Scalable real-time data assimilation with various data types for accurate spatiotemporal nearshore bathymetry estimation

Immediate estimation of nearshore bathymetry is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming, while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to apply interpolation and inverse modeling approaches to indirect remote sensed observations along with sparse direct survey data points. Example indirect observations include video-based observations such as time-series snapshots and time-averaged (Timex) images across the surf zone taken from tower-based platforms and Unmanned Aircraft Systems (UASs), while stationary LiDAR tower and UAS flights with infrared camera capability or imagery-based structure-from-motion (SfM) algorithms have been used to provide beach topographic data. In this work, we present three bathymetry estimation tools for real-time nearshore characterization using different types of information.