Analysis of multifactorial drivers of the Urban Heat Island effect in Vienna: A scalable method for urban settings using open data sources

To develop effective climate change mitigation and adaptation strategies for urban areas, it is crucial to consider building characteristics, urban morphology, and environmental interactions. The Urban Heat Island (UHI) effect, marked by higher temperatures in urban areas compared to rural surroundings, underscores the need to account for contributing factors. However, UHI studies often overlook key contributors at micro and mesoscales due to the complexity of urban settings.

This research addresses these gaps by developing a method to derive urban-scale data, including anthropogenic heat (AH) from industrial activities, building energy consumption, power plant emissions, and traffic. This data is analysed alongside urban morphology and thermal properties using open data sources. Previous research has noted AH’s impact on urban microclimates but often lacks the integration of diverse AH sources and urban morphology due to challenges in data availability and standardisation.

Our study builds on methods from projects like Cooling Singapore, employing the Weather Research and Forecasting (WRF) model coupled with urban schemes to simulate urban climates at the mesoscale and Palm4U for micro-climate analysis. We develop a method to include different AH sources and urban characteristics, such as buildings and vegetation, using open datasets to estimate AH emissions in Vienna, Austria. This approach provides a detailed urban context for evaluating the UHI effect and understanding AH impacts.

Data on buildings and road networks are derived from OpenStreetMap, urban morphology and vegetation data from the global map of Local Climate Zones (LCZ), and information on land use, energy consumption, and traffic from public datasets to calculate AH emissions. The data is integrated into the WRF model, enabling accurate simulations of urban temperature distributions and identification of key UHI contributors. The WRF model is calibrated using historical weather data and urban temperature measurements.

Preliminary findings show the feasibility of integrating multiple open datasets into meso- and micro-scale models, providing insights into UHI’s spatial and temporal dynamics. Results reveal significant impacts of industrial activities, building energy use, traffic, and urban morphology on temperature increases. These insights can guide urban planners and policymakers develop targeted UHI mitigation strategies, such as optimising urban design, enhancing green spaces, and improving energy efficiency.

In conclusion, this study highlights the effectiveness of a scalable approach for deriving urban information, including AH sources and urban characteristics. By using advanced modelling techniques and open data, we offer a robust framework for understanding and addressing UHI, contributing to more sustainable and resilient urban environments.