Uncertainty quantification and inference of Manning's friction coefficients using DART buoy data during the Tōhoku tsunami

Ihab Sraj, Kyle T. Mandli, Omar Knio, Clint N. Dawson, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Tsunami computational models are employed to explore multiple flooding scenarios and to predict water elevations. However, accurate estimation of water elevations requires accurate estimation of many model parameters including the Manning's n friction parameterization. Our objective is to develop an efficient approach for the uncertainty quantification and inference of the Manning's n coefficient which we characterize here by three different parameters set to be constant in the on-shore, near-shore and deep-water regions as defined using iso-baths. We use Polynomial Chaos (PC) to build an inexpensive surrogate for the G. eoC. law model and employ Bayesian inference to estimate and quantify uncertainties related to relevant parameters using the DART buoy data collected during the Tōhoku tsunami. The surrogate model significantly reduces the computational burden of the Markov Chain Monte-Carlo (MCMC) sampling of the Bayesian inference. The PC surrogate is also used to perform a sensitivity analysis.
Original languageEnglish (US)
Pages (from-to)82-97
Number of pages16
JournalOcean Modelling
Volume83
DOIs
StatePublished - Nov 2014

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Oceanography
  • Computer Science (miscellaneous)
  • Atmospheric Science

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