| 08 Jan 2026
Regional modeling of the impacts of tidal flooding in the context of mean sea level rise on low-lying in the Global South
Abstract. This study assessed the risks and impacts of rising average sea levels on Brazil's semi-arid coastline in a low-lying coastal area with limited response potential, using freely available data and based on the central hypothesis that, even in conservative scenarios, there will be risks with significant impacts. The methodology integrated DEM calibration, geodetic validation of tide gauge data, flood modeling, and overlay with real estate grids to quantify damage. The results showed relative stability of astronomical tides, with projected extremes of up to 2.975 m and 3.454 m, respectively, for a 20-year return period. Meteorological tides showed low values (≈ 0.11 m), although with episodic variability. The modeling indicated that up to 14 % of the total area (about 730 km²) could be affected in extreme scenarios, with progressive flooding of solar salt pans and low-lying urban areas. Cities such as Areia Branca, Macau, and Porto do Mangue are at the highest risk, with a 60–80 % probability of flooded days in severe scenarios. Economic losses were estimated at approximately R$ 36 million in residences (≈ US$ 6.7 million) and R$ 158 million in land (≈US$ 29 million), with Areia Branca being the most impacted municipality. Towns such as Barra, Cristóvão, and Baixa Grande also experienced significant risks and damage. The findings reinforce the usefulness of open data for regional risk analysis, even recognizing limitations in spatial resolution and vertical uncertainties. The methodology proved promising, replicable, and useful for supporting adaptive policies in regions with low institutional technical capacity.
The study evaluated the risks and impacts of rising mean sea levels on Brazil’s semi-arid, low-lying coastline using freely available datasets. The authors proposed a framework that integrates calibrated digital elevation models (DEM), validated tide-gauge data, flood modeling, and spatial overlays with real estate grids to estimate the impacts of tidal flooding. Overall, this study is meaningful for more informed coastal flood risk management, especially for areas with limited institutional and technical capacity. However, I still have several concerns and suggestions as follows.
1) Lines 90-94: As the authors mentioned, quite a few studies have been conducted to investigate the flooding risk analysis for low-lying coastal areas. Then what makes this study different from the literature?
2) It is suggested to add a list of acronyms mentioned in the manuscript. The full term of the acronym is only presented the first time it appears, e.g., SGB, however, does not make sense since it is short for "Brazilian Geodetic System". Should it be "BGS"?
3) Equation (1): Exposure is also an important factor in determining the degree of risk. Is it possible to incorporate the effect of exposure in your risk analysis? How would that affect the findings in this study?
4) In the caption of some figures, it is suggested to remove the unnecessary statement like "Map prepared by the authors (2025)".
5) Section 3.1: It is suggested to add a table to present the information of different datasets used in this study and make the corresponding text more concise.
6) Figure 3, Equations (3) and (4), Table 3, Table 4, etc.: To avoid confusion, please change the comma "," to decimal point "." in the elevation numbers.
7) Lines 245-248: The bathtub technique does not account for hydrodynamics and it is very likely that it will lead to overestimates in flood inundation extents. The applicability of the bathtub technique should be justified in more detail. Moreover, it should be noted that various uncertainty sources in the physics-based flood modeling process should not be ignored (Please refer to the paper below). Also, too many references are cited here. It is suggested to remove some old ones.
Reference:
"Uncertainty analysis and quantification in flood insurance rate maps using Bayesian model averaging and hierarchical BMA" (https://doi.org/10.1061/JHYEFF.HEENG-58)
8) Lines 264-266: 0 to 5 is actually six classes. According to Table 1, the hazard class value is from 1 to 5.
9) Table 2 and Table 3: Please add units to the numbers. Also in Table 2, the Kendall’s Tau is a normalized S. Why is S positive, while the Kendall’s Tau is negative?
10) Figure 4: Please add the area name to each sub-figure. For the bottom panel, how would you explain the observed data points of the tidal level are much lower than the fitted values?
11) Figure 6: To make the flooding probability clearer, it would be better to remove the ocean area from the figures.
12) Figure 9: It is suggested to add a scale bar to each sub-figure.
13) Should Figure 13 be Table 6?
14) Line 540: What does “addressing climate change” mean?