF andheight-to-width ratio of urban canyon with UHI intensity.The lower SVF value the higher urban air temperature.

Geographically, Singapore is located between latitudes1o09' North and 1o29' South, longitudes 103o36' East and

104o25' East. By its location, Singapore falls within hothumid climate region with characteristics of uniform hightemperature, humidity and rainfall throughout the year [3].Singapore as the most developed country within SoutheastAsian region has been experiencing rapid urban development.Commercial district is one of the highly developed areaswhich allows higher building site coverage and plot ratiowith rows of high-rise buildings for residential and commercialusage to encourage the country's strong economic growth.Current Singapore’s urban planning policy for commercialdistrict allows high rise developments with plot ratioranging from 5 to more than 11.2 which can be translatedto building height ranging from 25 to more than 50 storeysheight.

A study conducted by Wong [4] observed from thesatellite image that UHI in Singapore is seen during day-time with ‘hot spots’ were identified on commercial districtsbesides airport and industrial areas. However, ‘cool spots’were identified as well on large parks, the landscape inbetween housing estates and the catchment area. Jusut etal. [5] studied the relation between land use and ambienttemperature as shown in Fig.1. It is seen that during day-time commercial district experienced lower temperaturecompared to other land uses. But during night-time, itexperienced higher temperature.

Local climate condition is the existing factor thatpermanently affecting macro and micro climate condition.Katzschner [6] mentioned that climate is an ever existingfactor in a built environment and the study about climatecondition is purposed to improve the climate condition andto reduce the negative micro climate effects. Mills [7]proposed that examining the relationship between urbanforms and climate can employ the results of urban climatology

* Corresponding author.E-mail address: [email protected] historyReceived November 4, 2011Accepted December 23, 2011©2011 SUSB Press. All rights reserved.

324 SUSB Vol.2 No.4 Dec.2011 W. Hien, S. Jusuf, R. Samsudin, A. Eliza, and M. Ignatius

into urban design guidelines.To improve the urban environment quality and mitigate

UHI effect, a climatic map of an urban area is possible tobe developed by using Geographic Information System(GIS) platform with analysis on different information layers.Climatic mapping method has become widely used for

urban planning from macro to micro level and can be usedas reference for future urban planning and development.

The objective of this study is to see how different designoptions can be explored on developing a block within ahighly dense urban area, along with their impact on therelated urban microclimatic condition (in this case, urbantemperature on pedestrian level). The design options variationare limited on varying building massing and building physicaldimension accordingly, within the same plot ratio control.

2. SCREENING TOOL FOR ESTATE ENVI-RONMENT EVALUATION (STEVE TOOL)

STEVE has been developed based on the air temperatureprediction models. These prediction models were based onthe empirical data collected over a period of close to 3years as part of the development of an assessment methodto evaluate the impact of estate development, which includesthe assessment method of existing greenery condition [8]and greenery condition for a proposed master plan in anestate development [9].

In the development of the empirical model, air temperaturedata that has been gathered in the previous studies werecombined with the most recent data, which includes estate-wide and canyon types of measurements. The measurementpoints cover various types of land uses, including residential,commercial, business park, education, industrial, park, openspace and sport facility.

Daily minimum (Tmin), average (Tavg) and maximum(Tmax) temperature of each point of measurements werecalculated as the dependent variable of the air temperatureprediction model. The independent variables for the modelscan be categorized into:

Climate predictors: daily minimum (Ref Tmin), average(Ref Tavg) and maximum (Ref Tmax) temperature at referencepoint; average of daily solar radiation (SOLAR). For theSOLAR predictor, average of daily solar radiation total(SOLARtotal) was used in Tavg models, while average ofsolar radiation maximum of the day (SOLARmax) was usedin the Tmax model. SOLAR predictor is not applicable forTmin model. These data are obtained from the weatherstation.

