William C. O'REilly

We investigate conditions for small discharge plume formation and transport using observations near a low‐inflow estuary in San Diego, CA. We find that the precise timing of discharge and tides influences the source region of small plumes and determines when plume formation occurs. Two plume formation regimes (tidal and river) are identified and distinguished by a non‐dimensional parameter. The tidal regime occurs when tides dominate over discharge and plumes only form during ebb tide. The river regime occurs when river discharge dominates and resulting plume formation can persist over one or more full tidal cycles. Small plumes are known to be impacted by surf zone processes at the mouth and here we find that the “surf zone field,” which encompasses the unique dynamics of plumes within the surf zone, impacts the location and timing of plume arrival to the far‐field. Three case studies of river regime plumes show that direct wind forcing sets the direction of, and transports, inner‐shelf plumes in the far‐field. Buoyancy also contributes to plume speed in the far‐field during storm conditions. Under moderate winds and waves, the plume spreads predominantly alongshore, however if a storm has weak winds and waves, buoyancy can carry the plume over a large cross‐shore extent. The strength and timing of both the wind and wave forcings relative to precipitation, tides, and river discharge can alter transport, highlighting the sensitivity of small discharge plumes to variability in environmental conditions.

A 7‐year coastal cliff erosion record, developed from 320 ∼weekly LiDAR surveys of a 2.3 km‐long cliff in southern California, is used to examine the cliff geomorphological response to the variability of environmental forcing (water level, waves, rain, and groundwater) from weekly to interannual timescales at different cliff elevations. When the environmental factors were considered independently, the erosion of the lower‐cliff within the wave exposure zone best correlated with wave forcing ( r ² = 0.38) at weekly scales, while the upper‐cliff correlated best with rain ( r ² = 0.36) and groundwater ( r ² = 0.15). Higher correlations were found when considering combined environmental forcing, with the greatest lower/upper‐cliff geomorphological response to combined environmental changes at semiannual/monthly timescales, respectively ( R ² = 0.66/ R ² = 0.58). A wave‐forcing increase of 36% relative to the monthly climatology was observed during El Niño, coinciding with increased cliff‐erosion activity. Multi‐variable seasonal cliff erosion models accounting for the combined effects of environmental forcing captured the interannual variability in cliff erosion but presented limitations in capturing episodic cliff failure events. The results indicate that large‐scale interannual climate patterns, such as ENSO influence on wave forcing on cliffs at the study site, should be considered in rock coast evolution modeling.

The important role of wave climate variability in driving shoreline evolution has been demonstrated recently with improved satellite-derived shoreline detection algorithms, wave buoy records, and wave reanalysis and hindcast models. While severe beach erosion with extreme El Niño waves is well documented on Pacific coastlines, less clear is the broader link between interannual wave energy and shoreline response. Here, we show half of California’s interannual Landsat shoreline change is a coherent response to wave power anomalies originating from a specific central North Pacific swell generation region, which in turn is only weakly correlated with the Niño3.4 index. Positive wave power anomalies (beach narrowing) are strongly associated with El Niños, but the negative anomalies (beach widening) are not similarly tied to La Niñas. The North Pacific wave climate modulation of beach width narrowing and widening over interannual to multi-decadal time scales has implications for long-term coastal resilience planning.

Western Pacific islands, such as Palau, are susceptible to wave‐driven inundation due to low land elevations and limited space for coastal retreat. Assessing the impacts of extreme wave events on these low‐lying islands requires regional numerical models that account for remotely generated waves from all directions and that resolve the complex bathymetry surrounding the islands. Such models often are computationally expensive and impractical for operational forecasts. In this study, eight years of wave observations from 10 sites around the island chain of Palau, at depths ranging from 9.4 to 17.6 m, are used to characterize the spatial variability of the island group's wave climate and to test the ability of a spectral refraction model to downscale regional wave hindcasts and reproduce the observed incident conditions. The spectral refraction model demonstrates significant skill predicting reef edge significant wave heights (mean = 0.77, mean = 0.21 m) at the locations of our island scale wave gauge array and outperforms a regional 7 km resolution hindcast product based upon the WaveWatch III community model (Smith et al., 2021, https://doi.org/10.1002/gdj3.104 ). Furthermore, the spectral refraction model accurately downscales wave predictions during Typhoon Lan demonstrating its potential for operational forecasting and place‐based damage and risk assessments during extreme events. When used with deep‐water wind‐wave models as input, spectral refraction methods can significantly improve reef edge incident wave boundary conditions necessary for shallow water reef modeling with low computational cost.

