Comparison of STEVE and ENVIMET as temperature prediction model for Singapore Context

Marcel IgnatiusPhD Candidate National University of Singapore Singapore a0018034@nus.edu.sg

Anseina Eliza Postgraduate Student National University of Singapore Singapore anseina@yahoo.com

Nyuk Hien Wong, National University of Singapore, Singapore, bdgwnh@nus.edu.sg Steve Kardinal Jusuf, National University of Singapore, Singapore, stevekj@nus.edu.sg Summary In urban area, natural land soil has been replaced by asphalt roads and concrete buildings, which causes more heat is absorbed and retained during the day, creating so-called Urban Heat Island (UHI) phenomenon. Current studies show that UHI impact mitigation strategies are to increase the open spaces to allow urban ventilation and plant green cover. To complement this, temperature prediction model could be effective for simulating and quantifying the temperature reduction for every developed strategy. This paper will look into two prediction models: STEVE and ENVIMET. Screening Tool for Estate Environment Evaluation (STEVE) is a prediction tool which is able to calculate the T-min, T-avg and T-max of the point of interest for certain urban settings. Temperature in that particular point is the result of surrounding environment within the buffer zone. Output data from STEVE will be used as a database for Geographic Information System (GIS) to produce temperature maps. ENVIMET is a Computational Fluid Dynamics (CFD) based micro-climate and local air quality model. It calculates temperature within the interval times for 24 to 48 hours. The calculation based on the grid (x,y) with a specified distance between the grid. This resolution allows analyzing small-scale interactions between individual buildings, surfaces and plants. The major differences between both models are wind-speed variable, raster map, surface temperature and the local climate context. STEVE calculation focuses on any calm day conditions which excludes the wind speed variables, while ENVIMET includes it. GIS raster map that is produced from STEVE is based on buffer zone with specified diameter, while ENVIMET is based on pixel from the grid which will give more detail resolution. Objective of this study is to provide comparison of both prediction models to understand its benefits and limitations, in order to justify which model is more appropriate for Singapore local context. Keywords: Prediction Tool, ENVIMET, STEVE, Urban Heat Island, Tropical Climate

1. Introduction The Urban Heat Island (UHI) phenomenon is a condition which temperatures in urban areas are higher than its surrounding. It is resulting from urbanization which increases anthropogenic heat emissions in urban centres, while water, wind and greenery that can help to cool down an urban area have concurrently decreased. Also, due to a greater area of asphalt roads and concrete buildings, more heat is absorbed and retained, while the heat reflection ratio decreases. Strategies to mitigate the UHI impact are to increase the open spaces to allow urban ventilation and insert green cover to cool down the solar radiation during the day. Temperature prediction model could be effective for simulating and quantifying the temperature reduction for any strategies that will be used. This study will focus on comparing two prediction models: STEVE and ENVIMET.

Screening Tool for Estate Environment Evaluation (STEVE) is a web application that is specific to an estate and it calculates the Tmin, Tavg and Tmax of the point of interest for the existing condition and future condition (proposed master plan) of the estate. Temperature in that particular point is as a result of surrounding environment within the buffer zone. Output data from STEVE will be used as a database for Geographic Information System (GIS) to produce a raster map of temperature difference within the estate. Envi-Met is a CFD-based micro-climate and local air quality model. It calculates temperature within the interval times for 24 to 48 hours. The calculation based on the grid (x,y) with specified distance between the grid. This resolution allows analyzing small-scale interactions between individual buildings, surfaces and plants. The major differences between both are wind-speed variable, raster map, surface temperature and the local climate context. STEVE calculation is focus on calm day conditions which exclude the wind speed variables, while ENVIMET includes wind-speed as one of calculation variables. GIS raster map that is produced from STEVE is based on buffer zone with specified diameter, while ENVIMET is based on pixel from the grid which will give more detail resolution. ENVIMET could give prediction of surface temperature on different height while STEVE calculation is based on pedestrian height. STEVE has been validated with the field measurement in Singapore based on 3 years data measurement which is more appropriate for tropical climate, while ENVIMET is mostly used for temperate climate countries. Objective of this study is to give comparison of both prediction models to understand its benefits and limitations. By considering these factors, they can be used to justify which model is more appropriate for Singapore local context.

