Unravelling Uncertainties in Coastal Extremes

Hybrid physics-guided and data-driven attribution and uncertainty quantification of coastal extremes incorporating different sources of information

PI: Associate Professor Low Ying Min

National University of Singapore, Department of Civil and Environmental Engineering, College of Design and Engineering

What is this about?

This project aims to provide detailed insights into the factors influencing extreme coastal flooding events, enabling PUB, Singapore’s National Water Agency, to devise more effective risk mitigation strategies.

What is the goal?

Develop a hybrid approach that merges diverse data sources with physicsguided attribution to analyse coastal extremes.

Predict joint probabilities of extreme events and quantify the uncertainties of these predictions using confidence intervals.

Create stochastic climate emulators to generate extensive ensembles of scenarios reflecting different potential climate futures.

How is this done?

Employ the Coupled OceanAtmosphere-Wave-Sediment Transport (COAWST) modelling system to process both hindcast and forecast data, producing wave and wind parameters, currents and storm patterns as outputs. Apply a Bayesian network to variables, processes and uncertainty sources to generate a joint distribution model of coastal extremes.

Why does this matter?

This generates reliable statistical estimates for extreme sea levels and wave conditions with projections to 10,000-year return periods.

Scientifically quantify uncertainties to provide more precise forecasts of potential extreme events, supporting the development of more effective mitigation strategies.

Offer novel insights into the impact of regional climate changes and global sea level rise on extreme coastal water events, supporting strategic planning and policy formulation.