Strategies for Cooling Singapore
The catalogue of strategies aims to give a comprehensive overview of strategies and measures that are available to mitigate UHI and improve OTC. The catalogue contains 86 measures, grouped in seven clusters. Each measure describes its impact towards UHI and OTC, its applicability in the tropical climate, its integration into urban planning, and its current research status. With this catalogue, we hope to support the urban planning and design process with actionable knowledge.
The individual catalogue items have been compiled by reviewing current scientific articles that study and measure the causes of UHI and OTC, with special focus on Singapore and tropical regions. To verify and extend the list of the collected strategies, a questionnaire within the scientific community of SEC, SMART, TUMCREATE and NUS was conducted. The content of the catalogue is based on literature review and expert knowledge form various perspectives on urban design, transport, energy. build construction, and urban climatology.
A catalogue of 80+ measures to mitigate the urban heat island and improve Outdoor Thermal Comfort.
Links to other tasks
The catalogue is directly linked with the the “Assessment of OTC strategies at local scale” since it provides relevant information about the selected measures.
A catalogue of strategies for reducing UHI and improving OTC that are applicable to the local context of Singapore.
Assessment of OTC strategies at local scale
Strategies and measures to improve OTC have been assessed specifically for Singapore with the use of modelling techniques. Using microscale computational fluid dynamics (ENVI-met model) we have been able to study the thermal environment at the building and neighbourhood scale. This analysis has been performed in different meteorological conditions, representing the whole year, so that the general OTC levels of specific test areas (in Singapore) could be evaluated.
The mitigation measures have been analyzed in collaboration with two Singaporean Agencies: Urban Redevelopment Authority (URA) and Housing and Development Board (HDB). The aim was to involve the agencies and receive their feedback for the selection of relevant mitigations measures.
For each case study (HDB Punggol, JLD and CBD) different possible scenarios have been designed and assessed. The scenarios are composed by one or more measures at different scales, such as green coverage, green facades, increasing porosity. The results may impact the application of future measures and inform future guidelines.
Some of the results for Singapore are:
- It is relevant to use reference weather types to analyze OTC throughout the year,
- Porosity and increasing ventilation can improve OTC in courtyards and open areas,
- Green facades can only have an impact on OTC in areas very close to them (< 2 - 4 m),
- Green roofs have little impact on OTC, if applied only locally.
The impact of mitigation measures also depends on the use of different urban spaces. Different sites require different levels of thermal acceptability and thus specific mitigation measures can be adequate for certain urban spaces.
In this sense it is important to know the reliable range of thermal acceptability of the Singaporean population in different contexts (spaces, activities…).
Links to other tasks
This task is related to the development of a mitigation strategy catalogue for Singapore. Also the economic impact of these measures needs to be assessed together with the benefits in terms of OTC so that relevant information and strategies are included in the policy road map for Singapore.
Despite the fact that some relevant measures to improve OTC have already been tested, more evaluations are needed so that a whole set of feasible mitigation measures will be available for Singapore.
On the whole, the results will establish a framework of knowledge that will allow the impact of the implementation of mitigation measures to be determined.
Assessment of OTC Strategies
- Increase setbacks on ground floor from 4 to 9 meters.
- Re-arrange and re-orientate punctual buildings blocks.
- Adjust height, location and form of C-walk.
- Increase number of trees (33% and 60%).
- Install large-scale urban canopy/pergola to increase shading.
- Retrofit pavement and façade materials with green facades and retro-reflective materials at different heights.
Tools for Cooling Singapore
The content of the Tool Report has been collected mainly through literature review activities based on current scientific papers that study and use modeling software to assess the impact of mitigation strategies on Urban Heat Island (UHI) and Outdoor Thermal Comfort (OTC), with a special focus on Singapore and tropical regions. To verify and extend the list of the collected simulation tools on a scientific knowledge base, a questionnaire within the scientific community of SEC, SMART, TUMCREATE, and NUS was conducted. Additionally, people actually developing Singaporean tools and some others were interviewed with the aim of including their valuable and direct contribution into the report.
A simplified guide of 24 main simulation tools to assess the impact of different mitigation strategies in reducing Urban Heat Island (UHI) and improving Outdoor Thermal Comfort (OTC) in Singapore and similar local contexts. The guide provides useful links, references, and a matrix specifically developed to help users compare the features of the 24 simulation tools.
Links to other tasks
The Tool report is directly linked with the “WRF Modelling” (Omer) and the “Assessment of OTC strategies at local scale” (Juan, Lea, Gloria, Elliot) since it provides information on the software used for the mesoscale and microscale analysis.
A digital version of the tool report can be viewed at https://www.coolingsingapore.sg/tools/
Urban heat modelling by using WRF
The WRF model was used to analyze the impact of UHI for April (the inter-monsoon season) when the UHI intensity in Singapore is expected to be at its highest. The validity of the analysis was established by comparing the 2m temperature diurnal profile from the simulations with 15 meteorological stations across Singapore. From this, the various UHI metrics for policy and mitigation measures were established, which will help to improve liveability in the future. The impact of UHI is of interest to the URA for existing and future developments.
The key findings for Singapore include that the UHI effect can be as high as 4.2ºC during night time. The mitigation measures evaluated through WRF suggest that
- Using high reflective materials for roofs can lower the temperatures by about 1.47ºC
- A 20 % reduction in energy can be achieved by increasing the thermostat set temperature in commercial buildings by 4ºC.
- Increasing building density can adversely affect ventilation and thermal comfort, and blockage of major breezeways should hence be avoided for developments planned for 2030.