2. Literature Review 2.1 Urban Heat Island The Urban Heat Island (UHI) phenomenon is a condition which temperatures in urban areas are higher than its surrounding. Isotherms on a map show an urban area with higher temperatures emerging like a warm island floating on a cooler sea. This is the origin of the name of "Urban Heat Island.” [2]. Alterations of the urban surface by people result in diverse microclimates whose aggregate effect is reflected by the heat island [3]. Building, paving, vegetation, and other physical elements of the urban fabric are the active thermal interfaces between the atmosphere and land surface. Their composition and structure within the urban canopy layer, which extends from the ground to about roof level, largely determines the thermal behaviour of different sites within a city [4], [5]. UHI can also be observed in every town and city as the most obvious climatic manifestation of urbanization [3]. Over the past 100 years, Tokyo's average temperature has increased by about three degrees Celsius, and that of Osaka has increased by two degrees Celsius. Since it is said that global warming has raised the Japan's average temperature by about one degree, the temperature increase due to the UHI effect is probably about two degrees in Tokyo and about one degree in Osaka. In Washington and Tokyo, a rate of one-half to one degree Fahrenheit every ten years was observed in the context of the summer’s maximum temperatures during the last 30-80 years. In Singapore, the satellite image shows UHI effect during daytime. The ‘hot’ spots are normally observed on exposed hard surfaces in urban context, such as industrial area, airport and CBD. The satellite image also shows some ‘cool’ spots, which are mostly observed on the large parks, the landscape in-between the housing estates and the catchment area [6]

2.2 Mitigating UHI Urban morphology is a major factor that influences the thermal behavior in the city and creates UHI impact. [7] stated that designs for street canyons in high- and mid-latitude cities should (1) maximize shelter from wind for pedestrian comfort, (2) maximize dispersion of air pollutants, (3)

minimize urban warming to reduce the need for space cooling, and (4) maximize solar access. Oke’s analysis of urban geometry in relation to air-flow patterns, radiation exchange, and thermal behavior resulted in a range of canyon geometries and building densities that achieve a “zone of compatibility”.[8] Calculation of irradiances on urban canyon walls and floors provide quantitative guidelines on street geometry. He found that irradiance values generally were smaller for canyon walls than for floors, and that controlling canyon-floor irradiance was more critical at lower latitudes because of higher solar angles. North-South street orientation provided less summer and more winter canyon-floor irradiance than east-west street orientation. Using Cluster Thermal Time Constant model, [9] in hot regions, cities with east-west streets have weaker urban heat-island intensities (0.8 deg C) than cities with east-west streets. Their sensitivity analysis illustrated how increasing aspect ratios (canyon height to width) results in a stronger daytime “cool island” due to increased shade, and a weaker nocturnal heat island due to appreciably reduced maximum temperature. 2.3 STEVE tool [1]  

Screening Tool for Estate Environment Evaluation (STEVE) is a web application that is specific to an estate and it calculates the Tmin, Tavg and Tmax of the point of interest for the existing condition and future condition (proposed master plan) of the estate. Temperature in that particular point is as a result of surrounding environment within the buffer zone. Output data from STEVE will be used as a database for Geographic Information System (GIS) to produce a raster map of temperature difference within the estate. 3. Hypothesis Envi-met and STEVE tool have the same objective, which creates temperature map in the shape of raster images. In this case, since STEVE tool was built based on local climate data (Singapore), thus in term of predicting the temperature output, it will be more representative in term of reflecting the real condition, compared to Envi-met. However, since STEVE has limitation on creating the raster image based on interpolating result from 100 x 100 m grid, Envi-met will definitely produce a better and accurate output in term of resolution. 4. Methodology Figure 1. Model settings on GIS using STEVE (left) and Envi-Met (right) Comparison between STEVE tool and ENVIMET draws on scenario comprises five building massing, each with 48m height. In STEVE tool, the ground surface is assumed fully paved (Green Plot Ratio or GnPR value is zero). The same setting is also applied in ENVIMET, where the soil is set to be fully pavement (concrete) also.

