Articles | Volume 20, issue 10
https://doi.org/10.5194/nhess-20-2681-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-2681-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multivariate statistical modelling of the drivers of compound flood events in south Florida
Robert Jane
CORRESPONDING AUTHOR
Civil, Environmental and Construction Engineering and National Center for Integrated Coastal Research, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, USA
Luis Cadavid
Operational Hydraulics Unit – Applied Hydraulics Section, South Florida Water Management District, West Palm Beach, FL 35406, USA
Jayantha Obeysekera
Sea Level Solutions Center, Florida International University, Miami, FL 33199, USA
Thomas Wahl
Civil, Environmental and Construction Engineering and National Center for Integrated Coastal Research, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, USA
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Cited articles
Aas, K. and Berg, D.:
Models for construction of multivariate dependence – a comparison study,
Eur. J. Financ.,
15, 639–659, 2009.
Aas, K., Czado, C., Frigessi, A., and Bakken, H.:
Pair copula constructions of multiple dependence,
Insurance: Math. Econ.,
44, 182–198, 2009.
Arns, A., Wahl, T., Haigh, I. D., Jensen, J., and Pattiaratchi, C.:
Estimating extreme water level probabilities: A comparison of the direct methods and recommendations for best practise,
Coast. Eng.,
81, 51–66, 2013.
Bedford, T. and Cooke, R. M.:
Probability density decomposition for conditionally dependent random variables modeled by vines,
Ann. Math. Artif. Intel.,
32, 245–268, 2001.
Bedford, T. and Cooke, R. M.:
Vines – a new graphical model for dependent random variables,
Ann. Stat.,
30, 1031–1068, 2002.
Bender, J., Wahl, T., Müller, A., and Jensen, J.:
A multivariate design framework for river confluences,
Hydrolog. Sci. J.,
61, 3471–482, 2016.
Bengtsson, L.:
Probability of combined high sea levels and large rains in Malmö, Sweden, southern Öresund,
Hydrol. Process.,
30, 3172– 3183, 2016.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.:
Dominant flood generating mechanisms across the United States,
Geophys. Res. Lett.,
43, 4382–4390, 2016.
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.:
The relative importance of different flood-generating mechanisms across Europe,
Water Resour. Res.,
55, 4582–4593, 2019.
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017.
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.:
Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change,
Sci. Adv., 5, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019.
Bevacqua, E., Vousdoukas, M. I., Shepherd, T. G., and Vrac, M.: Brief communication: The role of using precipitation or river discharge data when assessing global coastal compound flooding, Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, 2020.
Bloetscher, F. H., Heimlich, B., and Meeroff, D. E.:
Development of an adaptation toolbox to protect southeast Florida water supplies from climate change,
Environ. Rev.,
19, 397–417, 2011.
Buishand, T. A.:
Bivariate extreme-value data and the station-year method,
J. Hydrol.,
69, 77–95, 1984.
Chen, L., Singh, V. P., Shenglian, G., Hao, Z., and Li, T.:
Flood coincidence risk analysis using multivariate copula functions,
J. Hydrol. Eng.,
17, 742–755, 2012.
Coles, S.:
An Introduction to Statistical Modelling of Extreme Values,
Springer series in statistics,
Springer-Verlag, London, 2001.
Coles, S. G., Heffernan, J. E., and Tawn, J. A.:
Dependence measures for extreme value analyses,
Extremes,
2, 339–365, 1999.
Couasnon, A., Sebastian, A., and Morales-Nápoles, O.:
A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas,
Water,
10, 1190, https://doi.org/10.3390/w10091190, 2018.
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.
Daneshkhah, A., Remesan, R., Chatrabgoun, O., and Holman, I. P.:
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model,
J. Hydrol.,
540, 469–487, 2016.
De Michele, C. and Salvadori, G.:
A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-Copulas,
J. Geophys. Res.,
108, 4067, https://doi.org/10.1029/2002JD002534, 2003.
Di Bernardino, E. and Rullière, D.:
On an asymmetric extension of multivariate Archimedean copulas based on quadratic form,
Depend. Model.,
4, 328–347, 2016.
