Are high-resolution urban datasets necessary for accurate heat exposure modelling in cities?

Maryam Fazeli , Negin Nazarian, Jason P. Evans, Mathew Lipson, Jacobo Gabeiras, Shankar Sharma, Alberto Martilli

Accurately capturing the spatial variability of urban heat exposure is important for planning heat-resilient cities. While regional climate models have historically simplified urban characteristics, high-resolution urban morphological datasets now present an opportunity to produce spatially accurate heat maps. In this vein, this study evaluates four morphological datasets for Sydney, Australia in the Weather Research and Forecasting (WRF) model during the extremely hot period of 10-20 January 2017: the default IGBP-MODIS data with no local morphology, a class-based Local Climate Zones (LCZ) dataset requiring parameter interpretation by modellers, and explicitly defined parameter datasets from World Settlement Footprint 3D (WSF-MB) and Geoscape. The latter three used the BEP-BEM-Comfort urban canopy model, which outputs subgrid-scale Universal Thermal Climate Index (UTCI), an indicator of human thermal stress. Comparison with observations showed any urban dataset over WRF default reduced 2m temperature mean absolute errors (MAEs) at peak solar hours by at least 1°C on average, while localized instantaneous and median temperature differences reached 13°C and 4.5°C across the domain. High-resolution gridded experiments outperformed LCZ temperature predictions by up to 0.37°C (MAE) before sunrise. LCZ and Gridded experiments revealed substantial UTCI differences, with LCZ predicting four times lower probability of extreme heat stress in the afternoon, and 2-3°C lower average UTCI exceedances in Western Sydney. Urban dataset choice mattered most under weak synoptic conditions, though simplified datasets could have critical discrepancies in estimating localised heat stress even during strong forcing. The findings underscore the importance of high-resolution data for identifying heat-vulnerable times and locations.