STEVE Envi-Met

5. Data and Analysis

5.1 ENVIMET Simulation on Different Wind Speed

Figure 2. ENVIMET simulation result on different wind speed

From the temperature maps from five different scenarios of wind speed (Figs. 2), it appears that ENVIMET simulation result are not accurate for the low wind-speed condition (below 1 m/s), as they show inconsistencies of wind directions in the low wind-speed condition. Consistent wind direction can only be seen when the speed is set starting from 1m/s. Therefore, to compare with STEVE tool result, it can only use simulation with minimum 1m/s wind speed.

5.2 Comparison of STEVE tool and ENVIMET 5.2.1 Measurement Point difference for raster map Figure 3. Comparison of temperature map result from both STEVE tool and ENVIMET To create a raster map, GIS use a circular buffer zone with certain radius. Then, STEVE tool will predict the temperature in that particular centre point of the buffer area based on the environment condition within (it uses 50m radius buffer zone). On the other hand, ENVIMET calculation is technically based on pixel with particular size of the grid as defined on the ‘create model domain’ (this study use 5m for x, y, z grid) These two different methods appear to vary the raster map output as well (see Figs. 3). For instance, in the red highlighted location, STEVE tool shows the equal temperature between buildings, while ENVIMET shows temperature variance in the same location. In the STEVE tool raster map, it also shows higher temperature in the purple highlighted location, while ENVIMET shows similar temperature for all canyons. The main reason for this is different measurement point resolution; ENVIMET provides better resolution as the measurement is for every pixel, while STEVE tool measures every 50m radius buffer zone. Therefore, in order to have better resolution of temperature map, it required more buffer zones for STEVE tool. To achieve better resolution of raster map temperature, this study tries to put additional 27 more measurement points within the same parcel. While normal calculation of STEVE tool use attached buffer zone, this study try to overlap the buffer zone as it required to put more measurement points.

Figure 4. Additional buffer zone on STEVE calculation to improve the accuracy of temperature map

Attached buffer zone (4 measurement points)

Overlapping buffer zone (31 measurement points)

Figure 4. Temperature map comparison as a result of overlapping buffer areas on STEVE calculation The diagram above (Figs. 4) shows the difference of utilizing more measurement point in order to achieve more accurate temperature map. The highlighted area shows the difference of both methods. In attached buffer zone, only the center part shows low temperature while in overlapping buffer zone, every space in between building shows low temperature. Color zone of the temperature map depends on the next measurement point location. Therefore, prediction of temperature with the GIS raster map might be misleading. Figure 5. STEVE and ENVIMET temperature map result comparison

STEVE tool ENVIMET

Raster map from overlapping buffer zone shows similar pattern with ENVIMET, which has lower temperature in between buildings. Temperature prediction from ENVIMET shows higher temperature compared to STEVE, STEVE temperature range from 32.1 0C to 32.68 0C, while ENVIMET temperature range from 302.33 K to 303.05 K (29.33 0C to 30.05 0C). 5.3 Wind Speed

Figure 6. STEVE and ENVIMET wind profile comparison STEVE calculation focuses on calm day conditions, which excludes the wind speed variables, while ENVIMET calculation also depends on wind-speed variables. Figure 6 shows the comparison of STEVE tool and ENVIMET if the initial wind speed for ENVIMET is changed. For ENVIMET, it has been tested out for 1m/s, 3m/s, and 5m/s wind speed. The result shows that when the wind-speed increases, the temperature will be reduced. ENVIMET results show that for 1m/s wind speed, the temperature range from 29.34 0C to more than 29.81 0C, for 3m/s the temperature range from 28.40 0C to 29.11 0C, and for 5m/s the temperature range from less than 27.93 0C to 28.63 0C. STEVE tool temperature prediction for calm day ranges from 32.1 0C to 32.68 0C. 5.4 Temperature profile difference

Results from the previous studies shows that prediction temperature from ENVIMET has always been lower compare to STEVE tool. Therefore, this study tries to test whether ENVIMET

STEVE tool ENVIMET

underestimates the initial temperature, as it is mostly used in temperate country. The scenario is that STEVE and ENVIMET simulate the same condition of empty land and with massing in every 1 hour interval during one day.

