Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Dependence between extreme CWL and river peak discharge. (a-c) Spatial maps of correlation and upper tail dependence between annual maxima CWL and d-day lagged daily peak discharge within ± 7 days of the occurrence of the extreme CWL using nonparametric dependence measures. The complete dependence between two variables is established using Kendall’s τ ( a ), while the Upper Tail Dependence Coefficients, UTDC ( b , c ), are computed using two nonparametric upper tail dependence metrics (see Methods). The location of SGs with significant (at 5% level) dependence between compound flood drivers are marked with colours, whereas the location of SGs with insignificant and low values of positive dependence (with values <0.1 and p-values ≥0.05), and negative dependence (values <0) are marked with white ( d ) Kernel density functions of complete and UTDC metrics illustrating the negative skewness in the spatial distribution. The two UTDC distributions are shifted significantly (as indicated by the p- values < 0.05) towards higher values relative to the distribution of complete dependence. The density curve in LOG estimator is flattened, with an elongated right (higher dependence) tail. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Stronger upper tail dependence relative to complete dependence increases the likelihood of compound flood events: Proof-of-concept illustrations of unconditional ( left panel ) and conditional (on high coastal CWL; right panel ) flood hazards in UK Rivers along the North shields TG: River Ribble (a, top panel) a tidally influenced river located at a geodesic distance of 157 km and in the River South Tyne (b, bottom panel), non-tidally influenced, located at a geodesic distance of 69 km from the TG. ( a ) Kendall’s τ correlation between Annual maxima CWL and peak discharge for River Ribble is 0.16 with p- value = 0.12 [the p- value indicates the evidence against the null hypothesis of independence: the smaller (larger) the p -value, the stronger is the evidence against (for) the null hypothesis; however, a p -value does not indicate the probability that the null hypothesis is true], while empirical upper tail dependence coefficients are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{CFG}^{Obs}$$\end{document} λ C F G O b s = 0.28 (p-value = 0.0054) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{LOG}^{Obs}$$\end{document} λ L O G O b s = 0.44 (p-value = 0.011). ( b ) Kendall’s τ correlation associated with compound event pairs in River South Tyne is 0.25 with p-value = 0.018, while empirical upper tail dependence coefficients are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{CFG}^{Obs}$$\end{document} λ C F G O b s = 0.35 (p-value = 0.001) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{LOG}^{Obs}$$\end{document} λ L O G O b s = 0.44 (p-value = 0.013). While circles with shades in yellow and red denote the year of occurrence of the compound event, the one in gray indicates copula-simulated samples. For clarity, return level estimates are rounded to their nearest decimal numbers. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: Spatial Variations in compound flood hazards for selected winter storm events. Spatial distribution of CHR index showing compound flooding hotspots for three winter storm episodes: Capella (1 st –5 th January, 1976; a and d ), Xynthia (26 th February–7 th March, 2010; b and e ), and Xaver (4 th –11 th December, 2013; c and f ) for T = 10- ( top panel ) and 50-year ( bottom panel ) return periods. The triangles indicate locations of TG. The colours in the TGs indicate the standardized anomaly of annual maximum CWL, while the size of the triangle is proportional to its value. The upward (positive) and downward (negative) triangles indicate the sign of the standardized anomaly at each TG location. The circles show SG locations where CHR is calculated. The darker shade represents a high value indicating a greater hazard, while a lighter shade denotes low hazard associated with the compound event. Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

doi: 10.1038/s41598-019-49822-6

Figure Lengend Snippet: The fraction of TG-SG pairs showing an increase in the likelihood of compound flood hazards for the three winter storm episodes ( a ) Fraction (expressed as a percentage) of TG-SG pair with an increase in T-year peak discharge associated with compound event relative to at-site peak discharge. Percentage relative increase in T- year peak discharge for ( b ) 10- and ( c ) 50-year events. The increase in discharge is quantified as the relative difference between the magnitude of the T- year flood peak conditional on AMWL and the seasonal maxima (November-March) at-site T- year peak discharge expressed as a percentage. The horizontal bars in red (figure b) and blue (Figure c) show TG-SG pairs with an increase in flood hazard. The dotted vertical line (in black) indicates the relative increase of the order of 50%. Figures are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Article Snippet: Maps are generated using MATLAB 2015a (Version 8.5, http://www.mathworks.com ), and then organized and labelled in Adobe Photoshop CS6 Desktop (Version 13.0.1 × 32, http://www.adobe.com ) [Software].

Techniques: Generated, Software