the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Nov 2025
Equatorial storm surge risks revealed by the 2001 tropical cyclone Vamei
Abstract. Tropical Cyclone Vamei, which emerged at 1.5° N on December 27, 2001, challenged the prevailing idea that near-equatorial areas are safe from storm surges, as it resulted in localised flooding in Singapore and Malaysia, revealing a rare yet critical regional hazard. Thus, we investigated storm surge risk assessment in Singapore based on numerical simulations using Delft3D in multiple scenarios. We first validated the accuracy of our simulation results by comparing them with nine observation points around Singapore Island. We then conducted the simulations as a suite of alternative scenarios created by moving the known track of Tropical Cyclone Vamei, modelling more intense storms corresponding to a 1-in-1000-year scenario and considering future sea level rise induced by global warming. When a 1000-year probability of occurrence was assumed, the maximum storm surge height around Singapore increased to 0.595 m. For a 1000-year cyclone with its path shifted 0.8° southward, sea level rise scenarios of +0.7 m and +2.0 m resulted in inundation areas of 34.5 km² and 90.7 km², respectively. While the calculated storm surge height remained largely unchanged despite future sea level rise, the inundation area in Singapore expanded significantly. This indicates that sea level rise is a primary contributor to this expansion, highlighting the importance of considering future sea levels in inundation assessments. Further research is necessary to assess potential changes in the frequency and intensity of tropical cyclones impacting Singapore under future climate scenarios.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-5703', Anonymous Referee #1, 22 Dec 2025
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AC1: 'Reply on RC1', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #1
- Line 27 - Suggest adding another example of losses from a historic storm surge, in order to better support the manuscript’s opening statement.
Answer: We agree with this suggestion. To better support the opening statement, we have added the following additional example of economic losses from storm surge and sea-level rise in the revised manuscript. “Storm surges induced by tropical cyclones threaten coastal communities worldwide (Dasgupta et al., 2011) and frequently cause devastating economic impacts. For example, the historic storm surge from Hurricane Katrina in 2005 resulted in over $125 billion in damages along the US Gulf Coast (Knabb et al., 2005), and future projections estimate that asset losses from combined storm surges and sea-level rise in the USA could reach $990 billion by 2100 (Neumann et al., 2015).”
- Line 36 - Have there been any additional cyclones in the last 25 years since 2001 that can further help support the statement about tropical cyclones being a possibility and concern?
Answer: Thank you for this insightful comment. To our knowledge, no tropical cyclones have been reported to form within 1.5° of the Equator in the South China Sea since Tropical Cyclone Vamei in 2001. While recent low-latitude events have been documented in the broader region, such as Tropical Storm Senyar (2025) forming near 5°N in the Malacca Strait (e.g., Lui et al., 2026; Abdillah et al., 2026), Vamei remains the only recorded case forming so close to the Equator within our study domain. These observations highlight both the extreme rarity and the potential significance of such events. We have revised the manuscript to include this clarification and the recent example, thereby better supporting the study’s motivation.
- Line 56 - Suggest changing the high precision coordinates to the coordinate range for the country.
Answer: We agree with your suggestion. We have added the general coordinate range for Singapore to improve clarity in the revised manuscript.
- Line 146 - Can the difference between the u and y parameters be clarified as they seem quite similar based on the provided description?
Answer: Thank you for pointing this out. We agree that the original wording could make u and y appear similar. We have revised the text to clarify that u is the predefined threshold wind speed, whereas y denotes the observed maximum wind-speed values that exceed this threshold.
- Line 151: Suggest including the empirical wind-pressure relationship employed here, or providing a corresponding reference.
Answer: We agree with your suggestion. In this section, we present a figure showing the empirical wind-pressure relationship.
- Line 156 - Was this maximum wind speed assumed for the simulated scenario or the observed cyclone? If the former, can the authors elaborate on the justification?
Answer: This is the observed value by JMA. We collected tropical cyclone datasets with maximum wind speeds exceeding 45 knots to determine the relationship between annual exceedance probability and the cyclone's maximum wind speed. In the revised manuscript, we have added this explanation.
- Paragraph 156 - The paragraph is unclear with regard to its relevance to modeling the simulated scenario/observed cyclone.
Answer: We have rewritten these sentences again in the revised manuscript.
- Line 197 - Reference is missing date.
Answer: We have added the publication date.
- Line 203 - Suggest adding a sentence to describe Case 1 prior to describing other cases.
Answer: We agree with your suggestion. We have revised the manuscript to introduce and describe Case 1 prior to presenting the other simulation cases for improved clarity.
- Line 204 - Suggest removing the linebreak at the end, which incorrectly specifies the shift as 0.2 degrees.
Answer: We have fixed this as you suggested.
- Line 204 - Can the authors add the reasoning for why the paths were only shifted southward, and not northward?
Answer: We shifted the cyclone track only southward to represent a physically plausible worst-case scenario for Singapore. Southward shifts bring the cyclone’s core closer to Singapore, increasing wind exposure and storm-surge potential. In contrast, northward shifts move the cyclone farther away and systematically reduce the associated impacts. We have add this explanation to the revised manuscript.
- Line 205 - “As we *explain* in Section 4.4…”, since the section is later in the manuscript.
Answer: This has been corrected as suggested.
- Table 2 - Which path of the cyclone is used in the 20 cases - JMA or JTWC?
Answer: The paths of the cyclone shown in Table 2 are the observed path by JMA or the path shifted southward. We have explicitly stated these in the revised version of Table 2.
- Line 228 - The statement seems to imply that the simulation accuracy is solely dependent on the observation parameters, however the simulation parameters have also affect the results. Additionally, the authors have not commented on the accuracy differences between JMA and JTWC. Suggest revising the statement to indicate better that simulation results vary by differences in reported observations.
Answer: Thank you for this comment. We agree that observational inputs alone do not determine the accuracy of storm surge simulations; the model formulation and simulation parameters also influence it. In this study, the wind and pressure fields were generated using Holland's (1980) parametric model, an empirical approach that introduces additional sources of uncertainty beyond the observational data themselves. Ideally, directly prescribing spatially resolved observed wind and pressure fields could improve simulation accuracy; however, such an approach is beyond the scope of the present study and limited by data availability.
Regarding the differences between the JMA- and JTWC-based simulations, there are systematic differences in the simulated maximum water levels. Simulations using JMA data tend to underestimate peak water levels, whereas those using JTWC data tend to overestimate them. This discrepancy primarily arises from differences in observational definitions: JMA reports 10-minute sustained wind speeds, while JTWC reports 1-minute sustained wind speeds. These differences affect the intensity of the constructed wind fields and, consequently, the storm surge response. We have revise the manuscript to clarify that simulation results depend on both observational datasets and model assumptions, and to explicitly describe the differences between the JMA- and JTWC-based simulations.
We agree that it is important to explicitly discuss uncertainties in the study. In addition to revising the statement in Line 228 as noted above, we have added a brief discussion of key sources of uncertainty, including differences in observational datasets (JMA vs JTWC), the use of parametric wind models, and the deterministic assumptions in the scenario design.
- Line 231 - Same as previous comment.
Answer: Thank you for this comment. The inconsistency in the water-level time series is due to the accuracy of the tide-simulation model, the use of Holland's (1980) parametric model to generate wind and pressure fields, and differences in observation methods between JMA and JTWC. To incorporate these explanations, we have modified the manuscript.
- Fig 4 - Suggest adding a legend on the figures and clarifying in the caption that the red line indicates JMA, and blue JTWC.
Answer: A legend has to be added to this figure. We have revised the figure caption to clearly state that red lines correspond to JMA-based simulations and blue lines to JTWC-based simulations. Please see the revised figure.
- Fig 4 - The JTWC simulation has a noticeable dip in the 0-3 hr range across most tide gauges. Can the authors clarify whether this dip is expected and is an artifact of the storm surge, and why is it observed only for JTWC, and not JMA?
Answer: Thank you for this insightful comment. The dip observed in the JTWC-based simulations during the 0–3 h period is an expected model response. This feature arises from differences in the wind representation between the JTWC and JMA datasets. Specifically, the JTWC dataset provides 1-minute sustained wind speeds, whereas the JMA dataset uses 10-minute sustained wind speeds. When constructing the parametric wind and pressure fields for the storm surge model, the JTWC-based wind field exhibits a sharper, more intense structure at model initialisation than the JMA-based wind field. As a result, when the JTWC-derived forcing is introduced into the numerical model, the hydrodynamic fields adjust rapidly, leading to a short-term dip in simulated water levels at many tide gauges. In contrast, the JMA-based simulations show a smoother temporal evolution because the 10-minute sustained wind speeds produce a less abrupt initial wind forcing. To add this explanation, we have modified the manuscript.
- Section 4.2 - Suggest consistent capitalization/lowercase of first letter of “domain”.
Answer: Thank you for this suggestion. We have use “domain” in the revised version.
- Section 4.2 - What is the difference between the results in Section 4.1 and 4.2 since they both seem to implement the observed cyclone parameters?
Answer: We clarified that Section 4.1 focuses on model validation, while Section 4.2 describes the spatial characteristics of storm surge induced by the 2001 tropical cyclone Vamei. But it is easier to understand if the two sections are combined, since they both mention the same simulation results. Thus, in the revised manuscript, we have combined both sections.
- Line 253 - Is 25 m/s the observed speed? Which path does the calculated speed correspond to?
Answer: This is the calculated result when JMA’s path and intensity were used. To clarify this, we have modified the manuscript.
- Line 254 - Are there tidal stations in domains 1 and 3 from Fig. 4 that can be used to compare the observed wave heights with the calculated ones?
Answer: The time resolution of observed time series data at tidal stations is 1 min. Thus, storm waves cannot be detected using these datasets. Therefore, we have validated the accuracy of our simulation results by focusing on water-level changes induced by tides and storm surges. To explain this, we have modified the manuscript.
- Line 260 - Is there a threshold water depth used to calculate the inundation area? It is unclear how to interpret Fig 5 to visualize the inundation area.
Answer: In this study, the water-depth threshold is set as 0.1 m, as in Vogt et al. (2024). To add this explanation, we have modified the manuscript. We have modified Fig 5 to visualise the inundation area more clearly.
- Line 261 - Can the authors mark the location of the island on the figures?
Answer: Key islands, including Tekong Island, have been clearly labelled on Fig. 1b.
- Fig 6a - Can the authors clarify how the cumulative probability of observed data is calculated?
Answer: The cumulative probability of the observed data shown in Fig. 6a is calculated using an empirical cumulative distribution function. Specifically, the observed wind speed data are first sorted in ascending order, and the cumulative probability for the i-th data point is defined as i/N, where N is the total number of observations. In the revised manuscript, we have add this explanation.
- Line 274 - How is the radius calculated for the 1000-year scenario?
Answer: The radius for the 1000-year cyclone is also derived using the empirical relationship proposed by Quiring et al. (2011).
- Fig 7 - Is it possible to add comparisons of the observed relationship with relationships derived in other studies, in order to better validate it?
Answer: Thank you for this valuable suggestion. We agree that comparing the observed wind–pressure relationship with those derived in previous studies would be helpful for validation. However, most widely used empirical relationships (e.g., Atkinson and Holliday, 1977; Knaff and Zehr, 2007) are based on 1-minute sustained wind speeds, whereas Fig. 7 in this study uses 10-minute sustained wind speeds observed by JMA. Because the conversion between 1-min and 10-min wind speeds introduces additional uncertainty and depends on assumptions that vary among studies, a direct quantitative comparison may not be appropriate. Instead, we have mentioned these previous wind–pressure relationships in the revised text and added the justification for establishing a new relationship in this study.
- Line 284 - Suggest specifying the figure reference as Fig. 8b.
Answer: We have specified the figure reference as Fig. 8b.
- Figure 8 - Suggest adding marker for “North” on the map. Applies to all maps missing the north marker.
Answer: North arrows have been added to all relevant maps.
- Line 294 - Why are the maximum inundation heights similar with different sea level rises, and are the inundation heights specified with respect to the mean sea level of that scenario?
Answer: Thank you for this comment. We clarify that the values shown and discussed here do not represent maximum inundation heights. Instead, they correspond to the maximum storm-surge–induced water level (maximum storm-surge height) within the third domain (D3) of the numerical model.
The maximum storm-surge height is the highest water level above mean sea level for each scenario. Consequently, while sea-level rise increases the absolute water depth, it does not necessarily increase storm-surge height.
To avoid confusion, we have modified the caption of Table 2 as “Studied cases and assumptions. The calculated maximum storm surge heights (maximum water levels from the mean sea level of each scenario) in Domain 3 (D3) and the calculated inundation area in Singapore are also shown.”
- Section 5.1 - In my opinion, moving this to the Introductions sections would improve the motivation for this work.
Answer: Thank you for your suggestion. We have deleted Section 5.1 and moved these sentences to the Introduction.
- Line 326 - Which 1999 event are the authors referencing here?
Answer: This event was generated by the December 1999 Northeast Monsoon. To explain this, we have modified the text.
- Line 332 - What is the authors’ expected reasoning for the storm surges being unaffected by sea level rise in Singapore while being affected in Macau.
Answer: Thank you for this comment. Li et al. (2018) focus on tsunami-induced flooding rather than storm surges. Because the physical mechanisms governing tsunamis and storm surges are fundamentally different, we have removed this comparison.
- Section 5 - Suggest adding the limitation of the authors’ study regarding only considering future sea level rise from climate change, and not considering changes in weather patterns thus impacting the expected 1000-year scenario levels.
Answer: Thank you so much for your suggestion. The limitation of this study is that, while it accounts for future sea level rise associated with climate change, it does not explicitly incorporate potential future changes in weather patterns, such as cyclone frequency, intensity, or large-scale atmospheric circulation, which could influence extreme water levels under the 1000-year scenario. We have added these explanations as a limitation of this study in the manuscript.
- Line 345 - The first statement does not provide support for the second statement but are connected by “therefore”. Suggest improving sentence structure.
Answer: Thank you so much for pointing this out. We have improved the sentences as you suggested.
- Line 367 - The calculated maximum water levels were under- and over-estimated based on Table 3. Suggest clarifying why these comparisons were deemed consistent.
Answer: As you mentioned, the calculated maximum water levels were under- and over-estimated based on Table 3. But our simulation could reproduce the correct order of magnitude and the observed data's time series. Thus, “consistent” is not accurate. Instead of this, we have modified “Our simulation reasonably reproduced the maximum water levels of the storm surge and its time series at the tide gauge data around Singapore Island caused by tropical cyclone Vamei” in the text.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC1
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AC1: 'Reply on RC1', Masashi Watanabe, 28 Mar 2026
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RC2: 'Comment on egusphere-2025-5703', Anonymous Referee #2, 20 Jan 2026
General comments
Typhoons passing near the equator and the damage they cause are rare, and the cases examined in this study are therefore interesting. However, as a scientific paper, there are major problems as outlined below, which may prevent readers from achieving a sufficient understanding of the study.
This study aims to evaluate storm surge risk with a 1,000-year return period; however, what is actually assessed is storm surge generated by a typhoon whose intensity corresponds to a 1,000-year return period for low-latitude typhoons. The method used to estimate this intensity is not sufficiently explained. Storm surge is determined not only by typhoon intensity but also by factors such as track, size, and translation speed, the effects of which cannot be ignored. Therefore, these two concepts (1,000-year storm surge and 1,000-year typhoon) are not equivalent. A proper evaluation of this distinction is required, and factors that are not accounted for should be clearly stated.
Although tides and waves are included in the analysis, it is questionable whether their interactions need to be considered. In the water-level changes used for validation, the majority of the temporal variation is dominated by tides, making it difficult to adequately verify the reproducibility of storm surge–induced sea level anomalies. If interaction with tides is essential, comparisons with and without tidal forcing should be conducted. As it stands, figures such as Figures 4, 5, and 8 make it difficult to distinguish the reproducibility and influence of storm surge components. Wave analysis results are shown in Figure 5, but similarly, their contribution is not discussed. There is insufficient explanation regarding how much high waves contribute to water level rise or whether overtopping analysis was conducted in the inundation simulations. Consequently, the reliability of the hindcast simulations cannot be properly evaluated.
Although some relationship can be observed between the radius of maximum wind and the central pressure (or maximum wind speed) of a typhoon, in practice there is considerable variability, and accurate estimation should be difficult. This issue is not addressed, and the validation is concluded by attributing inaccuracies to uncertainty in the typhoon track without presenting verification of temporal changes in wind speed, wind direction, or pressure. This approach is not appropriate.
Overall, the explanations of the methodology and results are insufficient, making the content difficult to understand. Furthermore, much of what is discussed in the Discussion section does not correspond to the results of the present simulations and is therefore inappropriate.
Specific comments
- (L44) There is no explanation of the specific approach for the following methods. ‘Downward counterfactual analysis’. Please provide a concise explanation and significance, not just a list of references.
- (L105) It is reported that significant flooding and mudslides have occurred, but supplementary explanation is needed regarding whether this was caused by rainfall or strong winds (storm surge), what kind of rainfall characteristics did the typhoon have? and where the damage occurred.
- (L112) Empirical rules of the radius of maximum winds (Quiring et al., 2011) are likely to exhibit considerable variation. While acceptable for simulations under hypothetical conditions, I cannot endorse their use for evaluating the accuracy of follow-up calculations. It is also necessary to verify the reproducibility of the wind field time series.
- (Table 1) If the definition of wind speed differs, it should be noted in the table's footnotes or converted to a comparable value.
- (L125) Generally, remnants of typhoons can sometimes affect storm surges. Particularly in cases like this one where the wind-driven effect is relatively small, the swinging back effect could potentially cause the second wave to become larger. An explanation is needed as to whether the current conditions are sufficient.
- (L156-L161) It is unclear what this paragraph is explaining. What is assumed the maximum wind speed 45 knots? If it refers to Table 1, it might be Typhoon Vamei, but why is it appropriate to adopt Vamei as an example? Please outline your reasoning process. How many typhoon cases are ultimately used for GPD parameter estimation?
- (L168) What exactly are large-scale parameters?
- (L163-L172) This method is insufficient to determine the target typhoons. Storm surges depend not only on a typhoon's intensity but also on its path, size, and translation speed. Crucially, whether the typhoon of certain intensity passes over the target area is also important. This problem should be explicitly stated.
- (L174-L179) I do not understand the meaning of this explanation. While an explanation of how Chang et al. made their estimation is also necessary, even considering that, it remains unclear why the recurrence interval for Typhoon Vamei can be determined as 250 years despite the availability of observational data, and how this aligns with creating a cumulative distribution from past typhoon data.
- While it is possible to estimate worst-case scenarios, I believe risk assessments incorporating probability are insufficient. I request explanation specifying what assumptions are required.
- (L201-202) To consider diverse storm surges, we must also account for the effects of their progression speed and scale. While typhoon intensity and scale are assumed one-to-one correspondences in this case, but actually there is considerable variation.
- (Table 2, Maximum storm surge height around Singapore) If it is the maximum value in region D3, the location where it occurs differs in each case. Shouldn't we use a comparable metric?
- (Table 3) Although the unit is not specified, a tidal deviation of around 10 cm would be considered quite small. Even considering only the static suction effect from the central pressure, an increase of about 5 cm would be expected. That is, even accounting for estimation errors in the central pressure, the effect of wind-driven surges appears to be quite small. This characteristic is not reflected in the discussion of the results. Interpreting everything as merely a matter of typhoon trajectory data is far too simplistic.
- (L225) ‘Thus, high storm surges were produced by higher wind velocity and lower pressure.’ Supporting data is required for comparisons of wind speed and air pressure, as well as the relationship between the closest approach distance and the radius of maximum wind speed.
- (L228-229) ‘Thus, the simulation accuracy depended on the accuracy of observation of the tropical cyclone’s track, minimum central pressure, and maximum wind speed.’ More specifically, a qualitative explanation is needed as to why the underestimation and overestimation occurred. First, regarding the wind field, the reproducibility has not been demonstrated.
- (Figure 4) Since Figure 4 should compare the reproducibility of sea level anomaly, please compare the results after applying a high-pass filter to both.
- (L234-236) If tidal calculations fall outside the scope of this research, there is no need to perform analyses incorporating tides, and the research direction becomes unclear. Even if the analysis includes tides, verification of the tidal component and verification of tidal level deviation must be conducted separately.
- (L253) ‘The maximum wind speed is 25 m/s in domain 1.’ How does this value correspond to the one shown in Table 1?
- (L254-256) ‘The maximum significant wave height is 5.86 m in Domain 1, while it was 4.3 m in Domain 3. The maximum significant wave height in domain 1 was calculated in southern Singapore because waves penetrated from the southeastward of Singapore, which faces the Pacific Ocean.’ I do not understand the necessity of presenting analysis results for waves. Was the contribution of wave-induced water level rise significant in this analysis? Are you analyzing wave overtopping and inundation? Without any explanation, I consider it unnecessary within the paper's structure.
- (Figure 5) Without understanding the temporal variations in the wind field, including wind direction, it is difficult to comprehend the phenomenon. Water levels also contain tidal components, making it nearly impossible to discern what to observe. The results for c and f do not appear to correspond.
- (L269) ‘The typhoon's maximum wind speed of 1000-year probability of occurrence is 84 knots’ How do you read the 1000-year probability value from this diagram?
- (L273) ‘Based on the relation proposed by Quiring et al. (2011), the radius of maximum winds of Typhoon Vamei was 76.4 km.’ There is no guarantee that this is the actual maximum wind radius of Typhoon Vamei.
- (L274) ‘The radius of maximum winds of a 1000-year typhoon is 54.7 km’ There is no guarantee that this assumption is correct. If the radius of maximum wind speed changes, the results will also change significantly.
- (L305-321) What is the purpose of this section? Its content is unrelated to the research findings.
- (L328-330) ‘Therefore, a sea level anomaly of the same size as Tropical Cyclone Vamei occurred in Singapore, aside from tropical cyclones. However, in this study, even when sea level rises were assumed, maximum water levels around Singapore remained the same (Table 2).’ I don't understand what you mean or which results you're referring to. Please explain more specifically.
- (L332-335) ‘In the case of our simulation, the intensity of storm surges around Singapore did not significantly change, so even if sea level rise were generated in the future, the intensity of storm surges would not significantly increase around Singapore.’ What exactly is meant by storm surge intensity? I don't understand what we're discussing. Please explain the hypothesis that storm surge intensity changes due to sea level rise.
- (L338-341) The scope of the discussion appears overly broad and unrelated. Please focus on topics directly relevant to the findings of this research.
- (L369-371) ‘When the path of Vamei is moved southward, the maximum storm surge height around Singapore increases from 0.127 m to 0.183 m, and inundation increases from 8.06 km2 to 8.83 km2.’ There is no explanation of the mechanism. It merely states the numerical results.
Technical corrections
- (Figure 1) The letters SS and BK are hard to read in Figure 1. Is the station RL not included in D3? Some additional explanation is needed.
- (L112 and L450) Typo concerning author name of reference, Fujii and Mitsuta (1986).
- (Table 1) There is an error in the table number.
- (L130 )An explanation of what the abbreviation stands for is required. ‘MPA’
- (L139-L141) The similar expressions appear repeatedly, which I believe constitutes redundant phrasing. ‘We achieved this by considering the return period using the generalised Pareto distribution (GPD). The generalised Pareto distribution (GPD) (e.g., Coles 2001; McInnes et al. 2016) was employed for the typhoon intensity.’
- (L183-L187) The same content is stated at the beginning of the paper, and I believe this constitutes redundant expression.
- (L197) An explanation of what the abbreviation stands for is required. ‘CCRS’
- (L221, the maximum water level rise) The term “maximum tidal deviation” is more appropriate.
- (Table 3) The unit of water level is not shown.
- (L258) ‘… Tropical Cyclone Vamei (Fig. 1)’ Is the referenced figure correct?
- (L261) ‘… especially west of Tekong Island (Fig. 5f).’ It is difficult to interpret the results from the figure.
Citation: https://doi.org/10.5194/egusphere-2025-5703-RC2 -
AC2: 'Reply on RC2', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #2
- General comments
Typhoons passing near the equator and the damage they cause are rare, and the cases examined in this study are therefore interesting. However, as a scientific paper, there are major problems as outlined below, which may prevent readers from achieving a sufficient understanding of the study.
This study aims to evaluate storm surge risk with a 1,000-year return period; however, what is actually assessed is storm surge generated by a typhoon whose intensity corresponds to a 1,000-year return period for low-latitude typhoons. The method used to estimate this intensity is not sufficiently explained. Storm surge is determined not only by typhoon intensity but also by factors such as track, size, and translation speed, the effects of which cannot be ignored. Therefore, these two concepts (1,000-year storm surge and 1,000-year typhoon) are not equivalent. A proper evaluation of this distinction is required, and factors that are not accounted for should be clearly stated.
Although tides and waves are included in the analysis, it is questionable whether their interactions need to be considered. In the water-level changes used for validation, the majority of the temporal variation is dominated by tides, making it difficult to adequately verify the reproducibility of storm surge–induced sea level anomalies. If interaction with tides is essential, comparisons with and without tidal forcing should be conducted. As it stands, figures such as Figures 4, 5, and 8 make it difficult to distinguish the reproducibility and influence of storm surge components. Wave analysis results are shown in Figure 5, but similarly, their contribution is not discussed. There is insufficient explanation regarding how much high waves contribute to water level rise or whether overtopping analysis was conducted in the inundation simulations. Consequently, the reliability of the hindcast simulations cannot be properly evaluated.
Although some relationship can be observed between the radius of maximum wind and the central pressure (or maximum wind speed) of a typhoon, in practice there is considerable variability, and accurate estimation should be difficult. This issue is not addressed, and the validation is concluded by attributing inaccuracies to uncertainty in the typhoon track without presenting verification of temporal changes in wind speed, wind direction, or pressure. This approach is not appropriate.
Overall, the explanations of the methodology and results are insufficient, making the content difficult to understand. Furthermore, much of what is discussed in the Discussion section does not correspond to the results of the present simulations and is therefore inappropriate.
Answer: Thank you very much for your detailed and constructive comments. We address the main points raised as follows:
(1) Distinction between a “1000-year typhoon” and a “1000-year storm surge”
We agree that these two concepts are not equivalent. In this study, we assess storm surge generated by a typhoon with an intensity corresponding to a 1-in-1000-year return period for low-latitude tropical cyclones, rather than estimating a probabilistic 1000-year storm surge. We recognise that our previous wording may have caused confusion. We have revised the abstract and relevant sections to clearly state this distinction and avoid implying that a 1000-year storm surge was directly estimated.
(2) Estimation of typhoon intensity and neglected factors (track, size, translation speed)
We have provided a clearer explanation of how the extreme wind intensity was estimated using regional data and explicitly state that track, size, and translation speed are prescribed based on Tropical Cyclone Vamei. We have also clarified that this is a scenario-based (deterministic) approach and discussed the limitations of neglecting multivariate variability.
(3) Treatment of tides and validation of storm surge reproducibility
We agree that tidal dominance can obscure surge signals. We have revised the validation by separating tidal and storm-surge components using high-pass filtering and have included additional comparisons without tidal forcing to better assess surge reproducibility.
(4) Role of waves and their contribution
We have clarified the role of wave–current interaction in the coupled model and quantified the contribution of waves to water-level changes through additional sensitivity analysis (e.g., with and without SWAN coupling). We have also explicitly stated that wave overtopping is not considered in this study.
(5) Uncertainty in radius of maximum wind and wind-field representation
We acknowledge the uncertainty associated with estimating the radius of maximum wind and the limitations of the parametric wind model. We have revised the manuscript to clarify these assumptions and, where possible, include additional validation of wind-field characteristics (e.g., temporal variations in wind speed and direction).
(6) Discussion section
We agree that the Discussion section requires substantial revision. We have revised it to focus more closely on the simulation results, explicitly link interpretations to the presented findings, and clearly state key limitations and assumptions (e.g., deterministic scenario design, parametric wind representation, and uncertainty in storm structure).
Specific comments
- (L44) There is no explanation of the specific approach for the following methods. ‘Downward counterfactual analysis’. Please provide a concise explanation and significance, not just a list of references.
Answer: Thank you for this helpful comment. We agree that the motivation for using the downward counterfactual approach should be more clearly emphasised. We have revised the Introduction to provide a concise definition and clarify its significance in the context of this study. Specifically, we have explained that downward counterfactual analysis is a scenario-based approach that evaluates plausible alternative outcomes by benchmarking them against a historical event, and that it is particularly useful in regions such as Singapore where event records are limited. In such cases, alternative scenarios can be constructed by systematically modifying key storm characteristics (e.g., track and intensity) to explore potential hazard outcomes beyond the limited historical record. We have also clarified how we apply this framework to Tropical Cyclone Vamei and its counterfactual scenarios and position our contribution relative to previous studies.
- (L105) It is reported that significant flooding and mudslides have occurred, but supplementary explanation is needed regarding whether this was caused by rainfall or strong winds (storm surge), what kind of rainfall characteristics did the typhoon have? and where the damage occurred.
