the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Feb 2025
Assessing the sensitivity of storm surge simulation to the atmospheric forcing resolutions across the estuary-sea continuum
Abstract. Estuaries are particularly vulnerable to flooding from extreme events such as storm surges, and this vulnerability can be exacerbated by climate change. Numerical models are valuable tools for supporting flood prevention and planning in these regions. However, despite recent improvements in storm surge modeling, most models use atmospheric forcing data with spatial resolutions of several tens of kilometers and temporal resolutions of a few hours, hence much coarser than their own resolution. This discrepancy may have an impact on the overall model accuracy. Here, we evaluate the impact of atmospheric forcing data’s spatial and temporal resolution on storm surge modeling within the Scheldt river-estuary-North Sea sea continuum. Atmospheric forcings were incorporated at spatial resolutions ranging from 2 km to 30 km and at temporal resolutions from 15 minutes to 6 hours. Using an unstructured-mesh multiscale hydrodynamic model, we assessed how these variations influenced the accuracy of storm surge simulations. Our findings indicate that increasing spatial resolution significantly improves the accuracy of peak surge predictions in estuarine areas, while higher temporal resolution further enhances model performance only at the finest spatial resolution. The effect of the temporal resolution diminishes as spatial resolution becomes coarser, suggesting that spatial resolution is more critical for improving storm surge forecasts in estuaries like the Scheldt. The timing of peak surges remained consistent across all configurations. The best results are obtained with 2 km and 15 min atmospheric forcing resolution. This study underscores the importance of aligning atmospheric forcing resolution with the hydrodynamic model's spatial scale to achieve optimal accuracy in storm surge predictions for estuaries.
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Status: open (until 18 Apr 2025)
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RC1: 'Comment on egusphere-2025-634', Marvin Lorenz, 14 Mar 2025
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Review of “Assessing the sensitivity of storm surge simulation to the atmospheric forcing resolutions across the estuary-sea continuum” by Riana Randresihaja et al.
The manuscript examines the impact of the spatial and temporal resolution of atmospheric forcing on extreme sea levels in estuaries. The study focuses on storm Xaver and the Scheldt estuary in the southern North Sea on the Belgian coast. The authors model the atmospheric conditions during the storm at resolutions from 2 to 30 km and provide the hydrodynamic model with forcing data at temporal resolutions from every 15 minutes to 6 hours. The highest temporal and spatial resolutions provide the best results, although peak sea levels are underestimated in all simulations. The manuscript is generally well written. While the results are not surprising, this is one of the first studies to systematically investigate these impacts of resolution for extreme sea levels in an estuary, although limited to one event and one estuary. I think the manuscript will be a very useful reference in the future, and despite its exemplary nature by studying only one system and one storm, I think general conclusions can be drawn from this study. However, I think the manuscript has potential for improvement since it does not dive into the dynamical aspects that are better resolved by the higher resolution of the meteorological data, leading to the improved results. Therefore, I have several comments and suggestions that I think would improve the manuscript.
General comments/suggestions:
I think the main benefit of the higher spatial resolution of the atmospheric data is that local wind speed differences over water and land within the estuary are much better represented. However, this point is not really elaborated in the manuscript. I think the impact of the paper can be greatly improved if the authors could elaborate on why the peak values are higher in the high resolution case. I think the main reason could be that the momentum transfer within the estuary is larger due to the potentially higher wind speed over the water inside the estuary, thus increasing the surge within the estuary even more. However, since the surge outside the estuary is also higher for the high-resolution case, this may not be the only or even the main reason. So how and why does the resolution affect the surge height at the coast before the surge propagates along the estuary? This also raises the question of whether the momentum transfer within the estuary is even a factor in the surge increase, or whether the better representation of the surge at the coast is the main reason for the improvement of the peak sea level within the estuary? A starting point could be the evaluation of the wind speed distributions within the different simulations and the estimation of the induced additional sea level within the estuary. I recommend that the authors try to disentangle or at least discuss the origins, as this will make the results and conclusions of this paper transferable to other systems. This will greatly increase the impact of this paper.
Since spatially high resolution atmospheric forcing data are generally not available, a possible solution could be a local bias correction in the momentum transfer equation of the hydrodynamic model, which somehow depends on the spatial resolution of the atmospheric data and the width of the estuary. While I do not expect the authors to test a way to correct for the resolution bias, I think it is worthwhile to discuss possibilities like this one or other in the discussion section.
Specific comments:
Title: I would write "across an estuary-sea continuum".
L18: Isn't this statement true for any place on Earth, since any place always has a 1% chance of experiencing such an event? Or are the authors referring to the fact that, on average, 1.3% of the population has experienced such an event annually in the past? Either way, this sentence needs to be clarified.
L25: heightened -> increased
L34/35: I do not think the statement needs references, as it is a very general statement.
L35-37: This statement is not relevant to the focus of this study.
L45/46: These are not smoothed, but simply not resolved.
