Bayesian-Motivated Probabilistic Model of Hurricane-Induced Multimechanism Flood Hazards
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 149, Issue 4
Abstract
Multimechanism floods (MMFs) are caused by the simultaneous occurrence of more than one flood mechanism such as storm surge, precipitation, tides, and waves. MMFs can lead to more severe or differing impacts than single-mechanism floods. As a result, comprehensive risk assessments require the ability to assess the multivariate probabilistic behaviors of hazards from MMFs. This study introduces a novel Bayesian-motivated approach for the probabilistic assessment of hurricane-induced hazards from the combination of the surge, precipitation, tides, and river antecedent flow. A Bayesian network (BN) is developed to capture the physical (conditional) relationship between variables and facilitate the generation of a hazard curve for river discharge that captures the contributions from multiple flood drivers. A case study located along the Delaware River is used to illustrate the proposed approach. Five computationally efficient representative predictive models are developed to estimate the conditional distributions required for the BN as a means of demonstrating the overall framework. The predictive models used in this study act as placeholders and can be replaced with more sophisticated and high-fidelity models depending on the desired accuracy level. While the predictive models are intended to be representative and illustrative, the model performance is evaluated using three historical storms that affected the area. Overall, the proposed framework is shown to be transparent, effective, and adaptable.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported by the US Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research, as part of the NRC Probabilistic Flood Hazard Assessment Research Program. SCK and STD are employees of UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the US Department of Energy. Accordingly, the US government retains, and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (DOE 2014).
This manuscript was prepared as an account of work sponsored by an agency of the US Government. Neither the US Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the US Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US Government or any agency thereof.
References
Al Kajbaf, A., and M. Bensi. 2020. “Application of surrogate models in estimation of storm surge: A comparative assessment.” Appl. Soft Comput. 91: 106184. https://doi.org/10.1016/j.asoc.2020.106184.
Bass, B., and P. Bedient. 2018. “Surrogate modeling of joint flood risk across coastal watersheds.” J. Hydrol. 558: 159–173. https://doi.org/10.1016/j.jhydrol.2018.01.014.
BayesFusion. 2023. “GeNIe Modeler.” https://www.bayesfusion.com/genie/.
Bensi, M., A. D. Kiureghian, and D. Straub. 2013. “Efficient Bayesian network modeling of systems.” Reliab. Eng. Syst. Saf. 112: 200–213. https://doi.org/10.1016/j.ress.2012.11.017.
Bensi, M., S. Mohammadi, S.-C. Kao, and S. T. DeNeale. 2020. Multi-mechanism flood hazard assessment: Critical review of current practice and approaches and example use studies. NUREG/CR-7296. Washington, DC: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission.
Brody, S. D., S. Zahran, W. E. Highfield, H. Grover, and A. Vedlitz. 2008. “Identifying the impact of the built environment on flood damage in Texas.” Disasters 32 (1): 1–18. https://doi.org/10.1111/j.1467-7717.2007.01024.x.
Cialone, M., et al. 2015. North Atlantic Coast Comprehensive Study (NACCS)—Coastal storm model simulations: Waves and water levels. Washington, DC: US Army Corps of Engineers.
Cigler, B. A. 2017. “U.S. floods: The necessity of mitigation.” State Local Gov. Rev. 49 (2): 127–139. https://doi.org/10.1177/0160323X17731890.
Couasnon, A., A. Sebastian, and O. Morales-Nápoles. 2018. “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 (9): 1190. https://doi.org/10.3390/w10091190.
de Kok, T. 2021. Temporal assessment of hybrid flood defenses: A dynamic Bayesian network. Delft, Netherlands: Delft Univ. of Technology.
DOE. (Department of Energy). 2014. Public access plan. https://www.energy.gov/articles/doe-public-access-plan.
Harris, R., E. Furlan, H. Vuong Pham, S. Torresan, J. Mysiak, and A. Critto. 2022. “A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis.” Clim. Risk Manage. 35: 100410.
Hawkes, P. J., B. P. Gouldby, J. A. Tawn, and M. W. Owen. 2002. “The joint probability of waves and water levels in coastal engineering design.” J. Hydraul. Res. 40 (3): 241–251. https://doi.org/10.1080/00221680209499940.
Helderop, E., and T. H. Grubesic. 2019. “Hurricane storm surge in Volusia County, Florida: Evidence of a tipping point for infrastructure damage.” Disasters 43 (1): 157–180. https://doi.org/10.1111/disa.12296.
Hsieh, B., and J. Ratcliff. 2013. “Coastal hurricane inundation prediction for emergency response using artificial neural networks.” In Engineering Applications of Neural Networks, Communications in Computer and Information Science, edited by L. Iliadis, H. Papadopoulos, and C. Jayne, 102–111. Berlin: Springer.
