Conceptual Design of Extreme Sea-Level Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods

Cléa Denamiel,Xun Huan,Ivica Vilibić

doi:10.3389/fmars.2021.650279

Abstract

Coastal hazards linked to extreme sea-level events are projected to have a direct impact (by flooding) on 630 million of people by year 2100. Numerous operational forecasts already provide coastal hazard assessments around the world. However, they are largely based on either deterministic tools (e.g., numerical ocean and atmospheric models) or ensemble approaches which are both highly demanding in terms of high-performance computing (HPC) resources. Through a robust learning process, we propose conceptual design of an innovative architecture for extreme sea-level early warning systems based on uncertainty quantification/reduction and optimization methods. This approach might be cost-effective in terms of real-time computational needs while maintaining reliability and trustworthiness of the hazard assessments. The proposed architecture relies on three main tools aligning numerical forecasts with observations: (1) surrogate models of extreme sea-levels using polynomial chaos expansion, Gaussian processes or machine learning, (2) fast data assimilation via Bayesian inference, and (3) optimal experimental design of the observational network. A surrogate model developed for meteotsunami events – i.e., atmospherically induced long ocean waves in a tsunami frequency band – has already been proven to greatly improve the reliability of extreme sea-level hazard assessments. Such an approach might be promising for several coastal hazards known to destructively impact the world coasts, like hurricanes or typhoons and seismic tsunamis.

Highlights

The size and number of global coastal communities have increased dramatically in the past century
Bayesian inference and optimal experimental design are mathematical tools widely used in statistics and computational engineering, they remain mostly unknown and marginally explored within the extreme sea-level and geosciences communities
In this article we describe how they can be applied to early warning systems for extreme sealevels driven by hurricanes, tsunamis, meteotsunamis, and other

Summary

INTRODUCTION

The size and number of global coastal communities have increased dramatically in the past century. The main advantage of this application is that both analytical synthetic forcing (e.g., Holland, 1980; DeMaria and Kaplan, 1994; Knaff et al, 2007; Wood et al, 2013) and high-resolution wave-current ocean model (i.e., ADCIRC + SWAN; Dietrich et al, 2011) were already developed and used to study hurricanes in the Gulf of Mexico and the US East coast and are publicly available (bottom green ellipse, Figure 2) This means that the principal work prior to building the surrogate model is reduced to define the most accurate possible distributions of the stochastic input parameters used, for example, in the Holland synthetic forcing (i.e., track, central and environmental surface pressures, maximum winds, radius of maximum winds, etc.). The surrogate models and Bayesian inference will first be used for historical storms in order to derive the capacity of the newly developed early warning system to produce accurate extreme sea-level hazard assessments based on saved historical atmospheric forecasts as well as real observational networks and their data. If the new system provides satisfactory results, it could be used in parallel and compared to the more traditional approaches in forecast and near real-time modes

DISCUSSION AND PERSPECTIVES

Findings

DATA AVAILABILITY STATEMENT