SSPipeline: A pipeline for estimating and characterizing uncertainty in coastal storm surge levels

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Authors

John Letey, Mingxuan Zhang, Tony Wong 

Abstract

Effective management of coastal risks demands projections of flood hazards that account for a wide variety of potential sources of uncertainty. Two typical approaches for estimating flood hazards include (1) direct physical process-based modeling of the storms themselves and (2) statistical modeling of the distributions and relevant characteristics of extreme sea level events. Recently, flexible and efficient mechanistically-motivated models for sea-level change have become widely used for characterizing uncertainty in projections of mean sea levels [Oppenheimer and Alley, 2016]. In order to complement these models for mean sea levels, there is also a need for fast and flexible estimates of extreme sea levels, and corresponding uncertainties. This is the motivating factor in the focus within the SSPipeline (Storm Surge Pipeline) project, that characterizes uncertainty in estimates of extreme sea levels, using a statistical modeling approach. Specifically, this work provides a high-level description of the input, methods and expected output from a software pipeline to process raw tide gauge information, and generate calibrated estimates of storm surge return levels.

DOI

https://doi.org/10.31223/osf.io/t6358

Subjects

Computer Sciences, Engineering, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

Keywords

Dates

Published: 2018-10-22 16:21

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License

GNU Lesser General Public License (LGPL) 2.1

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