An Approach to Approximate Wave Height from Acoustic Tide Gauges

The demand for nearshore wave observations is increasing due to spatial gaps and the importance of observations for accurate models and better understanding of inundation processes. Here, we show how water level (WL) standard deviation (sigma, sigma) measurements at three acoustic NOAA tide gauges that utilize an Aquatrak sensor [Duck, North Carolina, Bob Hall Pier (BHP) in Corpus Christi, Texas, and Lake Worth, Florida] can be used as a proxy for significant wave height (H-m0). Sigma-derived H-m0 is calibrated to best fit nearby wave observations and error is quantified through RMSE, normalized RMSE (NRMSE), bias, and a scatter index. At Duck and Lake Worth, a quadratic fit of sigma to nearby wave observations results in a R-2 of 0.97 and 0.83, RMSE of 0.11 and 0.11 m, and NRMSE of 0.09 and 0.22, respectively. A linear fit between BHP sigma and H-m0 is best, resulting in R-2 0.62, RMSE of 0.22, and NRMSE of 0.26. Regression fits deviate across NOAA stations and from the classic relationship of H-m0 = 4 sigma, indicating H-m0 cannot be accurately estimated with this approach at these Aquatrak sites. The dynamic water level (DWL = still WL & PLUSMN; 2 sigma) is calculated over the historic time series showing climatological and seasonal trends in the stations' daily maximums. The historical DWL and sigma wave proxy could be calculated for many NOAA tide gauges dating back to 1996. These historical wave observations can be used to fill observational spatial gaps, validate models, and improve understanding of wave climates. SIGNIFICANCE STATEMENT: There is a large spatial gap in nearshore real-time observational wave data that can provide critical information to researchers and resource managers regarding inundation and erosion, help validate coastal hydrodynamic models, and provide the maritime community with products that help ensure navigational safety. This study utilizes existing infrastructure to help fill the demand for nearshore wave observations by deriving a proxy for wave height at three sites. This work shows spatial variability in the regression fits across the sites, which should be explored at more stations in future work. Multidecadal length time series were also used at the sites to investigate climatological and seasonal trends that provide insight into wave climates and wave driven processes important for coastal flooding.