Bayesian Characterization of Uncertainties Surrounding Fluvial Flood Hazard Estimates

TL;DR

This paper develops a Bayesian framework to quantify uncertainties in flood hazard estimates, emphasizing the importance of accounting for model and parameter uncertainties for better water infrastructure planning.

Contribution

It introduces a nonstationary Bayesian model for flood hazard estimation based on the Indian Ocean Dipole Index, highlighting the impact of uncertainties on flood risk assessments.

Findings

Ignoring uncertainties biases flood hazard estimates

Model parameter uncertainty is more influential than model structure

Incorporating uncertainty improves flood risk management

Abstract

Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in river stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.