Mapping uncertainty in flood impacts: a multi-source assessment and catalogue of simulated, remotely sensed and reported floods in Europe

Abstract. Flooding is Europe’s most costly natural hazard, with riverine floods alone accounting for over a third of disaster related damages, which have increased nearly tenfold since the 1990s. Despite advances in large-scale hydrological modeling and flood hazard mapping, continental-scale flood risk assessments remain highly uncertain, also due to simplified representations of flood protections and limitations in observational datasets and reported impacts. In this study, we assess how flood protection assumptions and data sources influence flood impact and risk estimates across Europe. We compile and provide a catalogue of simulated flood events based on the European Flood Awareness System (EFAS, version 5.0) modeling chain and quantify differences across three flood protection scenarios. Simulated events are then systematically compared with satellite-derived flood extents and impacts from the Copernicus Global Flood Monitoring (GFM) system, as well as with reported impacts from the HANZE database, using an automated event-matching procedure. Results indicate that flood protections are a major source of uncertainty, strongly affecting simulated flood frequency, spatial distribution, and damages. Moreover, substantial inconsistencies are found between simulations, observations, and reported data. Simulations tend to overestimate flood extents and damages, especially for large events, while satellite observations underestimate small and short-lived floods, particularly in urban environments. Reported datasets capture only a subset of impactful events and are biased toward populated regions, resulting in limited agreement across datasets and in differing regional impact patterns. Overall, results show that no single dataset can be currently considered a ground truth for flood impacts at continental scale, highlighting the need for multi-dataset approaches in large-scale flood risk assessment. These findings contribute to a more robust interpretation of flood risk estimates and support the development of more reliable and uncertainty-aware multi-dataset approaches for continental-scale flood risk assessments.