Figure 7. Temperature profile comparison Diagram above indicates that prediction temperature from ENVIMET for empty land (blue line) is lower than STEVE tool (red line). Prediction temperatures with buildings added into the parcel also show lower values for ENVIMET (purple line) compared to STEVE (green line). Therefore, this concludes that initial temperature for ENVIMET is lower than STEVE, which means it under-predicts the temperature. Both STEVE and ENVIMET show similar trend of temperature profile. ENVIMET shows smooth curve line as a result of using only initial boundary condition to run the simulation. While STEVE implement one day weather data profile as input to generate the predicted temperature. Because of using the actual data (for this study 6 March 2010 was the selected day), the temperature profile will not be as smooth as the one which is generated by ENVIMET. Table 1 reveals that the prediction temperature difference for empty land between STEVE and ENVIMET can reach 3.77 0C maximum and 2.38 0C average, while temperature difference when there is massing can reach 3.50 0C maximum and 2.26 0C average. The temperature reduction from empty land to massing is higher in STEVE compare to ENVIMET. For STEVE, it shows maximum reduction of 0.99 0C, and average reduction of 0.430C, while ENVIMET shows maximum reduction of 0.660C and average reduction of 0.110C.

Table 1. Prediction Temperature Difference between STEVE and ENVIMET

5.5 Adjustment of ENVIMET Temperature Profile Comparison between STEVE tool and ENVIMET above shows that there is 20C to 30C difference between two prediction models. To achieve the most similar temperature profile between STEVE tool and ENVIMET, initial temperature in ENVIMET is raised into 20C and 30C become 302K (290C) and 303K (300C). Temperature profile from STEVE tool on 6 March 2010 shows a not smooth curve line, while ENVIMET shows a smoother curve line. Therefore, to have the proper assessment of the smooth curve line, this study attempted to use different dates, which are 28 February 2010 and 26 November 2009. The graphic below shows comparison of ENVIMET simulation with 3 different initial temperatures (300K, 302K, and 303K) and STEVE tool simulation with 3 different dates (6-Mar-10, 28-Feb-10, and 26-Nov-09). The result displays that ENVIMET on 302K and 303K show similar profile with

Time (24hrs)

Massing (STEVE

tool)

Empty (STEVE

tool)

Temp. diff. (STEVE

tool)

Point 4 & 5 (ENVI-MET)

Empty (ENVI-MET)

Temp. diff. (ENVI-MET)

STEVE-ENVI-MET

(empty)

STEVE-ENVI-MET (massing)