Jelesnianski, C. P. 1992. SLOSH: Sea, lake, and overland surges from hurricanes. Washington, DC: US Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.
Jensen, F. 1996. An introduction to Bayesian networks. London: Taylor & Francis.
Jia, G., and A. A. Taflanidis. 2013. “Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment.” Comput. Methods Appl. Mech. Eng. 261–262: 24–38. https://doi.org/10.1016/j.cma.2013.03.012.
Jia, G., A. A. Taflanidis, N. C. Nadal-Caraballo, J. A. Melby, A. B. Kennedy, and J. M. Smith. 2016. “Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms.” Nat. Hazard. 81 (2): 909–938. https://doi.org/10.1007/s11069-015-2111-1.
Kao, S.-C., and N.-B. Chang. 2012. “Copula-based flood frequency analysis at ungauged basin confluences: Nashville, Tennessee.” J. Hydrol. Eng. 17 (7): 790–799. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000477.
Kao, S.-C., G. R. Ghimire, C. Hansen, S. Gangrade, P. E. Thornton, and D. Singh. 2022. Dayflow: CONUS daily streamflow reanalysis, version 1. Oak Ridge, TN: Oak Ridge National Laboratory.
Kaplan, J., and M. DeMaria. 1995. “A simple empirical model for predicting the decay of tropical cyclone winds after landfall.” J. Appl. Meteorol. 34 (11): 2499–2512. https://doi.org/10.1175/1520-0450(1995)034%3C2499:ASEMFP%3E2.0.CO;2.
Kaplan, J., and M. DeMaria. 2001. “On the decay of tropical cyclone winds after landfall in the New England area.” J. Appl. Meteorol. 40 (2): 280–286. https://doi.org/10.1175/1520-0450(2001)040%3C0280:OTDOTC%3E2.0.CO;2.
Kick, E. L., J. C. Fraser, G. M. Fulkerson, L. A. McKinney, and D. H. De Vries. 2011. “Repetitive flood victims and acceptance of FEMA mitigation offers: An analysis with community-system policy implications.” Disasters 35 (3): 510–539. https://doi.org/10.1111/j.1467-7717.2011.01226.x.
Kim, S.-W., J. A. Melby, N. C. Nadal-Caraballo, and J. Ratcliff. 2015. “A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling.” Nat. Hazard. 76 (1): 565–585. https://doi.org/10.1007/s11069-014-1508-6.
Kjærulff, U., and A. Madsen. 2013. Bayesian networks and influence diagrams. A guide to construction and analysis. 2nd ed. New York: Springer.
Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, and C. J. Schreck. 2018. International Best Track Archive for Climate Stewardship (IBTrACS). Project NOAA National Centers for Environmental Information. https://doi.org/10.25921/82ty-9e16.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. 2010. “The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data.” Bull. Am. Meteorol. Soc. 91: 363–376. https://doi.org/10.1175/2009BAMS2755.1.
Kyprioti, A. P., A. A. Taflanidis, N. C. Nadal-Caraballo, and M. O. Campbell. 2021a. “Incorporation of sea level rise in storm surge surrogate modeling.” Nat. Hazard. 105 (1): 531–563. https://doi.org/10.1007/s11069-020-04322-z.
Kyprioti, A. P., A. A. Taflanidis, M. Plumlee, T. G. Asher, E. Spiller, R. A. Luettich, B. Blanton, T. L. Kijewski-Correa, A. Kennedy, and L. Schmied. 2021b. “Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases.” Nat. Hazard. 109 (2): 1349–1386. https://doi.org/10.1007/s11069-021-04881-9.
Luettich, R. A., A. Richard, J. J. Westerink, and N. W. Scheffner. 1992. ADCIRC: An advanced three-dimensional circulation model for shelves, coasts, and estuaries. Report 1, Theory and methodology of ADCIRC-2DD1 and ADCIRC-3DL. Vicksburg, MS: US Army Engineer Waterways Experiment Station, Coastal Engineering Research Center.
Mayo, T., and N. Lin. 2019. “The effect of the surface wind field representation in the operational storm surge model of the national hurricane center.” Atmosphere 10 (4): 193. https://doi.org/10.3390/atmos10040193.
Mileti, D. 1999. Disasters by design: A reassessment of natural hazards in the United States. Washington, DC: Joseph Henry Press.
Mohammadi, S., M. Bensi, S.-C. Kao, and S. Deneale. 2021. Multi-mechanism flood hazard assessment: Example use case studies. Oak Ridge, TN: Oak Ridge National Laboratory.
Nadal-Caraballo, N. C., J. A. Melby, V. M. Gonzalez, and A. T. Cox. 2015. Coastal storm hazards from Virginia to Maine. Technical Rep. Vicksburg, MS: Engineer Research and Development Center.
