Semi‐empirically modelling barrier sediment transport in response to hydrodynamic forcing using UAV‐derived topographical data (Holgate, New Jersey)

Abstract

We conducted monthly surveys, from October 2020 to May 2021, of coastal topography in Holgate, New Jersey. Using unmanned aerial vehicle (UAV)‐photogrammetry and RTK‐GNSS equipment, we generated digital elevations models and cross‐section profiles, capturing spatiotemporal variability in volumetric change. We measured a total loss of 27 500 ± 10 500 m3 of subaerial sediment through the study. From October 2020 to February 2021, over 59 600 ± 10 500 m3 of sediment was eroded, followed by 32 100 ± 10 500 m3 of deposition from February to May 2021. We developed a semi‐empirical model correlating the measured geomorphological change to wave energy and water‐level variations. The calibrated model identified storm conditions that caused erosion and that waves from the south to southeast caused more erosion than those from the east to northeast. These results emphasise that alongshore transport represents a critical component of sediment transport dynamics relevant to beach erosion. Using the calibrated model, we quantified the impact of water‐level variations and wave energy on net sediment transport for a stretch of barrier coastline. Specifically, a water‐level increase of 0.14 m (equivalent to a 1σ variation) generated slightly less erosion (1.18 m3/m per 48 h) than the same variance‐based increase in wave energy, which generates 1.44 m3/m of erosion per 48 h. These variables strongly covary. Alongshore transport modulates the relationship, increasing erosion 0.9 m3/m per 48 h with a 1σ shift in wave energy directed alongshore. Forcing from strong storms, hindcast from 8 years of data, can produce 15–20 m3/m of erosion per 48 h. This modelling approach represents a methodology to produce estimates of potential erosion under predicted storm conditions, which is inherently valuable to coastal management and resilience planning. Our study demonstrates cost‐effective data collection and robust analytical methods that can be applied globally to benefit both the understanding of coastal geomorphology and local communities through data‐driven natural resource management.