Abstract
The urban heat island (UHI) is one of the most critical issues for dense urban environments, as high outdoor temperatures and poor wind flow in high-density built-up areas can have negative impacts on the thermal comfort and health of city dwellers by trapping air pollutants and increasing energy demand for artificial cooling. The Intergovernmental Panel on Climate Change (IPCC) Assessment Reports recognized the potential impacts of climate change on cities. With global warming, an increased frequency of heat waves and the severity will certainly amplify the current problems associated with UHI effects and heat-related illnesses (HRI) in a highly dense urban environment.
This chapter showcases how an Integrated Multi-scale Environmental Urban Model (IMEUM) can be useful for building professionals, city planners, and policymakers in their decision-making related to minimizing the environmental impact due to continuously changing dense urban environment. The concept originates by downscaling environmental models from global scale (25 km) to a site-specific parcel or building. IMEUM provides a computationally efficient method which couples multi-scale atmospheric models with statistical model and computational fluid dynamic (CFD) to estimate weather parameters. IMEUM can be applied to integrate various UHI and heat-related illnesses mitigation measures at the early stage of design process.
This chapter also showcases the IMEUM calibration by means of ground sensing. Furthermore, a series of case studies of a hypothetical office building and the microclimate simulation is provided to illustrate how the simulation output can be fully converted into a localized weather data file for cooling load simulation. These results consequently can be used for urban microclimate on heat-related illnesses impact assessment. IMEUM is part of integrated quantitative urban environment simulation tool (QUEST), a platform for analysing and testing the immediate microclimatic impact of development plans and assessing their long-term impacts under future climate change scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arnfield AJ (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):1–26. https://doi.org/10.1002/joc.859
ASHRAE (2010) ANSI/ASHRAE standard 55–2010, thermal environmental conditions for human occupancy. American Society of Heating, Air-Conditioning and Refrigeration Engineers, Inc., Atlanta
Chakraborty D, Elzarka H, Bhatnagar R (2016) Generation of accurate weather files using a hybrid machine learning methodology for design and analysis of sustainable and resilient buildings. Sustain Cities Soc 24:33–41. https://doi.org/10.1016/j.scs.2016.04.009
Chan ALS (2011a) Developing a modified typical meteorological year weather file for Hong Kong taking into account the urban heat island effect. Build Environ 46(12):2434–2441. https://doi.org/10.1016/j.buildenv.2011.04.038
Chan ALS (2011b) Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energ Buildings 43(10):2860–2868. https://doi.org/10.1016/j.enbuild.2011.07.003
Chang E-C, Hong S-Y (2011) Projected climate change scenario over East Asia by a regional spectral model. JKESS 32(7):770–783
Chen F, Dudhia J (2001) Coupling an advanced land Surface–Hydrology Model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon Weather Rev 129(4):569–585. https://doi.org/10.1175/1520-0493(2001)129<0569:Caalsh>2.0.Co;2
Chen Y, Wong NH (2006) Thermal benefits of city parks. Energ Buildings 38(2):105–120. https://doi.org/10.1016/j.enbuild.2005.04.003
Chen F, Kusaka H, Bornstein R, Ching J, Grimmond CSB, Grossman-Clarke S, Loridan T, Manning KW, Martilli A, Miao S, Sailor D, Salamanca FP, Taha H, Tewari M, Wang X, Wyszogrodzki AA, Zhang C (2011) The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int J Climatol 31(2):273–288. https://doi.org/10.1002/joc.2158
Chong ZMA, Ignatius M, Wong NH, Jusuf SK (2012) The effect of urban heat island on heat gain increase. Paper presented in the 8th International Conference on Urban Climates (ICUC8), Dublin, Ireland, 6–10 August 2012