Diederen, D., Liu, Y., Gouldby, B., Diermanse, F., and Vorogushyn, S.: Stochastic generation of spatially coherent river discharge peaks for continental event-based flood risk assessment, Nat. Hazards Earth Syst. Sci., 19, 1041–1053, https://doi.org/10.5194/nhess-19-1041-2019, 2019.
Duong, T.:
ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R,
J. Stat. Softw.,
21, 1–16, 2007.
Embrechts, P., Lindskog, F., and McNeil, A.:
Modelling dependence with copulas and applications to risk management,
in: Handbook of Heavy Tailed Distributions in Finance,
edited by: Rachev, S. T.,
North-Holland, Elsevier, the Netherlands, 2003.
Fang, H.-B., Fang, K., and Kotz, S.:
The meta-elliptical distributions with given marginals,
J. Multivariate Anal.,
82, 1–16, 2002.
Fang, K. T., Kot, S., and Ng, K. W.:
Symmetric Multivariate and Related Distributions,
Chapman and Hall, London, 1990.
FEMA:
Guidance for flood risk analysis and mapping; combined coastal and riverine floodplain, No. Guidance Document 32,
FEMA, Washington, D.C., 2015.
Florida Office of Economic and Demographic Research: Florida Demographic Estimating Conference April 2015 and the University of Florida, Bureau of Economic and Business Research, Florida Population Studies, Bulletin 178, June 2015.
Ganguli, P. and Merz, B.:
Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe,
Sci. Rep.-UK,
9, 13165, https://doi.org/10.1038/s41598-019-49822-6, 2019.
Gilja, G., Ocvirk, E., and Kuspilić, N.:
Joint probability analysis of flood hazard at river confluences using bivariate copulas,
Gradevinar,
70, 267–275, 2018.
Gräler, B., van den Berg, M. J., Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B., and Verhoest, N. E. C.: Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation, Hydrol. Earth Syst. Sci., 17, 1281–1296, https://doi.org/10.5194/hess-17-1281-2013, 2013.
Gräler, B., Petroselli, A., Grimaldi, S., De Baets, B., and Verhoest, N.:
An update on multivariate return periods in hydrology,
P. Int. Ass. Hydrol. Sci.,
373, 175–178, 2016.
Gouldby, B., Méndez, F. J., Guanche, Y., Rueda, A., and Mínguez, R.:
A methodology for deriving extreme nearshore sea conditions for structural design and flood risk analysis,
Coast. Eng.,
88, 15–26, 2014.
Gouldby, B. P., Wyncoll, D., Panzeri,M., Franklin, M., Hunt, T., Hames, D., Tozer, N. P., Hawkes, P. J., Dornbusch, U., and Pullen T. A.:
Multivariate extreme value modelling of sea conditions around the coast of England,
P. I. Civil Eng.-Mar. En.,
170, 3–20, 2017.
Haigh, I. D., MacPherson, L. R., Mason, M. S., Wijeratne, E. M. S., Pattiaratchi, C. B., Crompton, R. P., and George, S.:
Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tropical cyclone-induced storm surges,
Clim. Dynam.,
42, 139–157, 2014.
Hallegatte, S., Green, C., Nicholls, R. J., and Corfee-Morlot, J.:
Future flood losses in major coastal cities,
Nat. Clim. Change,
3, 802–806, 2013.
Hawkes, P. J.:
Joint Probability Analysis for Estimation of Extremes,
J. Hydraul. Res.,
46, 246–256, 2008.
Hawkes, P. J., Gouldby, B. P., Tawn, J. A., and Owen, M. W.:
The joint probability of waves and water levels in coastal engineering design,
J. Hydraul. Res.,
40, 241–251, 2002.
Heffernan, J. E. and Tawn, J. A.:
A conditional approach for multivariate extreme values (with discussion),
J. Roy. Stat. Soc. B,
66, 497–546, 2004.
Hendry, A., Haigh, I. D., Nicholls, R. J., Winter, H., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S. E.: Assessing the characteristics and drivers of compound flooding events around the UK coast, Hydrol. Earth Syst. Sci., 23, 3117–3139, https://doi.org/10.5194/hess-23-3117-2019, 2019.