Urban morphology predictors: percentage of pavementarea over R 50m surface area (PAVE), average height tobuilding area ratio (HBDG), total wall surface area (WALL),

Nyuk Hien Wong is Associate Professor in the Department of Building,

National University of Singapore. His area of expertise and research interests

includes urban heat island, urban greenery, thermal comfort in the tropics

and building energy simulation. He is the principal investigator of a number

of research projects in collaboration with the various government agencies

in Singapore. Prof. Wong has published more than 150 international referred

journal and conference papers and was the co-authors of 3 books on rooftop

and urban greenery and has been invited to deliver keynote papers and

research findings in various conferences and symposiums. He has also been

invited to serve in the various advisory committees both locally and

internationally.

Steve Kardinal Jusuf has a Ph.D. degree in Building Science from the

Department of Building, National University of Singapore. Currently he is a

Research Fellow at Centre for Sustainable Asian Cities, NUS. His research

interests include urban microclimate and urban climatic mapping with

Geographical Information Systems. He has worked in a number of research

projects with various Singapore government agencies, mainly on urban

climatic mapping for sustainable urban development.

Rosita Samsudin is Research Assistant at Centre for Sustainable Asian

Cities, NUS. She is architect in practice and holds master degree in building

science from Department of Building, NUS. Her research interests include

urban heat island, urban climatic mapping, outdoor thermal comfort,

sustainable building and urban development.

Anseina Eliza is a Master Graduate in Building Science at Department of

Building, National University of Singapore. Topic of this paper embarks

from her Independent Study research, with Dr. Wong Nyuk Hien as her

supervisor, which highlights the building morphology and density effect on

urban temperature. Currently, she is working at green building consultant.

Marcel Ignatius is currently a PhD candidate at Department of Building,

National University of Singapore, under Prof. Wong Nyuk Hien supervision.

The focus of his research is mostly on urban climatic mapping and temperature

model for urban morphology in Singapore. He was a research assistant in

Centre of Sustainable Asian Cities (CSAC) at NUS in 2009, and he has done

his Master Degree in Building Science from the same university in 2008.

Fig.1 Urban Heat Island profile in Singapore (Source: Jusuf et al., 2007)


Green Plot Ratio (GnPR), sky view factor (SVF) andaverage surface albedo (ALB). These data are provided bythe government agency and cross-checked by field survey.

Before the model was developed, the radius of influencearea was determined. A radius of 50 meter was deemed asa suitable one after a series of influence area study bycomparing radius value from 25 – 100 m (see Fig.2). Thetemperature models were then developed by examining thevariables regression coefficient values and their correlationswith the dependent variables.

Wind speed, one of the most common variables, wasexcluded in the model development, since the models focuson calm day conditions (wind speed < 3 m/s). Meanwhilefor another common variable, altitude was excluded fromthe model development since the data collected showedaltitude has a very little influence on air temperaturecondition.

In the first stage of model development, trend analysis wasdone to identify and discuss the behaviour of the models’variables (based on the data collected on field measurement),

by examining the variables’ regression coefficient valuesand their correlations with the dependent variable. Not allof the independent variables are significant. However, it isimportant to analyze how these variables behave in determiningthe air temperature. The next stage is to develop the airtemperature prediction models that use only the significantvariables.

The air temperature regression models were developedbased on the data collected over a period of close to 3years. It is necessary to validate the models with anotherperiod of measurement data, which in this case, with fairlyclear and calm day conditions (wind speed < 3 m/s).

The air temperature prediction models can be written asfollows:

Tmin (oC) = 4.061 + 0.839 Ref Tmin (

oC) + 0.004 PAVE (%)– 0.193 GnPR – 0.029 HBDG + 1.339E-06 WALL (m2)

Tavg (oC) = 2.347 + 0.904 Ref Tavg (

oC) + 5.786E-05 SOLARtotal (W/m2) + 0.007 PAVE (%) – 0.06 GnPR – 0.015 HBDG + 1.311E-05 WALL (m2) + 0.633 SVF

Fig.2 Sample of urban area measurement point in influence area radius of 25 m, 50 m, 75 m and 100 m. Source [8]


Tmax (oC) = 7.542 + 0.684 Ref Tmax (

oC) + 0.003 SOLARmax

(W/m2) + 0.005 PAVE (%) – 0.016 HBDG + 6.777E-06 WALL (m2) + 1.467 SVF + 1.466 ALB

Since it is impossible to put the all the theoreticalbackground and prediction model development into thispaper, the author will only underline the essential elementsof the STEVE tool, while a more detailed explanation anddata validation can be read from the related paper, whichcan be found in [8-11].

