A polynomial chaos framework for probabilistic predictions of storm surge events

Pierre Sochala, Chen Chen, Clint Dawson, Mohamed Iskandarani

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)109-128
Number of pages20
JournalComputational Geosciences
Issue number1
StatePublished - Feb 1 2020


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

ASJC Scopus subject areas

  • Computer Science Applications
  • Computers in Earth Sciences
  • Computational Theory and Mathematics
  • Computational Mathematics


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