Answer: Thank you for this suggestion. We have revised the manuscript to clarify the causes of the reported flooding and mudslides by providing additional information on rainfall characteristics, affected locations, and distinguishing rainfall-driven impacts from storm-surge-related coastal effects considered in this study.
- (L112) Empirical rules of the radius of maximum winds (Quiring et al., 2011) are likely to exhibit considerable variation. While acceptable for simulations under hypothetical conditions, I cannot endorse their use for evaluating the accuracy of follow-up calculations. It is also necessary to verify the reproducibility of the wind field time series.
Answer: Thank you for your comment. We agree that verifying the reproducibility of the wind-field time series would strengthen the hindcast assessment. However, for Tropical Cyclone Vamei, continuous in situ wind observations in/around the study area with sufficient temporal coverage were not available to us for direct time-series validation of the constructed parametric wind fields. Therefore, in this study, we evaluated model performance primarily using the observed water-level time series at tide-gauge stations (Fig. 4) and the peak sea-level anomalies extracted by high-pass filtering (Fig. 3; Table 3), which provide the most reliable local observations for assessing storm-surge response. We acknowledge that the lack of direct wind-field time-series validation is a limitation of the present work. We have added this limitation to the revised manuscript and note that a more comprehensive assessment would require additional meteorological constraints (e.g., reanalysis-based winds and/or fully coupled meteorological–hydrodynamic modelling) to explicitly evaluate wind-field evolution, which is beyond the scope of this study. We have mentioned this limitation in Section 5.1.
- (Table 1) If the definition of wind speed differs, it should be noted in the table's footnotes or converted to a comparable value.
Answer: Thank you for your suggestion. We have unified the definition of wind speed in the manuscript.
- (L125) Generally, remnants of typhoons can sometimes affect storm surges. Particularly in cases like this one where the wind-driven effect is relatively small, the swinging back effect could potentially cause the second wave to become larger. An explanation is needed as to whether the current conditions are sufficient.
Answer: We thank the reviewer for this comment. We examined both observed and simulated water-level time series for evidence of a secondary peak associated with a potential “swinging back effect.” In the case of Tropical Cyclone Vamei, neither the tide-gauge observations nor the model results show a pronounced secondary surge peak following the main wind-forced maximum. Given the relatively short duration and rapid development of this near-equatorial system, the storm-surge response was primarily controlled by direct wind and pressure forcing rather than by delayed basin-scale oscillations. We have clarified this point in Section 4 by briefly noting that no significant secondary peak is observed in either the measured or simulated water levels, indicating that swinging-back effects were not a dominant process in this event.
- (L156-L161) It is unclear what this paragraph is explaining. What is assumed the maximum wind speed 45 knots? If it refers to Table 1, it might be Typhoon Vamei, but why is it appropriate to adopt Vamei as an example? Please outline your reasoning process. How many typhoon cases are ultimately used for GPD parameter estimation?
Answer: We agree that this paragraph was unclear and conflated several methodological steps. We have rewritten Lines 156–161 to clearly separate:
(i) the role of 45 kt as the lower-bound threshold for selecting strong tropical cyclone cases used in the statistical analysis;
(ii) the use of Tropical Cyclone Vamei as an illustrative example of a near-equatorial event rather than as the sole basis for parameter estimation; and
(iii) the number of tropical cyclone cases included in the GPD fitting.
The revised text has explicitly stated how many storms were used to estimate the GPD parameters and clarified the reasoning behind each step.
- (L168) What exactly are large-scale parameters?
Answer: Thank you for your comment. By large-scale parameters, we refer to atmospheric and oceanic environmental factors that influence tropical cyclone development, including sea surface temperature, wind shear, low-level vorticity, mid-level moisture, and large-scale ascent associated with monsoon variability. We have clarified this terminology in the revised manuscript.
- (L163-L172) This method is insufficient to determine the target typhoons. Storm surges depend not only on a typhoon's intensity but also on its path, size, and translation speed. Crucially, whether the typhoon of certain intensity passes over the target area is also important. This problem should be explicitly stated.
Answer: We agree with the reviewer that storm surge generation depends on multiple factors, including cyclone intensity, track, size, and translation speed, and that a “1000-year wind speed” does not directly correspond to a “1000-year storm surge.” In this study, we adopt a deterministic, scenario-based (“downward counterfactual”) framework rather than a fully probabilistic hazard assessment. Specifically, we estimate extreme wind intensity from regional climatology, while cyclone track, size, and translation speed are prescribed based on the historical Tropical Cyclone Vamei and its systematic variations. This approach is intended to explore plausible upper-bound impacts of an intensified Vamei-like event, rather than to quantify joint probabilities of all governing variables. Regarding the reviewer’s point on whether storms of a given intensity actually pass over the target region, we note that our statistical analysis is based exclusively on tropical cyclones that originated within or passed through the southern South China Sea (0–15°N, 100–125°E). Thus, the intensity distribution inherently reflects storms affecting this region. We have clarified these assumptions and limitations in the revised manuscript.
- (L174-L179) I do not understand the meaning of this explanation. While an explanation of how Chang et al. made their estimation is also necessary, even considering that, it remains unclear why the recurrence interval for Typhoon Vamei can be determined as 250 years despite the availability of observational data, and how this aligns with creating a cumulative distribution from past typhoon data.
Answer: We agree that this part of the manuscript was unclear. The ~250-year recurrence interval for Tropical Cyclone Vamei cited here is taken from Chang et al., who estimated it using regional tropical cyclone climatology and large-scale environmental conditions. This estimate is independent of our statistical analysis. Our study does not derive the recurrence interval of Vamei directly from wind-speed observations. Instead, we use historical tropical cyclone intensity data to construct a cumulative distribution of maximum wind speeds and then explore hypothetical extreme scenarios for hazard assessment.
We have revised Lines 174–179 to clearly distinguish between the recurrence estimate reported by Chang et al. and our own statistical framework and clarify that the 250-year value is cited only as contextual information rather than being derived from our cumulative distribution.
- While it is possible to estimate worst-case scenarios, I believe risk assessments incorporating probability are insufficient. I request explanation specifying what assumptions are required.
Answer: We agree with the reviewer that this study does not constitute a fully probabilistic risk assessment. Our objective is scenario-based hazard evaluation rather than estimation of event likelihood.
The analysis assumes:
(i) prescribed cyclone intensity and track scenarios;
(ii) fixed storm characteristics aside from the imposed modifications; and
(iii) specified sea-level rise scenarios.
These assumptions allow us to explore plausible upper-bound impacts but do not quantify joint probabilities of cyclone occurrence, track, intensity, and tidal phase. We have clarified throughout the manuscript that our approach represents scenario-based hazard screening, and explicitly state these assumptions and limitations in the Methods and Discussion sections.
- (L201-202) To consider diverse storm surges, we must also account for the effects of their progression speed and scale. While typhoon intensity and scale are assumed one-to-one correspondences in this case, but actually there is considerable variation.
Answer: We agree that storm surge depends not only on cyclone intensity but also on translation speed and storm size, which can vary substantially among events. In this study, these parameters were held fixed to isolate the effects of intensity, track position, and sea-level rise within a controlled scenario framework. We have explicitly acknowledged this simplification in the Methods and Discussion sections and state that variability in storm size and translation speed is not explored here and represents an important source of uncertainty for future work.
- (Table 2, Maximum storm surge height around Singapore) If it is the maximum value in region D3, the location where it occurs differs in each case. Shouldn't we use a comparable metric?
Answer: We acknowledge that the location of the maximum storm surge within Domain D3 differs among scenarios. In this study, we intentionally report the domain-wide maximum water level as a worst-case hazard indicator, rather than using a fixed reference point, because our objective is to assess potential upper-bound impacts under different cyclone scenarios. We have clarified in the caption of Table 2 and in Section 4 that the reported values represent the spatial maximum within Domain D3 for each case, and that variations in location reflect changes in storm forcing and coastal response rather than inconsistencies in the metric.
- (Table 3) Although the unit is not specified, a tidal deviation of around 10 cm would be considered quite small. Even considering only the static suction effect from the central pressure, an increase of about 5 cm would be expected. That is, even accounting for estimation errors in the central pressure, the effect of wind-driven surges appears to be quite small. This characteristic is not reflected in the discussion of the results. Interpreting everything as merely a matter of typhoon trajectory data is far too simplistic.
Answer: Thank you for your comment. First, we added the unit in Table 3. We also agree that the sea-level anomaly (storm-surge residual) around Singapore during Vamei is relatively small (on the order of 0.1 m). In the revised manuscript, we have added an explanation that this small residual implies a limited wind-driven contribution in this event and that even the pressure-driven component (static suction / inverse barometer effect) is only a few centimetres, given that the observed minimum central pressure of Vamei was 1006 hPa. Therefore, the remaining wind-driven setup is correspondingly small in Singapore, and modest uncertainties in the wind-field parameterisation and storm structure can lead to noticeable differences in the residual. In addition, we have revised the manuscript to better reflect this characteristic and to avoid attributing the differences solely to the typhoon trajectory. We now explicitly state that the surge magnitude is controlled by multiple factors, including not only track but also the reported intensity definition (e.g., 10-min vs 1-min winds), the parametric representation of the wind/pressure structure (including storm size/RMW), and local geographic effects (e.g., limited fetch and shielding by surrounding land and islands). To add these explanations, we have revise Section 4.1.
- (L225) ‘Thus, high storm surges were produced by higher wind velocity and lower pressure.’ Supporting data is required for comparisons of wind speed and air pressure, as well as the relationship between the closest approach distance and the radius of maximum wind speed.
Answer: We agree that this statement requires quantitative support. We have revised Section 4.2 to explicitly report representative values of maximum wind speed, minimum central pressure, radius of maximum winds, closest-approach distance, and the corresponding peak storm surge for selected scenarios. This has quantitatively demonstrated how increased wind velocity, reduced pressure, and storm geometry together contribute to higher surge levels.
- (L228-229) ‘Thus, the simulation accuracy depended on the accuracy of observation of the tropical cyclone’s track, minimum central pressure, and maximum wind speed.’ More specifically, a qualitative explanation is needed as to why the underestimation and overestimation occurred. First, regarding the wind field, the reproducibility has not been demonstrated.
Answer: We acknowledge that in-situ wind observations are not available for this event, which limits direct validation of the simulated wind field. The atmospheric forcing is therefore derived from parametric tropical cyclone models constrained by best-track data, introducing uncertainty in both wind-magnitude and spatial-structure estimates. Under- and overestimation of water levels across stations primarily reflect differences in storm-track representation and pressure forcing between datasets, combined with uncertainties in the parametric wind field. These factors lead to spatial shifts in peak forcing relative to tide-gauge locations. We have revised Lines 228–229 to clarify that discrepancies arise from uncertainties in track position, central pressure, and parametric wind reconstruction, and explicitly acknowledge the lack of direct wind-field validation as a limitation of this study.
- (Figure 4) Since Figure 4 should compare the reproducibility of sea level anomaly, please compare the results after applying a high-pass filter to both.
Answer: We agree with the reviewer that Fig. 4 should compare storm-surge residuals rather than raw water levels. We have revised Fig. 4 by applying the same high-pass filter to both observed and simulated water levels to extract sea-level anomalies, and have updated the figure accordingly to directly assess surge reproducibility.
- (L234-236) If tidal calculations fall outside the scope of this research, there is no need to perform analyses incorporating tides, and the research direction becomes unclear. Even if the analysis includes tides, verification of the tidal component and verification of tidal level deviation must be conducted separately.
Answer: We agree that the role of tides was not sufficiently clarified. Although tidal dynamics themselves are not the focus of this study, tidal forcing is required to reproduce realistic total water levels and to consistently extract storm surge residuals from both observations and simulations. We have revised Lines 234–236 to clarify that tides are included in establishing the baseline water level and enabling surge-residual validation. In addition, we have separately quantified tidal model performance (e.g., RMSE and phase error) prior to surge extraction, as noted above.
- (L253) ‘The maximum wind speed is 25 m/s in domain 1.’ How does this value correspond to the one shown in Table 1?
Answer: Thank you for your comment. We have unified the unit of maximum wind speed as knots in the revised manuscript. Please see the revised manuscript.
- (L254-256) ‘The maximum significant wave height is 5.86 m in Domain 1, while it was 4.3 m in Domain 3. The maximum significant wave height in domain 1 was calculated in southern Singapore because waves penetrated from the southeast of Singapore, which faces the Pacific Ocean.’ I do not understand the necessity of presenting analysis results for waves. Was the contribution of wave-induced water level rise significant in this analysis? Are you analysing wave overtopping and inundation? Without any explanation, I consider it unnecessary within the paper's structure.
Answer: We agree that the role of waves was not sufficiently justified in the current manuscript. Waves are included through two-way FLOW–SWAN coupling because wave setup can contribute to total water levels, particularly when surge magnitudes are relatively small. To directly assess this contribution, we have conducted additional sensitivity experiments with SWAN disabled (FLOW-only) and compared peak water levels, timing, RMSE, and bias against the fully coupled simulations at tide-gauge locations. These results have been presented in an Appendix. Wave overtopping is not explicitly modelled in this study and is therefore outside the scope of this study. We have revised Section 4.2 to clarify the purpose of wave analysis and explicitly state that overtopping is not simulated.
- (Figure 5) Without understanding the temporal variations in the wind field, including wind direction, it is difficult to comprehend the phenomenon. Water levels also contain tidal components, making it nearly impossible to discern what to observe. The results for c and f do not appear to correspond.
Answer: We agree that Fig. 5 does not sufficiently convey the temporal evolution of wind forcing and that tidal components obscure the interpretation of water-level patterns. We have revised Fig. 5 to present storm-surge residuals (after high-pass filtering) rather than total water levels, and we have added representative time series of wind speed and direction at selected locations. The colour scales have been adjusted for clarity, and figure captions have been expanded to explicitly explain the correspondence between panels (c) and (f). These revisions have improved the interpretability of both wind forcing and surge response.
- (L269) ‘The typhoon's maximum wind speed of 1000-year probability of occurrence is 84 knots’ How do you read the 1000-year probability value from this diagram?
Answer: Thank you for your comment. We agree that the procedure for obtaining the 1000-year value from Fig. 6 was not sufficiently explained. In this study, the “1000-year” level is defined as an annual exceedance probability of 0.001. We obtained the corresponding maximum wind speed by evaluating the fitted extreme-value model (GPD) at a return period of 1000 years (i.e., an annual exceedance probability of 0.001) and reading the associated wind speed, which is 84 knots. We have revised the text around Line 269 to explicitly describe this procedure and updated the figure/caption to indicate the 1000-year level (e.g., with a marker/vertical line) for clarity.
