the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Apr 2026
Valuing Salt Marshes as Nature-based Infrastructure for Coastal Flood Mitigation: A Case Study of Chatham County, GA
Abstract. Flooding poses significant economic challenges to coastal counties, affecting nearly 40 % of the U.S. population. Nature-based solutions, also known as green infrastructure, are increasingly recognized as effective alternatives or complements to traditional gray infrastructure for flood risk mitigation. This study examines the flood damage reduction benefits of salt marshes, a key type of green infrastructure. We use physics-based spatially explicit hydrodynamic models to simulate storm scenarios and the resulting inundation depths with and without salt marshes. We then translate hydrological data into economic benefits by applying two distinct approaches, one based on the traditional US Army Corps of Engineers depth-damage function and another with an estimated depth-damage function derived from the National Flood Insurance Program (NFIP) claims data. Applying our integrated approach to the case study area, Chatham County in Georgia, we find that salt marshes contribute to significant damage reductions, ranging from $ 30 million to $ 40 million for a storm representative of the 1 % annual exceedance probability event. This study offers policymakers valuable insights into implementing flood mitigation strategies through marshland conservation. Our integrated modeling framework is readily adaptable to coastal regions worldwide where salt marshes or similar coastal ecosystems provide flood-mitigation services.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1475', Anonymous Referee #1, 07 May 2026
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RC2: 'Comment on egusphere-2026-1475', Anonymous Referee #2, 20 May 2026
Dear editor, dear authors,
The current manuscript described a model framework to estimate the monetary benefits of salt marshes in Chatham County, GA. They couple a hydrodynamic model to a damage assessment to provide a monetary value of around $30 million to $40 million per storm (1% annual exceedance probability). The manuscript is easy to read and has barely any English mistakes. I do have some questions and concerns on the analysis. Therefore I recommend major revisions.
Major comments
1) In the introduction there is already a big focus on explaining the methodology. I recommend erasing these explanations (e.g., lines 49 – 56) and making sure this is all covered in the methodology. Aside from that, the contribution to the current literature can be made more clear. There are more models that translate the effectiveness of NBS into a monetary value. What is your specific novelty? The salt marshes that are studied? The NFIP data to construct depth-damage curves?
2) In your methodology you compare 2 situations with each other: the setup with and without salt marshes. The situation with the salt marshes is the current situation as I understood from your manuscript. I think the authors should place more emphasis on this. The title could make a reader believe that the authors are quantifying the effects of newly developed salt marshes.
3) For the hazard simulation only 2 storms were modelled of which only 1 storm is used for the main analysis. It is unclear to me why the authors chose to limit themselves to only 1 storm. I can imagine that a storm with a different magnitude (e.g. a 200 year, 500 or 1000 year storm) can drastically influence the results and the monetary benefits of the salt marshes. Moreover, the location where the storm makes landfall can also influence the flooding patterns. I recommend the authors to add an extra analysis testing a few different variations of storms to see how if the monetary benefits are equal in size for other storms.
4) The description of the model validation is lacking. Figure 3 shows the difference between the observed and simulated water levels for a station near the study area. However, there is no figure showing where this station is located. This could be added to Figure 2. Moreover, there is no performance metric reported, for example the RMSE. Right now it is hard to interpret whether the water level differences are very large. Lastly, there is no validation for the storm the authors used in the analysis. I can imagine that there are water levels available for storm 157, and even water extent data to validate the hydrodynamic model.
5) Figure 5 shows the USACE damage curves used in the study. However, when looking at the figure I am confused why there is already damage for buildings with no basements when water depths are below 0 meter. This requires further explanation. Moreover, the plot is hard to read with the lack of a grid. I recommend adding a grid to the figure instead of a white background.
6) In paragraph 3.2 the depth-damage curve derived by the authors from the NFIP is presented. Can the authors plot these curves together with the ASACE curves (from Figure 5)? This way a reader can easily compare the difference between both curves.
