Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Baldan, Damiano; Coraci, Elisa; Crosato, Franco; Ferla, Maurizio; Bonometto, Andrea; Morucci, Sara

doi:https://doi.org/10.5194/egusphere-2022-347

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https://doi.org/10.5194/egusphere-2022-347

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07 Jun 2022

| 07 Jun 2022

Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Damiano Baldan, Elisa Coraci, Franco Crosato, Maurizio Ferla, Andrea Bonometto, and Sara Morucci

Abstract. Coastal flooding caused by extreme sea levels (ESLs) is one of the major impacts related to the climate change. It is expected to increase in the future due to sea level rise and storm surge intensification. Estimates of return levels obtained under the framework provided by extreme events theory might be biased under climatic non-stationarity. Additional uncertainty is related to the choice of the model. In this work, we fit several extreme values models to a long-term (96 years) sea level record from the city of Venice (NW Adriatic Sea, Italy): a Generalized Extreme Value distribution (GEV), a Generalized Pareto Distribution (GPD), a Point Process (PP), and the Joint Probability Method (JPM) under different detrending strategies. We model non-stationarity with a linear dependence of the model’s parameters from the mean sea level. Our results show that non-stationary GEV and PP models fit the data better than stationary models even with detrended data. The non-stationary PP model is able to reproduce the rate of extremes occurrence fairly well. Actualized estimates of the return levels for non-stationary models are generally higher than estimates from stationary models. Thus, projections of return levels in the future might be significantly different from those calculated using stationary models. Overall, we show that non-stationary extremes analyses can provide more robust estimates of return levels to be used in coastal protection planning.

Received: 16 May 2022 – Discussion started: 07 Jun 2022

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14 Nov 2022

Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Damiano Baldan, Elisa Coraci, Franco Crosato, Maurizio Ferla, Andrea Bonometto, and Sara Morucci

Nat. Hazards Earth Syst. Sci., 22, 3663–3677, https://doi.org/10.5194/nhess-22-3663-2022,https://doi.org/10.5194/nhess-22-3663-2022, 2022

Short summary

Damiano Baldan et al.

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2022-347', Anonymous Referee #1, 30 Jun 2022

The manuscript compares the return levels obtained from four different extreme value analyses accounting for non-stationarity in the Punta Della Salute tide gauge, Venice. The extreme value distributions include: (i) generalized extreme value (GEV) applied to a block maxima sampling (BM); (ii) generalized Pareto distribution (GPD) and (iii) a point process (PP) method, both applied to peak over a threshold (POT); and (iv) a joint probability method (JPM). In addition, the authors tested the implications of using three different detrending techniques, including (i) removing the annual mean sea level from the time series (MSL); (ii) removing the last 19 years’ mean sea level (MSL_L); and (iii) not detrending the data before fitting the distributions.

I find the topic important for coastal flood risk assessment as traditional designs have been based on analyses using direct methods that ignore the non-stationarities, which can lead to an underestimation of the risk, as found in previous studies. In addition, having a comprehensive analysis of the different non-stationary extreme value methodologies would help on the way to obtaining a more standardized analysis, facilitating the comparison of the results between the studies. Thus, the results of the manuscript are relevant for the scientific community and coastal risk stakeholders after improving the work in some aspects, mainly related to

the level of comprehensiveness of the analysis: it will be interesting for the scientific community to include one of the most utilized indirect methods: the revised joint probability method. In doing so, the authors will also reduce the existing overlapping with previous studies,
the level of applicability to other study areas by including more tide gauge records in the analysis, and
the level of replicability (some information relevant for reproducibility is missed).