- (L273) ‘Based on the relation proposed by Quiring et al. (2011), the radius of maximum winds of Typhoon Vamei was 76.4 km.’ There is no guarantee that this is the actual maximum wind radius of Typhoon Vamei.
Answer: We agree with this comment. As the radius of maximum winds (RMW) of Tropical Cyclone Vamei is not directly observed, the value derived from Quiring et al. (2011) should be interpreted as an empirical estimate rather than the actual RMW. We have revised the manuscript to clarify this point and have explicitly acknowledged the associated uncertainty as a limitation of the study.
- (L274) ‘The radius of maximum winds of a 1000-year typhoon is 54.7 km’ There is no guarantee that this assumption is correct. If the radius of maximum wind speed changes, the results will also change significantly.
Answer: Thank you for this comment. We agree that the assumed radius of maximum winds (RMW) for the 1000-year cyclone scenario is uncertain and that variations in RMW may influence the simulated storm-surge and inundation results. We have revised the manuscript to clarify that the reported RMW represents an empirical estimate derived from Choiring et al. (2011) and have explicitly acknowledged this assumption and its associated uncertainty as a limitation of the study. We have also noted that the use of parametric tropical-cyclone wind models introduces structural uncertainty and that more physics-based assessments would require fully coupled meteorological–hydrodynamic modelling.
- (L305-321) What is the purpose of this section? Its content is unrelated to the research findings.
Answer: Thank you for your suggestion. Based on the comment from Reviewer 1, we have deleted this part from the Discussion and moved this to the Introduction session.
- (L328-330) ‘Therefore, a sea level anomaly of the same size as Tropical Cyclone Vamei occurred in Singapore, aside from tropical cyclones. However, in this study, even when sea level rises were assumed, maximum water levels around Singapore remained the same (Table 2).’ I don't understand what you mean or which results you're referring to. Please explain more specifically.
Answer: Thank you for this comment. We agree that the previous description was unclear about which results were referenced. We have revised this section to provide a more specific explanation of the simulated storm-surge results and clarify the comparison between cyclone-induced and non-cyclone-related sea-level anomalies in Singapore.
- (L332-335) ‘In the case of our simulation, the intensity of storm surges around Singapore did not significantly change, so even if sea level rise were generated in the future, the intensity of storm surges would not significantly increase around Singapore.’ What exactly is meant by storm surge intensity? I don't understand what we're discussing. Please explain the hypothesis that storm surge intensity changes due to sea level rise.
Answer: Thank you for this comment. We agree that the term “storm surge intensity” was unclear. In this study, it refers to storm-surge height (i.e., the sea-level anomaly). We have revised the manuscript to clearly distinguish between storm-surge height and total water level, and clarify that sea-level rise increases the baseline water level but does not substantially alter the simulated storm-surge anomaly itself.
- (L338-341) The scope of the discussion appears overly broad and unrelated. Please focus on topics directly relevant to the findings of this research.
Answer: Thank you so much for your suggestion. Based on all reviewers’ comments, we have modified all parts of the Discussion section.
- (L369-371) ‘When the path of Vamei is moved southward, the maximum storm surge height around Singapore increases from 0.127 m to 0.183 m, and inundation increases from 8.06 km2 to 8.83 km2.’ There is no explanation of the mechanism. It merely states the numerical results.
Answer: Thank you for this suggestion. We agree that the mechanism responsible for the increase in storm surge and inundation was insufficiently explained. We have revised the manuscript to include a physical explanation describing how the southward track shift brings the cyclone’s strongest wind field closer to Singapore and enhances onshore wind forcing, resulting in increased surge levels and inundation extent.
Technical corrections
- (Figure 1) The letters SS and BK are hard to read in Figure 1. Is the station RL not included in D3? Some additional explanation is needed.
Answer: Thank you so much for your suggestion. We have changed the colour of ‘SS’ and ‘BK’. Please see Fig. 1. RL is located in D2. To explain this, we have added “RL is in D2, and the other observation points are in D3” in the caption of Fig. 1.
- (L112 and L450) Typo concerning author name of reference, Fujii and Mitsuta (1986).
Answer: This has been corrected as suggested.
- (Table 1) There is an error in the table number.
Answer: This has been corrected as suggested.
- (L130 )An explanation of what the abbreviation stands for is required. ‘MPA’
Answer: Thank you for this comment. We have revised the manuscript to define the abbreviation “MPA” at its first occurrence as the Maritime and Port Authority.
- (L139-L141) The similar expressions appear repeatedly, which I believe constitutes redundant phrasing. ‘We achieved this by considering the return period using the generalised Pareto distribution (GPD). The generalised Pareto distribution (GPD) (e.g., Coles 2001; McInnes et al. 2016) was employed for the typhoon intensity.’
Answer: Thank you for this suggestion. We agree that the phrasing was redundant. We have revised the manuscript to remove the repeated expression and improve clarity.
- (L183-L187) The same content is stated at the beginning of the paper, and I believe this constitutes redundant expression.
Answer: Thank you for this comment. We agree that this section contains redundant material already presented in the Introduction. We have revised the manuscript to condense this paragraph and retain only the study-specific description needed to introduce the methodological framework.
- (L197) An explanation of what the abbreviation stands for is required. ‘CCRS’
Answer: Thank you for this comment. We have revised the manuscript to define the abbreviation “CCRS” at its first occurrence as the Centre for Climate Research Singapore.
- (L221, the maximum water level rise) The term “maximum tidal deviation” is more appropriate.
Answer: Thank you for this suggestion. We agree that the terminology should be clarified. We have revised the manuscript to replace “maximum water level rise” with a more appropriate term describing the extracted tidal deviation (storm-surge residual).
- (Table 3) The unit of water level is not shown.
Answer: Thank you for your suggestion. We have added the unit (m) in Table 3.
- (L258) ‘… Tropical Cyclone Vamei (Fig. 1)’ Is the referenced figure correct?
Answer: Thank you for this comment. We agree that the figure reference was incorrect. We have revised the manuscript to correct the corresponding figure citation.
- (L261) ‘… especially west of Tekong Island (Fig. 5f).’ It is difficult to interpret the results from the figure.
Answer: Thank you for this comment. We have revised Fig. 5 to improve interpretability by adjusting the colour scale and adding geographic labels, including island names, to clarify the spatial distribution of inundation.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC2
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RC3: 'Comment on egusphere-2025-5703', Giovanni Scardino, 20 Jan 2026
This work is very interesting and considers an important topic for coastal hazard. Here, a modelling approach was followed to simulate the impact of cyclone Vamei in Singapore, also in a framework of future sea-level rise. The methodology and results are well explained and support the increase of coastal flooding related to the joint action between storm surge and sea-level rise. I have just few comments that can be considered to improve the manuscript.
Minor comments
Line 19: In this sentence “For a 1000-year cyclone with its path shifted 0.8° southward, sea level rise scenarios of +0.7 m and +2.0 m resulted in inundation areas of 34.5 km²….” it is not clear why the cyclone track is shifted. Maybe a brief sentence could help the reader to better understand the dynamic of southward shift.
Line 90: rephrase like this: We used domain decomposition to simulate the area around Singapore using a fine computational grid.
Line 91: It is not clear if you applied a nesting for all the spatial domains (D1, D2, D3). Please highlight this aspect.
Lines 105-108: This sentence is almost a discussion. Maybe it could be better to move into the Section 5.
Lines 123-124: When you are reporting the tropical cyclone speed, do you mean the maximum wind speed?
Lines 182-183: This sentence represents a discussion.
Line 188: When modifying storm variables such as size and intensity, are you accounting for associated changes in thermodynamic fields (e.g., air temperature, sea surface temperature, specific humidity)? If so, please include a brief explanation of how your modifications are consistent with these thermodynamic changes.
Line 197: Are you sure that CCRS projections are undated?
Line 198-199: Consider to correct the sea-level rise with the value of vertical land movements of Singapore area (for example, see 10.1109/JSTARS.2025.3573299).
Table 2: How did you assess the inundated areas, report a description in the methodology. Then I suggest to insert also the intensity variables that you have changed, such us central pressure and maximum wind speed.
Fig. 5: Please also insert some toponyms in the maps, because you cited the island names and cities in the text.
Section 5 Discussion needs to be improved. Although tsunami contributions to the study areas have been documented in the literature, this study is limited to the simulation of tropical cyclones, a phenomenon that differs fundamentally in its mechanism of water level change. So I suggest to avoid a discussion with tsunami flooding compared to your cyclone simulation.
Citation: https://doi.org/10.5194/egusphere-2025-5703-RC3 -
AC3: 'Reply on RC3', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #3
- This work is very interesting and considers an important topic for coastal hazard. Here, a modelling approach was followed to simulate the impact of cyclone Vamei in Singapore, also in a framework of future sea-level rise. The methodology and results are well explained and support the increase of coastal flooding related to the joint action between storm surge and sea-level rise. I have just few comments that can be considered to improve the manuscript.
Answer: Thank you so much for giving us many helpful suggestions. We have modified the manuscript based on your comments.
Minor comments
- Line 19: In this sentence “For a 1000-year cyclone with its path shifted 0.8° southward, sea level rise scenarios of +0.7 m and +2.0 m resulted in inundation areas of 34.5 km²….” it is not clear why the cyclone track is shifted. Maybe a brief sentence could help the reader to better understand the dynamic of southward shift.
Answer: Thank you for your comment. We agree that the motivation for shifting the cyclone track should be stated more clearly. In this study, the southward track shift is used as a counterfactual sensitivity experiment to represent plausible variability/uncertainty in the cyclone path near the equator and to examine how a slightly closer approach to Singapore would affect storm surge and inundation. To ensure this is clear to the reader, we have added the following sentence to the manuscript: "A southward track shift was incorporated as a sensitivity experiment to account for track uncertainty and to simulate a closer approach of the cyclone to Singapore.
- Line 90: rephrase like this: We used domain decomposition to simulate the area around Singapore using a fine computational grid.
Answer: We have rephrased the sentences as you suggested in this part in the revised manuscript.
- Line 91: It is not clear if you applied a nesting for all the spatial domains (D1, D2, D3). Please highlight this aspect.
Answer: Thank you for your comment. We agree that the nesting configuration should be stated more clearly. In this study, all spatial domains (D1, D2, and D3) were coupled using two-way nesting in Delft3D, such that water levels and depth-averaged currents are exchanged between the parent and child domains during the simulation. To explain this, we have modified the manuscript.
- Lines 105-108: This sentence is almost a discussion. Maybe it could be better to move into the Section 5.
Answer: We agree with this suggestion. We have moved these sentences to the Discussion section.
- Lines 123-124: When you are reporting the tropical cyclone speed, do you mean the maximum wind speed?
Answer: Thank you for your comment. Here, tropical cyclone speed means maximum wind speed. To avoid confusion, we have rewritten these sentences.
- Lines 182-183: This sentence represents a discussion.
Answer: We have modified this part based on this suggestion.
- Line 188: When modifying storm variables such as size and intensity, are you accounting for associated changes in thermodynamic fields (e.g., air temperature, sea surface temperature, specific humidity)? If so, please include a brief explanation of how your modifications are consistent with these thermodynamic changes.
Answer: Thank you for your comment. We agree that changes in storm size and intensity may be accompanied by thermodynamic changes (e.g., air temperature, sea surface temperature, and humidity), which can influence air–sea fluxes and potentially affect the storm structure. In the present study, however, the tropical-cyclone wind and pressure fields were determined using a parametric formulation to generate controlled scenarios, and we did not explicitly account for the associated changes in thermodynamic fields. We have clarified this assumption and added it as a limitation in the revised manuscript.
- Line 197: Are you sure that CCRS projections are undated?
Answer: Thank you for your suggestion. In the revised manuscript, we have added the correct citation of this publication (Ng et al., 2025).
- Line 198-199: Consider to correct the sea-level rise with the value of vertical land movements of Singapore area (for example, see 10.1109/JSTARS.2025.3573299).
Answer: Thank you for this important comment. We agree that vertical land movement (VLM) plays an important role in relative sea-level rise and should be considered in coastal impact assessments. In this study, however, the sea-level-rise scenarios (+0.7 m by 2099 and +2.0 m by 2150) are based on the Singapore V3 projections (Centre for Climate Research Singapore, 2024), which already represent relative sea-level rise and explicitly include contributions from vertical land movement. Therefore, applying an additional correction for VLM would result in double-counting. We have revised the manuscript to clarify that the adopted sea-level-rise values already account for VLM.
- Table 2: How did you assess the inundated areas, report a description in the methodology. Then I suggest to insert also the intensity variables that you have changed, such us central pressure and maximum wind speed.
Answer: Thank you for your comment. We agree with your suggestion. Thus, we have add a description of how the inundation area was assessed in the Methodology section. In our simulations, a grid cell is classified as inundated when the simulated maximum inundation depth exceeds a threshold of 0.1 m (following, e.g., Vogt et al., 2024). We then calculate the inundation area by multiplying the number of inundated grid cells by the area of each grid cell.
In addition, as suggested, we have added the minimum central pressure and maximum wind speed to the Table’s caption.
- Fig. 5: Please also insert some toponyms in the maps, because you cited the island names and cities in the text.
Answer: Thank you for your suggestion. We have added some toponyms in the maps and revised the caption.
- Section 5 Discussion needs to be improved. Although tsunami contributions to the study areas have been documented in the literature, this study is limited to the simulation of tropical cyclones, a phenomenon that differs fundamentally in its mechanism of water level change. So I suggest to avoid a discussion with tsunami flooding compared to your cyclone simulation.
Answer: We agree with you. This paper investigated storm-surge disasters; therefore, sentences mentioning tsunamis have been removed.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC3
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AC3: 'Reply on RC3', Masashi Watanabe, 28 Mar 2026
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CC1: 'Comment on egusphere-2025-5703', Xingkun XU, 22 Jan 2026
Publisher’s note: this comment is a copy of RC4 and its content was therefore removed on 22 January 2026.
Citation: https://doi.org/10.5194/egusphere-2025-5703-CC1 -
RC4: 'Comment on egusphere-2025-5703', XINGKUN XU, 22 Jan 2026
This manuscript assesses storm surge and coastal inundation hazards for Singapore using Tropical Cyclone Vamei (27 Dec 2001) as a baseline and a “downward counterfactual” framework to explore hypothetical scenarios (southward track shifts, intensification to a 1-in-1000-year cyclone, and sea-level rise). Delft3D-FLOW coupled with SWAN is employed to simulate water level, currents, and wave effects through radiation-stress feedback (wave setup). Modelled water levels are compared against tide-gauge observations at nine locations, and scenario impacts are summarized using peak storm surge heights and inundation areas. The paper is potentially valuable, but the reliability of the headline results depends strongly on assumptions that are currently under-documented (e.g., pressure–wind conversion, extreme-value extrapolation and its uncertainty, wave contribution to water level, and inundation derivation).
