A polynomial chaos framework for probabilistic predictions of storm surge events

  • Pierre SochalaEmail author
  • Chen Chen
  • Clint Dawson
  • Mohamed Iskandarani
Original Paper


We present a polynomial chaos-based framework to quantify the uncertainties in predicting hurricane-induced storm surges. Perturbation strategies are proposed to characterize poorly known time-dependent input parameters, such as tropical cyclone track and wind as well as space-dependent bottom stresses, using a handful of stochastic variables. The input uncertainties are then propagated through an ensemble calculation and a model surrogate is constructed to represent the changes in model output caused by changes in the model input. The statistical analysis is then performed using the model surrogate once its reliability has been established. The procedure is illustrated by simulating the flooding caused by Hurricane Gustav 2008 using the ADvanced CIRCulation model. The hurricane’s track and intensity are perturbed along with the bottom friction coefficients. A sensitivity analysis suggests that the track of the tropical cyclone is the dominant contributor to the peak water level forecast, while uncertainties in wind speed and in the bottom friction coefficient show minor contributions. Exceedance probability maps with different levels are also estimated to identify the most vulnerable areas.


Tropical cyclones Uncertainty quantification Empirical orthogonal functions Global sensitivity analysis Exceedance probability Hurricane Gustav 


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Funding information

The work of P. Sochala is supported by a funding of BRGM (French Geological Survey) through its Institut Carnot sponsored by the ANR (French National Research Agency). This research was made possible in part by a grant from The Gulf of Mexico Research Initiative to the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) and by NSF 1639722 and NSF 1818847.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pierre Sochala
    • 1
    Email author
  • Chen Chen
    • 2
  • Clint Dawson
    • 2
  • Mohamed Iskandarani
    • 3
  1. 1.BRGMOrléansFrance
  2. 2.Oden Institute for Computational Engineering and SciencesUniversity of Texas at AustinAustinUSA
  3. 3.Rosenstiel School of Marine and Atmospheric ScienceUniversity of MiamiMiamiUSA

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