Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-163-2026
https://doi.org/10.5194/nhess-26-163-2026
Research article
 | 
16 Jan 2026
Research article |  | 16 Jan 2026

Deciphering the drivers of direct and indirect damages to companies from an unprecedented flood event: A data-driven, multivariate probabilistic approach

Ravikumar Guntu, Guilherme Samprogna Mohor, Annegret H. Thieken, Meike Müller, and Heidi Kreibich

Viewed

Total article views: 1,551 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,275 233 43 1,551 118 38 55
  • HTML: 1,275
  • PDF: 233
  • XML: 43
  • Total: 1,551
  • Supplement: 118
  • BibTeX: 38
  • EndNote: 55
Views and downloads (calculated since 25 Apr 2025)
Cumulative views and downloads (calculated since 25 Apr 2025)

Viewed (geographical distribution)

Total article views: 1,551 (including HTML, PDF, and XML) Thereof 1,551 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Feb 2026
Download
Short summary
The 2021 flood in Germany caused severe damage to companies, with over half reporting losses above € 100 000. Using probabilistic models, we identify key factors driving direct damage and business interruption. Water depth, flow velocity and company exposure were key factors, but preparedness played a crucial role. Companies that took good precaution recovered faster. Our findings stress the value of early warnings and risk communication to reduce damage from unprecedented flood events.
Share
Altmetrics
Final-revised paper
Preprint