Chow WTL, Salamanca F, Georgescu M, Mahalov A, Milne JM, Ruddell BL (2014) A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes. Atmos Environ 99:64–76. https://doi.org/10.1016/j.atmosenv.2014.09.053
Coutts A, Beringer J, Tapper N (2010) Changing urban climate and co2 emissions: implications for the development of policies for sustainable cities. Urban Policy Res 28(1):27–47. https://doi.org/10.1080/08111140903437716
Crutzen PJ (2004) New directions: the growing urban heat and pollution “island” effect—impact on chemistry and climate. Atmos Environ 38(21):3539–3540. https://doi.org/10.1016/j.atmosenv.2004.03.032
Donaldson RJ, Dyer RM, Kraus MJ (1975) An objective evaluator of techniques for predicting severe weather events. In: Norman O (ed) Preprints of the 9th conference on severe local storms. American Meteor Society, p 321–326
Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res Atmos 108(D22). https://doi.org/10.1029/2002jd003296
Eliasson I (2000) The use of climate knowledge in urban planning. Landscape Urban Plan 48(1):31–44. https://doi.org/10.1016/S0169-2046(00)00034-7
EPA (2017) Future of climate change. https://19january2017snapshot.epa.gov/climate-change-science/future-climate-change_.html. Accessed 30 Aug 2019
Fehrenbach U, Scherer D, Parlow E (2001) Automated classification of planning objectives for the consideration of climate and air quality in urban and regional planning for the example of the region of Basel/Switzerland. Atmos Environ 35(32):5605–5615. https://doi.org/10.1016/S1352-2310(01)00205-9
Glahn HR, Lowry DA (1972) The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteorol 11(8):1203–1211. https://doi.org/10.1175/1520-0450(1972)011<1203:Tuomos>2.0.Co;2
Guan L (2009) Preparation of future weather data to study the impact of climate change on buildings. Build Environ 44(4):793–800. https://doi.org/10.1016/j.buildenv.2008.05.021
Guan L (2011) Sensitivity of building cooling loads to future weather predictions. Archit Sci Rev 54(3):178–191. https://doi.org/10.1080/00038628.2011.590057
Ham S, Lee J-W, Yoshimura K (2016) Assessing future climate changes in the East Asian summer and winter monsoon using regional spectral model. J Meteorol Soc Jpn Ser II 94A:69–87. https://doi.org/10.2151/jmsj.2015-051
Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L (2006) Neighborhood microclimates and vulnerability to heat stress. Soc Sci Med 63(11):2847–2863. https://doi.org/10.1016/j.socscimed.2006.07.030
Hong S-Y, Pan H-L (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124(10):2322–2339. https://doi.org/10.1175/1520-0493(1996)124<2322:Nblvdi>2.0.Co;2
Hong S-Y, Juang H-MH, Zhao Q (1998) Implementation of prognostic cloud scheme for a regional spectral model. Mon Weather Rev 126(10):2621–2639. https://doi.org/10.1175/1520-0493(1998)126<2621:Iopcsf>2.0.Co;2
Ignatius M, Wong NH, Jusuf SK (2015) Urban microclimate analysis with consideration of local ambient temperature, external heat gain, urban ventilation, and outdoor thermal comfort in the tropics. Sustain Cities Soc 19:121–135. https://doi.org/10.1016/j.scs.2015.07.016
Ignatius M, Wong NH, Jusuf SK (2016) The significance of using local predicted temperature for cooling load simulation in the tropics. Energ Buildings 118:57–69. https://doi.org/10.1016/j.enbuild.2016.02.043
IHPC (2019) Development of quantitative urban environment simulation tool (QUEST). Institute of High Performance Computing, Singapore
Jentsch MF, Bahaj AS, James PAB (2008) Climate change future proofing of buildings—generation and assessment of building simulation weather files. Energ Buildings 40(12):2148–2168. https://doi.org/10.1016/j.enbuild.2008.06.005
Jentsch MF, James PAB, Bourikas L, Bahaj AS (2013) Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renew Energy 55:514–524. https://doi.org/10.1016/j.renene.2012.12.049
Juang H-MH (2000) The NCEP mesoscale spectral model: a revised version of the nonhydrostatic regional spectral model. Mon Weather Rev 128(7):2329–2362. https://doi.org/10.1175/1520-0493(2000)128<2329:Tnmsma>2.0.Co;2
