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

This study explores the drivers of both direct and indirect damages during the July 2021 extreme flash flood event in Germany. Results show that 62 % of companies incurred direct damages exceeding EUR 100 000. Machine learning models and Bayesian network analyses identify water depth and flow velocity as the primary drivers of both direct damage and business interruption.

However, company characteristics (e.g., size premise, number of employees) and preparedness also play critical roles. Companies that implemented precautionary measures experienced significantly shorter business interruption durations – up to 58 % for water depths below 1 m and 44 % for depths above 2 m. These findings offer important insights for policy development and risk-informed decision-making. Incorporation of behavioural indicators into flood risk management strategies and improving early warning systems could significantly enhance business preparedness.