3. METHODOLOGYTemperature map for Singapore’s commercial district in

this study is developed by overlaying layers of urbanmorphology parameters and predicted Tmax, Tavg and Tmin

using GIS platform. Tmax represents maximum temperatureduring daytime between and Tmin represents minimumtemperature during night-time. Predicted temperature arecalculated by interpolating historical climatic parameters oftemperature and solar radiation obtained from local weatherstation with urban morphology predictors of buildingheight, exposed surface area, average albedo and sky viewfactor (SVF). This study compares the existing urbanmorphology condition with proposed possible scenariosbased on current Singapore’s urban planning policy forcommercial district.

Models of 6 types massing configuration consist of 1 mass,2 masses, 3 masses, 5 masses, 10 masses and 16 masses aredeveloped and to be observed on 7 blocks in commercialdistrict which presently is densely built and have allowableplot ratio more than 11.2 [12], namely block A, B, C, D, E,F and G. By configuring different massing configuration,various building footprints and building heights areachieved. Building footprint determines urban corridorwidth and horizontal urban density are achieved whilebuilding height contributes in sky view factor (SVF). Table 1and Fig.3 shows the 6 type massing configuration used inthis study.

Total of 9 measurement points, out of other measurementpoints allocated within commercial districts, within 50 meterradius buffer are distributed around the selected blocks and

predicted Tmax, Tavg and Tmin are calculated by STEVE tool.This study mainly focuses on effect of urban structures

towards urban air temperature therefore greenery variable

Table 1. Matrix of different building configurations on each block

LOCATION

BLOCK

AREA

(m2)

PLOT

RATIO

GFA

(m2)

MASSING

1 2 3 5 10 16

HEIGHT (STOREYS)

80 80 80 36 24 24

FOOTPRINT FOR 1 MASSING (m2)

BLOCK A 15600 11.2 174720 2184.00 1092.00 728.00 970.67 728.00 455.00

BLOCK B 9180 11.2 102816 1285.20 642.60 428.40 571.20 428.40 267.75

BLOCK C 8190 11.2 91728 1146.60 573.30 382.20 509.60 382.20 238.88

BLOCK D 8775 11.2 98280 1228.50 614.25 409.50 546.00 409.50 255.94

BLOCK E 12920 11.2 144704 1808.80 904.40 602.93 803.91 602.93 376.83

BLOCK F 5550 11.2 62160 777.00 388.50 259.00 345.33 259.00 161.88

BLOCK G 5550 11.2 62160 777.00 388.50 259.00 345.33 259.00 161.88

Fig.3 Selected 7 blocks in commercial district with plot ratio 11.2

Fig.4 Types of different building configuration located on 7 blocksin Singapore’s commercial district


is not included in the predicted temperature calculations.The open areas in between buildings blocks are assumed aspavement areas. However it is confirmed from many earlierstudies that greenery contributes greatly in reducing theurban air temperature by the trees shading and vegetationevapotranspiration process.

4. FINDINGSTemperature map of predicted Tmax, Tavg and Tmin for all

scenarios show that there are changes on air temperatureaccordingly by changing the buildings configuration anddensity.

4.1 Temperature maximum (Tmax) mapTemperature map Tmax in Fig.5 indicates higher temperature

for some areas in type 1, 2 and 3 compared to type 4, 5 and6. Building configurations in type 1, 2 and 3 allow more

open spaces and receive more direct solar radiation duringday-time thus increase air temperature within urban canopylayer. Building height also contributes in reducing Tmax,benefits from the building shading that falls onto pavementarea, as shown in some area which indicate lower temperaturein type 1, 2 and 3. However, particular areas in type 1 stillshow higher temperature especially in between the buildingswhich rather far apart. This confirms Oke’s study [2] oncorrelation between ratio of building height and urbancorridor width with urban air temperature.