Hettiarachchi, S., Wasko, C., and Sharma, A.:
Can antecedent moisture conditions modulate the increase in flood risk due to climate change in urban catchments?,
J. Hydrol.,
571, 11–20, 2019.
Ikeuchi, H., Hirabayashi, Y., Yamazaki, D., Muis, S., Ward, P. J., Winsemius, H. C., Verlaan, M., and Kanae, S.:
Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh,
J. Adv. Model. Earth Syst.,
9, 1847–1862, 2017.
Jane, R.: MultiHazard R package, available at: https://github.com/rjaneUCF/MultiHazard, last access: 20 August 2020.
Jane, R., Dalla Valle, L., Simmonds, D., and Raby, A.:
A Copula Based Approach for the Estimation of Wave Records Through Spatial Correlation,
Coast. Eng.,
117, 1–18, 2016.
Joe, H.:
Families of m-variate distributions with given margins and bivariate dependence parameters,
in: Distributions with fixed marginals and related topics,
edited by: Rüschendorf, L., Schweizer, B., and Taylor, M. D.,
IMS – Institute of Mathematical Statistics, Hayward, CA, 120–141, 1996.
Keef, C., Tawn, J. A., and Svensson, C.:
Spatial risk assessment for extreme river flows,
Appl. Stat.-J. Roy. Stat. C,
58, 601–618, 2009a.
Keef, C., Tawn, J. A., and Svensson, C.:
Spatial dependence in extreme river flows and precipitation for Great Britain,
J. Hydrol.,
378, 240–252, 2009b.
Keef, C., Papastathopoulos, I., and Tawn, J. A.:
Estimation off the conditional distribution of a vector variable given that one of its components is large: additional constraints for the Heffernan and Tawn model,
J. Multivar. Anal.,
115, 396–404, 2013.
Keenan, J. M., Hill, T., and Gumber, A.:
Climate gentrification: from theory to empiricism in Miami-Dade County, Florida,
Environ. Res. Lett.,
13, 054001, https://doi.org/10.1088/1748-9326/aabb32, 2018.
Kew, S. F., Selten, F. M., Lenderink, G., and Hazeleger, W.: The simultaneous occurrence of surge and discharge extremes for the Rhine delta, Nat. Hazards Earth Syst. Sci., 13, 2017–2029, https://doi.org/10.5194/nhess-13-2017-2013, 2013.
Kulp, S. and Strauss, B. H.:
Rapid escalation of coastal flood exposure in US municipalities from sea level rise,
Climatic Change,
142, 477–489, 2017.
Lamb, R, Keef, C, Tawn, J. A., Laeger, S., Meadowcroft, I., Surendran, S., Dunning, P., and Batstone, C.:
A new method to assess the risk of local and widespread flooding on rivers and coasts,
J. Flood Risk Manage.,
3, 323–336, 2010.
Ledford, A. W. and Tawn, J. A.:
Modelling dependence within joint tail regions,
J. Roy. Stat. Soc. B,
59, 475–499, 1997.
Leonard, M., Westra, S., Phatak, A., Lambert, M., Van den Hurk, B., Mcinnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M.:
A compound event framework for understanding extreme impacts,
WIREs Clim. Change,
5, 113–128, 2014.
Li, G., Peng, H., Zhang, J., and Zhu, L.:
Robust rank correlation based screening,
Ann. Stat.,
40, 1846–1877, 2012.
Lian, J. J., Xu, K., and Ma, C.: Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China, Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, 2013.
Loganathan, G. V., Kuo, C. Y., and Yannacconc, J.:
Joint probability distribution of streamflows and tides in estuaries,
Nord. Hydrol.,
18, 237–246, 1987.
Ma, M., Song, S., Ren, L., Jiang, S., and Song, J.:
Multivariate drought characteristics using trivariate Gaussian and Student's t copulas,
Hydrol. Process.,
27, 1175–1190, 2013.