Shoreline position (e.g., beach width) is a critical component of flooding and overtopping forecasts but difficult to predict accurately. We model beach width changes with a supervised machine learning (ML) approach informed by equilibrium principles. The time history of wave energy anomalies that force equilibrium models is used as an ML input feature. The sweeping simplifying equilibrium model assumptions relating beach width change to anomalies are replaced with data‐based ML results. Supervised learning regression methods including linear, support vector, decision trees, and ensemble regressors are tested. Observations for model training and testing includes weekly to quarterly beach elevation surveys spanning approximately 500 m alongshore and 8 years at two beaches, each supplemented with several months of ∼100 sub‐weekly surveys. These beaches, with different sediment types (sand vs. sand‐cobble mix), both widen in summer in response to the seasonal wave climate, in agreement with a generic equilibrium model. Differences in backshore erodability contribute to differing beach responses in the stormiest (El Niño) year that are reproduced by a simple extra trees regression model but not by the equilibrium model. With sufficiently extensive training data, the ML model outperforms equilibrium by providing flexibility and complexity in the response to wave forcing. The present ML and equilibrium models both fail to simulate a uniquely stunted beach recovery unlike other recoveries in the training data.

Understanding and predicting changes in shoreline location are critical for coastal planners. In situ monitoring is accurate but not widely available. Satellite observations of shorelines have global coverage, but their accuracy and predictive capacity have not been fully explored. Abundant beach surveys and extensive wave observations in Southern California provide a unique ground truth for the interpretation of satellite‐derived recently wetted waterlines. We combine 23 years of waterline position estimates from satellite imagery with nearshore wave hindcasts and tides to train and test a deep neural network (DNN). The trained DNN uses only tides and waves as predictors at transects with satellite coverage and wave estimates to predict beach width and, for the first time, seasonal average beach slopes. Beach width changes hindcast using DNN have at least fair skill (>0.3) for 50% of transects, where the skill was calculated relative to the mean of extensive new in situ survey data from in San Diego County. The DNN also predicted shoreline changes to within 10 m (the nominal uncertainty in satellite‐derived shorelines) for 64% of transects lacking ground truth. DNN and survey estimates of seasonal beach slope changes also agree qualitatively.

An empirically based sediment budget model is developed for Cardiff State Beach CA to assess management strategies to maintain beach width subject to mean sea level rise (MSLR) and potentially more frequent El Niño storms. Two decades (2000-2019) of surveys support the hypothesis that the rocky reefs bounding this beach retain sand added to the nearshore zone, except during strong El Niño years with more severe storm waves. The subaerial beach has widened by ~60 m during the last 20 years owing to nourishment (~17K m3/yr) of imported sand, and sand bypassed annually by dredging a lagoon inlet at the beach’s updrift end. The observed widening yields 1 m/yr of mean beach width increase for each 6 m3/m-shoreline of added sand. A strong El Niño year is modeled with a permanent volume loss coupled with a shoreline retreat that recovers partially as the beach profile adjusts between El Niño years. Calibrated with observations from Cardiff and South Torrey Pines (a control beach), the model is used to project beach change through 2050. All modeled scenarios suggest that no bypassing or nourishment (no “management”) will result in tens of meters of beach width loss. However, continued bypassing would partially mitigate MSLR and El Niño beach width losses. An artificially built (living shoreline) dune that backs the beach, if completely undermined during strong El Niño storm waves, stores enough sand to balance one-third of the expected volume loss that year, and may make the beach more resilient and speed subsequent beach recovery.

Conventional and recently-developed methodologies for measuring beach, dune and seacliff change over entire coastal regions are described and compared with survey methods suitable only for subregional monitoring. The data needs for regional shoreline management are described and the applicability of three airborne systems to meeting these needs is investigated. These systems are conventional LIDAR laser ranging, modified LIDAR using a water-penetrating laser, and video photogrammetry. These approaches are being investigated by Scripps Institution of Oceanography along 80km of Southern California shoreline, often referred to as the Oceanside Littoral Cell, as part of a five year program funded by the U.S. Army Corps of Engineers. This regional approach, necessary because broad regions of coastline share a common sand resource, will help regulatory agencies make informed, cost effective and environmentally sound management decisions.