Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

• Main Authors: ALEXANDER, Paul John, BECHTEL, Benjamin, CHOW, Winston T. L., FEALY, Rowan, MILLS, Gerald
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: UEB; SUEWS; LCZ; Urban model evaluation; Flux measurements; Environmental Sciences
• Online Access: https://ink.library.smu.edu.sg/soss_research/3057; https://ink.library.smu.edu.sg/context/soss_research/article/4314/viewcontent/1_s2.0_S2212095516300347_main.pdf
• Institution: Singapore Management University

There are a number of models available for examining the interaction between cities and the atmosphere over a range of scales, from small scales - such as individual facades, buildings, neighbourhoods - to the effect of the entire conurbation itself. Many of these models require detailed morphological characteristics and material properties along with relevant meteorological data to be initialised. However, these data are difficult to obtain given the heterogeneity of built forms, particularly in newly emerging cities. Yet, the need for models which can be applied to urban areas (for instance to address planning problems) is increasingly urgent as the global population becomes more urban. In this paper, a modeling approach which derives the required land cover parameters for a mid-complex urban energy budget and water budget model (SUEWS) in a consistent manner is evaluated in four cities (Dublin, Hamburg, Melbourne and Phoenix). The required parameters for the SUEWS model are derived using local climate zones (LCZs) for land cover, and meteorological observations from off-site synoptic stations. More detailed land cover and meteorological data are then added to the model in stages to examine the impact on model performance with respect to observations of turbulent fluxes of sensible (QH) and latent (QE) heat. Replacing LCZ land cover with detailed fractional coverages was shown to marginally improve model performance, however the performance of model coupled with ‘coarse’ LCZ data was within the same range of error (20–40 W m− 2 for QE and 40–60 W m− 2 for QH) as high resolution data.