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Comparison of steve and envi-met as temperature prediction models for singapore context
In urban areas, natural land soil has been replaced by asphalt roads and concrete buildings, which absorb and retain more heat during the day, creating the Urban Heat Island (UHI) phenomenon.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, a temperature prediction model could be effective for simulating and quantifying the temperature reduction for every developed strategy. This paper will look into two prediction methods: STEVE and ENVI-met. Screening Tool for Estate Environment Evaluation (STEVE) is a prediction tool which is able to calculate the Tmin, Tavg and Tmax of the point of interest for certain urban settings. The temperature at that particular point is the result of its surrounding environment within the buffer zone. Output data from STEVE will be used as a database for a Geographic Information System (GIS) to produce temperature maps. ENVI-met 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 is based on the grid (x,y) with a specified grid distance. This resolution allows analysis of small-scale interactions between individual buildings, surfaces and plants. The major differences between the models are the wind-speed variable, raster map, surface temperature and the local climate context. STEVE calculation focuses on typical calm day conditions which excludes the wind speed variable, while ENVI-met consider it as one of the parameters. The GIS raster map generated from STEVE predicted temperature is based on a buffer zone with specified diameter, while ENVI-met is based on grid pixels or cells which produces temperature maps in more detail resolution. The objective of this study is to compare both prediction models so as to understand their benefits and limitations, in order to justify which model is more appropriate for a tropical urban context, and in this case Singapore.