L46/47: An additional point that can be added here is that an atmospheric model is not the "truth" either. Already the choice of a data set introduces uncertainties, because there is no perfect data set. Some storms are simply not represented well enough in a dataset to correctly capture sea level peaks everywhere. There may also be regional biases. For the Baltic Sea, Lorenz & Gräwe (2023) have studied this in a hindcast ensemble, where the ensemble spread is already quite large for 1 in 30 year surges.
L70: flood and dry out -> wet and dry
L80: Can you add references for this sentence, e.g. Familkhalili & Talke (2016) comes to my mind.
L81: flood control areas -> a better wording might be "designated flood retention areas”?
L90: sea level rises -> maybe better: “temporally elevated sea levels”
L90: drops in atmospheric pressure and wind stress -this could be interpreted as a drop in wind stress. Please reformulate.
L91: atmospheric pressure deficits - do you mean low pressure systems?
L91: main driver of storm surges - do you mean elevated sea levels by the inverse barometric effect? I wouldn't call that a storm surge.
L98: typical tidal signal -> mean tidal high water
Eq. (1) and (2) - I don't think these equations are needed, as this is textbook knowledge and any hydrodynamic model solves them.
L129: Delete “Finally, the last parametrization concerns”
L133: MAR - Can you spell it out as this is the first time that you are using this acronym?
L161: characterize - can you find a better word than characterize? Maybe “determine”?
L168/169: delete everything after “is used”, as this information in not needed here
L213: Here and in some other places: I suggest trying to avoid the use of “((“ and “))”.
Table 2 / validation metrics: I suggest adding the “bias” as a metric, bias = 1/N sum_i^N (m_i-o_i). I suggest this because I wonder if there is a bias in the mean sea level, possibly due to a bias in the tidal asymmetry (Fig. 4). This metric may be helpful to the reader.
L222: I think these are not averaged or smoothed out, but not resolved.
L223: observations
L229: I think the validation would benefit from a few more stations. There must be weather data available for many stations across the model domain.
Fig. 2: I think this plot and this study in general would benefit greatly if you could add an inset of the Scheldt estuary showing the resolution of the atmospheric data over the estuary for each resolution. This should show that the wind speeds over the water inside the estuary are higher than in the course resolution. This should help your argumentation of the dynamical origins, see main comment above.
Fig.2: I think it might also help if you plot some contours of isobars to illustrate the position of the low pressure system.
Fig.2: can you specify the height of the wind speed? I expect it to be u10.
Section 3.2: Can you also validate the hydrodynamic model against other tide gauges in the domain?
L267: You didn’t compute the bias, but the MAE. Also, I would call 20cm near zero.
Fig. 4 : I suggest adding more panels for more gauges.
Tab. 4: I suggest adding the bias as a metric.
L285/286: I think this statement depends strongly on the characteristics of the surge (long or short, one or more peaks etc.) and the size of the estuary. I just want to say that this general conclusion should not be drawn that quickly, although I agree with the statement.
L299: I do not think that ~5cm differences are “substantial” compared to a peak of 4m.
L301: “with coarser temporal resolution (Figs. 8a,c and e)”. - This is more evident in Fig. 7 for the spatial resolution!
Fig. 7: Why are the deviations in (e) and (g) in the open North Sea that large?
Fig. 7: Delete “two simulations with varying spatial resolutions.”
- Discussion: A paragraph discussing the general underestimation of peak sea levels would be useful to the reader
L338-339: It is not only in estuarine environments that this resolution may be insufficient. I think this statement can be made for some environments larger than estuaries. In general, wind speeds can be underestimated if local topography in coastal areas is not well resolved in the atmospheric model. For the Western Baltic Sea, Lorenz & Gräwe (2023) find that the coarse resolution of atmospheric models such as ERA5 is also insufficient to resolve wind speeds correctly, leading to underestimated peak sea levels.
L343/344: This statement needs arguments from the results of this study, see my main comment
L345: necessitates -> requires
L349: this raises the question of how much further refinement is both feasible and beneficial - a bias correction could be a computationally cheaper option. I think this point can be added to the discussion, see my second main point
L357: intensive -> expensive
L388: I would split the discussion and the conclusion into two sections, as only this paragraph is a conclusion
Appendix C: I think this detailed description of the drying and wetting is not necessary as the detailed implementation of the drying and flooding is not important for understanding this study. Therefore, I would suggest removing Appendix C from the manuscript and only mention wetting and drying in the methods section as it is already done and cite the reference where it is described in more detail.
References
Familkhalili, R., and S. A. Talke (2016), The effect of channel deepening on tides and storm surge: A case study of Wilmington, NC, Geophys. Res. Lett., 43, 9138–9147, doi:10.1002/2016GL069494.
Lorenz, M. and Gräwe, U.: Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea, Ocean Sci., 19, 1753–1771, https://doi.org/10.5194/os-19-1753-2023, 2023.
Citation: https://doi.org/10.5194/egusphere-2025-634-RC1
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