- The manuscript establishes a relationship between maximum wind speed and minimum central pressure using the collocated JMA dataset over 0–15°N and 100–125°E (Fig. 7). Because the subsequent “1000-year cyclone” construction and wind-field forcing depend strongly on both and , the results can be highly sensitive to the chosen conversion (and to scatter in Fig. 7). I strongly recommend a sensitivity test or uncertainty propagation。
- If I understood correctly, the model is run with two-way Delft3D-FLOW–SWAN coupling, so waves can affect water level through wave setup. Since the surge magnitudes at some tide gauges are relatively small (~0.1–0.2 m), even a modest wave-setup contribution could matter. Could the authors quantify this more directly? A clean way is to run a sensitivity test with SWAN coupling switched off (FLOW-only) and compare the tide-gauge water levels (peak, timing, RMSE/bias). This would clarify whether waves are a second-order detail here, or whether they noticeably affect the conclusions.
- The paper says surge is extracted using a high-pass filter, but the filter settings aren’t described. With small surge signals, different filter choices can shift the peak or change its magnitude. Please report the filter design clearly. Ideally, also show a quick robustness check (e.g., two reasonable cutoff choices) to demonstrate that the peak surge is not an artefact of the specific filter settings.
- Inundation area (km²) is a key outcome, yet the manuscript does not clearly document whether inundation is computed dynamically (wetting/drying in FLOW) or via static post-processing against a DEM/DTM, nor how vertical datums and coastal defenses/connectedness are handled.
- 5 is hard to read because the colour scale looks too compressed. I can’t easily tell spatial differences or infer values. I suggest adjusting the colour limits / tick marks (or using discrete levels) so the spatial gradients become more interpretable.
- Sections 5.1 and 5.3 read more like Introduction material than discussion of this study’s results. I suggest tightening them and keeping the Discussion closer to what is actually shown/diagnosed in the paper. 5.1: the equatorial cyclone genesis discussion is interesting, but unless the paper presents results directly related to genesis, it may fit better in the Introduction or as brief context. 5.3: similarly, if cold surges / Borneo vortex are not directly diagnosed in the analysis (no supporting diagnostics shown), they should be framed more cautiously as plausible mechanisms or limitations rather than as confirmed drivers.
- Table 2 and line 330 suggest that peak water levels do not change under the SLR scenarios. Could the authors explain why?
Minor
L125–127: Simulation period says “in January” although the event is Dec 27, 2001; likely should be December
L110–113 + Fig.2 caption: Text cites Holland (1980) parametric model, while the caption states Holland (2010). Please align and specify which formulation is used.
L95–99: Manning’s is set uniformly to 0.025 across beaches, seafloor, and paved surfaces. This is a strong simplification; please justify or clarify whether any spatially varying roughness was tested.
L145–170: Please clarify whether y is per-storm maximum wind, and how independence is ensured for POT sampling.
L255–260: Statement that waves penetrated from “southeastward of Singapore, which faces the Pacific Ocean” is geographically incorrect and should be revised (Singapore is sheltered by surrounding seas/straits; “Pacific Ocean” is misleading here).
Fig.7: y-axis says “Minumum central pressure (Pa)” but plotted values (~880–1020) indicate hPa; also spelling “Minumum” → “Minimum”.
In Fig. 4(e), the JTWC-forced simulation (blue) shows a small double-peaked feature during the first ~2–3 h that is not present in the observations and is much weaker in the JMA-forced run. Any reasons?
Line 155: Could the authors please check that the definition of “maximum wind” is consistent between the model forcing and the JMA best-track data (e.g., averaging period, reference height, and whether it is sustained wind vs gust)?
Citation: https://doi.org/10.5194/egusphere-2025-5703-RC4 -
AC4: 'Reply on RC4', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #4
- This manuscript assesses storm surge and coastal inundation hazards for Singapore using Tropical Cyclone Vamei (27 Dec 2001) as a baseline and a “downward counterfactual” framework to explore hypothetical scenarios (southward track shifts, intensification to a 1-in-1000-year cyclone, and sea-level rise). Delft3D-FLOW coupled with SWAN is employed to simulate water level, currents, and wave effects through radiation-stress feedback (wave setup). Modelled water levels are compared against tide-gauge observations at nine locations, and scenario impacts are summarized using peak storm surge heights and inundation areas. The paper is potentially valuable, but the reliability of the headline results depends strongly on assumptions that are currently under-documented (e.g., pressure–wind conversion, extreme-value extrapolation and its uncertainty, wave contribution to water level, and inundation derivation).
Answer: Thank you for your comment. We have revised the manuscript to better explain the reliability of the simulation results and the key assumptions, based on the reviewers’ feedback. Below, we provide a detailed point‑by‑point response to the reviewers' comments.
- The manuscript establishes a relationship between maximum wind speed and minimum central pressure using the collocated JMA dataset over 0–15°N and 100–125°E (Fig. 7). Because the subsequent “1000-year cyclone” construction and wind-field forcing depend strongly on both and , the results can be highly sensitive to the chosen conversion (and to scatter in Fig. 7). I strongly recommend a sensitivity test or uncertainty propagation.
Answer: Thank you for your comment. We agree that the construction of the 1000-year design cyclone depends on both the maximum wind speed and the minimum central pressure, and that uncertainty in the wind–pressure relationship could propagate into the parametric wind/pressure forcing and hence the simulated surge and inundation. In our manuscript, the wind–pressure relationship is derived from the collocated JMA dataset within 0–15°N and 100–125°E to represent low-latitude tropical cyclones relevant to Singapore. As shown in Fig. 7, the relationship is well constrained (R² = 0.956), indicating limited scatter in the regional dataset used here. To further assess this uncertainty, we estimated the 95% prediction interval of the minimum central pressure corresponding to the 1000-year wind-speed level based on the regression shown in Fig. 7. The resulting range indicates a moderate level of variability in the derived pressure values, while remaining within a physically reasonable range for the regional dataset.
Nevertheless, we acknowledge that the empirical conversion introduces uncertainty in the estimated minimum central pressure corresponding to the 1000-year wind-speed level. Because the primary aim of this study is a scenario-based assessment for Singapore (rather than a full uncertainty-propagation analysis), we did not perform an additional sensitivity experiment in the present revision. Instead, we have revised the manuscript to clarify this assumption and explicitly state it as a limitation, noting that a more rigorous treatment would require uncertainty propagation and/or fully coupled meteorological–hydrodynamic modelling, which we leave for future work.
To add this explanation, we have revised the manuscript.
- If I understood correctly, the model is run with two-way Delft3D-FLOW–SWAN coupling, so waves can affect water level through wave setup. Since the surge magnitudes at some tide gauges are relatively small (~0.1–0.2 m), even a modest wave-setup contribution could matter. Could the authors quantify this more directly? A clean way is to run a sensitivity test with SWAN coupling switched off (FLOW-only) and compare the tide-gauge water levels (peak, timing, RMSE/bias). This would clarify whether waves are a second-order detail here, or whether they noticeably affect the conclusions.
Answer: We thank the reviewer for this constructive suggestion. We agree that, given the relatively small surge magnitudes (~0.1–0.2 m at some stations), the contribution of wave setup should be quantified explicitly. In the revised manuscript, we have performed additional sensitivity experiments with SWAN coupling disabled (FLOW-only) and compared peak water levels, timing, RMSE, and bias at tide-gauge locations against the fully coupled FLOW–SWAN simulations. Storm surge residuals were extracted using the same high-pass filtering procedure for both configurations. The results of this comparison have been presented in an Appendix to directly assess the influence of wave setup on surge and inundation estimates.
- The paper says surge is extracted using a high-pass filter, but the filter settings aren’t described. With small surge signals, different filter choices can shift the peak or change its magnitude. Please report the filter design clearly. Ideally, also show a quick robustness check (e.g., two reasonable cutoff choices) to demonstrate that the peak surge is not an artefact of the specific filter settings.
Answer: Thank you for your comment. We agree that the filter settings should be reported clearly, particularly because the storm-surge signal around Singapore is relatively small. In the revised manuscript, we now describe the high-pass filtering procedure in detail. Specifically, we extracted the storm-surge sea-level anomaly from both observed and simulated 1-min water-level time series by removing the dominant tidal component using a digital high-pass filter with a cutoff period of 12 h. The filter was applied in a zero-phase manner (forward–backward filtering) to avoid shifting the timing of peaks. We then defined the “maximum storm surge height” at each station as the maximum value of the filtered sea-level anomaly within the analysis window. To add this explanation, we have revised the manuscript.
- Inundation area (km²) is a key outcome, yet the manuscript does not clearly document whether inundation is computed dynamically (wetting/drying in FLOW) or via static post-processing against a DEM/DTM, nor how vertical datums and coastal defenses/connectedness are handled.
Answer: Thank you for your comment. We agree that the derivation of the inundation area should be documented more clearly. In the revised manuscript, we now explicitly describe how the inundation area is calculated. We specify that inundation is quantified by applying a water-depth threshold of 0.1 m (consistent with previous storm-surge inundation studies; e.g., Vogt et al., 2024) and then identifying grid cells where the maximum simulated inundation depth exceeds 0.1 m during the event, counting those inundated cells, and multiplying by the area of an individual grid cell to obtain inundation area (km²). We also clarify that coastal defences are not implemented as separate hydraulic structures; instead, their effects are included only to the extent that they are represented in the elevation/topographic data used in the model. To add these explanations, we have revised the manuscript.
- 5 is hard to read because the colour scale looks too compressed. I can’t easily tell spatial differences or infer values. I suggest adjusting the colour limits / tick marks (or using discrete levels) so the spatial gradients become more interpretable.
Answer: Thank you for your suggestion. We have changed the colour bar of Fig. 5f.
- Sections 5.1 and 5.3 read more like Introduction material than discussion of this study’s results. I suggest tightening them and keeping the Discussion closer to what is actually shown/diagnosed in the paper. 5.1: the equatorial cyclone genesis discussion is interesting, but unless the paper presents results directly related to genesis, it may fit better in the Introduction or as brief context. 5.3: similarly, if cold surges / Borneo vortex are not directly diagnosed in the analysis (no supporting diagnostics shown), they should be framed more cautiously as plausible mechanisms or limitations rather than as confirmed drivers.
Answer: Thank you for this helpful suggestion. We agree that the Discussion should be more tightly focused on our actual findings. In the revised manuscript, we have moved the material on equatorial cyclone genesis (formerly Section 5.1) to the Introduction to provide better context.
Furthermore, we have significantly revised Section 5.3. Because we do not present supporting diagnostics for cold surges and the Borneo vortex in this study, we now clearly frame them as plausible mechanisms and limitations of our current analysis rather than confirmed drivers. We have updated the entire Discussion section accordingly.
- Table 2 and line 330 suggest that peak water levels do not change under the SLR scenarios. Could the authors explain why?
Answer: Thank you for your comment. We agree that this point needed a clearer explanation. In Table 2, “maximum storm surge height” is defined as the maximum sea-level anomaly relative to the mean sea level of each scenario, not the absolute total water level. In our simulations, sea-level rise is implemented as a uniform increase in the scenario mean sea level. This increases the overall maximum water level, but it does not necessarily amplify the sea-level anomaly itself; therefore, the maximum storm-surge heights reported in Table 2 remain nearly unchanged across the sea-level-rise scenarios. To incorporate this explanation, we revise the manuscript.
Minor
- L125–127: Simulation period says “in January” although the event is Dec 27, 2001; likely should be December
Answer: Thank you so much for bringing this to my attention. We have modified this as you suggested.
- L110–113 + Fig.2 caption: Text cites Holland (1980) parametric model, while the caption states Holland (2010). Please align and specify which formulation is used.
Answer: Thank you so much for indicating this. In this research, we used Holland (1980). Thus, we have changed “Holland (2010)” to “Holland (1980)” in the caption of Fig. 2.
- L95–99: Manning’s is set uniformly to 0.025 across beaches, seafloor, and paved surfaces. This is a strong simplification; please justify or clarify whether any spatially varying roughness was tested.
Answer: Thank you for your comment. In this study, we prescribed Manning’s coefficients uniformly as 0.025 across the model domain, including beaches, the seafloor, and paved/urban surfaces, following Kotani et al. (1998). The value of Manning’s coefficients is supported by the literature. We adopted this literature-based value in all our simulations. We have clarified this rationale in the revised manuscript and explicitly cite Kotani et al. (1998).
- L145–170: Please clarify whether y is per-storm maximum wind, and how independence is ensured for POT sampling.
Answer: Thank you for your comment. We have clarified that y represents independent peak wind speeds. Specifically, we first define a threshold wind speed u. We then identify tropical-cyclone events from the JMA best-track dataset and extract a single value per event: the event-wise maximum sustained wind speed. The value of y, therefore, consists of these per-storm maxima that exceed u, ensuring independence because multiple correlated records within the same cyclone are not included. To add this explanation, we have modified the manuscript.
- L255–260: Statement that waves penetrated from “southeastward of Singapore, which faces the Pacific Ocean” is geographically incorrect and should be revised (Singapore is sheltered by surrounding seas/straits; “Pacific Ocean” is misleading here).
Answer: Thank you for your suggestion. In the revised manuscript, we have modified the manuscript as you suggested.
- Fig.7: y-axis says “Minumum central pressure (Pa)” but plotted values (~880–1020) indicate hPa; also spelling “Minumum” → “Minimum”.
Answer: Thank you so much for indicating this! We have modified the unit and typo in this figure.
- In Fig. 4(e), the JTWC-forced simulation (blue) shows a small double-peaked feature during the first ~2–3 h that is not present in the observations and is much weaker in the JMA-forced run. Any reasons?
Answer: Thank you for this insightful comment. The dip observed in the JTWC-based simulations during the 0–3 h period is an expected model response. This feature arises from differences in the wind representation between the JTWC and JMA datasets. Specifically, the JTWC dataset provides 1-minute sustained wind speeds, whereas the JMA dataset uses 10-minute sustained wind speeds. When constructing the parametric wind and pressure fields for the storm surge model, the JTWC-based wind field exhibits a sharper, more intense structure at model initialisation than the JMA-based wind field. As a result, when the JTWC-derived forcing is introduced into the numerical model, a rapid adjustment of the hydrodynamic fields occurs, leading to a short-term dip in simulated water levels at many tide gauges. In contrast, the JMA-based simulations show a smoother temporal evolution because the 10-minute sustained wind speeds produce a less abrupt initial wind forcing. To add this explanation, we have modified the manuscript.
- Line 155: Could the authors please check that the definition of “maximum wind” is consistent between the model forcing and the JMA best-track data (e.g., averaging period, reference height, and whether it is sustained wind vs gust)?
