the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis
Byungho Kang
Rusty A. Feagin
Thomas Huff
Orencio Durán Vinent
Abstract. Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. This flooding also can greatly affect post-storm coastal dune recovery and reduce the long-term resilience of the back-barrier ecosystem. Recent analytical work has hypothesized that these frequent flooding events are uncorrelated in time and can be modeled as a marked Poisson process with exponentially distributed sizes, a result with important implications for the prediction of coastal flooding. Here we test this proposition using high-temporal-resolution field measurements of an eroding beach on the Texas coast. A time series of the flooded area was obtained from pictures using Convolutional Neural Network (CNN)-based semantic segmentation methods. After defining the flooding events using a peak-over-threshold method, we found that the size of the flooding events indeed followed an exponential distribution as hypothesized. Furthermore, the flooding events were uncorrelated with one another at daily time scales, but correlated at hourly time scales. Finally, we found relatively good statistical agreement between our CNN-based empirical flooding data and run-up predictions. Our results formalize the first probabilistic model of coastal flooding events driven by wave run-up which can be used in coastal risk management and landscape evolution models.
Byungho Kang et al.
Status: open (extended)
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RC1: 'Comment on egusphere-2023-238', Anonymous Referee #1, 11 May 2023
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The manuscript “Stochastic properties of coastal flooding events – Part 2: Probabilistic Analysis” describes the development of time series of beach flooding from pictures using the Convolutional Neural Network (CNN) model-based semantic segmentation methods described in Part 1 (this manuscript is a two-part submission). The authors QA/QC the output and find the statistics of the measured “flooding” match up well with those from predicted “flooding” using the total water level (still water level + wave runup (R2% as defined by Stockdon et al., 2006)) over a beach elevation based threshold (and as previously analyzed in Rinaldo et al., 2021).
The manuscript is well-written and concise. I think the methods are neat, and I like the characterizing “how much water in the frame” as a more detailed method for flood detection than a binary Y/N, but also less complicated than a full georectification of the water line elevation. While the authors display interesting findings, the main contribution of the research isn’t clear. The paper reads as a validation of the work presented by Rinaldo et al., 2021, however its broader importance to the field could be better explained and tied into other literature. The authors suggest in the second to last line of the manuscript that “Our results formalize … the first probabilistic model of coastal flooding events driven by wave runup.” But I’m not entirely sure what the probabilistic model is and/or how it might be used more broadly. This paper could be strengthened with more explanation about what the authors mean by probabilistic model and tightening up the key terminology. My specific suggestions are found below.
General comments:
The motivation of the study could be clearer. I understand the authors are more or less validating the Rinaldo et al., 2021 paper, but the details of why this is important and how this adds to the field are less clear.
The authors could better explain the comparisons between the Rinaldo et al., 2021 work and their results, specifically when it comes to the “beach elevation” threshold comparisons. In the introduction (when referring to the Rinaldo et al., 2021 paper) and later (section 4), the authors discuss beach elevations. I find this discussion/comparison a bit confusing, as there’s no context as to what the beach elevations mean or are related to and/or what the characteristic beach elevation is. First, there’s no datum to any of the elevation measurements, which is important when discussing height. Second, it seems the whole beach elevation discussion is a threshold analysis, where the authors are potentially deciding on an appropriate beach elevation in which to evaluate flooding over. I wonder if it makes sense to call these “beach thresholds” rather than “beach elevations.”
The authors use a peak over threshold approach for threshold selection to define flooding and choose the 2% threshold from the distribution of water area fraction. Was this threshold varied with each change in the time window? E.g., the 2% value with a 5-minute time step is not going to be the same as the 2% value of the daily time step. In general, did the authors test different threshold values (even for the same timestep)? The authors also test a few different “given beach elevations” and the HWE statistics over those thresholds, so it would seem natural to test different thresholds of the flood measurement model too.
There is a lot of focus on wave runup and the flood variability wave runup produces and the development of a flooding driven by wave runup. However, the flooding the authors are tracking is based on the total water level, not just the wave runup, as I don’t believe they are removing the tidal and/or SWL signal. So, it’s not accurate to say their model provides “runup predictions” (Line 11, also section 4 title). Yes, wave runup is a contributor to the beach flooding the authors are tracking, however there are examples from their results that to me suggest there’s more at play than just wave runup. One example of this is that the correlated events for timescales less than 10 hours. If you look at Figure 8C, the number of events really stabilizes >12 hours and certainly by 24 hours, which is also the duration of semi-diurnal tidal cycles. This hints to the relationship being related to the tide + weather (waves, anomalies) rather than related to timescale of local weather alone affected by the daily cycle as suggested by the last line on page 8. The authors should clearly distinguish between the total water level and the wave runup.