Juang H-MH, Kanamitsu M (1994) The NMC nested regional spectral model. Mon Weather Rev 122(1):3–26. https://doi.org/10.1175/1520-0493(1994)122<0003:Tnnrsm>2.0.Co;2
Juang H-MH, Hong S-Y, Kanamitsu M (1997) The NCEP regional spectral model: an update. Bull Am Meteorol Soc 78(10):2125–2144. https://doi.org/10.1175/1520-0477(1997)078<2125:Tnrsma>2.0.Co;2
Jusuf SK, Wong NH (2009) Development of empirical models for an estate level air temperature prediction in Singapore. In: Proceedings of the Second International Conference on Countermeasures to Urban Heat Islands, Berkeley
Jusuf SK, Wong NH (2016) Valuing green spaces as a heat mitigation technique. In: Santamouris M, Wong NH (eds) Urban climate mitigation techniques. Routledge, New York, pp 41–66
Jusuf SK, Wong NH, Hagen E, Anggoro R, Hong Y (2007) The influence of land use on the urban heat island in Singapore. Habitat Int 31(2):232–242. https://doi.org/10.1016/j.habitatint.2007.02.006
Klein WH, Glahn HR (1974) Forecasting local weather by means of model output statistics. Bull Am Meteorol Soc 55(10):1217–1227. https://doi.org/10.1175/1520-0477(1974)055<1217:Flwbmo>2.0.Co;2
Kodera S, Nishimura T, Rashed EA, Hasegawa K, Takeuchi I, Egawa R, Hirata A (2019) Estimation of heat-related morbidity from weather data: a computational study in three prefectures of Japan over 2013–2018. Environ Int 130:104907. https://doi.org/10.1016/j.envint.2019.104907
Kolokotroni M, Giannitsaris I, Watkins R (2006) The effect of the London urban heat island on building summer cooling demand and night ventilation strategies. Sol Energy 80(4):383–392. https://doi.org/10.1016/j.solener.2005.03.010
Labosier CF, Beckman J, Robinson T, Tennis D (2019) Preliminary findings of thermal safety in children’s outdoor playhouses. Int J Biometeorol 63(9):1303–1307. https://doi.org/10.1007/s00484-019-01732-y
Lee J-W, Ham S, Hong S-Y, Yoshimura K, Joh M (2014) Future changes in surface runoff over Korea projected by a regional climate model under A1B scenario. Adv Meteorol 2014:753790. https://doi.org/10.1155/2014/753790
Li X-X, Koh T-Y, Entekhabi D, Roth M, Panda J, Norford LK (2013) A multi-resolution ensemble study of a tropical urban environment and its interactions with the background regional atmosphere. J Geophys Res Atmos 118(17):9804–9818. https://doi.org/10.1002/jgrd.50795
Li H, Kanamitsu M, Hong S-Y, Yoshimura K, Cayan DR, Misra V, Sun L (2014) Projected climate change scenario over California by a regional ocean–atmosphere coupled model system. Clim Chang 122(4):609–619. https://doi.org/10.1007/s10584-013-1025-8
Li N, Wang K, Cheng J (2015) A research on a following day load simulation method based on weather forecast parameters. Energy Convers Manag 103:691–704. https://doi.org/10.1016/j.enconman.2015.06.073
Lim TK, Ignatius M, Miguel M, Wong NH, Juang H-MH (2017) Multi-scale urban system modeling for sustainable planning and design. Energ Buildings 157:78–91. https://doi.org/10.1016/j.enbuild.2017.02.024
Martin M, Wong NH, Hii DJC, Ignatius M (2017) Comparison between simplified and detailed EnergyPlus models coupled with an urban canopy model. Energ Buildings 157:116–125. https://doi.org/10.1016/j.enbuild.2017.01.078
McGregor JL (1997) Regional climate modelling. Meteorog Atmos Phys 63(1):105–117. https://doi.org/10.1007/BF01025367
MEWR (2016) Using energy responsibly: climate change. http://www.mewr.gov.sg/topic/climate-change
Mirzaei PA (2015) Recent challenges in modeling of urban heat island. Sustain Cities Soc 19:200–206. https://doi.org/10.1016/j.scs.2015.04.001
NUS (2016) Department of Geography Weather Station. https://inetapps.nus.edu.sg/fas/geog. Accessed 10 Aug 2016
Oke TR (1973) City size and the urban heat island. Atmos Environ (1967) 7(8):769–779. https://doi.org/10.1016/0004-6981(73)90140-6
Oke TR (1987) Boundary layer climates. Routledge, London
Pascal M, Laaidi K, Ledrans M, Baffert E, Caserio-Schönemann C, Le Tertre A, Manach J, Medina S, Rudant J, Empereur-Bissonnet P (2006) France’s heat health watch warning system. Int J Biometeorol 50(3):144–153. https://doi.org/10.1007/s00484-005-0003-x
