Flood impact on business downtime: analysis of post-flood observed data in Germany

Flood events can significantly disrupt economic activities, yet the relationship between flood characteristics and business downtime remains underexplored. Downtime estimates are currently based on expert evaluations, the differentiation by sector type is highly aggregated, and assessments based on observed data are very limited. This study leverages a comprehensive database of post-flood information collected in Germany to examine how flood hazard characteristics and exposure attributes of economic activities influence the duration of operational interruptions. The research objectives are: (1) to investigate the correlation between various flood hazard characteristics and resulting business downtime, and (2) to assess the relationship between direct damages and downtime, accounting for the specific attributes of exposed entities.

The database includes detailed information collected via telephone interviews conducted after flood events in the period of 2002 - 2013. Variables encompass hazard characteristics (e.g., water depth, event duration), exposure characteristics (e.g., industrial sector, number and type of buildings, equipment and stock values), impact measures (e.g., total damages to buildings, equipment, and goods, downtime duration), and adaptation strategies (e.g., emergency plans, alarm times, protective measures). Key variables are classified into independent (e.g., hazard characteristics), dependent (e.g., downtime measures), and control categories (e.g., qualitative and descriptive responses). The analysis is adopting traditional statistical methods, including Pearson's correlation, regression analysis, and ANOVA, to evaluate linear relationships, alongside machine learning techniques—such as clustering, decision trees, random forests, and neural networks—to uncover complex, non-linear interactions among variables.

The findings of this research will provide valuable insights into the dynamics of business interruption and contingent business interruption caused by flood events. By expanding the understanding of how hazard characteristics, exposure attributes, and adaptive strategies interact to influence downtime, this study lays the groundwork for advancing risk assessment models of natural hazard into economic sectors. These results will not only support the insurance sector in evaluating and managing collective risks but also contribute to the development of more robust strategies for enhancing societal and economic resilience to natural hazards.