Sprecher
Beschreibung
Risk from climate hazards is commonly defined as the interplay of hazard, exposure, and vulnerability. In recent years, response capacity and adaptation have been recognized as additional dimensions in comprehensive risk frameworks. Digital twins offer a powerful approach to integrate all these components into one coherent and dynamic system. However, to adequately assess risk, digital twins must be supplied with high-quality, spatially detailed data on each of these components, including exposure.
One key exposure indicator is the spatial distribution of population and its future mobility. Given the high uncertainty surrounding demographic developments, population projections are typically based on different socioeconomic scenarios, such as the Shared Socioeconomic Pathways (SSPs). In this context, we have developed spatial population projections at a 1 km resolution for EU countries, derived from national projections aligned with the SSPs. To achieve this, we extended a flexible downscaling model that distributes national population projections to finer spatial levels using proxies of location attractiveness. This approach offers valuable insights into future population patterns and can be easily adapted to different scales or extended to include additional drivers of population movement. Furthermore, the model allows for the integration of vulnerability indicators, policy responses, and adaptation pathways, enabling a more nuanced understanding of how demographic shifts influence exposure and contribute to overall climate risk.
By embedding these spatial population projections into a digital twin framework, we enhance the ability to explore the uncertainty space of diverse future scenarios and support data-informed planning for climate resilience.
