Characterizing storm-induced coastal change hazards along the United States West Coast

Abstract

Traditional methods to assess the probability of storm-induced erosion and flooding from extreme water levels have limited use along the U.S. West Coast where swell dominates erosion and storm surge is limited. This effort presents methodology to assess the probability of erosion and flooding for the U.S. West Coast from extreme total water levels (TWLs), but the approach is applicable to coastal settings worldwide. TWLs were derived from 61 years of wave and water level data at shore-perpendicular transects every 100-m along open coast shorelines. At each location, wave data from the Global Ocean Waves model were downscaled to the nearshore and used to empirically calculate wave run-up. Tides were simulated using the Oregon State University's tidal data inversion model and non-tidal residuals were calculated from sea-surface temperature and pressure anomalies. Wave run-up was combined with still water levels to generate hourly TWL estimates and extreme TWLs for multiple return periods. Extremes were compared to onshore morphology to determine erosion hazards and define the probability of collision, overwash, and inundation.

Fig. 1

Flow chart explaining the methodology employed in this study. The blue boxes indicate the individual components needed for the study, the red box indicates the final calculated product, and the green box indicates data available for download. The abbreviation NTRs represent non-tidal residuals described in the Extreme Total Water Levels section.

Fig. 2

Flow chart detailing the LiDAR-derives shoreline morphology analysis from input LiDAR dataset, profiles locations, and Environmental Sensitivity Index (ESI) category. Note there are two separate calculation branches: one to evaluate dune/cliff crest (z_c) and toe (z_t) and another to calculate beach slope (β) for a given profile. z_sm represents the intermediate calculation value of the most shoreward maximum along the simplified profile, d²z/dx² is the second derivative of the simplified elevation profile, and z_io is the first onshore point where the second derivative is >0.15 to define the upper bound for β calculation (as determined by testing).

Fig. 3

Example elevation profiles in Santa Cruz County, Calif. of a cliff-backed beach (a) and a dune (b and c), with identified cliff and dune crest locations (z_c) and toes (z_t) locations relative to NAVD88 highlighted in blue and red, respectively. (a) Example beach slope (β) calculation for use with the Stockdon and others (2006) run-up formulation extending from the MHW location along the profile to z_t, representing the cliff toe. (b) Example dune elevation with a modified elevation profile is shown in red, creating a continuous sloped profile onshore of the dune crest. (c) Dune elevation profile simplification and application of the Palaseanu-Lovejoy and others (2016) iterative adaptation to determine z_t location. The dashed black line represents the original cross-shore morphology, the blue line represents the modified morphology to highlight the dune, and the red lines represent fit lines to iteratively identify z_t.

Fig. 4

Flow chart detailing total water level (TWL) calculations from wave model output, Environmental Sensitivity Index (ESI) category, and Digital Elevation Model (DEM) topography including run-up methodology selection and TWL magnitude evaluation. The runup method selected is indicated by the R_2% subscript and TWL_10-yr refers to the 10-year return period TWL at the transect. (µ + σ)_{10-yr region} refers to the average 10-year TWL event for a predefined region including the transect plus the standard deviation of those regional values. The subscript i indicates the values used in TWL calculation at an individual time step.

Fig. 5

Flow chart detailing selection of extreme value analysis method to generate the extreme total water level (TWL) and return periods. The shaded arrows in grey indicate the next step in the process if the conditions for the Confidence Interval in the corresponding boxes are met. The dark grey arrow indicates that the annual maxima GEV method is selected without testing the peaks over threshold method.

Fig. 6

Example cumulative density function describing probabilities of potential total water levels (TWLs) and dynamic water levels (DWLs) plotted against z_t and z_c. The impact regime and fraction of the cumulative probability function is indicated on the right. The bold, black line represents the TWL probability; the bold, dash-dot line corresponds to the DWL probability; the dotted line is z_t; and the dashed line is z_c. Swash and collision probabilities are solely defined by the TWL probability cumulative density function (PCDF) intersecting z_t and z_c. Overtopping probability is defined by the difference of both TWL and DWL PCDF curves exceeding the z_c, and inundation probability is solely defined by the DWL PCDF exceeding z_c.

Fig. 7

Significant wave height (H_s) propagation versus observed conditions for NDBC station 46027 Northwest of Crescent City, Calif. (a) Observed (blue line) versus propagated (orange line) H_s time series. (b) Quantile-Quantile plot of observed and modeled reconstruction H_s values for 27641 matching reconstructed and buoy records between 2005 and 2009. The red line represents the 1:1 line indicating perfect fit, the blue circles represent the quantile scatter, the black Xs represent a sample quantile pairing at increasing thresholds, and the dashed black line represents the best fit linear regression line for the quantile scatter.