Tradition city-descriptive data uses classes (left). New datasets allow a truer representation of cities, with parameters unique to each grid representing a neighbourhood | Image supplied.
Cities are both affected by the impacts of climate change and also contribute to it—through pollution, changing landscapes, and localised warming caused by urbanisation. More than 55% of the global population live in urban areas, a number which is projected to grow to 68% by 2050. As urban populations continue to increase it’s vitally important to understand the effects of climate change on urban environments. Unfortunately our understanding of the interaction between climate change and cities is, for the moment, incomplete.
Dr Mathew Lipson, Dr Melissa Hart, and Dr Negin Nazarian at the UNSW ARC Centre of Excellence for Climate Extremes, with colleagues Kerry Nice and Brooke Conroy, are working to solve this problem. Using data made available through AURIN, Dr Lipson and his colleagues are finding a way to incorporate more detailed features of the built environment into existing climate models.
A new generation of urban datasets is beginning to be produced which is transforming the way cities can be represented in urban climate models. These datasets resolve the characteristics of individual buildings and trees over entire cities, regions, and continents by drawing on satellite imagery, crowdsourced and government data, remote sensing, and machine learning. One such dataset produced in Australia by Geoscape was made available to researchers through AURIN’s restricted data program. This Geoscape data is uniquely valuable because it is very high resolution, includes three-dimensional information (e.g. building and tree heights) and covers the whole of Australia in a consistent manner.
These new datasets can be used to produce direct inputs for climate modelling studies at the grid level. This makes it possible to understand and simulate the impacts of climate change on urban areas and can assist in the development of strategies to mitigate these effects. Unfortunately, due to the limited access to these datasets, the transition to this type of bottom-up approach is ongoing.
In trying to capture more details of the built environment in climate models Dr Lipson and his colleagues used two data sources for their city-descriptive data. The first was Local Climate Zone maps, which characterise urban neighbourhoods based on local urban form. This data source represented a top-down approach.
The second data source was Geoscape Buildings Data, available through AURIN’s restricted data program, which represented the bottom-up method for detailed three-dimensional form at the level of individual buildings and trees. From these data sources they produced new gridded datasets, which do not rely on classes. The list of urban parameters that are available through processing both data sources include: surface cover, morphology, canopy attribute, and thermal attributes.
The Geoscape data was particularly valuable because of the scale of the data—it’s very high-resolution and is standard to many areas. The data provides more details on urban characteristics than other datasets, allowing cities to be assigned more accurate descriptors. This meant it was possible to calculate the parameters describing a city and feed it directly into the team’s climate models.
Dr Lipson and his colleagues recently published a paper which explained the methodology of deriving city-descriptive data for urban climate models. They also created maps which define actual urban form through the different urban parameters necessary for urban climate models. Through an agreement with Geoscape, maps covering the greater Sydney region have been made openly available for anyone to use.
In their two case studies of Melbourne and Sydney they explained how to extract precise and localised ranges of model parameters using the continental-scale Geoscape datasets. The case studies included open access data tables for integration into urban climate models. The methods Dr Lipson and his colleagues used for finding important city-descriptive parameters for climate models provide a useful blueprint for further research in this area.
Urban environments are increasingly under threat from problems caused by climate change. Research like Dr Lipson’s makes it possible to create climate models that can replicate the impact of climate change on the built environment, as well as the effect of interventions aimed to tackle these problems. This allows researchers and policy makers to quantify the impact of these interventions and supports evidence-based decisions for making our cities more climate resilient.