Forecasting Water Levels, Waves, and Coastal Change During the Landfall of a Hurricane

Rapid decision-making information on coastal inundation and impacts during storms is needed to address coastal hazard risk. The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) addresses this need by providing coastal communities with 6-day notice of potentially elevated water levels due to tide, storm surge and wave runup, and dune erosion on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model, a collaboration between the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA), provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines at 500-meter (m) resolution. The TWL&CC Forecast is run operationally as a post-processing step in the NOAA Nearshore Wave Prediction System. In this presentation, we assess the skill of the TWL&CC Forecast during Hurricane Isaias in early August 2020, using a combination of observations and model hindcasts. Water level and wave observations at three locations near Wrightsville Beach, NC, USA provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra during the peak of Isaias. The SWASH model simulated nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = -0.05 m; r² = 0.99). This suggests that water level observations at the pressure sensor location were a useful proxy for the total water levels (TWL; sum of tide, storm surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach and the pressure sensor observations were consistent (bias = -0.38 m and -0.74 m for the two forecast model grids considered, respectively; a weighted regression considering model uncertainty explained 95 percent of the variability in observed TWL). The observed TWL was within the confidence interval of the TWL&CC Forecast for the five hours at the storm peak. Forecast mean water levels (MWL; sum of tide, storm surge and wave setup) were consistent with tide gage observations (bias = 0.07 m and 0.02 m for the two forecast model grids; r² = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. Wave-driven water levels contributed a significant portion of the forecast TWL during Isaias, 52 percent during the three peak hours of the storm. The results demonstrate that TWL was accurately represented using the forecast model. The mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient forecasting system indicates that it can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.