Answer: Thank you for your comment. We agree that the definition of “maximum wind” must be consistent between the model forcing and the JMA best-track data. In this study, we used the JMA best-track maximum sustained wind speed as the input intensity parameter for constructing the parametric wind field. The JMA best-track maximum wind is reported as a 10-min sustained wind at 10 m height, and we used this definition consistently when specifying the maximum wind speed in the model forcing. We did not use gusty winds. To add this explanation, we have modified the manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC4
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RC5: 'Comment on egusphere-2025-5703', Anonymous Referee #5, 08 Feb 2026
The manuscript provides a valuable downward counterfactual analysis of Tropical Cyclone Vamei, a rare near-equatorial event. It is an interesting work and approach that reconstracts the cyclonic path and intensity to estimate the inundation areas caused by sea level rise scenarios and storm surge component induced by the cyclone. While the motivation is clear, the study would benefit from a more rigorous justification of its modeling choices and a deeper physical interpretation of the results. Specifically:- the choice of a 2D depth-averaged model (single vertical layer) is not sufficiently justified. Given the complex bathymetry the neglect of vertical shear and mixing under extreme wind stress may lead to underestimations of current velocities and surge propagation, and given the horizontal resolution of the domain of interest.
- The application of the Generalised Pareto Distribution (GPD) and the Wind-Pressure Relationship (WPR) lacks transparency. There are inconsistencies in units (knots vs. m/s) and a lack of detail regarding the threshold (u) selection.
- conclusion that sea level rise does not amplify surge intensity is physically reductive. The authors should distinguish between the surge residual and the total water level (and associated momentum flux), as it is the latter that governs inundation extent and damage potential.
- section 3.3 would benefit from a clearer focus on how the modeled scenarios support the stated hazard objectives. At present, the manuscript does not quantitatively link the simulation results to changes in event likelihood or hazard characteristics beyond prescribed sea level rise scenarios.
- Section 4.4 largely reiterates values already presented in Table 2 and would rather benefit from synthesis across scenarios, highlighting key trends in storm surge and inundation response to cyclone intensity, track variation, and sea level rise.
- The manuscript would be strengthened by an explicit discussion of model limitations and uncertaintiess.
Line-wise comments:Line 84. It is not clear what the authors mean by the 'same schemes' when referring to the cited studies, whether that is the coupling mechanics or the models setup, nesting, or other.
Line 85. The hydrodynamic model is configured with a single vertical layer, resulting in depth-averaged currents. What is the rationale for this choice, and how is the neglect of vertical shear and mixing-particularly under storm conditions—justified? Such simplification may underestimate peak storm-induced currents and affect wave-current interactions relevant to wave setup. In addition, the coupling interval between Delft3D and SWAN is not specified and should be clarified, as it may influence the representation of rapidly evolving storm processes. While the high horizontal resolution (0.00048deg near the equator) is well suited for storm-surge analysis in the Singapore region, the lack of vertical discretization remains a potentially important limitation.
Line 121. Wind and pressure forcing are applied only from 00:00 on 27 December, while the model is run from 24 December with tidal forcing. Since storm surge and wave development depend on cumulative wind stress, and also the use of a single layer, which makes the model heavily depend on accurate forcing data, it would be helpful to clarify whether earlier tropical-cyclone forcing was negligible or to justify why meteorological forcing was not applied prior to 27 December.
Table 2: The paths, central pressures, and wind speeds of the tropical cyclone Vamei reported by JMA and JTWC - is the the Table 1? - numbering error.
Line 139. It would b helpful to briefly justify the choice of the Generalised Pareto distribution for typhoon intensity, and explain why it is more appropriate for this application than for example other extreme value distributions.
Line 146. From the text, the u and y values are said to be both set to the maximum wind speed, which is slightly missleading. Please clarify that u represents a high threshold and y the exceedance above this threshold with their corresponding values for the wind speed.
Line 151. The formulation of the empirical wind–pressure relationship (WPR) developed in this study is not described, which makes it diffiult to assess or reproduce the method. It would be helpful to briefly present this formulation and clarify why a new WPR was preferred over established ones (e.g., Knaff and Zehr, 2006).
Line 153. The method used to estimate the maximum storm surge is not clearly described here; please clarify whether this refers to numerical model output or an empirical/statistical calculation.
Line 156. The statement regardig the use of a maximum wind speed of 45 knots is unclear, whether this choice applies to the WPR, GPD, or the storm surge estimation.
Line 168. The statement that tropical cyclone climatology in the South China Sea is influenced by monsoon-driven variations in large-scale parameters requires supporting references and clarification on parametsrs.
Line 177. The choice of a 250-year return period appears ad hoc, given that TC Vamei was a rare, near-equatorial event with significant stochastic uncertainty, calibrating the threshold u and model parameters based on this midpoint lacks statistical rigor.
Line 182. The discussion regarding the downward counterfactual approach is largely redundant as it repeats information already established in the Introduction. Furthermore, the claim that this study 'aids the understanding of environmental changes on storm surge risks' (this phrasing here is not clear) and 'informs risk management strategies' feels disconnected from the results of the work.
Line 198. "and 2015 as 0.7 m" - probably the authros meant 2150?
Line 205. 'maximum inundation... was maximum' - this sentence needs to be rephrased for clarity.
Line 221. The statement that higher storm surges were produced by higher wind velocity and lower pressure is asserted without quantitative support.
Line 233. The manuscript attributes the discrepancy between simulated and observed water levels to the tidal model and states that improving tidal accuracy is beyond the scope. However, an inaccurate tidal baseline can bias the extracted surge residuals because of non‑linear tide–surge interactions (e.g., Xiao et al., 2021). Even small phase or amplitude errors in the tidal signal may alter surge peaks. It would strengthen the validation to quantify the tidal simulation error and discuss its potential influence on the storm surge estimates.
Line 255. If i understood the text well, there is a mismatch between what is stated in the text and what is shown in the figures. The colormap in domain 1 (Fig. 5b) appears saturated at 5.86 m, while the value of 4.3 m reported for Domain 3 (Fig. 5e) is not reached according to the colormap.
Line 260. The reported inundation area of 8.06 km2 in case 1 lacks methodological details. Please clarify the approach used for inundation assessment (were they computed within the Delft3D suite?), including whether it is based on modeled water levels, a coupled hydrodynamic–topography calculation, or a parametric approach.
Line 265. It would be helpful to label Tekong Island in Fig. 5f for clarity.
Line 271. "the relationship between maximum wind speed and minimum central pressure of tropical cyclones in the region" - does this relate to the developed WPR relationship mentioned earlier in section 3.2? Figure 7 is described as showing the “relationship” between minimum central pressure and maximum wind speed, but it appears to show a linear fit to observed data rather than a physically or empirically established wind–pressure relationship. Consider clarifying this distinction or labeling the figure as a regression fit of the observations to avoid confusion with standard WPR formulations.
Line 285. "The direction of the tropical cyclone’s wind is counterclockwise in the northern hemisphere; thus, storm surges propagate from east to west." - repeats previous section 4.2.
Lines 290–300. This paragraph largely restates values from Table 2 without providing additional analysis or interpretation, the authors could summarize the key trends in maximum storm surge and inundation with respect to cyclone intensity, track shifts, and sea level rise, and provide physical explanations for these patterns. Alternatively, if no synthesis is added, this section could be condensed or removed.
Line 305. To my opinion, this section (5.1) would be more appropriate in the Introduction.
Line 317. The citation should be Gray (1968) as in William M. Gray.
Line 332. The comparison between the tsunami flooding in Macau due to sea level rise and the storm induced surge is tenuous, as the tsunami waves and storm surges operate on different wavelengths and periods.
Line 334. The statement that 'the increase in inundation area in Singapore in cases 13–20 is due to sea level rise' is partly corect but could be misleading. As water depth increases, bottom friction reduces, slightly decreasing the surge residual through a depth-averaging effect, however, the total water level and associated momentum flux increase, meaning the surge propagates over higher base levels with greater destructive potential. Stating that “the intensity of storm surges would not significantly increase” risks might underestimate this hazard. The authors could clarify this distinction and, for example, reference studies on sea level rise impacts on inundation or present additional analysis separating surge residual and total water level to highlight their synergistic effect on flooding.
Line 570. Several provided DOIs do not correspond to the citation, e.g. "https://doi.org/10.1016/j.sedgeo.2017.11.003", "https://doi.org/10.1002/2016JC012361"
Citation: https://doi.org/10.5194/egusphere-2025-5703-RC5 -
AC5: 'Reply on RC5', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #5
- The manuscript provides a valuable downward counterfactual analysis of Tropical Cyclone Vamei, a rare near-equatorial event. It is an interesting work and approach that reconstracts the cyclonic path and intensity to estimate the inundation areas caused by sea level rise scenarios and storm surge component induced by the cyclone. While the motivation is clear, the study would benefit from a more rigorous justification of its modeling choices and a deeper physical interpretation of the results. Specifically:
Answer: Thank you for your positive summary of our work and for your constructive feedback. We agree that a stronger justification of our modelling choices and a deeper physical interpretation of the results significantly strengthen the manuscript. As detailed in our point-by-point responses below, we have expanded the Methodology section to better justify our modelling choices, and we have substantially revised the Discussion section to provide a deeper physical interpretation of the surge dynamics and inundation results. We appreciate your insights, which have greatly improved the paper.
- - the choice of a 2D depth-averaged model (single vertical layer) is not sufficiently justified. Given the complex bathymetry the neglect of vertical shear and mixing under extreme wind stress may lead to underestimations of current velocities and surge propagation, and given the horizontal resolution of the domain of interest.
Answer: We thank the reviewer for this comment. Storm surge is primarily a depth-integrated, long-wave process driven by surface wind stress and pressure gradients, and 2D depth-averaged hydrodynamic models are widely used and operationally applied for storm surge and coastal inundation studies (e.g., ADCIRC, Delft3D-FLOW 2DH, MIKE21). Vertical shear and mixing mainly affect current structure, whereas their influence on water-level setup is typically second-order compared to surface forcing and bathymetry. Given that our primary objectives are storm surge magnitude and inundation extent rather than detailed vertical current profiles, a depth-averaged configuration is appropriate and computationally efficient for scenario-based hazard assessment.
We have clarified this rationale in Section 3.1 and added a short statement in the Discussion acknowledging that while a 3D configuration could better resolve vertical current structure, it is not expected to substantially alter the storm surge elevations reported here.
- - The application of the Generalised Pareto Distribution (GPD) and the Wind-Pressure Relationship (WPR) lacks transparency. There are inconsistencies in units (knots vs. m/s) and a lack of detail regarding the threshold (u) selection.
Answer: We acknowledge that the descriptions of the GPD analysis and wind–pressure relationship (WPR) were insufficiently detailed and that unit inconsistencies may have caused confusion.
In the revised manuscript, we:
(i) Expanded Section 3.2 to clearly define the POT–GPD procedure, including threshold (u) selection, exceedance definition, parameter estimation, and goodness-of-fit diagnostics (Q–Q plots and parameter stability analysis).
(ii) Explicitly presented the formulation of the wind–pressure relationship (WPR) used in this study, including coefficients, assumptions, and units.
(iii) Standardised all wind-speed units to knots throughout the manuscript.
These revisions ensure transparency and reproducibility of both the statistical and parametric components of the methodology.
- - conclusion that sea level rise does not amplify surge intensity is physically reductive. The authors should distinguish between the surge residual and the total water level (and associated momentum flux), as it is the latter that governs inundation extent and damage potential.
Answer: We agree with the reviewer that our wording was imprecise. In our simulations, sea-level rise does not substantially change the storm-surge residual (meteorologically driven water-level anomaly), but it increases the total water level, which directly governs the extent of inundation and damage potential. We revised the Discussion and Conclusions to explicitly distinguish between storm-surge residuals and total water levels, clarifying that although surge residuals remain similar under sea-level rise scenarios, the elevated mean sea level results in higher total water levels and expanded inundation areas.
- - section 3.3 would benefit from a clearer focus on how the modeled scenarios support the stated hazard objectives. At present, the manuscript does not quantitatively link the simulation results to changes in event likelihood or hazard characteristics beyond prescribed sea level rise scenarios.
Answer: We agree that Section 3.3 requires clearer linkage between the modelled scenarios and the stated hazard objectives. Our study is intended as a scenario-based hazard assessment rather than a probabilistic evaluation of event likelihood. We have revised Section 3.3 to explicitly frame the scenarios in terms of hazard-relevant metrics (maximum total water level and inundation area), and to summarise how changes in cyclone intensity, track position, and sea-level rise systematically affect these indicators. We have also clarified that changes in event likelihood are not quantified in this study, and that the scenarios are designed to explore plausible upper-bound impacts.
- - Section 4.4 largely reiterates values already presented in Table 2 and would rather benefit from synthesis across scenarios, highlighting key trends in storm surge and inundation response to cyclone intensity, track variation, and sea level rise.
Answer: We agree that Section 4.4 currently reiterates numerical values already presented in Table 2 and would benefit from clearer synthesis. In the revised manuscript, we have restructured Section 4.4 to focus on key trends across scenarios rather than repeating individual values. Specifically, we have synthesised how (i) cyclone intensity enhancement, (ii) southward track shifts, and (iii) sea-level rise systematically influence maximum storm surge and inundation area. Redundant numerical repetition has been reduced, and emphasis has been placed on comparative and process-based interpretation.
- - The manuscript would be strengthened by an explicit discussion of model limitations and uncertaintiess.
Answer: Thank you for your comment. In the revised manuscript, we have added the limitations of this study based on all reviewers’ comments. Please see the Discussion section in the revised manuscript.
- Line 84. It is not clear what the authors mean by the 'same schemes' when referring to the cited studies, whether that is the coupling mechanics or the models setup, nesting, or other.
Answer: We agree that the phrase “same schemes” was ambiguous. We have revised Line 84 to explicitly state that it refers to the two-way Delft3D-FLOW–SWAN coupling framework, the nested-domain configuration, and the use of parametric tropical-cyclone wind and pressure forcing, as in the cited studies. This removes ambiguity regarding the modelling approach.
- Line 85. The hydrodynamic model is configured with a single vertical layer, resulting in depth-averaged currents. What is the rationale for this choice, and how is the neglect of vertical shear and mixing-particularly under storm conditions—justified? Such simplification may underestimate peak storm-induced currents and affect wave-current interactions relevant to wave setup. In addition, the coupling interval between Delft3D and SWAN is not specified and should be clarified, as it may influence the representation of rapidly evolving storm processes. While the high horizontal resolution (0.00048deg near the equator) is well suited for storm-surge analysis in the Singapore region, the lack of vertical discretization remains a potentially important limitation.
