the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The value of visualization in improving compound flood hazard communication: A new perspective through a Euclidean Geometry lens
Abstract. Compound flooding, caused by the sequence/co-occurrence of flood drivers (i.e. river discharge and elevated sea level ) can lead to devastating consequences for society. Weak and insufficient progress toward sustainable development and disaster risk reduction are likely to exacerbate the catastrophic impacts of these events on vulnerable communities. For this reason, it is indispensable to develop new perspectives on evaluating compound flooding dependence and communicating the associated risks to meet UN Sustainable Development Goals (SDGs) related to climate action, sustainable cities, and sustainable coastal communities. An indispensable first step for studies examining the dependence between these bivariate extremes is plotting the data in the variable space, i.e., visualizing a scatterplot, where each axis represents a variable of interest, then computing a form of correlation between them. This paper introduces the Angles method, based on Euclidean geometry of the so-called “subject space,” for visualizing the dependence structure of compound flooding drivers. Here, we evaluate, for the first time, the utility of this geometric space in computing and visualizing the dependence structure of compound flooding drivers. To assess the effectiveness of this method as a risk communication tool, we conducted a survey with a diverse group of end-users, including academic and non-academic respondents. The survey results provide insights into the perceptions of applicability of the Angles method and highlight its potential as an intuitive alternative to scatterplots in depicting the evolution of dependence in the non-stationary environment. This study emphasizes the importance of innovative visualization techniques in bridging the gap between scientific insights and practical applications, supporting more effective compound flood hazard communication in a warming climate.
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RC1: 'Comment on gc-2024-7', Anonymous Referee #1, 07 Feb 2025
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Review Report
Manuscript: “The value of visualization in improving compound flood hazard communication: A new perspective through a Euclidean Geometry lens” by “Soheil Radfar, Georgios Boumis, Hamed Moftakhari, Wanyun Shao, Larisa Lee, Alison Rellinger”
General comments
The manuscript introduces the Angles method (based on Euclidean geometry of the so-called “subject space”) for visualizing the dependence structure of compound flooding drivers. Then it is evaluated the utility of the methodology for risk communication through a survey with diverse group of end-users, including academic and non-academic respondents.
The Authors use a geometrical interpretation of Pearson’s correlation coefficient (Eq.s 4-9). This issue is interesting and promising.
However, the Pearson’s correlation coefficient has some weaknesses: 1) problems of existence [see e.g., Salvadori er al. 2007 and De Michele et al. 2005]; 2) represent the linear association between the variables (as highlighted also by the Authors); 3) It is not invariant under monotonous transformation (only linear ones), issue of great importance for the application of Sklar’s theorem and thus copulas applications (see Salvadori er al. 2007). In this respect, why not using the Spearman correlation coefficient? According to the connection between the Pearson’s correlation coefficient and the Spearman’s one, you can write easily Eq.s 3-9 in terms of the pseudo-observations / transformed variables F(Q) and F(S). I suggest to develop this case in substitution (better) or alternative.
In the manuscript you have considered/referred to two variables (Q and S). If you have more than two variables, it could be interesting to say how to proceed, through a pairwise analysis?
Specific issues
Lines 84-87: I suggest to report also the p-value of the correlation coefficients to show the statistical significance.
In eq.(9) it is missing a parenthesis “(”
Line 121 clarify the acronym “CCF”.
Lines 153-154 “From Figure 4, it is clear that the correlation coefficient of the period 1997-2022 is greater than that of 1972-1996 since θ is smaller (thus, the cosine is greater).” I suggest also here to calculate the statistical significance of the estimates of the coefficient, also in light of the non-stationarities claim made by the authors (lines 163-164).
In Figure 8, it is not clear if all the correlations are significant. It is important to clarify which are the significant ones.
Mentioned references
Salvadori G. et al. 2007. Extremes in Nature: An approach using Copulas, volume 56, Springer, Dordrecht
De Michele C. et al. 2005. Bivariate Statistical Approach to check adequacy of dam spillway. ASCE J. Hydrologic Engineering 10(1), 50-57.
Citation: https://doi.org/10.5194/gc-2024-7-RC1 -
AC1: 'Reply on RC1', Soheil Radfar, 18 Feb 2025
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The comment was uploaded in the form of a supplement: https://gc.copernicus.org/preprints/gc-2024-7/gc-2024-7-AC1-supplement.pdf
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AC1: 'Reply on RC1', Soheil Radfar, 18 Feb 2025
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Data sets
Survey results Soheil Radfar and Georgios Boumis https://github.com/sradfar/CFnonStatViz/blob/main/Survey%201_Converted%20-%20Copy.xlsx
Washington gauge data Georgios Boumis and Soheil Radfar https://github.com/sradfar/CFnonStatViz/blob/main/Washington-Q_S.csv
Houston gauge data Georgios Boumis and Soheil Radfar https://github.com/sradfar/CFnonStatViz/blob/main/Houston-Q_SWL.csv
Model code and software
Dependence analysis code Georgios Boumis https://github.com/sradfar/CFnonStatViz/blob/main/CF_viz_Analysis.R
Fig 06 plot code Soheil Radfar https://github.com/sradfar/CFnonStatViz/blob/main/CFviz_Fig06.R
Fig 07 plot code Soheil Radfar https://github.com/sradfar/CFnonStatViz/blob/main/CFviz_Fig07.R
Interactive computing environment
Fig 08 plot code Soheil Radfar https://github.com/sradfar/CFnonStatViz/blob/main/CFviz_Fig08.R
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