A polynomial chaos framework for probabilistic predictions of storm surge events

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 stres...

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Bibliographic Details
Published in:Computational geosciences 2020-02, Vol.24 (1), p.109-128
Main Authors: Sochala, Pierre, Chen, Chen, Dawson, Clint, Iskandarani, Mohamed
Format: Article
Language:English
Subjects:
Bottom friction
Chaos
Coefficient of friction
Coefficients
Computer simulation
Cyclones
Earth and Environmental Science
Earth Sciences
Flooding
Friction
Geotechnical Engineering & Applied Earth Sciences
Hurricane tracking
Hurricanes
Hydrogeology
Life Sciences
Mathematical Modeling and Industrial Mathematics
Mathematical models
Original Paper
Perturbation
Polynomials
Probability theory
Reliability analysis
Sciences of the Universe
Sensitivity analysis
Soil Science & Conservation
Statistical analysis
Statistical methods
Storm surges
Storms
Time dependence
Tropical climate
Tropical cyclones
Uncertainty
Water levels
Wind
Wind speed
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Summary: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.
ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-019-09898-5