Proceedings of IAHS
Proceedings of IAHS
PIAHS
Articles | Volume 386
Articles | Volume 386
https://doi.org/10.5194/piahs-386-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/piahs-386-181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 386
Post-conference publication
 | 
19 Apr 2024
Post-conference publication |  | 19 Apr 2024

Flood damage model bias caused by aggregation

Seth Bryant, Heidi Kreibich, and Bruno Merz

Viewed

Total article views: 1,106 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
868 188 50 1,106 59 113
  • HTML: 868
  • PDF: 188
  • XML: 50
  • Total: 1,106
  • BibTeX: 59
  • EndNote: 113
Views and downloads (calculated since 19 Apr 2024)
Cumulative views and downloads (calculated since 19 Apr 2024)

Viewed (geographical distribution)

Total article views: 1,066 (including HTML, PDF, and XML) Thereof 1,066 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2026
Download
Short summary
Our study found that simplifying data in flood risk models can introduce errors. We tested 344 damage functions and found errors up to 40 % of the total asset value. This means large-scale flood risk assessments may have significant errors due to the modelling approach. Our research highlights the need for more attention to data aggregation in flood risk models.
Read more
Share
Special issue
ICFM9 – River Basin Disaster Resilience and Sustainability...