Martius, O., Pfahl, S., and Chevalier, C.:
A global quantification of compound precipitation and wind extremes,
Geophys. Res. Lett.,
43, 7709–7717, 2016.
Moftakhari, H., Schubert, J. E., AghaKouchak, A., Matthew, R. A., and Sanders, B. F.:
Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries,
Adv. Water Resour.,
128, 28–38, 2019.
Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F., and Matthew, R. A.:
Compounding effects of sea level rise and fluvial flooding,
P. Natl. Acad. Sci. USA,
114, 9785–9790, 2017.
NCHRP:
Estimating Joint Probabilities of Design Coincident Flows at Stream Confluences, Report 15-36,
National Cooperative Highway Research Program (NCHRP), Washington, USA, 2010.
NOAA: National Climatic Data Center, National Oceanic and Atmospheric Administration, available at: https://www.ncdc.noaa.gov/cdo-web, last access: 12 April 2019.
Nott, J.:
Synthetic versus long-term natural records of tropical cyclone storm surges: problems and issues,
Geosci. Lett.,
3, 1–9, 2016.
Paprotny, D., Vousdoukas, M. I., Morales-Nápoles, O., Jonkman, S. N., and Feyen, L.: Compound flood potential in Europe, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-132, 2018.
Parkinson, R. W. and Donoghue, J. F.:
Bursting the bubble of doom and adapting to sea level rise,
Shoreline,
2010, 12–20, 2010.
Pathak, C. S.:
Frequency analysis of daily rainfall maxima for central and south Florida,
SFWMD Technical Publication EMA 390, SFWMD, West Palm Beach, FL, 2001.
Patton, A. J.:
Modelling asymmetric exchange rate dependence,
Int. Econ. Rev.,
47, 527–556, 2006.
Peng, Y., Chen, K., Yan, H., and Yu, X.:
Improving flood-risk analysis for confluence flooding control downstream using copula Monte Carlo method,
J. Hydrol. Eng.,
22, 04017018, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001526, 2017.
Peng, Y., Shi, Y., Yan, H., Chen, K., and Zhang, J.:
Coincidence Risk Analysis of Floods Using Multivariate Copulas: Case Study of Jinsha River and Min River, China,
J. Hydrol. Eng.,
24, 05018030, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001744, 2018.
Provost, A. M., Werner, A. D., Post, V. E., Michael, H. A., and Langevin, C. D.:
Rebuttal to “The case of the Biscayne Bay and aquifer near Miami, Florida: density-driven flow of seawater or gravitationally driven discharge of deep saline groundwater?” by Weyer (Environ Earth Sci 2018, 77:1–16),
Environ. Earth Sci.,
77, 710, https://doi.org/10.1007/s12665-018-7832-5, 2018.
Pugh, D. J.:
Tide, surge and mean sea level. A handbook for Engineers and Scientists,
John Wiley, Chichester, UK, 472 pp., 1987.
Randazzo, A. F. and Jones, D. S. (Eds.):
The Geology of Florida,
University Press of Florida, Gainesville, FL, 76–80, 1997.
Salas, J. D.:
Analysis and modeling of hydrologic time series,
in:
Handbook of Hydrology,
edited by:
Maidment, D.,
McGraw-Hill, New York, 19.1–19.72, 1993.
Salvadori, G. and De Michele, C.:
Frequency analysis via copulas: Theoretical aspects and applications to hydrological events,
Water Resour. Res.,
40, W12511, https://doi.org/10.1029/2004WR003133, 2004.
Salvadori, G. and De Michele, C.:
Multivariate Extreme Value Methods,
in: Extremes in a Changing Climate,
edited by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S., Springer, Dordrecht, the Netherlands, 2013.
Salvadori, G., De Michele, C., and Durante, F.: On the return period and design in a multivariate framework, Hydrol. Earth Syst. Sci., 15, 3293–3305, https://doi.org/10.5194/hess-15-3293-2011, 2011.
Salvadori, G., Durante, F., and De Michele, C.:
Multivariate return period calculation via survival functions,
Water Resour. Res.,
49, 2308–2311, 2013.
