| 26 Jun 2025
Alongshore Varying Dune Retreat at a Barrier Island
Abstract. Barrier islands often exhibit spatially uneven morphological change due to alongshore variations in wave exposure, sediment supply, and dune morphology, which are influenced by both long-term evolution and short-term storm impacts. We investigate the alongshore variability of shoreline and dune evolution on Culatra Island (Ria Formosa, S. Portugal) over a two-year period (Nov 2009–Nov 2011) encompassing 31 storm events. A coupled modeling framework was developed, combining the one-line ShorelineS coastline model with an impact-based dune erosion and recovery module. The model was forced with hourly offshore wave data from the ERA5 reanalysis (corrected for biases in wave height, direction and period using Faro buoy observations) and transformed to the nearshore with the unstructured-grid SnapWave model. In situ LiDAR surveys and satellite-derived shorelines and dune vegetation lines (using Normalized Difference Vegetation Index (NDVI)) were used for calibration and validation. Model results show that longshore sediment transport gradients dominated shoreline change across most of the island, with net erosion in the sediment-starved western sector and net accretion in the eastern sector. Notably, an erosion "hotspot" in the central-west (transects T42–77) experienced intense storm-induced dune retreat, which supplied sand to the adjacent beach and caused local shoreline advance counter to the regional trend. Overwash at the eastern end contributed to the accretion aligned with the regional trend. The dune module's performance was sensitive to its calibration: a higher wave impact coefficient Cs led to greater dune erosion, while coarse sand d50 and low winds markedly reduced modeled post-storm dune recovery. Model validation using satellite-derived shoreline and dune positions employed R² and Pearson correlation metrics, revealing moderate shoreline performance and weaker dune agreement – though general retreat trends were captured across several transects. A correlation analysis indicates that cumulative cross-shore sediment flux (corr = 0.73) and sturm duration (corr = 0.48) exert the strongest control on dune retreat whereas peak storm wave direction and initial berm width had minimal influence. Overall, the coupled model captured the observed pattern of alongshore-varying coastal response – including the emergence of a dune erosion hotspot – highlighting the importance of cross-shore sediment exchanges in barrier island evolution and providing useful insights for coastal management of dune systems.
This study investigated the alongshore variability of shoreline and dune evolution on Culatra Island, a barrier island in southern Portugal, over a two-year period with 31 storm events. A coupled modeling framework was developed to combine the ShorelineS one-line coastline model and a dune erosion/recovery module. The model validation was performed using LiDAR surveys and satellite-derived vegetation lines. Overall, the study is comprehensive, and some findings might be helpful in practice. However, I have several concerns and suggestions regarding the methodology and results of this study as follows.
Methodology
1) Section 3.2: The temporal resolutions of buoy records and ERA5 datasets are different. How did you compare both? Given the uncertainty and measurement errors in the buoy data, it seems that a linear correction is good enough to “correct” the original ERA5 data.
2) Lines 213 and 214: How did you justify the definition of a storm? Is the number of storms sensitive to the thresholds of the wave height (2.5 m) and the duration (4 hours)?
3) What does RSLR in Equation (3) stand for? What is “K” in Line 248 in Equation (4)? Please make sure the denotations of different variables are consistent in both equations and the text.
4) Lines 270-272: The description about Figure 5 is redundant, and it is already provided in the figures.
5) Section 4.4: How did you consider the value of qscal as 0.3? The calibration of Cs and d50 was mentioned in both the Abstract and Conclusions, but the relevant details were only presented in Appendix B. Part of Section 4.4 already presented some results on calibration. It is suggested to move those to the Results and Discussion Section.
Results
6) Figure 9 clearly shows that the model output could not capture the net shoreline change, either overestimation or underestimation (and why?). Was the model calibration sufficient for further analysis, or maybe a better model is needed? Given the poor performance of the model, how would you justify the validity of the conclusions drawn based on the model output?
7) Figure 9: How did you calculate the BIAS? Is it a total bias or average bias over space?
8) Lines 397-404: what is the regression model the R2 is measuring? If it is a linear model with an intercept term, R2 is actually equal to the square of the Pearson correlation coefficient, r. Please note that R2 and r are not able to capture the bias in the model prediction. Given the sampling uncertainty in both time and space, it is also suggested to present the values of metrics with a statistical distribution instead of a fixed number. The authors can refer to the article below for more information about the limitations of some commonly used evaluation metrics.
Reference: “Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis” (https://doi.org/10.1111/jfr3.12982)
9) Line 483: The term “dSds” looks confusing. Please elaborate on this denotation.
10) Section 5.5 and Lines 646-647: To avoid confusion, it is suggested to use lowercase, “r”, to represent the correlation coefficient.
11) Figures 2, 4 and 7: Please change the color of the cell size labels and transect numbers, for example, it is hard to tell “800 m” and “50 m” from the figure. And add units to the legend of bed elevation. What is the reference level (i.e., 0 m) of bed elevation? What do the contour lines represent in Figure 4? A scale is needed for most maps. Also, please improve the resolution of all the figures.