7) In Table 3 the results from the regression analysis are presented. All 4 regressions have quite low R2 values (0.0165 – 0.0440). The authors don’t reflect on these low values, which indicate quite some uncertainty in the presented depth-damage curves. Endendijk et al. (2023) also used a regression analysis with damage data collected through a survey after a recent flood and had adjusted R2 values of 0.193 – 0.196, which are significantly higher.
8) In lines 293 – 295 the authors write the following: “The average flood damage per claim was $19,022 in Chatham County and $27,177 nationwide, according to the NFIP data over the last 40 years. This indicates that salt marshes’ flood damage mitigation benefits, about $2,000 per storm per house, equal about 10.5% of the average flood damage recorded in the past across homes in Chatham County”. I do not understand how the authors conclude that the mitigations benefits are around $2,000 per storm per house. Please add a further explanation.
9) In Figure 9 the authors present two histograms of the monetary benefits per water depth class. Figure A and B are hard to compare because they do not use the same water depth bins. Moreover, they use a different y-axis (0–10,000 and 0–7,000) I recommend to make the bins the same, and ideally to plot the subfigures together with the bars next to each other. This allows for easier comparison.
10) In Figure 10 the colour scale is an odd choice. Negative benefits are plotted in blue and positive benefits in red. Usually red is associated with something negative. I recommend the authors to change this colour scale to (for example) red (negative) to green (positive).
11) In paragraph 3.5 the authors present a spatial distribution of mitigation benefits for several socioeconomic factors like racial composition and poverty rate. This adds an interesting angle to the results. However, the source of the data is not described in the methodology, as well as the details of the method. I also find it odd that the main focus is placed on the racial composition, while the poverty rate is only mentioned in 9 lines. I think this paragraph should be rewritten to be more balanced. Moreover, it is impractical that some of the figures which the authors refer to are put in Appendix B. I recommend at least adding some of the results for the poverty analysis to the main manuscript.
12) The discussion chapter lacks a thorough discussion of the uncertainties in the results, especially in the hydrodynamic modelling (e.g., model choice, storm choice, method of modelling salt marshes) and the creation of their own depth-damage curve. Some uncertainties now are only mentioned, but it is not very well explained how they effect the main findings of the study.
13) The authors in the discussion emphasize that the monetary benefits of the salt marshes are around $30 million to $40 million per storm. How do these benefits compare to similar NBS? Is it a lot, or quite little?
Endendijk, T., Botzen, W. J. W., deMoel, H., Aerts, J. C. J. H., Slager, K., & Kok, M. (2023). Flood vulnerability models and household flood damage mitigation measures: An econometric analysis of survey data. Water Resources Research, 59, e2022WR034192. https://doi.org/10.1029/2022WR034192
Citation: https://doi.org/10.5194/egusphere-2026-1475-RC2
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The manuscript under consideration for publication addresses the capacity of salt marsh vegetation to reduce coastal flood damages to buildings at Chatham County, Georgia, USA. The study used an existing hydrodynamic model in combination with depth damage functions to estimate flood damages and corresponding reductions provided by current salt marsh cover. Finally, spatial distribution of these benefits are studied using a GIS overlay with socioeconomic data.
The novelty is in the expansion of the damage function based on National Flood Insurance Program (NFIP) starting from the work by Wing et al. (2020). Specifically, fitting a non-linear function and including residential homes up to three stories with and without basement. I found the research gap poorly described and making a too large claim, as combining hydrodynamic modelling with depth-damage function is to-date a standard procedure and is even applied at the global scale (e.g. Tiggeloven et al. (2022), and local scale (e.g. Philippines by Menendez et al. (2018)). In addition, the hydrodynamic model which is presented as state-of-the-art is omitting critical physical processes to accurately predict coastal inundation especially in vicinity of coastal wetlands. Finally, the level of validation of the integrated framework is lacking and is in my opinion a critical component in the analysis. In this review I highlight a number of attention points that could be implemented as part of a major revision, but would require substantial alternation of the approach and/or structure of the manuscript. A detailed review is provided in the attachment.