Abstract
The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.
Highlights
Coastal regions throughout much of the world are facing increasing flooding risk due to accelerating sea level rise (e.g., Sweet et al 2017) and more intense storms with lower central pressure (e.g., Knutson et al 2010; Emanuel 2013)
While FEMA (2014) has developed probabilistic coastal inundation maps using the traditional joint probability method (JPM, see, e.g., Myers 1970) and the joint probability method with optimal sampling (JPM-OS), their maps do not include the effect of the sea level rise (SLR) and more intense storms in the twenty-first century
To ensure accurate probabilistic coastal inundation maps, it is important to analyze the differences between maps interpolated by JPM-OS and maps produced by JPM
Summary
Coastal regions throughout much of the world are facing increasing flooding risk due to accelerating sea level rise (e.g., Sweet et al 2017) and more intense storms with lower central pressure (e.g., Knutson et al 2010; Emanuel 2013). To measure the accuracy of these maps, the high fidelity model must be so efficient that it can be used to produce the inundation maps using JPM with tens of thousands of storms, and to create a benchmark, against which the optimal sampling and interpolation scheme can be compared. The optimal storms are simulated using high fidelity CH3D-SSMS (Sheng et al 2010a); a state-of-the-art numerical surge model with grid size > 20 m (vs > 1000 m for SLOSH) and probabilistic coastal inundation maps with various return periods are computed using JPM-OS interpolation process. It is important to point out that surge level with 50-year return period does not mean that surge level will occur only once every 50 years, but the expected recurrence interval of that surge height is 50 years with an annual chance occurrence of 2%
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