Technical Papers
Jul 30, 2021

Spatial Variation in Sensitivity of Hurricane Surge Characteristics to Hurricane Parameters

Publication: Journal of Engineering Mechanics
Volume 147, Issue 10

Abstract

Mitigating losses from a storm surge requires an accurate prediction of its peak, duration, and current speed along the coastline. Because this prediction depends on varying atmospheric and oceanic conditions, the uncertainty of predicted values must also be evaluated. Sensitivity analysis can serve to assess how variations in storm parameters can impact the surge characteristics. Such analysis is usually performed by varying one of the parameters, e.g., central pressure, by a certain percentage while assuming all other parameters, e.g., radius of maximum wind and forward speed, constant. A more reliable sensitivity analysis can be obtained when performed together with stochastic analysis. Uncertainty quantification approaches that are based on polynomial approximations of the output values with respect to input parameters can be effectively implemented to provide output sensitivities to variations in input parameters. Towards that objective, we implement a nonintrusive polynomial chaos expansion to a series of idealized hurricane storm surge simulations to quantify the sensitivity of storm surge height, duration, and current speed to variations in hurricane parameters, including size, speed, and central pressure. Particular attention is paid to the spatial variation in the sensitivity of the surge height along the shoreline, which is not well investigated. Physical reasoning behind quantified sensitivities are discussed.

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Data Availability Statement

Data sets for this research are available in this in-text data citation reference (Ayyad 2020), with Creative Commons Attribution 4.0 (CC BY 4.0) International license.

Acknowledgments

This work was partially funded by a research task agreement entered between the trustees of the Stevens Institute of Technology and the Port Authority of New York and New Jersey, effective September 1, 2019.

