High‐Tide Floods and Storm Surges During Atmospheric Rivers on the US West Coast

Abstract

Atmospheric rivers (ARs) are long, narrow filaments of strong horizontal water vapor transport in the lower troposphere, typically associated with cold fronts of extratropical cyclones (Cordeira et al., 2013;Ralph et al., 2004Ralph et al., , 2017. ARs play an important role in the hydrological cycle, accomplishing most of the poleward moisture transport in the atmosphere at midlatitudes (Newman et al., 2012;Zhu & Newell, 1998). Landfalling ARs can be forced upwards by orography, leading to extreme precipitation and a range of hydrological impacts (Neiman et al., 2008). In California, for example, precipitation due to ARs has ended droughts and caused floods, landslides, and other debris flows (

Contributions of Different Sea‐Level Processes to High‐Tide Flooding Along the U.S. Coastline

“…TA is defined here as the predicted tide relative to MHHW; in San Francisco it has a range of ∼2.2 m and exhibits a 4.4 year cycle associated with the perigean tide (Figure 2e). NTR at San Francisco shows values from ∼-1.0 m (in 1964, as a result of a Tsunami triggered in Alaska (Dengler & Magoon, 2005)) to ∼0.75 m (Figure 2f), where high positive values are typically connected to extreme weather events including landfalling atmospheric rivers (e.g., Piecuch et al, 2022). Note that gaps in the TA and NTR components can arise due to insufficient hourly data (i.e., years with less than 75% completeness) to perform the tidal analysis; monthly and annual MSL values are often still available and hence the other components are not always affected.…”

Establishing flood thresholds for sea level rise impact communication

“…All the clusters show a symmetrical distribution, whereas the Northeast coast and the Gulf and Southeast coastal regions demonstrate a wider range of values. This regional variability of HTF thresholds highlights the limitations of current methods that rely solely on univariate linear regression 12,13,28,31,32 and calls for a shift towards more advanced nonlinear approaches. ML algorithms can provide better estimates of HTF thresholds and help identify regional and continental patterns of HTF, which is crucial for effective coastal flood risk communication, adaptation planning, and management.…”

“…Moreover, this means a linear regression solely based on one independent variable (here GTR) generally fails to capture the stochastic nature of spatial variability in HTF threshold above the local high tide datum (i.e., MHHW). This approach, if generalized as done in previous studies 12,13,28,31,32 , could yield in a large error in estimated flood frequency. Therefore, the variability in HTF thresholds necessitates the incorporation of multiple features, and a simple linear regression method proves insufficient to unravel the complex patterns inherent in these thresholds.…”

Yesterday's High Tide Is Today's New Normal

“…Orange line shows the same data with surge removed after Piecuch et al. (2022): the predicted tides are subtracted from the water level data in blue; the nontidal residuals are then smoothed with a 20‐day moving median filter isolate longer‐term mean sea level and remove shorter‐term storm surge; finally, the predicted tides are added back to give the orange time series. (b.)…”