the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deciphering the drivers of direct and indirect damages to companies from an unprecedented flood event: A data-driven, multivariate probabilistic approach
Abstract. Floods are among the most destructive natural hazards, causing extensive damage to companies through direct impacts on assets and prolonged business interruptions. The July 2021 flood in Germany caused unprecedented damages, particularly in North Rhine-Westphalia and Rhineland-Palatinate, affecting companies of all sizes. To date, no study has examined the factors influencing company damages during such an extreme event. This study addresses this gap using survey data from 431 companies affected by the July 2021 flood. Results show that 62 % of companies incurred direct damages exceeding €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., premises size, 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 behavioral indicators into flood risk management strategies and improving early warning systems could significantly enhance business preparedness.
Competing interests: The author Heidi Kreibich is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-1715', Anonymous Referee #1, 16 Jun 2025
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AC1: 'Reply on RC1', Ravi Kumar Guntu, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1715/egusphere-2025-1715-AC1-supplement.pdf
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AC1: 'Reply on RC1', Ravi Kumar Guntu, 15 Sep 2025
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RC2: 'Comment on egusphere-2025-1715', Anonymous Referee #2, 19 Jun 2025
This manuscript quantifies drivers of damages to companies by rare flood events via 3 data-driven techniques, which ultimately lead to a Bayesian Network. This study could have potential, but its possible novelty is currently hidden behind a rather complicated and untransparent chain of calculations. In particular, the justification of using the 3 data-driven models is unclear. Why not less? Why not more? Why these? This could easily be arbitrary. And what does the Bayesian Network add to the variable importance analysis via those 3 models? I raise more questions below. I believe these should be addressed before the paper can be reconsidered for publication.
Title: I suggest a different word than “deciphering” because that’s not what is being done in this study.
L44-52: The message needs to be streamlined here with regard to rare/high-impact events.
L107, 109, 199 and elsewhere: Consider something like “rare” in place of “unprecedented”, because there now is a precedent.
L141, L214: The analyses for each damage type could have been combined, as they are also internally related, via a multivariate regression. Why employ this more elegant solution making optimal use of all information (by not considering the responses as independent)?
L143: Across what scale where the missing data imputed, i.e. how far were they apart on average.
L155: J(beta) is not in the equation.
L157: What does use of the MAE as objective function imply about the nature of the residuals given a response which is between 0 and 1 or counts between 0 and 540?
L159f: It’s not entirely true that the model cannot handle nonlinearities – it can do so via transformations or in Generalised Linear Model form.
L201ff: What are the implications of combining the variable importance across the 3 models?
Eq9, L219, Appendix: It’s conditional probabilities, not fractions in Bayes Rule! I.e. X_i|E and E|X_i.
L222f: Why not leave it discrete rather than introducing another layer of assumptions?
L228f: How do the five models relate to the Bayesian Network?
L230-242: This part is redundant – see above. The function of the 3 models, despite factor selection is unclear. And why 3 models and not more or less?
Results & discussion: Too much time is spent describing univariate results. And the bivariate correlations kinf o defeat the purpose of multivariate analysis.
L394f: Purpose of sentence unclear.
L373: Who’s expert knowledge?
Fig6: What is it’s function for the manuscript?
Fig7: The directions matter here, no? And some of them are not intuitive!
Conclusion: Too short and doesn’t add sufficient novelty.
Citation: https://doi.org/10.5194/egusphere-2025-1715-RC2 -
AC2: 'Reply on RC2', Ravi Kumar Guntu, 15 Sep 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1715/egusphere-2025-1715-AC2-supplement.pdf
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AC2: 'Reply on RC2', Ravi Kumar Guntu, 15 Sep 2025
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This is an interesting and highly relevant topic that can significantly contribute to a deeper understanding of the various factors influencing both direct and indirect damages to businesses caused by flooding events. The research methods employed are notably technical and innovative, offering fresh perspectives and valuable insights into the complexity of flood-related impacts on commercial sectors. However, despite the strengths of the approach, there are certain points that require further attention and refinement. These include the justification of chosen methodologies, the interpretation of the survey results, a more clear interpretation of the results, and the need for a more comprehensive discussion of the limitations and potential implications of the findings. Addressing these aspects would enhance the overall robustness and applicability of the study.
Review comments:
Method
Results and discussion
Please also add a discussion that elaborates on any shortcomings such as low sample size for some company sizes/sectors and outliers, potential selection bias etc. Directions for future research.
Conclusion: