the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jun 2025
Quantifying the influence of coastal flood hazards on building habitability following Hurricane Irma
Abstract. Appropriate management of coastal flood risk is critical for creating resilient communities. An important part of this is estimating what buildings will become uninhabitable due to a flood event such as a tropical cyclone. To increase the accuracy of these estimations, habitability functions are developed to quantify the relationship between hydrodynamic hazards and the probability of a building becoming uninhabitable following Hurricane Irma. Hazards like maximum flood depths are determined by modeling Hurricane Irma flooding in Delft3D-FM coupled with the wave model SWAN. These modeled hazard levels are then extracted at building locations where Location Based Services (LBS) data provide information on buildings that were uninhabitable following Hurricane Irma. The developed habitability functions provide valuable insights into how different hydrodynamic parameters and regression models perform for estimating building habitability, where maximum depth is generally the best predictor of habitability. Furthermore, we find that while wooden structure habitability is significantly influenced by hazard level, concrete structure habitability is not. These findings provide novel methods for estimating coastal flooding induced building uninhabitability, enhancing how planners can prepare for floods.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2758', Anonymous Referee #1, 28 Jul 2025
- AC1: 'Reply on RC1', Benjamin Nelson-Mercer, 17 Sep 2025
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RC2: 'Comment on egusphere-2025-2758', Anonymous Referee #2, 01 Aug 2025
The manuscript outlines a methodology for defining habitability functions, which are purported to be a more accurate reflection of the impact of natural hazards and the ability of a community to recover than earlier work on damage or fragility functions. The authors focus on the impact of Hurricane Irma on locations on the Atlantic coast of Florida. The method couples information from a hurricane surge model (Delft3D-FM) with information from location based services and property data to deduce if residents of damaged buildings have resumed normal routines, linking this deduction to habitability of their homes. They indicated that the impact of water depth (flood depth) appears to be the major influence on habitability, greater than wave height or water velocities.Â
I think this is very interesting, perceptive work. I do have a few comments:Â
1) Figure 1 shows the model grid. This seems very small for hurricanes. The grid implies the assumption that water level changes generated outside the grid due to the hurricane are negligible. This may or may not be the case for this specific storm event, but is not generally the case, as many hurricane researchers using ADCIRC use their standard grid, which covers half of the Atlantic Ocean. There has been work that suggests that a small grid might miss surge forerunners and other possible motions that can cause additional damage aside from the main surge event. This might explain why the model is incapable of simulating the long surge buildup (Figure 2d). If the emphasis is on peak surge, then perhaps it doesn't matter, as the model seems to be sufficiently tuned to get the max surge right. But to what degree does this impact the velocities (and, in turn, impact the finding that the habitability functions developed with velocities perform poorly)?Â
2) Also regarding modeling: the tidal conditions from Egbert and Erofeeva can be less accurate in very shallow water, such as that near the Bahamas, where the offshore boundary is located. Was this accounted for?
3) While I understand the presumed connection between habitability and the resumption of a normal routine originating from the same dwelling, the definition of "habitability" might be somewhat ambiguous. After Katrina, many residents lived in their homes while being compelled to return to their routines, yet many of these homes had no power or water. These homes served as functional shelters but that shouldn't be confused with recovery, since they were far from recovered. In many cases, these residents were out of options. This might actually bias the reliability of these habitability functions against those with lower incomes and fewer options. I guess I would like to either see a clearer definition of "habitability" (i.e. dwelling with sufficient cover from the elements), or these habitability functions placed in a more general context.Â
4) The method relies on the availability and amount of LBS data. Collier and Monroe Counties have almost half a million residents between them. What would be possible in a place like the Louisiana coast? St Mary Parish, the largest coastal parish, has barely 50,000 residents. Is there a lower limit on data which would make the method meaningless?
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Citation: https://doi.org/10.5194/egusphere-2025-2758-RC2 - AC2: 'Reply on RC2', Benjamin Nelson-Mercer, 17 Sep 2025
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This paper introduces a new method for quantifying storm hazards and their effect on building habitability, specifically in the context of Hurricane Irma. The proposed methods offer some insight into how hazard levels (e.g., water depth, flow velocity, etc.) can be used to estimate the probability of a building becoming uninhabitable following the hurricane. However, there are several missing discussions and insights. The criteria for building habitability are overly simplified and do not account for human behavior, such as voluntary displacement despite a building remaining structurally sound. The sample size is also limited—only 920 buildings were analyzed, with just 12% classified as uninhabitable—raising concerns about potential overfitting in the regression models. The model validation is less robust than expected; for example, it lacks overland flood comparisons and relies primarily on offshore water level gauges. Despite statistical significance, there is substantial overlap between habitable and uninhabitable buildings, which undermines confidence in hazard level as a strong predictor of uninhabitability. The addition of a multivariable model does not improve predictive performance, calling into question the benefit of increased complexity. Several improvements could strengthen this work: increase the sample size by including more regions, hurricanes, or buildings; revisit and expand the discussion of model limitations; clarify the assumptions behind using cell phone data as a proxy for habitability; and test whether altering the return-date threshold affects the results. Finally, the applicability of this method to regions outside the U.S. is unclear. Given the U.S.-centric dataset, it remains uncertain whether this approach could be generalized globally.
The following items show concerns of the paper itself that I think should be remedied before publication acceptance: