the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2024
Storm surge frequency, magnitude, and cumulative storm beach impact along the U.S. east coast
Abstract. This study extracted historical water level data from 12 National Oceanographic and Atmospheric Administration tide gauge stations, spanning the period from the early 20th century to 2022 from central Maine to southern Florida, in order to determine if temporal and spatial trends existed in frequency and magnitude of storms along the U.S. Atlantic Ocean coast. We used the Storm Erosion Potential Index (SEPI) to identify and quantify storms. We then use the timing and magnitude of those storms to determine the cumulative effect of storm clustering and large magnitude storms on sandy beaches using the cumulative storm impact index (CSII) empirical model. The results from this study showed (1) no appreciable increase in storm frequency at any of the stations (except for sheltered stations susceptible to storm tide augmentation); (2) statistically significant, but modest increases in storm magnitudes over time for eight of the 12 tidal stations; (3) regional differences in storm magnitudes (SEPI) and cumulative storm impacts (CSII) characteristic of more frequent extratropical storms (temporal clustering) in the north and less frequent tropical storms in the south; and (4) a four to 10 year recovery period for regional beach recovery.
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RC1: 'Comment on egusphere-2024-656', Anonymous Referee #1, 27 Jun 2024
The manuscript “Storm surge frequency, magnitude, and cumulative storm beach impact along the U.S. east coast” describes temporal and spatial trends of the frequency and magnitude of storms, defined using the Storm Erosion Potential Index (SEPI). The cumulative impact of storms on beaches is compared to the impact of large magnitude storms using the Cumulative Storm Impact Index (CII). Findings suggest an increase in storm magnitude at most stations, and an increasing trend of cumulative storm impacts for four stations in the middle of the domain (NY – VA). The paper is concisely written and easy to read. I believe this paper is suitable for publication with some modifications that I describe below.
General comments
1. I don’t agree with some of the jargon used in the title and in the paper’s findings related to “storm” and “storm surge.” The definition of “storm” comes from the thresholding of extreme water level and non-tidal residual (defined as storm surge here) used in the SEPI, and the “storm magnitude” is the magnitude of the index, NOT of the water level variables. Similarly, the title suggests the paper is evaluating storm surge frequency, yet the variable that is evaluated is not storm surge. While a high storm surge is necessary for causing the SEPI, the storm tide also has to meet a threshold. This means there is the potential that not all storm surge events on record are analyzed. For example, there could be events that don’t cross the water level boundary, if the duration was low or the tide was low. So the suggestion the paper is evaluating storm surge frequency and magnitude (which is the elevation of the storm surge) is misleading. That said, there are inconsistencies in jargon throughout – using SEPI storm magnitude, average annual SEPI, storm magnitude, SEPI, and SEPI Values, SEPI measures of storm magnitude, all to describe the same value.2. The authors justify the lack of inclusion of waves by stating storm tide and duration as primary factors contributing to beach erosion from older studies, but many studies since then (e.g., Stockdon et al., 2007; Stockdon et al., 2023, Cohn et al., 2019, to name a few) show that wave runup (swash and setup processes) are important for spatially varying erosion impacts along coastlines. Other studies have suggested that wave runup/setup can be a large contributor to extreme water levels at the coast (e.g., Parker et al., 2023; Serafin et al., 2017; Stockdon et al., 2023). A brief discussion of the importance of these processes and potential for missing impacts is important.
3. How is storm duration computed? It seems important to the computation of the SEPI. It seems that the SEPI may be the sum of all hourly data over the MHW threshold for the surge “event” but this isn’t explicitly stated, beyond interpretation of eqn (1). Line 166 says that there is no minimum time duration for a storm, but Line 392 says a storm needed to persist for a minimum of 12 hours.
4. How is the scaling factor, f chosen for weighting beach recovery, and how much does this choice impact the model result? How sensitive is the periodicity of beach recovery to cumulative storm impacts to the parameters chosen? Is 1 year a good approximation for beach systems along gradient that may experience both ETC and TCs?
Line by line
Line 45: Typo after intensities “)”
Line 97: Seems like Stockdon et al., 2007 would be a good reference to include here too which built off the Sallenger, 2000 publication
Line 103: Nuance here, but I disagree the authors are assessing the frequency of and magnitude of TC and ETCs, as they’re evaluating water levels, which aren’t necessarily descriptive of JUST the storm climatology.
Line 135: Shouldn’t it just be SEPI, rather than “SEPI storm index”? Otherwise, you’re really saying Storm Erosion Potential Index storm index.
Lines 208 – 210: The Wilmington and Battery stations might also be subject to river discharge within the non-tidal residual/storm surge signals.
Line 226 – 227: While the justification that the selection of storms with MHW vs MHHW is similar is positive, a quantification of how the SEPI or duration of events is affected could be important. I believe MHHW was justified as a threshold in the original paper to infer more wave attack on dunes/back barrier from the storm, and does this relationship hold for MHW?
Line 262: How was the standard deviation over time computed? (if saying they are constant must have looked at time variability in this parameter?)
MHW threshold takes into account sea level rise, why not just remove the MSL trend from the data and use a stationary MHW threshold? How does sea level rise effect results? Or is the inclusion of a time varying MHW/MSL take care of that?
Line 365 – 370: Hurricane Florence impacted the Carolinas with an incredible amount of precipitation too. Is the signal Wilmington seeing due to river flow rather than coastal driven storm surge? Especially if potentially the duration of the event was impacted, e.g., gauge water levels staying high for much longer due to river flow outletting post storm surge event.
Line 390: Again, not looking at spatial and temporal trends in storm surge/storm tide, looking at trends in SEPI
Line 396: What are they typical problems associated with empirical data analyses the authors are referring to?
In Figure 1, what is considered the duration? This might be a good place to include itReferences mentioned:
Cohn, N., Ruggiero, P., García-Medina, G., Anderson, D., Serafin, K. A., & Biel, R. (2019). Environmental and morphologic controls on wave-induced dune response. Geomorphology, 329, 108-128.
Parker, K., Erikson, L., Thomas, J., Nederhoff, K., Barnard, P., & Muis, S. (2023). Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast. Natural Hazards, 117(3), 2219-2248.Serafin, K. A., Ruggiero, P., & Stockdon, H. F. (2017). The relative contribution of waves, tides, and nontidal residuals to extreme total water levels on US West Coast sandy beaches. Geophysical Research Letters, 44(4), 1839-1847.
Stockdon, H. F., Sallenger Jr, A. H., Holman, R. A., & Howd, P. A. (2007). A simple model for the spatially-variable coastal response to hurricanes. Marine Geology, 238(1-4), 1-20.
Stockdon, H. F., Long, J. W., Palmsten, M. L., Van der Westhuysen, A., Doran, K. S., & Snell, R. J. (2023). Operational forecasts of wave-driven water levels and coastal hazards for US Gulf and Atlantic coasts. Communications Earth & Environment, 4(1), 169.
Citation: https://doi.org/10.5194/egusphere-2024-656-RC1 -
RC2: 'Comment on egusphere-2024-656', Anonymous Referee #2, 28 Jun 2024
In the paper titled "Storm surge frequency, magnitude, and cumulative storm beach impact along the U.S. east coast," Rachele Dominguez and co-authors analyzed historical water level data from 12 tide gauge stations along the U.S. Atlantic Ocean coast, spanning from central Maine to southern Florida. Their objective was to investigate temporal and spatial trends in storm frequency and magnitude. The authors utilized the Storm Erosion Potential Index (SEPI) to identify and quantify storms, and the Cumulative Storm Impact Index (CSII) to evaluate the cumulative effects of storm clustering and large magnitude storms on sandy beaches. The findings indicate no significant increase in storm frequency, but statistically significant yet modest increases in storm magnitudes over time. Regional variations in storm magnitudes and cumulative impacts were observed, and a recovery period for regional beach restoration was identified. While the paper does not introduce novel concepts, ideas, or methods (already published in their JGR 2022 and applied at Sewells Point tide gauge), it draws substantial conclusions for the region. It is worth noting that the study employs observation-based methods to corroborate or challenge conclusions derived from hydrodynamic or physics models. However, the scientific methods and assumptions lack clear delineation, and the inclusion of justifications in the article would enhance reader comprehension. The manuscript is well-written, addressing an important topic, and while I do have some comments and suggestions, they are aimed at potentially improving the readability of the article for publication in ESurf.
General comments
1- Introduction : Although the introduction is interesting and well-written, it primarily presents general information about global cyclone dynamics and lacks specific attention to the regional context and key concerns. Only a few lines in the entire introduction provide an overview of the region of interest. It would be more appropriate to concentrate on the US East Coast or at least the North Atlantic in the introduction.
In addition, considering that numerous storm erosion predictive indices exist, it is important to clarify why SEPI and CSII were chosen, what unique contributions they offer, and what their limitations are.
2- Method : The SEPI is calculated from 𝑆2𝑆𝐷, representing the storm surge above the threshold for detecting storm surges, which is set at two standard deviations, and with a duration of 12 hours. The choice of two standard deviations and a duration of 12 hours is based on previous research. If the threshold were changed to 1.5 or 3 standard deviations or if a different duration were selected, the results would likely be affected. The choice of threshold and duration can influence the identification and quantification of storms, potentially altering the frequency and magnitude trends observed. Therefore, it is crucial to assess the robustness of the results and consider the sensitivity of the findings to different threshold and duration choices.
While the methodology for the CSII is presented in the article by Fenster and Dominguez (2022), it would be beneficial for readers if the method were further elaborated in the manuscript. For example, the justification for choosing the exponentially decaying weighting factor and the selection of tc (time constant) as one year for beach systems on the U.S. East Coast should be provided. Additionally, the determination of the delta parameter should be explained, as it plays a role in quantifying the impacts of storm clustering and large magnitude storms on sandy beaches. Justifying these choices would enhance the understanding of the methodology and the interpretation of the CSII results.
The estimation of 𝑃𝐶𝑇𝐸 (𝑡) is conducted over the period from 1983 to 2001. The specific choice of this time period should be justified to provide a clear rationale for the selection. Additionally, if the analysis did not include the consideration of seasonal and interannual variations of tidal components, it is crucial to explain the reason behind this decision. Providing this clarification will enhance the transparency and facilitate the interpretation of the 𝑃𝐶𝑇𝐸 (𝑡) estimates.
3- results/discussions :
Figure 5b: Do the results in terms of significance remain the same if a low-pass filter of 3-5 years is applied?
Figure 6: What is the significance of error bars? Are the results presented over the same time period? If not, are the values comparable? It would be helpful to specify this in both the figure caption and the text.
Line 330 : "the CSII peaks appear to have a periodicity on the order of 3-10 years" Is there any explanation for this observation?
Line 448-451 : Is there any variation in the distribution of the required recovery time throughout the observation period? Do certain stations require more or less time for recovery? As the authors pointed out, the time spans associated with beach recovery range from 3 years to >10 years, depending on the variability of storms in both time and space. It would be interesting to further develop this aspect, particularly in relation to existing studies in geomorphology if available.
Line 451-454 : It would be interesting to investigate whether these aperiodic clusters truly correspond to the interdecadal to decadal scale variability observed in cyclonic development attributed to the North Atlantic Oscillation and El Niño Southern Oscillation phases.
Citation: https://doi.org/10.5194/egusphere-2024-656-RC2
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