The manuscript by Pianosi et al. proposes a framework to analyse the sensitivity of large-scale flood models, taking into account the most relevant sources of uncertainty. In particular, the framework aims at reducing the complexity of the anaysis while maintaining the significance of results. 

I appreciated the opportunity to review this work. It's good to see a collaboration between academic and private sectors and I believe the topic is of high interest for the scientific community working on large-scale flood models. 

Having said so, I have some remarks mainly regarding the application of GSA to the two case studies. 

Main points

• The authors do not describe the JBA flood loss model but do provide some references to past works. However, my impression is that the outcomes of the application examples might be influenced by some modelling assumptions (see my following points) and therefore I would suggest including at least a short description of the JBA model.
• Uncertainty in hazard maps: previous studies have shown that hazard maps from large-scale flood models might show limited changes in flood extent and depths across return periods. For example, the 1-in-50-year and 1-in-100-year flood maps might be similar (see Bernhofer et al. 2018, and Aerts et al. 2018). If this is the case for JBA model, this might explain why this parameter has a limited importance in GSA outcomes. Moreover, the authors should explain how flood protection standards are considered in the model, given that they are a major component of  hazard map uncertainty (Paprotny et al 2025).   
• - Uncertainty ranges in Table 1: the ranges of exposed values and damage functions applied in GSA come from a previous work by Sarailidis (2023). This is fair enough, however, the assumptions and limitations of the study by Sarailidis should be better explained. For instance, my understanding is that Sarailidis obtained the range of damage ratio reported in table 1 by perturbing the values from multiple damage functions found in literature, which in my opionion might lead to overestimate the uncertainty associated to this parameter. Given the outcomes of GSA for the Rhine case study, it is important to communicate that these outcomes might be influenced by this and other assumptions.  

Minor points:

• Abstract: The authors begin with "Flood loss models are increasingly used in the (re)insurance sector to inform a range of financial decisions." I would add that flood loss models are increasingly important also in research, (for instant, to understand present and future risk trends as discussed in Ward et al., 2015), as well as in policy support (see for instance the PESETA programme, https://joint-research-centre.ec.europa.eu/projects-and-activities/peseta-climate-change-projects/jrc-peseta-iv/river-floods_en). This would highlight the relevance of the present study beyond the insurance sector.
• One of the main outcomes is the relevance of vulnerability functions in driving model uncertainty, I believe this should be mentioned in the abstract.
• Reference list: missing title for Galloway et al (2025)

References

Aerts, J. P. M., Uhlemann-Elmer, S., Eilander, D., and Ward, P. J.: Comparison of estimates of global flood models for flood hazard and exposed gross domestic product: a China case study, Nat. Hazards Earth Syst. Sci., 20, 3245–3260, https://doi.org/10.5194/nhess-20-3245-2020, 2020.

Bernhofer

Bernhofen, M. V., Whyman, C., Trigg, M. A., Sleigh, P. A., Smith, A. M., Sampson, C. C., Yamazaki, D., Ward, P. J., Rudari, R., Pappenberger, F., Dottori, F., Salamon, P., and Winsemius, H. C.: A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique, Environ. Res. Lett., 13, 104007, https://doi.org/10.1088/1748-9326/aae014, 2018. 

Paprotny, D., ’t Hart, C.M.P. & Morales-Nápoles, O. Evolution of flood protection levels and flood vulnerability in Europe since 1950 estimated with vine-copula models. Nat Hazards 121, 6155–6184 (2025). https://doi.org/10.1007/s11069-024-07039-5

Ward, P. J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T., Muis, S., de Perez, E. C., Rudari, R., Trigg, M. A., and Winsemius, H. C.: Usefulness and limitations of global flood risk models, Nat. Clim. Change, 5, 712–715, https://doi.org/10.1038/nclimate2742, 2015.

The manuscript ‘Global sensitivity analysis of large-scale flood loss models’ applies the GSA method to two case studies. As the main author has published about GSA in the past, the main contribution are the case studies.  However, I see several issues with how those are described and evaluated, as already also mentioned by other commenters here. It reduces the value of an otherwise interesting and potentially useful publication, but I believe the authors will be able to address all issues in the revision.

I would particularly stress the importance of comments by the other reviewer, Francesco Dottori. I concur that the lack of description of the JBA flood model is an issue to be addressed. No mention of flood protection levels is also troubling, given that it is often found to be the most sensitive parameter (see e.g. https://doi.org/10.5194/nhess-18-2127-2018). Also, the inconsistency of uncertainty ranges in Table 1 is problematic: vulnerability curves are assumed to have wide margins, much more than the flood event set and hazard maps (which are given arbitrary +/-50% range) or exposure (which has about  +/-25% range). This rather unsurprisingly leads to conclusion that vulnerability is the most sensitive component for the Rhine. Better explanation of the choice of uncertainty ranges is needed.

I would highlight that the Rhine catchment was subject to a GSA study before, which is not mentioned by the authors (https://doi.org/10.5194/nhess-18-3089-2018). There, a major sensitivity of impacts to changes in flood protection, land use and reservoirs is highlighted. The authors of that analysis also used well-designed uncertainty ranges with a strong rationale from literature and empirical data. It would be important to compare and discuss those results in context of the results presented here.

The case studies are implemented with very different methods and presented also completely differently. With the selection of model components and the choice of uncertainty distributions being quite arbitrary, the paper shows that there is a wide possibility of pre-selecting an outcome of any “validation test” (mentioned in the introduction), hence going against the authors’ final sentence; “We hope this paper will provide motivation as well as practical ideas to foster the application of GSA to flood risk models and contribute to increasing their transparency and legitimacy.”  The challenge remains to fairly evaluate different models components with realistic uncertainty ranges and indeed identify the elements that matter: not only flood protection, reservoirs, land use, vulnerability beyond depth-damage functions, but also inundation modelling method (e.g. https://doi.org/10.1029/2024EF005164, model resolution (e.g. https://doi.org/10.1029/2023WR035100 or hydrodynamic parameters (e.g. https://doi.org/10.1016/j.coastaleng.2024.104541). An improved discussion on this would be beneficial, given that the title, abstract and introduction suggests a more comprehensive, overarching study.

Minor points:

Several figures include underlining of errors created by MS Office

CRESTA acronym is explained after one of the subsequent uses, not the first one.

L339: what level of CRESTA units is used here?