Articles | Volume 24, issue 11
https://doi.org/10.5194/nhess-24-4091-2024
© Author(s) 2024. 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-24-4091-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A multivariate statistical framework for mixed storm types in compound flood analysis
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USA
Thomas Wahl
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USA
Sara Santamaria-Aguilar
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USA
Robert Jane
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
National Center for Integrated Coastal Research, University of Central Florida, Orlando, FL 32816, USA
James F. Booth
Department of Earth and Atmospheric Sciences, City University of New York, City College, New York, NY 10031, USA
Hanbeen Kim
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA
Gabriele Villarini
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
High Meadows Environmental Institute, Princeton University, Princeton, NJ 08544, USA
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Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Barth, N. A., Villarini, G., and White, K.: Accounting for Mixed Populations in Flood Frequency Analysi.: Bulletin 17C Perspective, J. Hydrol. Eng., 24, 4019002, https://doi.org/10.1061/(asce)he.1943-5584.0001762, 2019.
Bass, B. and Bedient, P.: Surrogate modeling of joint flood risk across coastal watersheds, J. Hydrol. (Amst), 558, 159–173, https://doi.org/10.1016/j.jhydrol.2018.01.014, 2018.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis-Gruss, S., Freeman, N. B., Houser, T., Delgado, M., Hamidi, A., Bolliger, I., E. McCusker, K., Emanuel, K., Ferreira, C. M., Khalid, A., Haigh, I. D., Couasnon, A., E. Kopp, R., Hsiang, S., and Krajewski, W. F.: Combined Modeling of US Fluvial, Pluvial, and Coastal Flood Hazard Under Current and Future Climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020WR028673, 2021.
Bauer, M., Tselioudis, G., and Rossow, W. B.: A new climatology for investigating storm influences in and on the extratropics, J. Appl. Meteorol. Clim., 55, 1287–1303, https://doi.org/10.1175/JAMC-D-15-0245.1, 2016.
Bender, J., Wahl, T., Müller, A., and Jensen, J.: A multivariate design framework for river confluences, Hydrolog. Sci. J., 61, 471–482, https://doi.org/10.1080/02626667.2015.1052816, 2016.
Bermúdez, M., Farfán, J. F., Willems, P., and Cea, L.: Assessing the Effects of Climate Change on Compound Flooding in Coastal River Areas, Water Resour. Res., 57, e2020WR029321, https://doi.org/10.1029/2020WR029321, 2021.
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.
Camus, P., Haigh, I. D., Nasr, A. A., Wahl, T., Darby, S. E., and Nicholls, R. J.: Regional analysis of multivariate compound coastal flooding potential around Europe and environs: sensitivity analysis and spatial patterns, Nat. Hazards Earth Syst. Sci., 21, 2021–2040, https://doi.org/10.5194/nhess-21-2021-2021, 2021.
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, https://doi.org/10.1061/(asce)he.1943-5584.0000504, 2012.
City of Gloucester: Official Website, https://www.cityofgloucester.org/, last access: 24 February 2024.
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.
Codiga, D. L.: Unified Tidal Analysis and Prediction Using the UTide Matlab Functions, 59 pp., https://doi.org/10.13140/RG.2.1.3761.2008, 2011.
Codiga, D.: UTide Unified Tidal Analysis and Prediction Functions, MATLAB Central File Exchange, https://www.mathworks.com/matlabcentral/fileexchange/46523-utide-unified-tidal-analysis-and-prediction-functions, last access: 5 August 2023.
Dullaart, J. C. M., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., and Aerts, J. C. J. H.: Accounting for tropical cyclones more than doubles the global population exposed to low-probability coastal flooding, Commun. Earth Environ., 2, 135, https://doi.org/10.1038/s43247-021-00204-9, 2021.
Dvoretzky, A., Kiefer, J., and Wolfowitz, J.: Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator, Ann. Math. Stat., 27, 642–669, https://doi.org/10.1214/aoms/1177728174, 1956.
