Probabilistic storm surge and flood-inundation modeling of the Texas gulf coast using super-fast INundation of CoastS (SFINCS)
Wonhyun Lee, Alexander Y. Sun, Bridget R. Scanlon
Abstract
Accurately predicting flood extent and depths, encompassing storm surge, pluvial, and fluvial flooding , is important for protecting coastal communities. However, high computational demands associated with detailed probabilistic models highlight the need for simplified models to enable rapid forecasting. In this study we developed an ensemble-based probabilistic forecast framework using a reduced-complexity, hydrodynamic solver – the Super-Fast INundation of CoastS (SFINCS) model. The framework was showcased over Hurricane Ike that significantly impacted the Texas Gulf Coast in 2008. Results demonstrate the capability of the SFINCS model to generate probabilistic predictions (e.g., ≤4 h for a 100-member ensemble on a single multi-core CPU). The model agrees well with observed data from NOAA tidal, USGS stream gage height, and FEMA high water mark stations. Compared to a deterministic approach, the ensemble method reduced errors by an average 16% across all water level and hydrograph stations. Sensitivity analysis indicated consistent patterns of flood inundation across varying ensemble sizes (81, 189, 1,000) and lead times (1–3 days before landfall), with a slight increase in uncertainty for smaller ensembles and longer lead times. In particular, counties adjacent to the Trinity River Basin had ≥80% probability of exceeding the critical 3-m flooding depth during Hurricane Ike. Our study highlights the effectiveness of the SFINCS-based ensemble framework in providing probabilistic flood extent/depth forecasts over long lead times in a timely manner. Thus, the framework constitutes a valuable tool for effective flood preparedness and response planning during flooding events.