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

• Published in: Ocean modelling (Oxford) 2014-11, Vol.83, p.82-97
• Main Authors: Sraj, Ihab, Mandli, Kyle T., Knio, Omar M., Dawson, Clint N., Hoteit, Ibrahim
• Format: Article
• Language: English
• Subjects: Bayesian inference; Manning’s n friction coefficient; Polynomial Chaos; Sensitivity analysis; Tsunami
• ISSN: 1463-5003; 1463-5011
• DOI: 10.1016/j.ocemod.2014.09.001

•We present an efficient approach for the inference of the Manning’s n coefficient.•We use Polynomial Chaos to build an inexpensive surrogate for the GeoClaw model.•We use Bayesian inference to infer the parameters using data from Tōhoku tsunami.•We report pdfs and Maximum-A-Posteriori values of the uncertain parameters. 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 GeoClaw 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.