Comment on egusphere-2025-5172

Abstract. This study addresses the challenges of exposure modelling at the building or object-level in Germany, motivated by the need for harmonized, open-access data in next generation risk assessments. While aggregated exposure data suffice for many applications, detailed object-level data are increasingly essential for tasks such as local risk management and impact forecasting. However, this object-level information is often proprietary, protected by regulation, poorly documented, and fragmented because data on building usage, structural type, or replacement costs is often not readily available or not compiled in one dataset. To address this gap, we present an evaluation of potential exposure modelling frameworks utilizing various disaggregation approaches and source data from cadastre-derived, crowd-sourced, national accounts, and fit-for-purpose datasets. Using information collected from an area recently affected by a flood disaster and a weighted scoring model, we evaluate the ability of candidates to assign a building’s economic sector and asset value against our hand-labelled benchmark dataset. Ultimately, we find an exposure modelling framework disaggregating national-accounts onto cadastre-derived building footprints slightly out-performs other candidates owing mainly to its transparency and adaptability. However, we conclude that all but the land-use derived candidate are defensible exposure modelling frameworks — so long as some relevant validation is performed. The frameworks presented here enable the transparent, reproducible, and maintainable multi-sector object-level exposure modelling necessary for the next generation of risk analysis and impact forecasting.