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Predicted relative sea-level and sea-level data for validation

We provide the model results of the manuscript "Glacial-isostatic adjustment models using geodynamically constrained 3D Earth structures" (Bagge et al. 2020, Paper) including the (1) predicted relative sea-level and (2) applied sea-level data. The predicted relative-sea level is calculated with the VIscoelastic Lithosphere and MAntle model VILMA (Klemann et al. 2008, 2015, Martinec et al. 2018, Hagedoorn et al. 2007, Martinec & Hagedoorn 2005, Kendall et al. 2005). The glacial-isostatic adjustment models uses different Earth structures (3D, 1D global mean and 1D regionally adapted; Bagge et al. 2020, Paper; Bagge et al. 2020, https://doi.org/10.5880/GFZ.1.3.2020.004) and ice histories (ICE-5G, Peltier 2004; ICE-6G, Peltier et al. 2015, Argus et al. 2014; NAICE, Gowan et al. 2016) resulting in 44 3D models, 54 1D global mean models and 162 1D regionally adapted models. For more information on model description and input data see Bagge et al. (2020, Paper) and Bagge at al. (2020, https://doi.org/10.5880/GFZ.1.3.2020.004). The provided output data include (1a) the global distribution of predicted relative-sea level at 14 kilo years before present as ensemble range of the 3D GIA models for three ice histories as netCDF files, (1b) the predicted relative-sea level at eight locations at 14 kilo years before present for all models as ASCII file and (1c) the predicted relative sea-level for the deglaciation period for all models as ASCII files. Eight locations include Churchill, Angermanland, Ross Sea (Antarctica), San Jorge Gulf (Patagonia), Central Oregon Coast, Rao-Gandon Area (Senegal), Singapore and Pioneer Bay (Queensland, Australia). (2) The about 520 applied sea-level data provide information on time, relative sea-level and type of sea-level data. They are extracted for the eight locations from the GFZ database using SLIVisu (Unger et al. 2012, 2018) and provided as ACSII files.

Supraglacial Debris Cover

This dataset is supplementary to the article of Scherler et al. (submitted), in which the global distribution of supraglacial debris cover is mapped and analyzed. For mapping supraglacial debris cover, we combined glacier outlines from the Randolph Glacier Inventory (RGI) version 6.0 (RGI consortium, 2017) with remote sensing-based ice and snow identification. Areas that belong to glaciers but that are neither ice nor snow were classified as debris cover. This dataset contains the outlines of the mapped debris-covered glaciers areas, stored in shapefiles (.shp).For creating this dataset, we used optical satellite data from Landsat 8 (for the time period 2013-2017), and from Sentinel-2A/B (2015-2017). For the ice and snow identification, we used three different algorithms: a red to short-wavelength infrared (swir) band ratio (RATIO; Hall et al., 1988), the normalized difference snow index (NDSI; Dozier, 1989), and linear spectral unmixing-derived fractional debris cover (FDC; e.g., Keshava and Mustard, 2002). For a detailed description of the debris-cover mapping and an analysis of the data, please see Scherler et al. (2019) to which these data are supplementary material.This dataset includes debris cover outlines based on either Landsat 8 (LS8; 30-m resolution) or Sentinel 2 (S2; 10-m resolution), and the three algorithms RATIO, NDSI, FDC. In total, there exist six different zip-files that each contain 19 shapefiles. The structure of the shapefiles follows that of the RGI version 6.0 (RGI consortium, 2017), with one shapefile for each RGI region. The original RGI shapefiles provide each glacier as one entry (feature) and include a variety of ancillary information, such as area, slope, aspect (RGI Consortium 2017a, Technical Note p. 12ff). Because the debris-cover outlines are based on the RGI v6.0 glacier outlines, all fields of the original shapefiles, which refer to the glacier, are retained, and expanded with four new fields:- DC_Area: Debris-covered area in m². Note that this unit for area is different from the unit used for reporting the glacier area (km²).- DC_BgnDate: Start of the time period from which satellite imagery was used to map debris cover.- DC_EndDate: End of the time period from which satellite imagery was used to map debris cover.- DC_CTSmean: Mean number of observations (CTS = COUNTS) per pixel and glacier. This number is derived from the number of available satellite images for the respective time period, reduced by filtering pixels due to cloud and snow cover.The dataset has a global extent and covers all of the glaciers in the RGI v. 6.0, but it exhibits poor coverage in the RGI region Subantarctic and Antarctic, where the debris cover extents are based on very few observations.

Chhota Shigri Glacier simulations and cosmogenic nuclide data

The files in this dataset are supplementary to the article of Scherler and Egholm (2020), in which experiments with the ice flow and landscape evolution model iSOSIA (Egholm et al., 2011) have been conducted to analyze the production and transport of cosmogenic 10Be in the catchment of the Chhota Shigri Glacier, India. This dataset contains different subsets, including the source code of the model, results from different models and time steps (see Scherler and Egholm, 2020a), and the sample data. The model code is a further development of iSOSIA (Integrated second order shallow ice approximation). The new branch (iSOSIA Version 3.4.3) is published by Egholm and Scherler (2020, https://doi.org/10.5880/fidgeo.2020.032) and also included in the "2020-004_Scherler-Egholm_modelcode" folder of this data publication.

Integrated second order shallow ice approximation (iSOSIA)

iSOSIA is an ice sheet model based on depth integration of the second‐order shallow ice approximation. Although depth integration is not guaranteed to maintain so‐called second‐order accuracy, the results of computational benchmark experiments show that iSOSIA, in spite of its efficient depth‐integrated structure, performs well in situations with steep topography (Egholm et al., 2011). Version 3.4.3 of iSOSIA includes a routine for Lagrangian particle tracing and the production and decay of in situ-produced cosmogenic 10Be. For more details on the implementation of the particle tracing, see Scherler and Egholm (2020).

Model data and code for the steady-state surface-elevation distribution of idealized glaciers and rivers

This data publication contains: • the source codes for the 1-D finite-difference glaciofluvial model (directory "model_code"), • the model results presented in Banerjee and Scherler (2025) (directory "model_results"), • and codes to produce the plots in Banerjee and Scherler (2025) To compile and run the source codes to generate the output files presented in Banerjee and Scherler (2025), use the commands given in “run_commands.txt”. The output files from the above runs are provided in the directory "model_results". To reproduce the figures 2 & 3 in the main text of Banerjee and Scherler (2025), and figures S1, S2 & S3 in the supplementary material, use the commands given in plot_commands.txt. This requires AWK and GNUPLOT commandline tools. Figure S2 is based on a Matlab script.

PISM simulation results of the Antarctic Ice Sheet deglaciation

This dataset contains PISM simulation results (http://www.pism-docs.org) of the Antarctic Ice Sheet based on code release pik-holocene-gl-rebound: http://doi.org/10.5281/zenodo.1199066 .With the help of added python scripts, Fig. 3 and other model related extended data figures can be reproduced as in the journal publication: Kingslake, Scherer, Albrecht et al. (2018, http://dx.doi.org/10.1038/s41586-018-0208-x).