Fall, G., Kitzmiller, D., Pavlovic, S., Zhang, Z., Patrick, N., St. Laurent, M., Trypaluk, C., Wu, W., and Miller, D.: The Office of Water Prediction’s Analysis of Record for Calibration, version 1.1: Dataset description and precipitation evaluation, J. Am. Water Resour. As., 59, 1246–1272, https://doi.org/10.1111/1752-1688.13143, 2023.
Gori, A. and Lin, N.: Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods, Earths Future, 10, e2022EF003097, https://doi.org/10.1029/2022EF003097, 2022.
Gori, A., Lin, N., and Xi, D.: Tropical Cyclone Compound Flood Hazard Assessmen.: From Investigating Drivers to Quantifying Extreme Water Levels, Earths Future, 8, e2020EF001660, https://doi.org/10.1029/2020EF001660, 2020.
Hallegatte, S., Green, C., Nicholls, R. J., and Corfee-Morlot, J.: Future flood losses in major coastal cities, Nat. Clim. Change, 3, 802–806, https://doi.org/10.1038/nclimate1979, 2013.
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.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, Y., Xuan Do, H., Kessler, J., Fry, L., Read, L., Rafieei Nasab, A., Gronewold, A. D., Mason, L., and Anderson, E. J.: Evaluation of gridded precipitation datasets over international basins and large lakes, J. Hydrol. (Amst), 607, 127507, https://doi.org/10.1016/j.jhydrol.2022.127507, 2022.
Imada, Y. and Kawase, H.: Potential Seasonal Predictability of the Risk of Local Rainfall Extremes Estimated Using High-Resolution Large Ensemble Simulations, Geophys. Res. Lett., 48, e2021GL096236, https://doi.org/10.1029/2021GL096236, 2021.
Jane, R., Cadavid, L., Obeysekera, J., and Wahl, T.: Multivariate statistical modelling of the drivers of compound flood events in south Florida, Nat. Hazards Earth Syst. Sci., 20, 2681–2699, https://doi.org/10.5194/nhess-20-2681-2020, 2020.
Jane, R. A., Malagón-Santos, V., Rashid, M. M., Doebele, L., Wahl, T., Timmers, S. R., Serafin, K. A., Schmied, L., and Lindemer, C.: A Hybrid Framework for Rapidly Locating Transition Zone.: A Comparison of Event- and Response-Based Return Water Levels in the Suwannee River FL, Water Resour. Res., 58, e2022WR032481, https://doi.org/10.1029/2022WR032481, 2022a.
Jane, R., Wahl, T., Cadavid, L., Obeysekera, J., and Solari, S.: MultiHazard R package, Zenodo [code], https://doi.org/10.5281/zenodo.6772478, 2022b.
Jones, K. A., Niknami L. S., Buto S. G., and Decker D.: Federal standards and procedures for the national Watershed Boundary Dataset (WBD), vol. 11-A3, U.S. Department of the Interior/U.S. Geological Survey, https://pubs.usgs.gov/tm/11/a3/ (last access: 9 October 2023), 2022.
Khanam, M., Sofia, G., Koukoula, M., Lazin, R., Nikolopoulos, E. I., Shen, X., and Anagnostou, E. N.: Impact of compound flood event on coastal critical infrastructures considering current and future climate, Nat. Hazards Earth Syst. Sci., 21, 587–605, https://doi.org/10.5194/nhess-21-587-2021, 2021.
Kim, H. and Villarini, G.: Evaluation of the Analysis of Record for Calibration (AORC) Rainfall across Louisiana, Remote Sens.-Basel, 14, 3284, https://doi.org/10.3390/rs14143284, 2022.
Kim, H., Villarini, G., Jane, R., Wahl, T., Misra, S., and Michalek, A.: On the generation of high-resolution probabilistic design events capturing the joint occurrence of rainfall and storm surge in coastal basins, Int. J. Climatol., 43, 761–771, https://doi.org/10.1002/joc.7825, 2023.
Kumbier, K., Carvalho, R. C., Vafeidis, A. T., and Woodroffe, C. D.: Investigating compound flooding in an estuary using hydrodynamic modelling: a case study from the Shoalhaven River, Australia, Nat. Hazards Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/nhess-18-463-2018, 2018.
