BEDMAP1 - Ice thickness, bed and surface elevation for Antarctica - standardised shapefiles and geopackages ...

• Main Authors: Lythe, M., Vaughan, David, BEDMAP 1, consortia, Fremand, Alice, Bodart, Julien
• Format: Dataset
• Language: English
• Published: NERC EDS UK Polar Data Centre 2022
• Subjects: "EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","GLACIER ELEVATION/ICE SHEET ELEVATION"; "EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","GLACIER THICKNESS/ICE SHEET THICKNESS"; "EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS","GLACIER TOPOGRAPHY/ICE SHEET TOPOGRAPHY"; "EARTH SCIENCE","CRYOSPHERE","GLACIERS/ICE SHEETS"; Antarctica; BEDMAP; Elevation; Ice thickness; Antarctic; Antarc*; Ice Sheet
• Online Access: https://dx.doi.org/10.5285/925ac4ec-2a9d-461a-bfaa-6314eb0888c8; https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01620

We present here the Bedmap1 ice thickness, bed and surface elevation aggregated points. The aggregated points consist of statistically-summarised shapefile points (centred on a continent-wide 500 m x 500 m grid) that reports the average values of Antarctic ice thickness, bed and surface elevation from the full-resolution survey data and information on their distribution. The points presented here correspond to the points used to grid Bedmap1. The data comes from 127 individual surveys. They are available as geopackages and shapefiles. The associated datasets consist of: - Bedmap2 statistically-summarised data points (shapefiles): https://doi.org/10.5285/0f90d926-99ce-43c9-b536-0c7791d1728b - Bedmap3 statistically-summarised data points (shapefiles): https://doi.org/10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a - Bedmap1 standardised CSV data points: https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029 This work is supported by the SCAR Bedmap project and the British Antarctic Survey's core programme: ... : The data consist of the Bedmap3 standardised CSV (doi: ) data converted to statistically-summarised points and lines. Lines were calculated automatically from the CSV data and split each time a gap of more than 5 km between two data points was found. The spatial distribution of the full-resolution survey CSV point data is extremely heterogeneous with, for example, dense, metre-scale sampling along modern flight-lines that are often separated across-track by kilometres to hundreds of kilometres, and this heterogeneity varies between campaigns and data providers. Such an uneven data distribution can cause gridding algorithms to be overly weighted to those areas with the highest sampling frequency, to the detriment of adjacent areas with valid data but sparser sampling. To reduce the impact of data density on gridding, the statistically-summarised shapefile point dataset (centred on a continent-wide 500 m x 500 m grid) reports the average values of the full-resolution survey data plus information on their ...