References

Ayyad, M. 2020. “Sensitivity-analysis-data.” Mendeley Data, V1. https://doi.org/10.17632/jw9tpd3ggg.1.
Beck, A. T., and W. J. de Santana Gomes. 2013. “Stochastic fracture mechanics using polynomial chaos.” Probab. Eng. Mech. 34 (Oct): 26–39. https://doi.org/10.1016/j.probengmech.2013.04.002.
Ben Ayed, S., A. Abdelkefi, F. Najar, and M. R. Hajj. 2014. “Design and performance of variable-shaped piezoelectric energy harvesters.” J. Intell. Mater. Syst. Struct. 25 (2): 174–186. https://doi.org/10.1177/1045389X13489365.
Conner, W. C., R. H. Kraft, and D. L. Harris. 1957. “Empirical methods for forecasting the maximum storm tide due to hurricanes and other tropical storms.” Mon. Weather Rev. 85 (4): 113–116. https://doi.org/10.1175/1520-0493(1957)085%3C0113:EMFFTM%3E2.0.CO;2.
Crestaux, T., O. Le Maître, and J.-M. Martinez. 2009. “Polynomial chaos expansion for sensitivity analysis.” Reliab. Eng. Syst. Saf. 94 (7): 1161–1172. https://doi.org/10.1016/j.ress.2008.10.008.
Field, R. V., Jr., and M. Grigoriu. 2004. “On the accuracy of the polynomial chaos approximation.” Probab. Eng. Mech. 19 (1–2): 65–80. https://doi.org/10.1016/j.probengmech.2003.11.017.
Fossell, K. R., D. Ahijevych, R. E. Morss, C. Snyder, and C. Davis. 2017. “The practical predictability of storm tide from tropical cyclones in the Gulf of Mexico.” Mon. Weather Rev. 145 (12): 5103–5121. https://doi.org/10.1175/MWR-D-17-0051.1.
Georgas, N., et al. 2016. “The stevens flood advisory system: Operational H3E flood forecasts for the greater New York/New Jersey Metropolitan region.” Int. J. Saf. Secur. Eng. 6 (3): 648–662. https://doi.org/10.2495/SAFE-V6-N3-648-662.
Ghanem, R., and P. D. Spanos. 1993. “A stochastic Galerkin expansion for nonlinear random vibration analysis.” Probab. Eng. Mech. 8 (3–4): 255–264. https://doi.org/10.1016/0266-8920(93)90019-R.
Ghanem, R. G., and P. D. Spanos. 2003. Stochastic finite elements: A spectral approach. Chelmsforth, MA: Courier.
Gutmann, E. D., R. M. Rasmussen, C. Liu, K. Ikeda, C. L. Bruyere, J. M. Done, L. Garrè, P. Friis-Hansen, and V. Veldore. 2018. “Changes in hurricanes from a 13-yr convection-permitting pseudo–global warming simulation.” J. Clim. 31 (9): 3643–3657. https://doi.org/10.1175/JCLI-D-17-0391.1.
Helton, J. C., and F. J. Davis. 2000. Sampling-based methods for uncertainty and sensitivity analysis. Albuquerque, NM: Sandia National Labs.
Helton, J. C., and F. J. Davis. 2003. “Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems.” Reliab. Eng. Syst. Saf. 81 (1): 23–69. https://doi.org/10.1016/S0951-8320(03)00058-9.
Holland, G. 2008. “A revised hurricane pressure–wind model.” Mon. Weather Rev. 136 (9): 3432–3445. https://doi.org/10.1175/2008MWR2395.1.
Holland, G. J. 1980. “An analytic model of the wind and pressure profiles in hurricanes.” Mon. Weather Rev. 108 (8): 1212–1218. https://doi.org/10.1175/1520-0493(1980)108%3C1212:AAMOTW%3E2.0.CO;2.
Hoover, R. A. 1957. “Empirical relationships of the central pressures in hurricanes to the maximum surge and storm tide.” Mon. Weather Rev. 85 (5): 167–174. https://doi.org/10.1175/1520-0493(1957)085%3C0167:EROTCP%3E2.0.CO;2.
Hosder, S., R. W. Walters, and R. Perez. 2006. “A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations.” In Proc., 44th AIAA Aerospace Sciences Meeting and Exhibit, 891. Reston, VA: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2006-891.
Hsu, S. A., and Z. Yan. 1998. “A note on the radius of maximum wind for hurricanes.” J. Coastal Res. 14 (2): 667–668.
Irish, J. L., D. T. Resio, and J. J. Ratcliff. 2008. “The influence of storm size on hurricane surge.” J. Phys. Oceanogr. 38 (9): 2003–2013. https://doi.org/10.1175/2008JPO3727.1.
Iskandarani, M., M. Le Hénaff, W. C. Thacker, A. Srinivasan, and O. M. Knio. 2016a. “Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions.” J. Geophys. Res. Oceans 121 (7): 4819–4832. https://doi.org/10.1002/2015JC011573.
Iskandarani, M., S. Wang, A. Srinivasan, W. C. Thacker, J. Winokur, and O. M. Knio. 2016b. “An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations.” J. Geophys. Res. Oceans 121 (4): 2789–2808. https://doi.org/10.1002/2015JC011366.
Jacquelin, E., S. Adhikari, J.-J. Sinou, and M. I. Friswell. 2015. “Polynomial chaos expansion and steady-state response of a class of random dynamical systems.” J. Eng. Mech. 141 (4): 04014145. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000856.
Jelesnianski, C. P. 1967. “Numerical computations of storm surges with bottom stress.” Mon. Weather Rev. 95 (11): 740–756. https://doi.org/10.1175/1520-0493(1967)095%3C0740:NCOSSW%3E2.3.CO;2.
Knio, O. M., and O. P. Le Maître. 2006. “Uncertainty propagation in CFD using polynomial chaos decomposition.” Fluid Dyn. Res. 38 (9): 616-640. https://doi.org/10.1016/j.fluiddyn.2005.12.003.
Luettich, R. A., Jr., 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-2DDI and ADCIRC-3DL. Vicksburg, MS: Coastal Engineering Research Center.
Mola, A., M. Ghommem, and M. Hajj. 2011. “Multi-physics modelling and sensitivity analysis of olympic rowing boat dynamics.” Sports Eng. 14 (Dec): 85–94. https://doi.org/10.1007/s12283-011-0075-2.