Naz, B. S., S.-C. Kao, M. Ashfaq, D. Rastogi, R. Mei, and L. C. Bowling. 2016. “Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations.” Global Planet. Change 143: 100–117. https://doi.org/10.1016/j.gloplacha.2016.06.003.
NOAA. n.d. Station Home Page - NOAA Tides & Currents: Trenton Marine Terminal, NJ - Station ID: 8539993. https://tidesandcurrents.noaa.gov/stationhome.html?id=8539993.
Oubeidillah, A. A., S.-C. Kao, M. Ashfaq, B. Naz, and G. Tootle. 2014. “A large-scale, high-resolution hydrological model parameter dataset for climate change impact assessment for the conterminous United States.” Hydrol. Earth Syst. Sci. Discuss. 10: 9575–9613. https://doi.org/10.5194/hessd-10-9575-2013.
Park, Y. H., and D. Youn. 2021. “Characteristics of storm surge based on the forward speed of the storm.” J. Coastal Res. 114 (SI): 71–75. https://doi.org/10.2112/JCR-SI114-015.1.
Rajasekaran, S., S. Gayathri, and T.-L. Lee. 2008. “Support vector regression methodology for storm surge predictions.” Ocean Eng. 35 (16): 1578–1587. https://doi.org/10.1016/j.oceaneng.2008.08.004.
Sahana, M., S. Rehman, H. Sajjad, and H. Hong. 2020. “Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India.” CATENA 189: 104450. https://doi.org/10.1016/j.catena.2019.104450.
Shashank, V. G., S. Mandal, and S. Sil. 2021. “Impact of varying landfall time and cyclone intensity on storm surges in the Bay of Bengal using ADCIRC model.” J. Earth Syst. Sci. 130 (4): 194. https://doi.org/10.1007/s12040-021-01695-y.
Taflanidis, A. A., G. Jia, N. C. Nadal-Caraballo, A. B. Kennedy, J. A. Melby, and J. M. Smith. 2014. “Development of real-time tools for hurricane risk assessment.” In Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management, edited by M. Beer, S.-K. Au, and J. W. Hall, 1341–1350. Reston, VA: ASCE.
Tuleya, R. E., M. DeMaria, and R. J. Kuligowski. 2007. “Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms.” Weather Forecasting 22 (1): 56–70. https://doi.org/10.1175/WAF972.1.
Turan, C. K., Y. P. Kinfu, M. A. Samad, A. Farhadzadeh, and K. Ng. 2018. “Comparison of ADCIRC and SLOSH model simulations for hurricanes Andrew and Irma near Miami, Florida.” In World Environmental and Water Resources Congress 2018: Hydraulics and Waterways, Water Distribution Systems Analysis, and Smart Water, edited by S. Kamojjala, 176–187. Reston, VA: ASCE.
University of North Carolina at Chapel Hill. n.d. “ADCIRC.” Accessed February 23, 2022. https://adcirc.org/.
USACE. n.d. “HEC-HMS.” Accessed February 23, 2022. https://www.hec.usace.army.mil/software/hec-hms/.
UASCE/ERDC. n.d. “Coastal Hazards System V2.0.” Accessed October 20, 2020. https://chs.erdc.dren.mil/.
USACE/HEC-RAS. n.d. “Hydrologic Engineering Center's River Analysis System (HEC-RAS) website.” Accessed February 23, 2022. https://www.hec.usace.army.mil/software/hec-ras/.
USGS. n.d. “Delaware River at Trenton NJ.” Accessed July 31, 2020. https://waterdata.usgs.gov/monitoring-location/01463500/.
Wang, Y., Z. Guo, S. Zheng, M. Zhang, X. Shu, J. Luo, L. Qiu, and T. Gao. 2021. “Risk assessment for typhoon-induced storm surges in Wenchang, Hainan Island of China.” Geomatics Nat. Hazards Risk 12 (1): 880–899. https://doi.org/10.1080/19475705.2021.1899060.
Westerink, J. J., J. Luettich, C. A. Blain, and N. W. Scheffner. 1994. ADCIRC: An advanced three-dimensional circulation model for shelves, coasts, and estuaries. Report 2. Vicksburg, MS: Army Engineer Waterways Experiment Station.
Yang, W., Q. Yang, M. Gong, X. Xu, and B. Yang. 2021. “Assessment on typhoon prevention capability of fishing port based on numerical model calculations.” IOP Conf. Ser.: Earth Environ. Sci. 861 (7): 072007. https://doi.org/10.1088/1755-1315/861/7/072007.
Information & Authors
Information
Published In
Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 149 • Issue 4 • July 2023
Copyright
© 2023 Published by American Society of Civil Engineers.
History
Received: May 10, 2022
Accepted: Jan 5, 2023
Published online: Mar 28, 2023
Published in print: Jul 1, 2023
Discussion open until: Aug 28, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.