Pascal M, Wagner V, Corso M, Laaidi K, Ung A, Beaudeau P (2018) Heat and cold related-mortality in 18 French cities. Environ Int 121:189–198. https://doi.org/10.1016/j.envint.2018.08.049
Rankin D (1959) Mortality associated with heat wave conditions in the Melbourne Metropolitan area, January and February 1959. Aust Meteorol Mag 26:96–98
Roth M, Chow WTL (2012) A historical review and assessment of urban heat island research in Singapore. Singap J Trop Geogr 33(3):381–397. https://doi.org/10.1111/sjtg.12003
Sailor DJ (2011) A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. Int J Climatol 31(2):189–199. https://doi.org/10.1002/joc.2106
Santamouris M (ed) (2001) Energy and climate in the urban built environment. James & James, London
Santamouris M (2014) On the energy impact of urban heat island and global warming on buildings. Energ Buildings 82:100–113. https://doi.org/10.1016/j.enbuild.2014.07.022
Santamouris M, Cartalis C, Synnefa A, Kolokotsa D (2015) On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—a review. Energ Buildings 98:119–124. https://doi.org/10.1016/j.enbuild.2014.09.052
Smoyer-Tomic KE, Kuhn R, Hudson A (2003) Heat wave hazards: an overview of heat wave impacts in Canada. Nat Hazards 28(2):465–486. https://doi.org/10.1023/A:1022946528157
Sun Y, Augenbroe G (2014) Urban heat island effect on energy application studies of office buildings. Energ Buildings 77:171–179. https://doi.org/10.1016/j.enbuild.2014.03.055
Takahashi K, Yoshida H, Tanaka Y, Aotake N, Wang F (2004) Measurement of thermal environment in Kyoto city and its prediction by CFD simulation. Energ Buildings 36(8):771–779. https://doi.org/10.1016/j.enbuild.2004.01.033
Versteeg HK, Malalasekera W (2007) An introduction to computational fluid dynamics: the finite volume method, 2nd edn. Pearson Education, Harlow
Wong NH, Ignatius M (2016) Urban heat island in Singapore: contributing factors and mitigation solutions. Innovation Magazine 15(1):33–39
Wong NH, Jusuf SK (2008) GIS-based greenery evaluation on campus master plan. Landscape Urban Plan 84(2):166–182. https://doi.org/10.1016/j.landurbplan.2007.07.005
Wong NH, Jusuf SK, Syafii NI, Chen Y, Hajadi N, Sathyanarayanan H, Manickavasagam YV (2011) Evaluation of the impact of the surrounding urban morphology on building energy consumption. Sol Energy 85(1):57–71. https://doi.org/10.1016/j.solener.2010.11.002
WWRP/WMO (2008) Recommendations for the verification and intercomparison of QPFs and PQPFs from operational NWP models. World Meteorological Organization, Switzerland
Xu H-Y, Fu X, Lim CL, Ma S, Lim TK, Tambyah PA, Habibullah MS, Lee GKK, Ng LC, Goh KT (2017) Weather impact on heat-related illness in a Tropical City State, Singapore. Atmos Clim Sci 8(1):97–110
Zhu M, Pan Y, Huang Z, Xu P (2016) An alternative method to predict future weather data for building energy demand simulation under global climate change. Energ Buildings 113:74–86. https://doi.org/10.1016/j.enbuild.2015.12.020
Acknowledgements
The authors wish to thank members of the staff at the Singapore Land Authority (SLA), the National Environment Agency (NEA) and the Urban Redevelopment Authority (URA) for the support of this work. Lim Tian Kuay also wish to thank Mr. Ronnie Tay (former CEO, NEA), Mr. Richard Hoo (URA), Dr. Raj Thampuran (Surbana Jurong) and Dr. Vijay Tallapragada (NOAA’s NCEP/EMC) for their support and encouragement. We also wish to thank Dr. Chew Kian Hoe and Dr. Stefan Ma for their comments on the heat-related illnesses.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lim, T.K. et al. (2021). Singapore: An Integrated Multi-scale Urban Microclimate Model for Urban Planning in Singapore. In: Ren, C., McGregor, G. (eds) Urban Climate Science for Planning Healthy Cities. Biometeorology, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-87598-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-87598-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87597-8
Online ISBN: 978-3-030-87598-5
eBook Packages: Social SciencesSocial Sciences (R0)