Answer: We thank the reviewer for this detailed comment. Storm surge is primarily a depth-integrated long-wave process, and 2D depth-averaged configurations are widely used in operational and research storm-surge modelling (e.g., Delft3D-FLOW 2DH, ADCIRC, MIKE21). Vertical shear mainly affects current structure, whereas its influence on water-level setup is typically secondary compared to surface wind stress, pressure forcing, and bathymetry. As our focus is on storm surge magnitude and inundation extent rather than detailed vertical current profiles, the depth-averaged approach is appropriate for this scenario-based hazard assessment. We acknowledge that a fully 3D configuration could better resolve vertical currents and wave–current interactions, and we have clarified this as a limitation. To assess the practical impact on water levels, we have additionally performed FLOW-only versus coupled FLOW–SWAN sensitivity tests, as described in responses above. Regarding model coupling, we acknowledge that the Delft3D–SWAN coupling interval was not specified.
We have revised Section 3.1 to (i) explicitly justify the depth-averaged configuration, (ii) acknowledge its limitations regarding vertical current structure, and (iii) specify the FLOW–SWAN coupling interval (60 minutes). Horizontal resolution (~53 m in Domain D3) was also reported in meters for clarity.
- Line 121. Wind and pressure forcing are applied only from 00:00 on 27 December, while the model is run from 24 December with tidal forcing. Since storm surge and wave development depend on cumulative wind stress, and also the use of a single layer, which makes the model heavily depend on accurate forcing data, it would be helpful to clarify whether earlier tropical-cyclone forcing was negligible or to justify why meteorological forcing was not applied prior to 27 December.
Answer: We thank the reviewer for this important point. Tropical Cyclone Vamei developed rapidly near the equator on 27 December 2001, and prior to 00:00 on 27 December, wind speeds in the Singapore region were weak and not associated with organised cyclone forcing. Therefore, the meteorological contribution to surge generation before this time was negligible relative to the peak-forcing period. Storm surge response in shallow coastal regions, such as Singapore, is primarily controlled by the period of the strongest wind stress and pressure gradients. The tidal spin-up period (24–27 December) was included to establish hydrodynamic equilibrium under tidal forcing.
We have clarified in Section 3 that meteorological forcing prior to 27 December was negligible based on best-track wind data, and that the model spin-up period was designed to stabilize tidal dynamics before cyclone forcing was applied.
- Table 2: The paths, central pressures, and wind speeds of the tropical cyclone Vamei reported by JMA and JTWC - is the the Table 1? - numbering error.
Answer: You are correct. This should be Table 1. We revised this.
- Line 139. It would b helpful to briefly justify the choice of the Generalised Pareto distribution for typhoon intensity, and explain why it is more appropriate for this application than for example other extreme value distributions.
Answer: We thank the reviewer for this comment. The Generalised Pareto Distribution (GPD) was selected because this study adopts a peaks-over-threshold (POT) framework, for which extreme-value theory demonstrates that exceedances above a sufficiently high threshold asymptotically follow a GPD, regardless of the parent distribution (Coles, 2001). In contrast, the Generalised Extreme Value (GEV) distribution is typically applied to block maxima (e.g., annual maxima). Given the limited number of near-equatorial tropical cyclone events, the POT–GPD approach allows more efficient use of available extreme wind-speed data than block-maxima methods.
We have added this justification to Section 3.2 and clarified the methodological distinction between POT–GPD and GEV approaches.
- Line 146. From the text, the u and y values are said to be both set to the maximum wind speed, which is slightly missleading. Please clarify that u represents a high threshold and y the exceedance above this threshold with their corresponding values for the wind speed.
Answer: We agree that the current wording is misleading. We have revised Section 3.2 to explicitly clarify that u represents the selected high threshold wind speed, while y denotes the exceedance above this threshold. The corresponding numerical values of u were reported, and the definitions were stated consistently throughout the revised manuscript.
- Line 151. The formulation of the empirical wind–pressure relationship (WPR) developed in this study is not described, which makes it diffiult to assess or reproduce the method. It would be helpful to briefly present this formulation and clarify why a new WPR was preferred over established ones (e.g., Knaff and Zehr, 2006).
Answer: We agree that the empirical wind–pressure relationship (WPR) was insufficiently described. Established formulations, such as Knaff and Zehr (2006), are primarily calibrated for typical tropical cyclone environments and may not be directly applicable to rare near-equatorial systems, such as Tropical Cyclone Vamei. Because our objective was to characterise the wind–pressure relationship specifically for low-latitude tropical cyclones in the study region, we derived a regional empirical relationship based on collocated JMA best-track data.
We have explicitly presented the WPR formulation used in this study (including regression equation and coefficients) in Section 3.2 and clarified that this regional relationship was adopted to better represent near-equatorial cyclone conditions, rather than relying on globally calibrated WPRs. The limitations of this approach were also acknowledged.
- Line 153. The method used to estimate the maximum storm surge is not clearly described here; please clarify whether this refers to numerical model output or an empirical/statistical calculation.
Answer: We agree that this description was unclear. We have clarified in Section 3.3 that the maximum storm surge heights reported in this study are directly obtained from the Delft3D-FLOW numerical model outputs as the peak water-level anomaly at each grid cell during the simulation period, rather than from any empirical or statistical calculation.
- Line 156. The statement regardig the use of a maximum wind speed of 45 knots is unclear, whether this choice applies to the WPR, GPD, or the storm surge estimation.
Answer: We agree that the role of the 45-kt wind speed was unclear in the current manuscript. We have revised Section 3.2 to clarify that 45 kt is used as a lower-bound threshold for fitting the Generalised Pareto Distribution to observed maximum wind speeds and for deriving the empirical wind–pressure relationship. The revised text has explicitly distinguished this threshold from wind speeds used in numerical forcing or storm-surge simulations, and has provided the rationale for selecting 45 kt as the cutoff for extreme-event analysis.
- Line 168. The statement that tropical cyclone climatology in the South China Sea is influenced by monsoon-driven variations in large-scale parameters requires supporting references and clarification on parametsrs.
Answer: We agree that the term “large-scale parameters” was insufficiently specific. We have revised Line 168 to explicitly refer to monsoon-related variations in low-level vorticity, vertical wind shear, and moisture convergence, and have added appropriate references documenting their influence on tropical cyclone activity in the South China Sea. This clarifies both the physical meaning and the literary basis of this statement.
- Line 177. The choice of a 250-year return period appears ad hoc, given that TC Vamei was a rare, near-equatorial event with significant stochastic uncertainty, calibrating the threshold u and model parameters based on this midpoint lacks statistical rigor.
Answer: We agree that the description of the 250-year return period was potentially misleading. This value is adopted from Chang et al. as contextual information on the rarity of Tropical Cyclone Vamei and is not used to calibrate the GPD parameters or to select the threshold in our analysis. We have revised Line 177 to clarify that the 250-year estimate is cited solely for contextual reference and does not influence the statistical fitting or scenario construction in this study.
- Line 182. The discussion regarding the downward counterfactual approach is largely redundant as it repeats information already established in the Introduction. Furthermore, the claim that this study 'aids the understanding of environmental changes on storm surge risks' (this phrasing here is not clear) and 'informs risk management strategies' feels disconnected from the results of the work.
Answer: We agree that portions of the discussion on the downward counterfactual approach were repetitive of the Introduction and that some statements overstated the implications for risk management. We have streamlined Section 4 by removing redundant explanations of the methodological framework and focusing the discussion on the results derived from the simulations. Statements regarding implications for environmental change and risk management were revised to more clearly link to the quantified scenario outcomes (e.g., changes in total water level and inundation extent) and have been phrased more cautiously as scenario-based insights rather than direct policy recommendations.
- Line 198. "and 2015 as 0.7 m" - probably the authros meant 2150?
Answer: Your suggestion is correct. We have edited this sentence.
- Line 205. 'maximum inundation... was maximum' - this sentence needs to be rephrased for clarity.
Answer: You are correct. Based on another reviewer’s comment, we have rewritten the entire section.
- Line 221. The statement that higher storm surges were produced by higher wind velocity and lower pressure is asserted without quantitative support.
Answer: We agree that this statement requires quantitative support. We have revised Section 4.2 to explicitly report representative values of maximum wind speed, minimum central pressure, and corresponding peak storm surge for selected scenarios, thereby quantitatively demonstrating how increased wind velocity and reduced pressure lead to higher surge levels in our simulations.
- Line 233. The manuscript attributes the discrepancy between simulated and observed water levels to the tidal model and states that improving tidal accuracy is beyond the scope. However, an inaccurate tidal baseline can bias the extracted surge residuals because of non‑linear tide–surge interactions (e.g., Xiao et al., 2021). Even small phase or amplitude errors in the tidal signal may alter surge peaks. It would strengthen the validation to quantify the tidal simulation error and discuss its potential influence on the storm surge estimates.
Answer: We agree that tidal model errors can influence the extracted storm surge signal through tide–surge interactions. In the revised manuscript, we have quantified the performance of the tidal simulations at each tide gauge using RMSE and phase error prior to surge extraction. We have also clarified that storm surge residuals are evaluated after consistent tidal removal from both observations and model output. A short discussion has been added to assess how remaining tidal errors may affect peak surge estimates, acknowledging this as a source of uncertainty.
- Line 255. If i understood the text well, there is a mismatch between what is stated in the text and what is shown in the figures. The colormap in domain 1 (Fig. 5b) appears saturated at 5.86 m, while the value of 4.3 m reported for Domain 3 (Fig. 5e) is not reached according to the colormap.
Answer: We agree that there is an inconsistency between the reported values and the colour scales in Fig. 5. We have revised Fig. 5 to ensure consistent colour limits across domains and adjusted the colour scales so that reported maximum values (e.g., 5.86 m in Domain 1 and 4.3 m in Domain 3) are correctly represented.
- Line 260. The reported inundation area of 8.06 km2 in case 1 lacks methodological details. Please clarify the approach used for inundation assessment (were they computed within the Delft3D suite?), including whether it is based on modeled water levels, a coupled hydrodynamic–topography calculation, or a parametric approach.
Answer: Thank you for your comment. In the revised manuscript, we have added an explanation of how to output the inundation area from Delft3D simulation results.
- Line 265. It would be helpful to label Tekong Island in Fig. 5f for clarity.
Answer: We have indicated the location of Tekong Island in Fig. 5f.
- Line 271. "the relationship between maximum wind speed and minimum central pressure of tropical cyclones in the region" - does this relate to the developed WPR relationship mentioned earlier in section 3.2? Figure 7 is described as showing the “relationship” between minimum central pressure and maximum wind speed, but it appears to show a linear fit to observed data rather than a physically or empirically established wind–pressure relationship. Consider clarifying this distinction or labeling the figure as a regression fit of the observations to avoid confusion with standard WPR formulations.
Answer: We agree that Fig. 7 represents a regression fit to observed regional data rather than a standard physically derived wind–pressure relationship, and that this distinction was not sufficiently clear. We have revised the text and figure caption to explicitly describe Fig. 7 as a regional empirical regression between maximum wind speed and minimum central pressure based on JMA best-track observations. We have also clarified that this regression is used to characterise near-equatorial cyclone behaviour, which is not well represented by globally calibrated WPR formulations, and distinguish it from established theoretical WPR models.
- Line 285. "The direction of the tropical cyclone’s wind is counterclockwise in the northern hemisphere; thus, storm surges propagate from east to west." - repeats previous section 4.2.
Answer: Thank you for your comment. We already stated this sentence in the previous section. But, to clarify this, we have revised the manuscript.
- Lines 290–300. This paragraph largely restates values from Table 2 without providing additional analysis or interpretation, the authors could summarize the key trends in maximum storm surge and inundation with respect to cyclone intensity, track shifts, and sea level rise, and provide physical explanations for these patterns. Alternatively, if no synthesis is added, this section could be condensed or removed.
Answer: We agree that this paragraph largely reiterates values already presented in Table 2 and lacks synthesis. We have rewritten Lines 290–300 to summarise key trends in maximum storm surge and inundation area in relation to cyclone intensity, southward track shifts, and sea-level rise, and provide physical explanations for these patterns. Redundant numerical repetition was reduced, and the focus was placed on comparative interpretation. If necessary, this paragraph was condensed accordingly.
- Line 305. To my opinion, this section (5.1) would be more appropriate in the Introduction.
Answer: Based on this comment and another reviewer’s comments, we have moved this part to the Introduction.
- Line 317. The citation should be Gray (1968) as in William M. Gray.
Answer: We have revised this sentence based on your suggestion.
- Line 332. The comparison between the tsunami flooding in Macau due to sea level rise and the storm induced surge is tenuous, as the tsunami waves and storm surges operate on different wavelengths and periods.
Answer: Thank you for your comment. We have deleted this part based on another reviewer’s comments. We then revised the entire Discussion section.
- Line 334. The statement that 'the increase in inundation area in Singapore in cases 13–20 is due to sea level rise' is partly corect but could be misleading. As water depth increases, bottom friction reduces, slightly decreasing the surge residual through a depth-averaging effect, however, the total water level and associated momentum flux increase, meaning the surge propagates over higher base levels with greater destructive potential. Stating that “the intensity of storm surges would not significantly increase” risks might underestimate this hazard. The authors could clarify this distinction and, for example, reference studies on sea level rise impacts on inundation or present additional analysis separating surge residual and total water level to highlight their synergistic effect on flooding.
Answer: We agree that our previous wording could have been misleading. In our simulations, sea-level rise does not substantially increase the storm surge residual (i.e., the meteorologically driven water-level anomaly). However, it raises the mean water level, thereby increasing the total water level and expanding the extent of inundation. We acknowledge that bottom-friction effects in deeper water may slightly modify surge residuals, but the inundation hazard is primarily governed by the total water level rather than by the surge residual alone. We have revised Line 334 and the associated discussion to explicitly distinguish between storm-surge residuals and total water levels. The term “surge intensity” was replaced with clearer terminology. We have also clarified that although surge residuals remain similar, sea-level rise increases flood risk by elevating baseline water levels and associated hydrodynamic impacts.
- Line 570. Several provided DOIs do not correspond to the citation, e.g. "https://doi.org/10.1016/j.sedgeo.2017.11.003", https://doi.org/10.1002/2016JC012361
Answer: Thank you for your comment. We have modified these DOIs.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC5
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AC5: 'Reply on RC5', Masashi Watanabe, 28 Mar 2026
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RC6: 'Comment on egusphere-2025-5703', Anonymous Referee #6, 09 Feb 2026
As a reviewer for the manuscript egusphere-2025-5703 titled "Equatorial storm surge risks revealed by the 2001 tropical cyclone Vamei," I have evaluated the paper against the standards for coastal risk and hydrodynamic modeling. Based on several critical methodological flaws, inconsistencies in physical forcing, and insufficient validation, I recommend a Reject.