Salvadori, G., Durante, F., De Michele, C., Bernardi, M., and Petrella, L.:
A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities,
Water Resour. Res.,
52, 3701–3721, 2016.
Schedel, J. R. and Schedel, A. L.:
Analysis of Variance of Flood Events on the U. S. East Coast: The Impact of Sea-Level Rise on Flood Event Severity and Frequency,
J. Coast. Res.,
341, 50–57, 2018.
Schepsmeier, U., Stoeber, J., Brechmann, E. C., Gräler,B., Nagler, T., and Erhardt, T.:
VineCopula: Statistical Inference of Vine Copulas,
R package version 2.1.8, 2018.
Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Sorteberg, A., Vera, C., and Zhang, X.:
Changes in climate extremes and their impacts on the natural physical environment,
in:
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), chap. 3,
edited by: Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G. K., Allen, S. K., Tignor, M., and Midgley, P. M.,
Cambridge Univ. Press, Cambridge, 109–230, 2012.
Serafin, K. A., Ruggiero, P., Parker, K., and Hill, D. F.: What's streamflow got to do with it? A probabilistic simulation of the competing oceanographic and fluvial processes driving extreme along-river water levels, Nat. Hazards Earth Syst. Sci., 19, 1415–1431, https://doi.org/10.5194/nhess-19-1415-2019, 2019.
Serinaldi, F.:
Dismissing return periods,
Stoch. Environ. Res. Risk A.,
29, 1179–1189, 2015.
SFWMD:
Get the Facts: Saltwater Intrusion and Water Supply,
South Florida Water Management District, available at: https://www.sfwmd.gov/sites/default/files/documents/getthefacts_052616_saltwater_intrusion.pdf (last access: 20 June 2019),
2016.
Sklar, A.:
Fonctions de répartition à n dimensions et leurs marges,
Publ. Inst. Stat. Univ. Paris,
8, 229–231, 1959.
Smith, R. L. and Weissman, I.:
Estimating the extremal index,
J. Roy. Stat. Soc. B,
56, 515–528, 1994.
Southeast Florida Regional Climate Change Compact Sea Level Rise Work Group (Compact):
Unified Sea Level Rise Projection for Southeast Florida. A document prepared for the Southeast Florida Regional Climate Change Compact Steering Committee,
p. 35, October 2015.
Strauss, B. H., Ziemlinski, R., Weiss, J. L., and Overpeck, J. T:
Tidally adjusted estimates of topographic vulnerability to sea level rise and flooding for the contiguous United States,
Environ. Res. Lett.,
7, 14033, https://doi.org/10.1088/1748-9326/7/1/014033, 2012.
Svensson, C. and Jones, D. A.:
Dependence between extreme sea surge, river flow and precipitation in eastern Britain,
Int. J. Climatol.,
22, 1149–1168, 2002.
Svensson, C. and Jones, D. A.: Dependence between sea surge, river flow and precipitation in south and west Britain, Hydrol. Earth Syst. Sci., 8, 973–992, https://doi.org/10.5194/hess-8-973-2004, 2004.
Svensson, C. and Jones, D. A.:
Joint Probability: Dependence between extreme sea surge, river flow and precipitation: A study in South and West Britain, R&D Technical Report FD2308/TR3, DEFRA, London, UK, 2006.
Sweet, W. V., Kopp, R. E., Weaver, C. P., Obeysekera, J., Horton, R. M., Thieler, E. R., and Zervas, C.:
Global and Regional Sea Level Rise Scenarios for the United States,
NOAA, Silver Spring, MD, USA, 2017.
Towe, R. P., Tawn, J. A., Lamb, R., and Sherlock, C.:
Model-based inference of conditional extreme value distributions with hydrological applications,
Environmetrics, 30, env.2575, https://doi.org/10.1002/env.2575, 2019.
Valle-Levinson, A., Dutton, A., and Martin, J. B.:
Spatial and temporal variability of sea level rise hot spots over the eastern United States,
Geophys. Res. Lett.,
44, 7876–7882, 2017.
van den Hurk, B., van Meijgaard, E., de Valk, P., van Heeringen, K.-J., and Gooijer, J.:
Analysis of a compounding surge and precipitation event in the Netherlands,
Environ. Res. Lett.,
10, 035001, https://doi.org/10.1088/1748-9326/10/3/035001, 2015.