In relation to the wave runup, the authors discuss beach slope a few times. It’s unclear to me how the authors determined the beach slope, as the paper cited doesn’t have much information about beach slope for their study site. I also wonder if some of the mismatch between predicted and measured flooding is due to the beach slope and/or slope variability at their site, since it’s a broad amount of coastline they view. Some assessment of the local beach slope, especially over the period of data collection would be important, especially since they also state in their conclusions that “ and our measurements take into account local beach erosion due to hurricane Harvey.” On this note, I don’t doubt that the Stockdon formulation may overpredict TWLs at time, especially on the hourly/daily scale, since it’s the wave runup, which is the 2% highest R values. I wonder if you used wave setup + SWL, more of the mean wave-driven water level component, that you’d see better agreement with your flood hits/misses. I also wonder if there are more limitations to this comparison besides just the variability of waves. For example, what about the fact that the authors are looking at a water surface over a large stretch of beach and the % coverage rather than just a transect like the HWE prediction methodology is doing? Because of site-to-site variability, you could have some areas that would consistently “flood” but that wouldn’t be the same for the whole stretch of the beach.
Terminology: The authors use many different terms to describe the same phenomena throughout the article, for example, coastal flooding, high-water events, flooding events, high-water events overtopping the beach, … I think it would be good to define what you’re measuring and stay consistent with the terminology throughout the article. I find the use of the terminology “coastal flooding” (especially in the title) a little broad and misleading, as it seems this is really looking at “beach flooding.” The authors also use terminology of 24 h or 1 day for their daily signal, and I suggest keeping it consistent and choosing one to use throughout.
Line by Line:
Line 22: “In this work…” I believe the authors are referring to the Rinaldo et al., 2021 paper, but the way it is written it could be referring to this present manuscript.
Line 25: The term overtopping here is odd to me. Flooding the beach, exceeding the shoreline, etc makes more sense for the types of events being discussed here, as overtopping typically refers to total water levels exceeding barriers such as dunes, seawalls, etc.
Line 26-27: “They also found that the size and intensity of an event, defined by the maximum total water level during the event, does not vary with increasing elevation” I think the authors are referring to some beach elevation threshold, but it’s not clear from this sentence if readers are not familiar with the Rinaldo et al., 2021 work.
Line 43: Why was the observation period chosen to be 6 months?
Line 74: worded weirdly, “seems to follow” Or “followed” or “follows” but not “seems followed”
Line 77 – 78: The authors suggest here that no event in their time series lasts more than 3 hours, which means there’s a physical upper limit to sustained flooding when there’s not a large storm. Do the authors have a long enough record to make this statement? Where there storms over this time period? There are also other factors that increase water levels – to me, this statement suggests tides are a big contributor to “when”/”how long” flooding occurs, if they’re only occurring over a few hours.
Line 89- 90: Do the authors have beach slope measurements during the time period? Did the beach slope vary a lot? How spatially and temporally varying is beach slope in this location? I’m not sure the authors can say much about robustness with respect to beach slope without any measurements (this is mentioned above too).
Line 118: Did the authors use the data developed in Rinaldo et al., 2021 or create a new time series using the same methodology? It’s unclear from this section. Either way, I think the authors should provide details about the tide gauge and buoy data used in this analysis. I’m interested also in what the water depth of the buoy used was? Stockdon et al., 2006 was developed from waves at the 20m contour that were linearly backshoaled to be “deep water waves” and they recommend linearly backshoaling waves if nearshore buoys are used rather than deep-water buoys.
Figures:
Even though there is a companion paper, I think this paper still needs a map to show where the study site/camera is located. Since they’re two separate papers I don’t think we can assume the readers will read both…(or should have to read both)!
Figure 1: Plotting the 2% threshold on the time series panel would be helpful!
Figure 4: Why are the two figures on different x-axes? It feels misleading as my eyes are trying to draw similarities between the two. Please put on the same axis by extending A or decreasing the time window of B. It’s important to know that (it looks like) everything in A is one event in B at the daily scale…!
Figure 6: I would remind readers here how you are defining event size, either in the caption or on the y-axis, e.g., max % water area over the 2% threshold
Figure 7B: is never cited in text – can authors put rejection range/plot? Legend overrides data on plot Figure 7C is not cited until much later. Good practice is to describe figures in order of appearance
Figure 10: You note that this represents when the predicted flood frequencies are equal, but it doesn’t look like that from the figure. Is it that the duration of the predicted water levels are much longer?
Figure 11: what does the shading represent?
I didn’t see any data availability statement or a location where codes could be found – please see the journal’s Data Policy.
Citation: https://doi.org/10.5194/egusphere-2023-238-RC1
Byungho Kang et al.
Byungho Kang et al.
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