DATA FROM: Global raster dataset on historic coastline positions and shelf sea extents since the Last Glacial Maximum

Posted on 12.07.2022 - 13:40 authored by Johannes De Groeve

Datasets and scripts supporting the analyses performed in "Global raster dataset on historic coastline positions and shelf sea extents since the Last Glacial Maximum".


SETUP R-PROJECT:

To facilitate the reuse of scripts and datasets we suggest to use the following workflow to recreate the rproject and directory structure. This workflow uses the AGE and SLW datasets using GEBCO 2019 on which figures in the manuscript are based. However both global raster datasets

are also generated using GEBCO 2021 as reference bathymetry.


STEP 1:

Download and unzip the SETUP-file (SETUP for rproject GEB). A directory 17229620 will be created. Do not rename this directory.

STEP 2:

Download all other files of the collection without unzipping. To download a dataset, click on the dataset and then click the "download all" button. This will download all files per dataset as a zip.

STEP 3:

Navigate to directory 17229620 and click GEB.rproj to open the R project in rstudio.

By clicking this file the root directory of the environment is set to the project directory (17229620)

STEP 4:

In rstudio, open init.R and run the two lines of code in the script. The script will run a function called 'data_prep' which automatically unzips and stores datasets in a DATA directory and scripts in a SCRIPTS directory. Please set the directory where files were downloaded to as the download_dir (default: '~/Downloads').


Your r project is all set and should now have the following directory structure:


├── DATA
│ ├── AGE_mosaic.tif
│ ├── AGE_tiles
│ ├── BATHYMETRY.tif
│ ├── SLW_mosaic
│ ├── SLW_tiles
│ ├── STC.cpg
│ ├── STC.dbf
│ ├── STC.prj
│ ├── STC.shp
│ └── STC.shx

├── GEB.Rproj
├── SCRIPTS
│ ├── Rcode_Fig2e.R
│ ├── Rcode_Fig2f.R
│ ├── Rcode_Fig2g.R
│ ├── Rcode_FigS1.R
│ ├── Rcode_FigS2c.R
│ └── Rcode_FigS2d.R

├── functions.R
└── init.R



1. DATASETS:

The archive includes seven datasets, including the output spatial models (AGE, SLW140) for which the workflow is described in Figure 1, a resampled version of the bathymetric model (BATHYMETRY) and spatial polygons of three relevant shelf regions (STC). These datasets are necessary to perform the analysis and to generate the figures presented in the manuscript. 


Datasets and figures referenced in the manuscript are based on GEBCO 2019, however AGE and SLW140 based on GEBCO 2021 are also added to this repository. To generate AGE and SLW140 we also used a spatio-temporal sea level curve (RSL) stored as well in this repository.


More detailed descriptions of each dataset is provided in a readme file named _README. Related identifiers of three resources (software SELEN4, raster DEMSRE3a, raster GEBCO 2019) were added as they were used as input to generate AGE and SLW140.

  • AGE (2019/2021)

Global coastline age raster indicating the most recent 500 yr time period a pixel was land. The reconstruction was done using the global bathymetric model (GEBCO 2019 & GEBCO 2021) and a state-of-the-art global spatiotemporal sea level curve (RSL).

  • SLW140 (2019/2021)
  • Global paleoreconstruction of shelf sea extent for every 500 yr time period using the global bathymetric model (GEBCO 2019 & GEBCO 2021) and a state-of-the-art global spatiotemporal sea level curve (RSL).
  • STC
    Spatial polygons for Sunda, Timor, and Caribbean regions. These are used for drawing region-scale figures (Fig. S2)
  • BATHYMETRY
    Modern global bathymetric map. The original data is GEBCO_2019. The spatial resolution was reduced to 2-minutes grid level.


  • RSL
  • The spatio-temporal relative sea level curve (RSL) was developed using the software SealEveL EquatioN solver - version 4 (SELEN4; https://zenodo.org/record/3520451) (Spada et al. 2019), the global ice sheet reconstruction (ICE-5G (VM2); https://pmip2.lsce.ipsl.fr/design/ice5g/) (Peltier 2004) and DEMSRE3a (https://zenodo.org/record/1637816).

2. R SCRIPTS


The R-scripts include the analysis scripts to generate the following figures.

  • Rcode_Fig2e
  • Rcode_Fig2f
  • Rcode_Fig2g
  • Rcode_FigS1
  • Rcode_FigS2c
  • Rcode_FigS2d

3. GIT-REPOSITORY


The linked git-repository stores the R-scripts and functions which were used to generate AGE and SLW. 

CITE THIS COLLECTION

De Groeve, Johannes; Kusumoto, Buntarou; Koene, Erik; Kissling, W.D.; Seijmonsbergen, A.C.; Hoeksema, Bert W.; et al. (2022): DATA FROM: Global raster dataset on historic coastline positions and shelf sea extents since the Last Glacial Maximum. University of Amsterdam / Amsterdam University of Applied Sciences. Collection. https://doi.org/10.21942/uva.c.5754779.v1
or
Select your citation style and then place your mouse over the citation text to select it.

SHARE

email
need help?