B1209
Title: Predicting extreme surges from sparse data using a copula-based hierarchical Bayesian spatial model
Authors: Jonathan Jalbert - Polytechnique Montréal (Canada) [presenting]
Melina Mailhot - Concordia (Canada)
Christian Genest - McGill University (Canada)
Nicholas Beck - McGill University (Canada)
Abstract: A hierarchical Bayesian model is proposed to quantify the magnitude of extreme surges on the Atlantic Coast of Canada with limited data. Generalized extreme-value distributions are fitted to surges derived from water levels measured at 21 buoys along the coast. The parameters of these distributions are linked together through a Gaussian field whose mean and variance are driven by atmospheric sea-level pressure and the distance between stations, respectively. This allows for information sharing across the original stations and for interpolation anywhere along the coast. The use of a copula at the data level of the hierarchy further accounts for the dependence between locations, allowing for inference beyond a site-by-site basis. It is shown how the extreme surges derived from the model can be combined with the tidal process to predict potentially catastrophic water levels.