Najm, H. N. 2009. “Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics.” Ann. Rev. Fluid Mech. 41 (Jan): 35–52. https://doi.org/10.1146/annurev.fluid.010908.165248.
Nielsen, P. 2009. “How storm size matters for surge height.” Coastal Eng. 56 (9): 1002–1004. https://doi.org/10.1016/j.coastaleng.2009.02.006.
NOAA (National Oceanic and Atmospheric Administration). 2019. “Hurricane archive.” Accessed March 17, 2016. https://www.nhc.noaa.gov/archive/.
O’Hagan, A. 2013. “Polynomial chaos: A tutorial and critique from a statistician’s perspective.” SIAM/ASA J. Uncertainty Quantif. 20 (May): 1–20.
Pandey, S., and A. D. Rao. 2019. “Impact of approach angle of an impinging cyclone on generation of storm surges and its interaction with tides and wind waves.” J. Geophys. Res. Oceans 124 (11): 7643–7660. https://doi.org/10.1029/2019JC015433.
Peng, M., L. Xie, and L. J. Pietrafesa. 2004. “A numerical study of storm surge and inundation in the Croatan–Albemarle–Pamlico Estuary System.” Estuarine Coastal Shelf Sci. 59 (1): 121–137. https://doi.org/10.1016/j.ecss.2003.07.010.
Pettit, C. L., M. R. Hajj, and P. S. Beran. 2010. “A stochastic approach for modeling incident gust effects on flow quantities.” Probab. Eng. Mech. 25 (1): 153–162. https://doi.org/10.1016/j.probengmech.2009.08.007.
Ramos-Valle, A., E. N. Curchitser, and C. L. Bruyère. 2020. “Impact of tropical cyclone landfall angle on storm surge along the Mid-Atlantic Bight.” J. Geophys. Res. Atmos. 125 (4): e2019JD031796. https://doi.org/10.1029/2019JD031796.
Reagan, M. T., H. N. Najm, R. G. Ghanem, and O. M. Knio. 2003. “Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection.” Combust. Flame 132 (3): 545–555. https://doi.org/10.1016/S0010-2180(02)00503-5.
Rego, J. L., and C. Li. 2009. “On the importance of the forward speed of hurricanes in storm surge forecasting: A numerical study.” Geophys. Res. Lett. 36 (7): 1–5. https://doi.org/10.1029/2008GL036953.
Sakamoto, S., and R. Ghanem. 2002. “Polynomial chaos decomposition for the simulation of non-Gaussian nonstationary stochastic processes.” J. Eng. Mech. 128 (2): 190–201. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:2(190).
Simpson, R. H., and H. Saffir. 1974. “The hurricane disaster—Potential scale.” Weatherwise 27 (8): 169–186. https://doi.org/10.1080/00431672.1974.9931702.
Sobol, I. M. 2001. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Math. Comput. Simul. 55 (1–3): 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.
Sudret, B. 2008. “Global sensitivity analysis using polynomial chaos expansions.” Reliab. Eng. Syst. Saf. 93 (7): 964–979. https://doi.org/10.1016/j.ress.2007.04.002.
Tyson, S., D. Donovan, B. Thompson, S. Lynch, and M. Tas. 2015. Uncertainty modelling with polynomial chaos expansions: Stage 1—Final report. Brisbane, Australia: Univ. of Queensland.
Vickery, P. J., and D. Wadhera. 2008. “Statistical models of Holland pressure profile parameter and radius to maximum winds of hurricanes from flight-level pressure and H*Wind data.” J. Appl. Meteorol. Climatol. 47 (10): 2497–2517. https://doi.org/10.1175/2008JAMC1837.1.
Wang, S., M. Iskandarani, A. Srinivasan, W. C. Thacker, J. Winokur, and O. M. Knio. 2016. “Propagation of uncertainty and sensitivity analysis in an integral oil-gas plume model.” J. Geophys. Res. Oceans 121 (5): 3488–3501. https://doi.org/10.1002/2015JC011365.
Wei, X., H. Chang, B. Feng, and Z. Liu. 2019. “Sensitivity analysis based on polynomial chaos expansions and its application in ship uncertainty-based design optimization.” Math. Probl. Eng. 2019: 19. https://doi.org/10.1155/2019/7498526.
Wiener, N. 1938. “The homogeneous chaos.” Am. J. Math. 60 (4): 897–936. https://doi.org/10.2307/2371268.
Zhang, C., and C. Li. 2019. “Effects of hurricane forward speed and approach angle on storm surges: An idealized numerical experiment.” Acta Oceanolog. Sin. 38 (7): 48–56. https://doi.org/10.1007/s13131-018-1081-z.

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Go to Journal of Engineering Mechanics
Journal of Engineering Mechanics
Volume 147Issue 10October 2021

History

Received: Dec 4, 2020
Accepted: May 10, 2021
Published online: Jul 30, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 30, 2021

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Authors

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Graduate Student, Davidson Laboratory, Dept. of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030 (corresponding author). ORCID: https://orcid.org/0000-0001-5800-9313. Email: [email protected]
Muhammad R. Hajj, F.ASCE [email protected]
George Meade Bond Professor, Davidson Laboratory, Dept. of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030. Email: [email protected]
Reza Marsooli, A.M.ASCE [email protected]
Assistant Professor, Davidson Laboratory, Dept. of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030. Email: [email protected]

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  • Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics, Journal of Geophysical Research: Atmospheres, 10.1029/2022JD037617, 127, 24, (2022).

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