Health-relevant Heat Indices for Urban Areas: A Machine Learning Approach with Downscaled Climate Data and City Measurement Networks

In a warming climate, heatwaves are becoming more intense and more frequent.  Their effects have already proven to be one of the highest contributors to climate hazard related mortality. Additionally, due to the urban heat island (UHI) effect, heat is intensified in the most densely populated areas and since urbanization is expected to continue increasing, more and more people are facing enhanced risks.

In this project, we investigate the severity of hot spells in cities with the help of health-relevant heat indices, namely wet bulb temperature, the universal thermal climate index (UTCI) and the amount of tropical nights, among others. We process reanalysis data from ERA5-Land from 1950 onwards and simulation data from the downscaled EURO-CORDEX simulations for various climate scenarios until 2100 to generate these indicators for Europe at a resolution of 0.1°. After this step, we plan to train a shallow machine learning model (XGBoost) to downscale reanalysis and simulation data to the city level at a resolution of 100m for selected European cities. For model validation, temperature series from 12 European cities’ urban measurement networks will be used. Finally, the indicators will be applied to four pilot cities (Oslo, Bern, Lyon and Naples), as part of the EU project healthRiskADAPT under the framework of Horizon Europe. In a subsequent phase, advanced modeling techniques such as Weather Research and Forecasting (WRF) models or computational fluid dynamics (CFD) may be applied to better understand the compound effects of heat and pollution in cities.