Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Mapping uncertainty in flood impacts: a multi-source assessment and catalogue of simulated, remotely sensed and reported floods in Europe
Lorenzo Scarpellini,Andrea Ficchì,Claudia D'Angelo,Andrea Betterle,Peter Salamon,and Andrea Castelletti
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.
Received: 15 May 2026 – Discussion started: 01 Jun 2026
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The manuscript presents a comprehensive assessment of the uncertainties associated with continental-scale flood risk estimation in Europe. By comparing EFAS-LISFLOOD simulations under three flood protection scenarios (FLOPROS, P25, and No Protection) with satellite-derived observations (Copernicus GFM) and historical impact records (Hanze database), the authors examine the characteristics and limitations of each data source.
The analysis shows that flood protection assumptions are a major source of uncertainty, substantially influencing estimated losses and the relative risk ranking of European countries. The manuscript also examines the limitations of different reference datasets, including the tendency of the model to overestimate inundation due to static hazard map aggregation and the tendency of satellite observations to underestimate flooding in urban areas because of SAR-related limitations.
The manuscript is well organized, and the conclusions are generally supported by the presented analyses. I recommend this manuscript for Moderate/Minor Revisions. The following comments are intended to further clarify the discussion and strengthen the manuscript.
Spatial mismatch in flood protection (limitations of FLOPROS): While the manuscript demonstrates the influence of using FLOPROS versus P25, the discussion could further address the implications of applying polygon-based protection standards at the NUTS3 level to localized hydrological processes. Applying a uniform protection standard across an entire NUTS3 region may overrepresent protection in rural and agricultural areas where such infrastructure is limited. Discussing how this spatial mismatch contributes to differences in agricultural damage estimates between the model and GFM would improve the interpretation of the results.
Addressing the low return-period gap: The manuscript identifies that the absence of hazard maps for return periods between 2.5 and 10 years accounts for nearly 20% of the unmatched events. Because EFAS is an operational system, the authors may consider emphasizing this finding in the Abstract or Conclusions and suggesting the development of hazard maps for return periods below 10 years in future Copernicus/EFAS releases.
Satellite limitations in urban areas: The manuscript attributes the underdetection of flooding in residential and industrial areas by GFM to SAR backscatter limitations and the 0.1 m water-depth threshold. The discussion could briefly mention potential directions for improving urban flood mapping, such as integrating multiple remote sensing data sources (e.g., SAR and optical imagery) or data assimilation and AI-based approaches.
Integration of multiple data sources: The manuscript demonstrates that each dataset (hydrodynamic simulations, SAR observations, and reported impacts) has different limitations, suggesting that combining multiple information sources could improve flood risk assessment. The discussion could mention recent developments in Earth system foundation models or flood foundation models as a possible research direction. Such models could potentially integrate hydrodynamic simulations, remote sensing observations, and historical impact records to improve consistency among different data sources and reduce the uncertainties identified in this study.
The use of a 90-day window is justified based on economic recovery considerations. However, from a hydrological perspective, this criterion may merge independent flood events occurring within the same period. A brief discussion of the potential influence of this assumption on event matching with the Hanze and GFM datasets, or a short sensitivity statement, would help clarify its implications.
In Section 3.2, the large number of events detected by both GFM and the model but absent from the Hanze database in Scandinavia is attributed to sparse population and limited reporting. It may be useful to further characterize Hanze as an exposure-dependent dataset, whose spatial coverage is influenced by population distribution and reporting practices in addition to hydrological hazard. This distinction would provide a more precise interpretation of the observed discrepancies.
Lorenzo Scarpellini,Andrea Ficchì,Claudia D'Angelo,Andrea Betterle,Peter Salamon,and Andrea Castelletti
Data sets
[Dataset] Mapping uncertainty in flood impacts: a multi-dataset assessment and catalog of simulated, remote sensed and reported floods in EuropeLorenzo Scarpellini, Andrea Ficchì, Claudia D'Angelo, Andrea Betterle, Peter Salamon, and Andrea Castelletti https://doi.org/10.5281/zenodo.19666073
Model code and software
[Dataset] Mapping uncertainty in flood impacts: a multi-dataset assessment and catalog of simulated, remote sensed and reported floods in EuropeLorenzo Scarpellini, Andrea Ficchì, Claudia D'Angelo, Andrea Betterle, Peter Salamon, and Andrea Castelletti https://doi.org/10.5281/zenodo.19666073
Interactive computing environment
[Dataset] Mapping uncertainty in flood impacts: a multi-dataset assessment and catalog of simulated, remote sensed and reported floods in EuropeLorenzo Scarpellini, Andrea Ficchì, Claudia D'Angelo, Andrea Betterle, Peter Salamon, and Andrea Castelletti https://doi.org/10.5281/zenodo.19666073
Lorenzo Scarpellini,Andrea Ficchì,Claudia D'Angelo,Andrea Betterle,Peter Salamon,and Andrea Castelletti
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This study shows that flood protection assumptions strongly affect risk estimates, and that flood model simulations, satellite observations, and historical records often disagree. Simulations overestimate large floods, satellites miss small ones, and historical records favour populated areas. No single data source provides a complete picture, highlighting the need to combine multiple datasets for more reliable continental-scale flood risk assessments.
This study shows that flood protection assumptions strongly affect risk estimates, and that...
The manuscript presents a comprehensive assessment of the uncertainties associated with continental-scale flood risk estimation in Europe. By comparing EFAS-LISFLOOD simulations under three flood protection scenarios (FLOPROS, P25, and No Protection) with satellite-derived observations (Copernicus GFM) and historical impact records (Hanze database), the authors examine the characteristics and limitations of each data source.
The analysis shows that flood protection assumptions are a major source of uncertainty, substantially influencing estimated losses and the relative risk ranking of European countries. The manuscript also examines the limitations of different reference datasets, including the tendency of the model to overestimate inundation due to static hazard map aggregation and the tendency of satellite observations to underestimate flooding in urban areas because of SAR-related limitations.
The manuscript is well organized, and the conclusions are generally supported by the presented analyses. I recommend this manuscript for Moderate/Minor Revisions. The following comments are intended to further clarify the discussion and strengthen the manuscript.