Herein, the authors create a set of 25 counterfactual extreme precipitation events to simulate the catastrophic flooding seen in the Ahr Valley, Germany during July of 2021. Their use of downscaled precipitation data and hydrological modelling showed that small shifts in the trajectory of the storm systems could have resulted in even worse flooding events than what was experienced. This type of analysis shows stakeholders and policy makers how best to be prepared for natural disasters and emerging climate risks.



Overall, I found the paper to be of excellent quality. I had one major comment on the precipitation data used and a few minor comments (see attached). After those are addressed, I am confident this paper will be ready for publication.

Dear Authors,

I really enjoyed reading your manuscript, as it is written clearly and drives all key information in the proper way.

As you can see from my comments below, I do not see critical points in the text, and I am generally in favour of its publication after revision. I hope my comments will help you in clarifying the overall approach and what we can learn from this example.

General comments

I suggest adding some more comments on the numerical model in the Abstract (which one? calibrated/validated how?), as results depend on it.

Ludwig et al. (2023) pointed out a significant role of sediment in the 2021 event. As you used a hydrodynamics model assuming clear water, could you please comment on potential uncertainties connected with river/valley morphodynamics? What about considering other floating materials constituting the accumulated debris?

Figure 4 shows that the model tends to anticipate the observed/reconstructed flood wave. Does this affect your results? Could you please comment a bit more on this, while presenting the results (Sec. 4.2)

Secs. 4.5 & 4.6: As the results are influenced by simplifications in the modelling scheme and uncertainties in the input data, I suggest providing not only final exact results, but also discussing more in detail uncertainties, providing their estimate

In the Discussion, you pointed out that, out of infinite spatial counterfactual scenarios, you used only 25. Could you please provide more details on how you selected them, also expanding the Introduction by adding some more comments on the study rationale?

From your results, one can argue that flood mapping should be done by considering multiple (potentially infinite) scenarios. Do you think that flood risk mapping and communication should be improved? How? In what direction should research go, to help communities to increase their willingness to undertake risk reduction measures for unprecedented events? This point could be discussed also in light of the very recent floods caused by storm Boris.

Specific comments & Technical corrections

Figure 1: please add a world map and a map of Germany to better locate the study area for readers not familiar with the region. DEM elevation is [m asl]

line 200: “RANDOLAN” is wrongly spelled

line 318: please add the units of Manning’s roughness n

line 449: you stated that “differences are small”. Is it possible to have an estimate of how much small?

line 559: I suggest deleting “an apocalyptic scenario beyond the imagination of decision-makers and flood-prone people” as it sounds a bit too personal statement and not a scientific one (even if it’s true)

Figs. A1-A6: I think there is a typo in the legend, as the red box should read “… statistics”

The work of Montanari is now published, so please update the references. Montanari, A., Merz, B., & Blöschl, G. (2024). HESS Opinions: The sword of Damocles of the impossible flood. Hydrology and Earth System Sciences, 28(12), 2603-2615. https://doi.org/10.5194/hess-28-2603-2024

Please update the reference Khosh Bin Ghomash, S., Apel, H., & Caviedes-Voullième, D. (2024). Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event. Natural Hazards and Earth System Sciences, 24(8), 2857-2874. https://doi.org/10.5194/nhess-24-2857-2024