Lai, Y., Li, J., Gu, X., Liu, C., and Chen, Y. D.: Global Compound Floods from Precipitation and Storm Surge: Hazards and the Roles of Cyclones, J. Climate, 34, 8319–8339, https://doi.org/10.1175/JCLI-D-21-0050.1, 2021.
Landsea, C. W. and Franklin, J. L.: Atlantic hurricane database uncertainty and presentation of a new database format, Mon. Weather Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1, 2013.
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.
Maduwantha, P.: Compound flood potential, Zenodo [code], https://doi.org/10.5281/zenodo.13755288, 2024.
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, https://doi.org/10.1016/j.advwatres.2019.04.009, 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, https://doi.org/10.1073/pnas.1620325114, 2017.
Nagler, T., Schepsmeier, U., Stoeber, J., Brechmann, E., Graeler, B., and Erhardt, T.: VineCopula: Statistical Inference of Vine Copulas, R package version 2.5.1, https://github.com/tnagler/VineCopula, last access: November 2023.
Nasr, A. A., Wahl, T., Rashid, M. M., Jane, R. A., Camus, P., and Haigh, I. D.: Temporal changes in dependence between compound coastal and inland flooding drivers around the contiguous United States coastline, Weather Clim. Extrem., 41, 100594, https://doi.org/10.1016/j.wace.2023.100594, 2023.
National Oceanic and Atmospheric Administration, Center for Operational Oceanographic Products and Services: Tides and Currents Data, NOAA [data set], https://tidesandcurrents.noaa.gov/, last access: 1 November 2023.
National Oceanic and Atmospheric Administration, National Climatic Data Center: Archive of Global Historical Weather and Climate Data, NOAA [data set], https://www.ncdc.noaa.gov/cdo-web, last access: 2 November 2023.
National Oceanic and Atmospheric Administration, National Hurricane Center: HURDAT2 Atlantic Hurricane Database (1851–Present), NOAA [data set], https://www.nhc.noaa.gov/data/hurdat, last access: 8 February 2024.
National Oceanic and Atmospheric Administration, National Weather Service: Analysis of Record for Calibration (AORC) Gridded Data, NOAA [data set], https://hydrology.nws.noaa.gov/pub/AORC/V1.1/, last access: 2 November 2023.
Nederhoff, K., Leijnse, T. W. B., Parker, K., Thomas, J., O'Neill, A., van Ormondt, M., McCall, R., Erikson, L., Barnard, P. L., Foxgrover, A., Klessens, W., Nadal-Caraballo, N. C., and Massey, T. C.: Tropical or extratropical cyclone: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?, Nat. Hazards, 120, 8779–8825, https://doi.org/10.1007/s11069-024-06552-x, 2024.
Nicholls, R., Hanson, S., Herweijer, C., Ranger, N., Hallegatte, S., Corfee-Morlot, J., Chateau, J., and Muir-Wood, R.: Ranking Port Cities with High Exposure and Vulnerability to Climate Extreme.: Exposure Estimates, OECD Environment Working Papers, OECD, Environment Directorate, https://doi.org/10.1787/011766488208, 2008.
Orton, P. M., Hall, T. M., Talke, S. A., Blumberg, A. F., Georgas, N., and Vinogradov, S.: A validated tropical-extratropical flood hazard assessment for New York Harbor, J. Geophys. Res.-Oceans, 121, 8904–8929, https://doi.org/10.1002/2016JC011679, 2016.
Orton, P. M., Conticello, F. R., Cioffi, F., Hall, T. M., Georgas, N., Lall, U., Blumberg, A. F., and MacManus, K.: Flood hazard assessment from storm tides, rain and sea level rise for a tidal river estuary, Nat. Hazards, 102, 729–757, https://doi.org/10.1007/s11069-018-3251-x, 2020.
Pfahl, S. and Wernli, H.: Quantifying the relevance of cyclones for precipitation extremes, J. Climate, 25, 6770–6780, https://doi.org/10.1175/JCLI-D-11-00705.1, 2012.
