Potential impacts of climate change on the siting of U-space infrastructure

The integration of Unmanned Aircraft Systems (UAS) into the U-Space system requires dedicated infrastructure planning, such as vertiports and aerial corridors, that is not only efficient but also resilient to future meteorological changes. In this study, focused on the Emilia-Romagna region, we develop a methodological framework based on Geographic Information Systemspatial analysis to estimate the annual hours of flight operability (flyability). The methodology is applied by comparing the current meteorological scenario, derived from historical climatological data, with a scenario featuring a temperature increase of + 6 °C. This increase is not presented as a most likely projection, but rather as a high-end sensitivity analysis designed to assess how the spatial distribution of flyability may respond to strong warming conditions. The procedure starts from the creation of a Digital Elevation Model (DEM), from which additional thematic layers such as aspect and surface roughness are derived. The influence of local meteorological conditions is simulated using a Random Forest predictive model, designed to identify the areas with the highest probability of exceeding the maximum operational thresholds for drones, mainly related to wind, rainfall, and temperature. As a result, we obtain raster maps that quantify, for each territorial pixel, the annual hourly fraction available for UAS flight. The comparative analysis highlights a heterogeneous reduction in flight hours in the stress-test scenario, with particularly pronounced decreases in hilly and foothill areas. Conversely, coastal plains maintain high levels of operability. Based on this evidence, we propose two guidelines for U-Space planning: (i) preferentially select sites that preserve high flyability even under future climate scenarios; (ii) adopt targeted adaptation strategies in the most vulnerable areas, such as alternative routes, satellite vertiports, energy storage systems, and real-time meteorological monitoring.