Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

Abstract

Accurate delineation of compound flood hazard requires joint simulation of rainfall-runoff and storm surges within high-resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM-OS), which has been widely used for storm surge assessment, and apply it for rainfall-surge compound hazard assessment under climate change at the catchment-scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics-based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM-OS-BQ) to select a small number (∼100) of storms, which are simulated within a high-resolution flood model to characterize the compound flood hazard. We show that the limited JPM-OS-BQ simulations can capture historical flood return levels within 0.25 m compared to a high-fidelity Monte Carlo approach. We find that the combined impact of 2100 sea-level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100-year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM-OS-BQ framework leads to different 100-year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the United States Atlantic and Gulf coasts under a variety of climate and environment scenarios.