Nine months of daily LiDAR, orthophotos and MetOcean data from the eroding soft cliff coast at Happisburgh, UK

Abstract

The dynamic interaction between cliff, beach and shore-platform is key to assessing the sediment balance for coastal erosion risk assessments, but this is poorly understood. We present a dataset containing daily, 3D,colour LiDAR scans of a 450 m coastal section at Happisburgh, Norfolk, UK. This previously para-glaciated region comprises mixed sand-gravel sediments, which are less well-understood and well-studied than sandy beaches. From Apr-Dec 2019, 236 daily surveys were carried out. The dataset presented includes: survey areas, transects LiDAR scans, georeferenced orthophotos, meteorological- and oceanographical conditions during the Apr-Dec observation period. Full LiDAR point-clouds are available for 67 scans (Oct-Dec). Hourly time-series of offshore sea-state parameters (significant wave height, mean propagation direction, selected spectral periods) were obtained by downscaling the ERA5 global reanalysis data (global atmosphere, land surface and ocean waves) using the numerical model Simulating Waves Nearshore (SWAN). We indicate how to obtain hourly precipitation time-series by interpolating ERA5 data. This dataset is important for researchers understanding the interaction between cliff, beach and shore-platform in open-coast mixed-sand-gravel environments.

Fig. 1

Study site location and examples of LiDAR scans outputs provided in this dataset; (a) The location of the study site (white arrow) in the UK and local (Happisburgh, marked as white rectangle) contexts. Map source: Esri, Maxar, Earthstar Geographics, and the GIS User Community https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/3; (b) Zenital view of the LiDAR cloud point collected on day 1 of the dataset and location of the LIDAR transects (black rectangles of 1 m width and 200 m length, numbered from 0 to 59) and areas (black polygons, numbered from 1 to 4). The density of cloud points is depicted on a grey to white scale, where grey indicates higher density. LiDAR data © ScanLAB Projects 2023 and © BGS 2023.

Fig. 2

The experimental setup of the TLS used at Happisburgh. In panels: (a) The permanent scanner position on the staircase (location 1). When the scanner is attached, each leg of the tripod connects to the steel elements, and tripod leg lengths are kept equal between scans; (b) The TLS fully set up at the staircase (location 1); (c) The permanent tribrach attached to the pillbox (location 2) allows for consistent scanner positioning; (d) The TLS set up on the pillbox (location 2). Photographs © ScanLAB Projects 2023.

Fig. 3

Workflow georeferencing orthophotos using python’s GDAL library.

Fig. 4

Position and extent of the two computational domains used for the SWAN computation. Notable points for the computation and validation of the MetOcean data and the location for which the precipitation data are obtained (P3) are also indicated. Map Data: Google ©2023/SIO, NOAA, U.S. Navy, NGS, GEBCO. Image generated by University of Nottingham.

Fig. 5

TLS survey temporal coverage. Green represents days where scans were performed at both anchors; orange represents a single scan, and red represents no scans. Diagonal lines mark days outside of the analysed period. Contains scheduling data © ScanLAB Projects 2023. The black box indicates the availability for the entire LiDAR point cloud dataset, transects and areas datasets and orthophotos are available for the entire survey temporal coverage.

Fig. 6

(a) H_s modelled (H_s,SWAN) vs H_s measured (H_s,meas), gray markers, samples, red line, perfect fit line, blue line, best fit line (equation indicated in the panel. (b) Joint distribution of H_s and T_m02, dashed lines: empirical joint probability density isolines for the observations, continuous lines: corresponding isolines for the model. Contains Wave Buoy data provided by Channel Coastal Observatory on behalf of © the Environment Agency and the Anglian Coastal Monitoring Programme (https://wavenet.cefas.co.uk/Map).