Bias and Efficiency Tradeoffs in the Selection of Storm Suites Used to Estimate Flood Risk

Abstract

Modern joint probability methods for estimating storm surge or flood statistics are based on statistical aggregation of many hydrodynamic simulations that can be computationally expensive. Flood risk assessments that consider changing future conditions due to sea level rise or other drivers often require each storm to be run under a range of uncertain scenarios. Evaluating different flood risk mitigation measures, such as levees and floodwalls, in these future scenarios can further increase the computational cost. This study uses the Coastal Louisiana Risk Assessment model (CLARA) to examine tradeoffs between the accuracy of estimated flood depth exceedances and the number and type of storms used to produce the estimates. Inclusion of lower-intensity, higher-frequency storms significantly reduces bias relative to storm suites with a similar number of storms but only containing high-intensity, lower-frequency storms, even when estimating exceedances at very low-frequency return periods.

Keywords

• flood risk
• joint probability methods
• storm selection
• computational efficiency