Links to other tasks
This tasks links not only to the UHI metrics and road map but also to the evaluation of emissions and energy. It also provides support to the outdoor thermal comfort campaign by providing visualizations that can motivate Singaporean inhabitants in their willingness to invest in the UHI mitigation. The task was an effective “ice breaker” that helped increase communications with regulators through powerful visualization, which allowed other tasks, such as outdoor thermal comfort, to link up with the agencies in Singapore.
The local climate zone classification and its integration with the Multilayer Urban Canopy Model in WRF considering the passive anthropogenic effects provides an excellent opportunity to analyze the impact of UHI and propose possible mitigation measures on the island wide scale suitable for regulators to plan new developments. It thus helped in developing the UHI metrics for the regulators and policy road map.
WRF modelling and simulations support the mitigation of UHI and propose countermeasures to offset the impact of UHI in Singapore. Further improvements in the modelling effort are currently underway and will help to develop a strong UHI mitigation framework.
The goal of the transportation model is to project the high level inputs provided by the scenario generator onto the road network of the transportation system by providing road-specific traffic attributes such as congestion levels, average velocity on roads, estimated heat and CO2 emissions. TUMCREATE’s expertise in traffic simulation is embodied by the CityMoS platform, which is used as a basis for the transportation model.
There are two main data sets which should be provided as input to the simulator in order for it to generate the desired traffic characteristics of the system: First, the origin destination (OD) matrix, which describes from where to where and when do people want to travel. This is typically either extracted from travel survey data such as HITS (small volume of data), derived from traffic counts (high level of error), or extrapolated from smart card public transport data like the CEPAS data set. If semantically enriched, the OD matrix can also give information about the mode of travel and the type of vehicles used.
Second, the road network, which represents the medium which people utilize to move around the city. The road network provides information about the connectivity of the road system and attributes for every road such as number of lanes, presence of traffic lights, phases of traffic lights, road types, etc.
An agent generation process constructs all agents that will participate in the simulation, parametrizing all their models by sampling the parameters from distributions determined by the user. Furthermore, the type of vehicle is defined in terms of dimensions, fuel type (petrol, diesel, electric etc.) and engine capabilities.
Once the OD and the road network are known, the routes that the population needs to take are computed using traffic assignment methodology. It computes the route of every generated agent in the simulation, getting them from the origin to the destination specified by the OD matrix while satisfying certain equilibrium criteria.
The majority of heat energy due to traffic is released during the morning and evening rush hours. Spatially, the biggest amount of heat is produced along the major highways in Singapore and especially at their intersections.
The output of the transportation model provides a macro and mesoscopic view of the system; from the set of routes of all agents we can derive the amount of agents that want to pass through every road within a given time interval, which can be translated into the expected level of congestion of the road, and thus the average speed. Using the average speed, the fuel consumption on the road can be estimated, which enables the utilization of macroscopic models for heat and particle emissions due to traffic on a road scale. By knowing the dimensions of the roads we can further compute the heat and particle fluxes which are required if coupling with a climate simulator is desired.
Links to other tasks
This task can be related to the WRF modelling task by providing inputs of energy fluxes from the transportation sector and to the mapping of energy flow in Singapore as it gives a spatial and temporal view of the energy losses in the transportation sector.
To summarise, the transportation model is designed such that it allows for variation in the amount of agents in the system, modes of transportation, and the splitting of the whole agent population into different subpopulations of vehicles (electric, autonomous, normal vehicles). The outputs of the simulator can be fed to a climate model as part of its inputs, thus coupling the two and allowing the scenario designer to observe the changes on climate resulting from the introduction of, for example, electric vehicles.
Mapping the Energy Flow in Singapore
The framework for the analysis of the energy flow in Singapore consists of observing the energy on a national scale in a Sankey diagram, and identifying the main sources of energy together with its losses, which are directly responsible for the increase of the Urban Heat Island effect (UHI). The breakdown of heat losses in a fine spatiotemporal resolution allows us to understand the areas that contribute most to UHI during a typical day. Once a comprehensive understanding of the current baseline scenario is conducted, it is possible to include future predictions and mitigation scenarios in order to analyse possibilities for energy usage in Singapore.
Main findings for Singapore’s energy include:
- The Industry responsible for transformation of energy (i.e. Power Stations and Refineries) is responsible for more than 50% of the heat losses;
- From the energy consumed (end-use), buildings are estimated to be responsible for 27% of the heat loss, while transportation is responsible for 35%. The chemical industry is responsible for around 18% and manufacturing and other industries for the remaining 21%.
The quantification of energy losses on a national level, per sub sector, clarifies the biggest contributors of heat emissions. The anthropogenic heat is observed in its active (e.g. AC from buildings, combustion from transportation and industry) and passive contributions (e.g. urban surfaces such as buildings, roads and pavements). The spatiotemporal distribution maps the contribution of each factor in terms of heat losses, using a combination of top-down and bottom-up approaches, depending on the data available.
Links to other tasks
A comprehensive understanding of the energy losses in Singapore assists in the development of policies for the roadmap, and the establishment of priorities for the analysis of mitigation measures, depending on how much each area is contributing to heat loss. Furthermore, the spatiotemporal map allows for overlapping with other observed maps, such as the UHI map generated by the WRF model to establish a relationship of cause (heat loss) and effect (temperature increase).
The energy mapping of Singapore is significant in increasing our understanding of the causes of UHI on the island. By quantifying the emissions in terms of heat losses for both passive and active components and breaking these down into sub-sectors, one is able to understand the proportions of contributions for each region.