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

Pennington, Catherine; Shaw, Matthew; Brooks, Thomas; Briganti, Riccardo; Gómez-Pazo, Alejandro; Ruffini, Gioele; Appleton, Matthew; Payo, Andres. 2024 Nine months of daily LiDAR, orthophotos and MetOcean data from the eroding soft cliff coast at Happisburgh, UK. Scientific Data, 11 (1), 846. https://doi.org/10.1038/s41597-024-03499-3

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Official URL: http://dx.doi.org/10.1038/s41597-024-03499-3

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1038/s41597-024-03499-3
ISSN:	2052-4463
Date made live:	06 Sep 2024 14:18 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537977

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1038/s41597-024-03499-3

ISSN:

2052-4463

Date made live:

06 Sep 2024 14:18 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/537977