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An early warning system for wave-driven coastal flooding at Imperial Beach, CA

  • Author(s): Merrifield, Mark A;
  • Johnson, Mele;
  • Guza, RT;
  • Fiedler, Julia W;
  • Young, Adam P;
  • Henderson, Cassandra S;
  • Lange, Athina MZ;
  • O'Reilly, William C;
  • Ludka, Bonnie C;
  • Okihiro, Michele;
  • Gallien, Timu;
  • Pappas, Kyle;
  • Engeman, Laura;
  • Behrens, James;
  • Terrill, Eric
  • et al.
Abstract

AbstractWaves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

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