Salvadori, G. and De Michele, C.: Multivariate Extreme Value Methods, in: Extremes in a Changing Climate: Detection, Analysis and Uncertainty, edited by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S., Springer Netherlands, Dordrecht, 115–162, https://doi.org/10.1007/978-94-007-4479-0_5, 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, https://doi.org/10.1002/wrcr.20204, 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, https://doi.org/10.1002/2015WR017225, 2016.
Sebastian, A., Dupuits, E. J. C., and Morales-Nápoles, O.: Applying a Bayesian network based on Gaussian copulas to model the hydraulic boundary conditions for hurricane flood risk analysis in a coastal watershed, Coast. Eng., 125, 42–50, https://doi.org/10.1016/j.coastaleng.2017.03.008, 2017.
Silva-Araya, W. F., Santiago-Collazo, F. L., Gonzalez-Lopez, J., and Maldonado-Maldonado, J.: Dynamic modeling of surface runoff and storm surge during hurricane and tropical storm events, Hydrology, 5, 13, https://doi.org/10.3390/hydrology5010013, 2018.
Sinclair, V. A., Rantanen, M., Haapanala, P., Räisänen, J., and Järvinen, H.: The characteristics and structure of extra-tropical cyclones in a warmer climate, Weather Clim. Dynam., 1, 1–25, https://doi.org/10.5194/wcd-1-1-2020, 2020.
Sklar, A.: Fonctions de répartition à n dimensions et leurs marges, Publ. Inst. Stat. Univ. Paris, 8, 229–231, 1959.
Smith, J. A., Villarini, G., and Baeck, M. L.: Mixture Distributions and the Hydroclimatology of Extreme Rainfall and Flooding in the Eastern United States, J. Hydrometeorol, 12, 294–309, https://doi.org/10.1175/2010JHM1242.1, 2011.
Smitha, P. S., Narasimhan, B., Sudheer, K. P., and Annamalai, H.: An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment, J. Hydrol. (Amst), 556, 100–118, https://doi.org/10.1016/j.jhydrol.2017.11.010, 2018.
Torres, J. M., Bass, B., Irza, N., Fang, Z., Proft, J., Dawson, C., Kiani, M., and Bedient, P.: Characterizing the hydraulic interactions of hurricane storm surge and rainfall–runoff for the Houston–Galveston region, Coastal Engineering, 106, 7–19, https://doi.org/10.1016/j.coastaleng.2015.09.004, 2015.
Towey, K. L., Booth, J. F., Frei, A., and Sinclair, M. R.: Track and Circulation Analysis of Tropical and Extratropical Cyclones that Cause Strong Precipitation and Streamflow Events in the New York City Watershed, J. Hydrometeorol., 19, 1027–1042, https://doi.org/10.1175/JHM-D-17-0199.1, 2018.
Towey, K. L., Booth, J. F., Rodriguez Enriquez, A., and Wahl, T.: Tropical cyclone storm surge probabilities for the east coast of the United States: a cyclone-based perspective, Nat. Hazards Earth Syst. Sci., 22, 1287–1300, https://doi.org/10.5194/nhess-22-1287-2022, 2022.
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, https://doi.org/10.1038/nclimate2736, 2015.
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.
Xu, H., Xu, K., Bin, L., Lian, J., and Ma, C.: Joint risk of rainfall and storm surges during typhoons in a coastal city of Haidian Island, China, Int. J. Env. Res. Pub. He., 15, 1377, https://doi.org/10.3390/ijerph15071377, 2018.
Zheng, F., Westra, S., and Sisson, S. A.: Quantifying the dependence between extreme rainfall and storm surge in the coastal zone, J. Hydrol. (Amst), 505, 172–187, https://doi.org/10.1016/j.jhydrol.2013.09.054, 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, https://doi.org/10.1002/2013WR014616, 2014.
Short summary
When assessing the likelihood of compound flooding, most studies ignore that it can arise from different storm types with distinct statistical characteristics. Here, we present a new statistical framework that accounts for these differences and shows how neglecting these can impact the likelihood of compound flood potential.
When assessing the likelihood of compound flooding, most studies ignore that it can arise from...
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