1:00 27.26 27.47 -0.21 25.03 25.34 -0.31 2.13 2.23

2:00 26.92 27.11 -0.19 24.85 25.06 -0.21 2.05 2.07

3:00 26.55 26.72 -0.17 24.64 24.82 -0.18 1.90 1.91

4:00 26.19 26.35 -0.16 24.48 24.61 -0.13 1.74 1.71

5:00 25.85 25.99 -0.14 24.30 24.42 -0.12 1.57 1.55

6:00 25.71 25.85 -0.13 24.13 24.24 -0.11 1.61 1.58

7:00 25.88 26.02 -0.14 23.96 24.09 -0.13 1.93 1.92

8:00 25.76 25.89 -0.14 23.84 23.97 -0.13 1.92 1.92

9:00 26.41 26.58 -0.17 24.89 25.22 -0.33 1.36 1.52

10:00 28.30 28.56 -0.26 26.04 26.43 -0.39 2.13 2.26

11:00 29.82 30.30 -0.49 27.18 27.69 -0.51 2.61 2.64

12:00 30.75 31.45 -0.70 27.87 28.46 -0.59 2.99 2.88

13:00 31.51 32.37 -0.86 28.66 29.16 -0.50 3.21 2.85

14:00 32.10 33.09 -0.99 29.31 29.71 -0.4 3.38 2.79

15:00 30.62 31.29 -0.67 29.44 30.06 -0.62 1.23 1.18

16:00 30.65 31.32 -0.67 29.5 30.16 -0.66 1.16 1.15

17:00 30.85 31.57 -0.72 29.35 29.99 -0.64 1.58 1.50

18:00 31.15 31.93 -0.78 28.97 29.50 -0.53 2.43 2.18

19:00 30.57 31.23 -0.66 28.17 28.43 -0.26 2.80 2.40

20:00 30.48 31.12 -0.64 27.24 27.51 -0.27 3.61 3.24

21:00 30.11 30.67 -0.55 26.61 26.90 -0.29 3.77 3.50

22:00 29.48 29.90 -0.41 26.04 26.41 -0.37 3.49 3.44

23:00 28.99 29.28 -0.29 25.67 26.00 -0.33 3.28 3.32

24:00 28.63 28.90 -0.27 25.34 25.65 -0.31 3.25 3.29

Max 32.10 33.09 -0.99 29.50 30.16 -0.66 3.77 3.50

Min 25.71 25.85 -0.13 23.84 23.97 -0.11 1.16 1.15

Avg 28.77 29.21 -0.43 26.48 26.83 -0.35 2.38 2.29

STEVE tool at 8.00am to 11.00am, but the gap in between become bigger when the profile goes to the maximum temperature. Maximum temperature in STEVE tool is shifting for every dates. On 28-Feb-10 temperature profile, the maximum temperature is at 16.00, which is the same for three ENVIMET temperature profile, but the difference between both prediction models is quite high, which is 1.88 to 4.10. Figure 7. Comparison of STEVE tool and ENVIMET Temperature Table 2. Comparison of STEVE tool and ENVIMET temperature

Time STEVE (06-Mar-10)

STEVE (28-Feb-10)

STEVE (26-Nov-09)

ENVIMET on 300K

ENVIMET on 302K

ENVIMET on 303K (24hrs)

1:00 27.47 27.96 27.02 25.34 26.8 27.5

2:00 27.11 27.66 26.77 25.06 26.54 27.24

3:00 26.72 27.62 26.50 24.82 26.3 27.01

4:00 26.35 27.33 26.13 24.61 26.1 26.80

5:00 25.99 26.84 26.00 24.42 25.92 26.62

6:00 25.85 26.54 25.81 24.24 25.76 26.47

7:00 26.02 26.27 25.63 24.09 25.61 26.33

8:00 25.89 26.45 25.68 23.97 25.5 26.21

9:00 26.58 26.96 26.76 25.22 26.71 27.42

10:00 28.56 28.40 28.42 26.43 27.59 28.12

11:00 30.30 30.02 29.92 27.69 29.11 29.79

12:00 31.45 31.38 31.28 28.46 29.92 30.64

13:00 32.37 32.79 32.36 29.16 30.63 31.35

14:00 33.09 33.17 33.36 29.71 31.19 31.91

15:00 31.29 33.74 34.43 30.06 31.54 32.27

16:00 31.32 34.26 33.86 30.16 31.65 32.38

17:00 31.57 34.10 33.94 29.99 31.49 32.22

18:00 31.93 33.66 33.56 29.50 31.01 31.74

19:00 31.23 32.40 32.29 28.43 29.81 30.48

20:00 31.12 31.57 30.80 27.51 28.92 29.60

21:00 30.67 30.49 29.55 26.90 28.31 29.00

22:00 29.90 29.35 28.77 26.41 27.83 28.52

23:00 29.28 28.63 28.14 26.00 27.43 28.12

24:00 28.90 28.31 27.87 25.65 27.09 27.79

Max 33.09 34.26 34.43 30.16 31.65 32.38

Min 25.85 26.27 25.63 23.97 25.50 26.21

Avg 29.21 29.83 29.37 26.83 28.28 28.98 6. Conclusion Comparison of temperature prediction model between STEVE and ENVIMET shows that resolution for ENVIMET raster map output is more detail compare to STEVE, as ENVIMET calculation is based on pixel while STEVE is based on the attached buffer zone. To achieve better resolution of raster map, there should be more measurement point in STEVE. The study shows that overlapping buffer zone give different raster map output compare to the attached buffer zone. The reason is because GIS raster map interpolate one measurement point to the next measurement point, so the color chart is depend on the next measurement point location. Raster map with overlapping buffer zone from STEVE shows similar pattern with ENVIMET, as the resolution is closer. Results from the previous studies shows that prediction temperature from ENVIMET is always lower compare to STEVE tool. The result indicates that prediction of initial temperature from ENVIMET is lower than STEVE tool. Prediction temperatures with massing also show lower temperature for ENVIMET compare to STEVE (green line). Therefore, it can be concluded that initial temperatures for ENVIMET is lower than STEVE, which means it under-predicts the temperature.

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