Nicholas Cirrito

Bathymetric data in surf zone environments are vital for coastal monitoring and hazard assessment, yet these regions remain challenging to survey due to shallow water and breaking waves. Jet ski-based survey methods are highly effective for rapidly mapping large stretches of coastline, but their cost, transport difficulties, permitting requirements, and safety limitations can restrict deployment, particularly in shallow water areas with swimmers and surfers. This study presents the BathyBoard, a low-cost bathymetric survey platform designed to address this observational gap. The platform is portable, can be transported on commercial aircraft to remote locations, operates safely around swimmers and surfers, and can collect data near breaking waves in targeted areas. The system integrates an echo sounder and a global navigation satellite system (GNSS) receiver on a surfboard-based platform. A GNSS-based wave correction methodology was used to remove wave-induced free-surface fluctuations from echo sounder measurements. Spectral analysis demonstrated strong agreement between GNSS elevation fluctuations and echo sounder depth fluctuations within the sea-swell frequency band, indicating that both sensors respond to the same wave motions. The performance of the BathyBoard was evaluated against two bathymetric datasets. Comparisons with a sonar-equipped survey jet ski along cross-shore transects near Scripps Pier, California, showed excellent agreement between the two platforms, while demonstrating the ability of the BathyBoard to collect measurements in appreciably shallower water. Repeated cross-shore transects demonstrated consistent bathymetric measurements. Additional validation was performed at two coral reef sites on the south shore of O‘ahu, Hawai‘i, against topobathymetric aerial light detection and ranging (LiDAR) data. Strong agreement was observed between BathyBoard and LiDAR bathymetry at both sites. These results demonstrate that the BathyBoard provides an effective, low-cost approach for expanding access to high-resolution bathymetric surveys in shallow, historically data-deficient coastal environments.

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.