Building configuration in type 4, 5 and 6 results in lowertemperature considering effect of shading that falls ontopavement and lower SVF value because of the urbandensity setting regardless lower building height planned forthese types. Predicted Tmax also takes account of exposedsurface area therefore lower building may possibly haveless exposed surface area.

Fig.5 Tmax temperature map on existing block condition compared with 6 types of urban structures configuration and density


4.2 Temperature average (Tavg) mapSimilar types of building configuration are modeled to

calculate predicted Tavg. Temperature maps in Fig.6 showthat type 1, 2 and 3 indicate lower air temperature comparedto existing condition and the other 3 types and it seems thatreduction of building height impacts on the increasing ofTavg as shown in type 4, 5 and 6. However, amongst the last3 building configurations, type 4 which has the lowestbuilding density but highest building height indicates thelowest air temperature.

Temperature map Tavg also confirms correlation betweenratio building height and urban corridor width with SVFvalue which affect amount of solar radiation coming intourban area. Solar radiation is one of climatic predictors thatdetermine the level of air temperature generated withinurban canopy layer.

4.3 Temperature minimum (Tmin) mapFrom Fig.7, it can be seen that type 1, 2 and 3 with lesser

density of building configuration have lower air temperaturecompared to existing condition, type 4, 5 and 6. Sparselyplanned urban structures allow heat released from buildingsurface to go up and leave urban canopy layer. Inversely,higher density building configurations seem to trap theheat within urban canopy layer and result in higher airtemperature which confirms the presence of potential UHIeffect. In this study type 1, 2 and 3 have the highest buildingheight compared to type 4, 5 and 6 therefore type 1, 2 and3 allow more open spaces compared to the other types.

5. CONCLUSIONSBesides local climate condition, urban morphology

predictors affect air temperature generated within urbancanopy layer which later impact on UHI intensity. Building

Fig.6 Tavg temperature map on existing block condition compared with 6 types of urban structures configuration and density


density and building height are some urban morphologypredictors observed in this study.

Urban configuration with lower building density allowsmore open spaces that potentially increases air temperatureduring day-time due to the amount of solar radiationcoming into urban canopy layer. But a sparsely plannedbuilding helps for the heat that is released from urbansurfaces into urban area to go up and leave urban canopylayer. Inversely, densely planned urban area provides moreshading and reduce amount of solar heat absorbed thuspotentially reduce air temperature during day-time but ittraps the heat released during night-time and causes higherair temperature compared to surrounding areas which lessdensely planned.

Combination of lower density urban configuration withhigher building height confirms in to reducing air temperature

during night-time as it allow more open space and allowsthe heat that is release into urban area to go up and leaveurban canopy layer. Proportionally planned building heightand urban corridor width affect in minimizing SVF valueand solar heat radiation coming into urban canopy layerwhich help to lower air temperature during day-time.

Figure 7 compiles the differences of Tmax, Tavg and Tmin

observed between existing condition on 7 blocks inSingapore’s commercial district with 6 types of differentbuilding configuration proposed. It shows that there is athreshold of optimum density that potentially applied forthese blocks. In general all building configuration typesreduce existing condition air temperature. However, type 5and 6 do not seem to have significant contribution. Therefore,it can be concluded that urban configuration with 1 to 5buildings are effective in reducing UHI effect in the context

Fig.7 Tmin temperature map on existing block condition compared with 6 types of urban structures configuration and density


of blocks used in this study. However, this threshold maynot be applicable for other blocks depending on the groundarea and allowable plot ratio therefore further detailedstudy needs to be conducted for other blocks in order toobserve particular optimum threshold.

This parametric study confirms that understanding andapplication of climatic responsive urban planning contributesgreatly in improving thermal performance within urbanarea which in further impacts on outdoor thermal comfort,health, air quality and urban energy usage.

Limitation to this study is that vegetation variable andurban wind ventilation are not included thus further detailedstudy can be conducted for more comprehensive urbanthermal performance findings and analysis.

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of The Impact of The Surrounding Urban Morphology on

Building Energy Consumption, Solar and Energy, In Press.

[12] www.ura.gov.sg

Fig.8 Average temperature difference on massing configurationtypes

a climatic responsive urban planning model

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