A Transformation in City-Descriptive Input Data for Urban Climate Models

Abstract

In urban climate studies, datasets used to describe urban characteristics have traditionally taken a class-based approach, whereby urban areas are classified into a limited number of typologies with a resulting loss of fidelity. New datasets are becoming increasingly available that describe the three-dimensional structure of cities at sub-metre micro-scale resolutions, resolving individual buildings and trees across entire continents. These datasets can be used to accurately determine local characteristics without relying on classes, but their direct use in numerical weather and climate modelling has been limited by their availability, and because they require processing to conform to the required inputs of climate models. Here, we process building-resolving datasets across large geographical extents to derive city-descriptive parameters suitable as common model inputs at resolutions more appropriate for local or meso-scale modelling. These parameter values are then compared with the ranges obtained through the class-based Local Climate Zone framework. Results are presented for two case studies, Sydney and Melbourne, Australia, as open access data tables for integration into urban climate models, as well as codes for processing high-resolution and three-dimensional urban datasets. We also provide an open access 300 m resolution building morphology and surface cover dataset for the Sydney metropolitan region (approximately 5,000 square kilometres). The use of building resolving data to derive model inputs at the grid scale better captures the distinct heterogenetic characteristics of urban form and fabric compared with class-based approaches, leading to a more accurate representation of cities in climate models. As consistent building-resolving datasets become available over larger geographical extents, we expect bottom-up approaches to replace top-down class-based frameworks.

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

“…In addition to building-related features, the amount of green space inside a city is a beneficial measure to determine urban structures, as green spaces promote leisure and human well-being [35,168]. For instance, Lipson et al (2022) used the fractions of trees and low vegetation to distinguish different urban structures within Sydney and Melbourne, Australia [169]. Urban vegetation and its fraction can be calculated using satellite imagery, as extensively reviewed by Neyns and Canters (2022) [170], but open datasets also exist, such as the Street Tree Layer provided for European cities within the Copernicus Programme [171] or the globally available ESA WorldCover maps [172].…”