Citation: https://doi.org/10.5194/egusphere-2022-347-RC1

AC1: 'Reply on RC1', Damiano Baldan, 13 Jul 2022

Thank you for the constructive comments. In the revised submission we will perform some additional analyses and edit some sections of the manuscript to meet the comments from referee #1. See below a more detailed overview:

Reviewer’s comment	Authors' reply
Improving the work in the level of comprehensiveness of the analysis: it will be interesting for the scientific community to include one of the most utilized indirect methods: the revised joint probability method. In doing so, the authors will also reduce the existing overlapping with previous studies	We will include the revised joint probability method to the list of tested methods.
Improving the work in the level of applicability to other study areas by including more tide gauge records in the analysis	We acknowledge the sea level data from the case study in Venice might not be representative of other geographic locations given their high degree of non-stationaries (which in turn was useful for detecting the non-stationary pattern). We will add this point to the discussion. If the editor feels it would be an added value to the manuscript, we could replicate the same analysis with data from another tide gauge station with a lower degree of non-stationarity, present the results in a dedicated figure, and include a short discussion of the results.
Improving the level of replicability (some information relevant for reproducibility is missed).	We will include more details in the methods section based on the specific reviewer’s comments.

Citation: https://doi.org/10.5194/egusphere-2022-347-AC1

RC2:
'Comment on egusphere-2022-347', Anonymous Referee #2, 08 Jul 2022

In this submission, authors study the value of nonstationary analysis of coastal flooding under sea level rise. They have fitted various extreme value models (e.g. GEV, GPD, PP and JPM) to a long-term record of observational water level record at Venice, Italy and quantified the difference in estimated flood risk with and without consideration of nonstationarity. The idea is very interesting and timely, the research is well designed, and the manuscript is well written. I have no significant comment, except a few very minor suggestions below:

- "extreme event theory" has been used in multiple occasions (i.e. Page 1, lines 8 and 25); I've ween usually called "extereme value theory" in the literature. Are the authors referring to a different concept/theory?

- Some recent studies on nonstationary extreme value analysis must be cited and their contribution be acknowledged

1. Cheng et al., (2014),Non-stationary extreme value analysis in a changing climate, Climatic Change volume 127, pages353–369, doi:10.1007/s10584-014-1254-5.

2. Ragno E., AghaKouchak A., Cheng L., Sadegh, M., 2019, A Generalized Framework for Process-informed Nonstationary Extreme Value Analysis, Advances in Water Resources, 130, 270-282, doi: 10.1016/j.advwatres.2019.06.007

Well done!

Citation: https://doi.org/10.5194/egusphere-2022-347-RC2
- AC2: 'Reply on RC2', Damiano Baldan, 13 Jul 2022
  
  Thank you for the overall appreciation of our work. We will correct the terminology as suggested and we will include the suggested references in the revised submission.
  
  Citation: https://doi.org/10.5194/egusphere-2022-347-AC2

Interactive discussion

Status: closed

RC1: 'Comment on egusphere-2022-347', Anonymous Referee #1, 30 Jun 2022

the level of comprehensiveness of the analysis: it will be interesting for the scientific community to include one of the most utilized indirect methods: the revised joint probability method. In doing so, the authors will also reduce the existing overlapping with previous studies,
the level of applicability to other study areas by including more tide gauge records in the analysis, and
the level of replicability (some information relevant for reproducibility is missed).

Citation: https://doi.org/10.5194/egusphere-2022-347-RC1

AC1: 'Reply on RC1', Damiano Baldan, 13 Jul 2022

Reviewer’s comment	Authors' reply
Improving the work in the level of comprehensiveness of the analysis: it will be interesting for the scientific community to include one of the most utilized indirect methods: the revised joint probability method. In doing so, the authors will also reduce the existing overlapping with previous studies	We will include the revised joint probability method to the list of tested methods.
Improving the work in the level of applicability to other study areas by including more tide gauge records in the analysis	We acknowledge the sea level data from the case study in Venice might not be representative of other geographic locations given their high degree of non-stationaries (which in turn was useful for detecting the non-stationary pattern). We will add this point to the discussion. If the editor feels it would be an added value to the manuscript, we could replicate the same analysis with data from another tide gauge station with a lower degree of non-stationarity, present the results in a dedicated figure, and include a short discussion of the results.
Improving the level of replicability (some information relevant for reproducibility is missed).	We will include more details in the methods section based on the specific reviewer’s comments.