1. The claimed novelty is neither new nor apparent. The risks associated with near-equatorial cyclones and surges have been previously explored, and this study does not provide a sufficiently unique advancement to the field.2. The use of degree-based resolution is insufficient for coastal impact studies. Resolutions in meters are preferable to accurately capture the hydrodynamic interactions near complex coastlines and urban environments.
3. It is unclear how the model deals with buildings, coastal structures, and riverlines. If these features are considered, their specific impact on the inundation results must be explicitly discussed. If they are omitted, the model cannot be considered a realistic assessment of urban resilience.
4. Standard meteorological physics dictates that wind speeds on the right side of the center (in the direction of motion) are higher than on the left due to the translation speed of the typhoon. However, Fig. 2 shows inconsistent wind fields that do not align with this physical reality.
5. The assumption regarding the Japan Meteorological Agency (JMA) maximum wind speed is unacceptable. The study appears to use JMA-offered maximum wind speeds directly, ignoring that these are not equivalent to 10m wind speeds above sea surface level required for surge and wave simulations. Furthermore, these speeds are often inconsistent with the wind speed at the radius of maximum winds (RMW).
6. Why was the Generalized Pareto Distribution (GPD) selected? The authors have not demonstrated that this is the optimized or best-fit function. Validation, such as a Q-Q plot, must be provided to justify this statistical choice.
7. The manuscript fails to validate the wave results of the simulations. Without comparison against buoy data or established hindcasts, the wave component remains speculative.
8. The specific configuration and parameters used for the SWAN model are not detailed, making the results impossible to replicate or verify.
9. In Fig. 4, there is a noticeable shift in tidal phases between observations and simulations, and the magnitude of the differences is quite large. To ensure the simulations are robust, the authors must use quantitative metrics such as correlation coefficients or Root Mean Square Error (RMSE) to assess the results. Also, the timing of landfall is ambiguous at each station. It has to be discussed.
10. Why did you conduct simulations on high tidal conditions? It must be discussed as well.
11. What are the results of waves in the 20 study cases? What are the effects of waves on surges and flooding in the 20 study cases?
Citation: https://doi.org/10.5194/egusphere-2025-5703-RC6 -
AC6: 'Reply on RC6', Masashi Watanabe, 28 Mar 2026
Thank you very much for the constructive comments on our manuscript from the anonymous reviewer. Based on all comments, we have improved our manuscript. We summarised our responses to each comment as follows.
Reply to comments by Reviewer #6
- As a reviewer for the manuscript egusphere-2025-5703 titled "Equatorial storm surge risks revealed by the 2001 tropical cyclone Vamei," I have evaluated the paper against the standards for coastal risk and hydrodynamic modeling. Based on several critical methodological flaws, inconsistencies in physical forcing, and insufficient validation, I recommend a Reject.
Answer: We thank the reviewer for their thorough evaluation of our manuscript. We acknowledge that our methodology, physical forcing parameters, and validation steps were not explained with sufficient clarity or detail in the original submission. We have taken your feedback very seriously and conducted major revisions. Specifically, we have revised the methodology, strengthened our discussion following all six reviewers' comments, and provided further scientific justification for our physical forcing data. We believe these revisions successfully demonstrate that our approach is robust and aligned with standard hydrodynamic modelling practices. Please see our detailed, point-by-point responses below, which address each of your concerns.
- The claimed novelty is neither new nor apparent. The risks associated with near-equatorial cyclones and surges have been previously explored, and this study does not provide a sufficiently unique advancement to the field.
Answer: We respectfully disagree that the novelty is unclear, but we acknowledge that it was not sufficiently articulated in the original manuscript. While near-equatorial tropical cyclones and their dynamics have been studied previously, most existing work has focused on cyclone genesis and atmospheric mechanisms rather than on coastal storm surge and inundation impacts.
Tropical Cyclone Vamei remains the closest-to-equator tropical cyclone on record, and to our knowledge, this study presents the first physics-based assessment of storm surge and inundation impacts for this event in Singapore using coupled hydrodynamic–wave modelling. This is particularly important because Singapore is a major global maritime hub, hosting one of the world’s largest container ports, and even low-probability coastal hazards may have disproportionate socio-economic consequences.
Furthermore, this study is among the first to integrate a downward counterfactual framework with sea-level-rise scenarios to systematically assess how variations in cyclone intensity, track, and background sea level influence coastal flooding in a near-equatorial setting.
We have revised the Introduction to more clearly emphasise these aspects of novelty and contribution.
To ensure this novelty is immediately apparent to the reader, we have substantially revised the Introduction by adding the following text:
"This downward counterfactual approach was previously applied to Tropical Cyclone Vamei by Lin et al. (2020), who investigated the consequences of a track shift resulting in a direct landfall on Singapore. However, their analysis centred strictly on wind-related impacts, leaving the potential for coastal inundation from storm surges unquantified. To our knowledge, no previous studies have conducted a physics-based coastal inundation assessment for this unique near-equatorial event. Building on this foundation, our study addresses this critical gap by presenting the first coupled hydrodynamic–wave modeling of Tropical Cyclone Vamei to explicitly quantify the storm surge hazard around Singapore. Furthermore, we expand upon previous work by assessing the compound effects of alternative cyclone intensities, track shifts, and future sea-level rise scenarios on this low-surge coastal environment."
- The use of degree-based resolution is insufficient for coastal impact studies. Resolutions in meters are preferable to accurately capture the hydrodynamic interactions near complex coastlines and urban environments.
Answer: We thank the reviewer for raising this point and allowing us to clarify. We entirely agree that resolutions on the order of tens to hundreds of meters are essential for accurately capturing hydrodynamic interactions near complex coastlines. However, there appears to be a misunderstanding regarding our grid spacing. Although our computational grids are defined in geographic coordinates (degrees), the effective horizontal resolution in our innermost domain (D3) is 0.00048°. At Singapore’s latitude, this corresponds to approximately 53 meters. Therefore, our resolution is already on the order of tens of meters and is fully consistent with high-resolution coastal modelling studies. Model accuracy is governed by the effective grid spacing, bathymetric/topographic quality, numerical schemes, and forcing, rather than whether the coordinate system itself is expressed in degrees or meters. To avoid any future confusion for readers, we have revised Section 3.1 to explicitly report the grid resolution in meters alongside the degree notation. The innermost domain (D3) features a horizontal resolution of 0.00048°. At Singapore’s latitude, this corresponds to a grid spacing of approximately 53 m, providing the high resolution necessary to capture complex coastal hydrodynamic interactions and inundation in urban environments.
- It is unclear how the model deals with buildings, coastal structures, and riverlines. If these features are considered, their specific impact on the inundation results must be explicitly discussed. If they are omitted, the model cannot be considered a realistic assessment of urban resilience.
Answer: We thank the reviewer for this important point. Individual buildings and engineered coastal structures are not explicitly represented in our simulation. Instead, we employ a high-resolution digital terrain model (10 m DTM from the Singapore Land Authority) together with an effective horizontal grid spacing of ~53 m in Domain D3, which resolves major coastal morphology and low-lying terrain. Accordingly, our inundation estimates should be interpreted as first-order, terrain-controlled estimates of flooding extent rather than as a detailed assessment of urban-scale resilience. We agree that explicit representation of buildings, drainage infrastructure, and flood defences would be required for a fully realistic urban resilience analysis. To add these explanations, we have clarified this limitation in Section 3.1 and Discussion, explicitly stating that buildings and engineered structures are not resolved and that the results represent scenario-based hazard screening rather than a detailed urban resilience assessment.
- Standard meteorological physics dictates that wind speeds on the right side of the center (in the direction of motion) are higher than on the left due to the translation speed of the typhoon. However, Fig. 2 shows inconsistent wind fields that do not align with this physical reality.
Answer: We thank the reviewer for this comment. Our wind/pressure forcing is generated using a parametric Holland-type model, and we explicitly include the forward-motion asymmetry following Fujii and Mitsuta (1986), as stated in Section 3.1. Therefore, the wind field is not intended to be azimuthally symmetric; the right-of-track enhancement associated with translation is included in the forcing. We suspect the current presentation of Fig. 2 may make it difficult to visually confirm the asymmetry of this wind field. To avoid ambiguity, we have revised Fig. 2 and its caption.
- The assumption regarding the Japan Meteorological Agency (JMA) maximum wind speed is unacceptable. The study appears to use JMA-offered maximum wind speeds directly, ignoring that these are not equivalent to 10m wind speeds above sea surface level required for surge and wave simulations. Furthermore, these speeds are often inconsistent with the wind speed at the radius of maximum winds (RMW).
Answer: We thank the reviewer for this comment. In this study, wind forcing is derived from the JMA best-track dataset, in which the reported maximum wind speed represents a 10-minute sustained wind at a reference height of 10 m. This definition is consistent with the wind input required by Delft3D and SWAN, and therefore, no height conversion was applied. Regarding consistency with the radius of maximum winds (RMW), the Holland-based parametric wind model is constructed so that the specified maximum wind speed occurs at the estimated RMW. We acknowledge that this was not clearly described in the manuscript. Therefore, we have revised Section 3.1 to explicitly state the wind reference height and averaging period used (10 m, 10-minute mean from JMA) and to add a short explanation of how the parametric wind model ensures consistency between the maximum wind speed and the RMW.
- Why was the Generalized Pareto Distribution (GPD) selected? The authors have not demonstrated that this is the optimized or best-fit function. Validation, such as a Q-Q plot, must be provided to justify this statistical choice.
Answer: We thank the reviewer for this important comment. The Generalized Pareto Distribution (GPD) was selected following standard extreme-value theory for peaks-over-threshold (POT) analysis, which demonstrates that exceedances above a sufficiently high threshold converge to a GPD irrespective of the parent distribution (e.g., Coles, 2001; McInnes et al., 2016). Our original manuscript did not sufficiently document goodness-of-fit diagnostics for this choice.
In the revised manuscript, we have substantially expanded Section 3.2 to:
(i) justify the use of GPD within the POT framework;
(ii) explicitly describe the threshold-selection procedure (using a mean residual life plot and parameter stability checks);
(iii) present Q–Q and P–P plots for threshold exceedances to assess distributional fit
These additions have allowed readers to directly evaluate whether the GPD provides an appropriate representation of the tail behaviour in our dataset.
- The manuscript fails to validate the wave results of the simulations. Without comparison against buoy data or established hindcasts, the wave component remains speculative.
Answer: We acknowledge that in situ wave observations are not available for Tropical Cyclone Vamei in the Singapore region, and therefore, direct validation of SWAN wave fields is not possible. Our primary validation is based on tide-gauge water levels. To address this limitation and avoid speculative interpretation of wave effects, we have added sensitivity experiments without wave coupling (FLOW-only) and quantitatively compare peak water levels and timing with the coupled FLOW–SWAN simulations. This explicitly assesses the contribution of wave setup to total water levels. The revised manuscript presents FLOW-only versus coupled results to demonstrate whether waves materially affect the conclusions.
- The specific configuration and parameters used for the SWAN model are not detailed, making the results impossible to replicate or verify.
Answer: We agree that the SWAN configuration was insufficiently documented, which limits reproducibility. We have added more explanations detailing the SWAN setup, including wind input formulation, whitecapping and bottom-friction parameterisations, spectral discretisation, frequency and directional resolution, coupling interval with Delft3D-FLOW, and boundary conditions. These additions ensure that the wave simulations can be independently reproduced and evaluated.
- In Fig. 4, there is a noticeable shift in tidal phases between observations and simulations, and the magnitude of the differences is quite large. To ensure the simulations are robust, the authors must use quantitative metrics such as correlation coefficients or Root Mean Square Error (RMSE) to assess the results. Also, the timing of landfall is ambiguous at each station. It has to be discussed.
Answer: We agree that a purely visual comparison in Fig. 4 is insufficient to evaluate model performance, and that the phase shift and amplitude differences require quantitative assessment. In the revised manuscript, we have validated simulated sea-level anomalies (after consistent removal of tidal components from both observations and model output) using quantitative metrics, including RMSE at each tide-gauge station. To clarify event timing, we define “landfall timing” here as the time of closest approach of the cyclone centre to Singapore derived from the JMA best-track data. This closest-approach time was indicated by vertical dashed lines in Fig. 4 and explicitly reported in the caption. A brief discussion has also be added to address remaining tidal-phase discrepancies and their potential influence on the extracted storm-surge signal.
- Why did you conduct simulations on high tidal conditions? It must be discussed as well.
Answer: We selected high-tide conditions to evaluate potential worst-case inundation extents under compound conditions of storm surge and elevated water levels. The objective of this study is a scenario-based hazard assessment rather than a probabilistic joint-occurrence analysis; therefore, we intentionally examined a conservative high-water-level case to assess the maximum plausible inundation. We acknowledge that storm surge and tidal phase interactions can influence peak water levels, and that the assumed coincidence of peak surge with high tide represents a simplified scenario. In the revised manuscript, we have clarified in Section 3 and Discussion that the simulations represent a scenario-based upper-bound assessment rather than a fully probabilistic joint tide–surge analysis. The limitations associated with assuming high-tide conditions were explicitly discussed.
- What are the results of waves in the 20 study cases? What are the effects of waves on surges and flooding in the 20 study cases?
Answer: We acknowledge that wave results were not sufficiently discussed across the 20 scenario simulations. Although SWAN is coupled to account for potential wave setup, waves were not treated as a primary hazard variable in this study, and our focus was on storm surge–driven water levels and inundation. To clarify the role of waves, we have added sensitivity experiments without wave coupling (FLOW-only) and quantified differences in peak water levels and inundation extents across representative scenarios. This allows us to explicitly assess whether wave setup materially affects storm surge and flooding outcomes. In the revised manuscript, we have clarified that storm surge is the dominant contributor to flooding in this study and report quantitative wave contributions from FLOW-only versus coupled simulations. Results for waves were summarised succinctly, and the interpretation was accordingly restricted.
Citation: https://doi.org/10.5194/egusphere-2025-5703-AC6
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AC6: 'Reply on RC6', Masashi Watanabe, 28 Mar 2026
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Watanabe et al have presented the results from Delft-3D simulations of the 2001 cyclone Vamei and additional scenarios based on the cyclone path and parameters. Using the scenarios, they have estimated inundation areas in Singapore. This research illustrates the importance of modeling extreme events in order to better prepare urban centers from expected damages.
The manuscript is generally clear and appropriately succinct. I have listed some comments below for further improvement and clarifications regarding the methodology and conclusions: