Characterizing multivariate coastal flooding events in a semi-arid region: the implications of copula choice, sampling,  and infrastructure

Lucey, Joseph T. D.; Gallien, Timu W.

doi:https://doi.org/10.5194/nhess-22-2145-2022

Articles | Volume 22, issue 6

https://doi.org/10.5194/nhess-22-2145-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-22-2145-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 6

Research article

|

27 Jun 2022

Research article |

| 27 Jun 2022

Characterizing multivariate coastal flooding events in a semi-arid region: the implications of copula choice, sampling, and infrastructure

Joseph T. D. Lucey and Timu W. Gallien

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Final revised paper (published on 27 Jun 2022)
Preprint (discussion started on 12 Aug 2021)

Interactive discussion

Status: closed

RC1:
'Comment on nhess-2021-241', Anonymous Referee #1, 08 Sep 2021

see pdf for detailed comments

Citation: https://doi.org/10.5194/nhess-2021-241-RC1
- AC1: 'Reply on RC1', Timu Gallien, 02 Nov 2021
  
  The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2021-241/nhess-2021-241-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/nhess-2021-241-AC1
RC2:
'Comment on nhess-2021-241', Anonymous Referee #2, 18 Sep 2021

The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2021-241/nhess-2021-241-RC2-supplement.pdf

Citation: https://doi.org/10.5194/nhess-2021-241-RC2
- AC2: 'Reply on RC2', Timu Gallien, 02 Nov 2021
  
  The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2021-241/nhess-2021-241-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/nhess-2021-241-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) (11 Nov 2021) by Philip Ward

AR by Timu Gallien on behalf of the Authors (23 Nov 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (25 Nov 2021) by Philip Ward

RR by Anonymous Referee #2 (10 Dec 2021)

Suggestions for revision or reasons for rejection

The paper attempts to analyze the compound flood resulting from high OWL and pluvial floods over the Pacific coast using the observational network. After reading their revision, I still have certain issues, which I described below:
1. Their definition of the compound event is wrong. The authors have sampled the largest OWL and the largest precipitation event within a given year without considering a short/limited time window. Typically for compound floods, it is assumed that the time between each driver is from 0 to a few days, preferably within a week to account for synoptic circulation. Since the ‘time’ of elapsing of one event to another is sufficiently large here – it merely resulted in a bivariate/multivariate event series rather than a compound pair. Likewise, pairing within a wet season month does not contribute towards compound event construction.

Although they quote a certain publication from FEMA pointing ‘observations within a wet season’ as worst possible pairs - In fact, FEMA (2015, 2018) do not provide any unified guidelines for analyzing compound floods. In FEMA (2020), a recommendation for combined occurrence for coastal and riverine flooding is provided, however, no sampling methods were discussed.

2. The authors have used AIC and ML methods as a goodness-of-fit estimate to select copula. However, the AIC and ML method does not provide any goodness-of-fit statistics of the population fit and the power of the test - it compares the empirical vs theoretical fit of the data. For the selection of copulas, a double parametric bootstrap method is to be adopted, other than the AIC-based criteria (Genest et al., 2009; Rémillard, 2017).

3. In compound event perspective, it is of interest to analyze when the design level exceeds both variables simultaneously, and not either of the variables in isolation. Therefore, the 'OR' criteria is suitable to analyze multivariate probability and not the compound extreme per se. The papers they have followed, also emphasize multivariate events and estimation of associated hazard and therefore, do not explicitly consider compound events in particular.

References:
Genest C, Rémillard B, Beaudoin D. 2009. Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics 44 (2): 199–213.

Rémillard B. 2017. Goodness-of-fit tests for copulas of multivariate time series. Econometrics 5 (1): 13.

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RR by Anonymous Referee #3 (21 Jan 2022)

ED: Reconsider after major revisions (further review by editor and referees) (26 Jan 2022) by Philip Ward

AR by Timu Gallien on behalf of the Authors (06 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (20 Apr 2022) by Philip Ward

RR by Anonymous Referee #3 (06 May 2022)

ED: Publish as is (10 May 2022) by Philip Ward

AR by Timu Gallien on behalf of the Authors (14 May 2022) Manuscript

Short summary

Coastal flooding can result from multiple flood drivers (e.g., tides, waves, river flows, rainfall) occurring at the same time. This study characterizes flooding events caused by high marine water levels and rain. Results show that wet-season coinciding sampling may better describe extreme flooding events in a dry, tidally dominated region. A joint-probability-based function is then used to estimate sea wall impacts on urban coastal flooding.