Citation: https://doi.org/10.5194/egusphere-2022-347-AC1

RC2:
'Comment on egusphere-2022-347', Anonymous Referee #2, 08 Jul 2022

In this submission, authors study the value of nonstationary analysis of coastal flooding under sea level rise. They have fitted various extreme value models (e.g. GEV, GPD, PP and JPM) to a long-term record of observational water level record at Venice, Italy and quantified the difference in estimated flood risk with and without consideration of nonstationarity. The idea is very interesting and timely, the research is well designed, and the manuscript is well written. I have no significant comment, except a few very minor suggestions below:

- "extreme event theory" has been used in multiple occasions (i.e. Page 1, lines 8 and 25); I've ween usually called "extereme value theory" in the literature. Are the authors referring to a different concept/theory?

- Some recent studies on nonstationary extreme value analysis must be cited and their contribution be acknowledged

1. Cheng et al., (2014),Non-stationary extreme value analysis in a changing climate, Climatic Change volume 127, pages353–369, doi:10.1007/s10584-014-1254-5.

2. Ragno E., AghaKouchak A., Cheng L., Sadegh, M., 2019, A Generalized Framework for Process-informed Nonstationary Extreme Value Analysis, Advances in Water Resources, 130, 270-282, doi: 10.1016/j.advwatres.2019.06.007

Well done!

Citation: https://doi.org/10.5194/egusphere-2022-347-RC2
- AC2: 'Reply on RC2', Damiano Baldan, 13 Jul 2022
  
  Thank you for the overall appreciation of our work. We will correct the terminology as suggested and we will include the suggested references in the revised submission.
  
  Citation: https://doi.org/10.5194/egusphere-2022-347-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (28 Jul 2022) by Philip Ward

AR by Damiano Baldan on behalf of the Authors (22 Aug 2022) Author's response Author's tracked changes Manuscript

ED: Reconsider after major revisions (further review by editor and referees) (24 Aug 2022) by Philip Ward

ED: Referee Nomination & Report Request started (25 Aug 2022) by Philip Ward

RR by Anonymous Referee #1 (06 Sep 2022)

Suggestions for revision or reasons for rejection

(1) The authors claim that one of the objectives of the present work is to assess “which parametric method best accommodates non-stationarity conditions”. The discussion and conclusion sections are written as if the results can be extrapolated to any other location (except for one sentence in line 338). However, the non-stationary conditions change with the conditions of the area of study. As I noted in the first review of this paper: “As is, the manuscript conclusions are appropriate for the study case (Punta Della Salute) only. In order to provide a quasi-standardized method for non-stationary analysis, the paper will benefit from applying the analysis to a larger set of tide gauge records, so the authors will be able to assess whether the conclusions can be extrapolated to other areas of study. Likewise, readers will be able to decide which method should be used according to the conditions of each case: tide range, relative relevance of surge vs tide in extremes – as in Dixon et al 1999-, location, record length, etc. In this sense, Haigh et al 2010 showed that differences between return levels estimated using different methods highly depend on the record length and on the presence of outliers (when using direct methods).”

In the second version, the authors included a new location, the Marseille tide gauge, which indeed shows that results change from one location to another (lines 246 to 247; 271 to 272). The inclusion of this second tide gauge is not, however, sufficient to generalize the results. The comparison of different extreme value distributions has been performed previously. The novelty of this work relies on the fact that they use a set of methods that have not been compared yet (to the best of my knowledge). Although this provides some novelty, I strongly believe that the paper can make a difference in the literature by performing the analysis using a comprehensive set of locations (globally ideally).

Concluding, I suggest the authors choose one of two options 1) a local study in which the results are limited to the two tide gauges used. If this option is chosen, the narrative of the paper should be modified to reflect this, or; 2) include a full set of tide gauges so that differences in conditions (record length, tidal range, surge vs tide, etc.) are included in the analysis and the results can be extrapolated.

(2) The main objective of the paper is to evaluate the performance of various extreme value distributions to account for non-stationarity, so it is very methodologically focused. Acknowledging this, the exposition of the methods is short and poor. A discussion of the pros and cons of each method would fit the paper. Also, the uncertainties of the models should be discussed in the results and conclusions.

(3) The article will benefit from a rewrite to avoid the "telegram style" taking particular attention to punctuation and typos, which are frequent throughout the paper (e.g., lines 247, 278, 299). It will also benefit from the integration into the narrative of the new tide gauge results. As is, the Marseilles tide gauge seems to be shoehorned into the paper.

Referee Report: PDF

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ED: Reconsider after major revisions (further review by editor and referees) (09 Sep 2022) by Philip Ward

Dear authors,

Thank you for the submission of your revised manuscript “Importance of non-stationary analysis for assessing extreme sea levels under sea level rise” to NHESS.

I have now received the reviewer’s comments on the revised manuscript. They have suggested 3 major areas of concern, as well as several suggestions for minor changes. The main concern is review comment 1, related to the fact that the paper’s objective is to assess “which parametric method best accommodates non-stationarity conditions”., and that the discussion and conclusion are written as though the results could be extrapolated to other locations. They suggest to rewrite the manuscript to either of 2 options, namely “1) a local study in which the results are limited to the two tide gauges used. If this option is chosen, the narrative of the paper should be modified to reflect this, or; 2) include a full set of tide gauges so that differences in conditions (record length, tidal range, surge vs tide, etc.) are included in the analysis and the results can be extrapolated”. I agree with this concern of the reviewer which would need to be thoroughly addressed in a manuscript with major revisions.

I would like to invite you to submit a revised version of your manuscript to address this concern, and the other remaining points. Would you please also provide an ‘author’s reply’ to the reviewers. Please can you also include a track changes document between the old manuscript and the new one (you can include this as part of your ‘author’s reply’).

I look forward to seeing the next version of your manuscript which I will then send out for further review to either the previous reviewer (if they agree) or a new reviewer.

Please be aware, that this is most likely the last possibility for you to change and improve the manuscript. Thus, I suggest that you carefully go through the manuscript again and improve everything which you still find useful to improve (even if the referees have not pointed it out).

Best regards

Philip Ward

NHESS Editor
Professor of Global Water Risk Dynamics, VU University Amsterdam

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AR by Damiano Baldan on behalf of the Authors (20 Sep 2022)

EF by Polina Shvedko (30 Sep 2022) Manuscript Author's response Author's tracked changes Supplement

ED: Referee Nomination & Report Request started (07 Oct 2022) by Philip Ward

RR by Anonymous Referee #1 (10 Oct 2022)

ED: Publish subject to minor revisions (review by editor) (11 Oct 2022) by Philip Ward

AR by Damiano Baldan on behalf of the Authors (11 Oct 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (26 Oct 2022) by Philip Ward

AR by Damiano Baldan on behalf of the Authors (26 Oct 2022) Manuscript

Journal article(s) based on this preprint

14 Nov 2022

Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Damiano Baldan, Elisa Coraci, Franco Crosato, Maurizio Ferla, Andrea Bonometto, and Sara Morucci

Nat. Hazards Earth Syst. Sci., 22, 3663–3677, https://doi.org/10.5194/nhess-22-3663-2022,https://doi.org/10.5194/nhess-22-3663-2022, 2022

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Damiano Baldan et al.

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Damiano Baldan et al.

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Short summary

Extreme events analysis is widely used to provide information for the design of coastal protection structures. Non-stationarity due to e.g. sea level rise can affect such estimates. Using different methods on a long time series of sea level data, we show that estimates of the magnitude of extreme events in the future can be inexact due to relative sea level rise. Thus, considering non-stationarity is important when analyzing extremes sea level events.


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