Verhoest, N. E.C., Vandenberghe, S., Cabus, P., Onof, C., Meca-Figueras, T., and Jameleddine, S.:
Are stochastic point rainfall models able to preserve extreme flood statistics?,
Hydrol. Process.,
24, 3439–2445, 2010.
Villarini, G. and Smith, J. A.:
Flood peak distributions for the eastern United States,
Water Resour. Res.,
46, W06504, https://doi.org/10.1029/2009WR008395, 2010.
Volpi, E. and Fiori, A.:
Hydraulic structures subject to bivariate hydrological loads: Return period, design, and risk assessment,
Water Resour. Res.,
50, 885–897, 2014.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.:
Increasing risk of compound flooding from storm surge and rainfall for major US cities,
Nat. Clim. Change,
5, 1093–1097, 2015.
Wahl, T., Plant, N. G., and Long, J. W.:
Probabilistic assessment of erosion and flooding risk in the northern, Gulf of Mexico,
J. Geophys. Res.-Oceans,
121, 3029–3043, 2016.
Wang, C., Chang, N.-B., and Yeh, G.-T.:
Copula-based flood frequency (COFF) analysis at the confluences of river systems,
Hydrol. Process.,
23, 1471–1486, 2009.
Ward, P. J., Couasnon, A., Eilander, D., Haigh, I. D., Hendry, A., Muis, S., Veldkamp, T. I. E., Winsemius, H. C., and Wahl, T.:
Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries,
Environ. Res. Lett.,
13, 084012, https://doi.org/10.1088/1748-9326/aad400, 2018.
Wdowinski, S., Bray, R., Kirtman, B. P., and Wu, Z.:
Increasing flooding hazard in coastal communities due to rising sea level: Case study of Miami Beach, Florida,
Ocean Coast. Manage.,
126, 1–8, 2016.
White, C. J.:
The use of joint probability analysis to predict flood frequency in estuaries and tidal rivers,
PhD thesis,
School of Civil Engineering and the Environment, University of Southampton, Southampton, 2009.
Wong, G., Lambert, M. F., and Metcalfe, A. V.:
Trivariate copulas for characterisation of droughts,
Anziam J.,
49, 306–323, 2008.
Wu, W., McInnes, K., O'Grady, J., Hoeke, R., Leonard, M., and Westra, S.:
Mapping dependence between extreme rainfall and storm surge,
J. Geophys. Res.-Oceans,
123, 2461–2474, 2018.
Wyncoll, D., Haigh, I., Gouldby, B., Hames, D., Laeger, S., Wall, A., Hawkes, P., and Hammond, A.:
Spatial analysis and simulation of extreme coastal flooding scenarios for national-scale emergency planning,
in: 3rd European Conference on Flood Risk Management,
edited by: Lang, M., Klijn, F., and Samuels, P.,
EDP Sciences, London, 2016.
Zhang, K.:
Analysis of non-linear inundation from sea-level rise using LIDAR data: a case study for South Florida,
Climatic Change,
106, 537–565, 2011.
Zheng, F., Westra, S., and Sisson, S. A.:
Quantifying the dependence between extreme rainfall and storm surge in the coastal zone,
J. Hydrol.,
505, 172–187, 2013.
Zheng, F., Westra, S., Leonard, M., and Sisson, S. A.:
Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk,
Water Resour. Res.,
50, 2050–2071, 2014.
Zscheischler, J., Westra, S., Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.:
Future climate risk from compound events,
Nat. Clim. Change,
8, 469–477, 2018.
Short summary
Full dependence is assumed between drivers in flood protection assessments of coastal water control structures in south Florida. A 2-D analysis of rainfall and coastal water level showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. The vine copula and HT04 model outperformed five higher-dimensional copulas in capturing the dependence between rainfall, coastal water level, and groundwater level.
Full dependence is assumed between drivers in flood protection assessments of coastal water...
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