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|Storage:||The size of this resource is 10.0 GB|
|Created:||Oct 06, 2022 at 10:27 a.m.|
|Last updated:|| Oct 07, 2022 at 6:27 a.m.
|Citation:||See how to cite this resource|
|Content types:||Multidimensional Content|
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The dataset contains netcdf outputs from global-scale landscape evolution model assimilating paleo-elevation and paleo-climate reconstructions over the past 541 Myr. The results are provided as global 0.05 degree resolution grids and include high resolution paleo-physiography maps, water and sediment fluxes, long-term erosion/deposition rates, and several morphometrics related to landscape dynamics (i.e., drainage basin ids, topographic position index, physiographic diversity).
The simulations are performed using goSPL model (Global Scalable Paleo Landscape Evolution - https://gospl.readthedocs.io) and rely on the paleo-elevation reconstructions from Scotese & Wright (2018) (PALEOMAP Project - https://doi.org/10.5281/zenodo.5460860) and precipitation grids from Valdes et al. (2021) (https://doi.org/10.5194/cp-17-1483-2021 | data available from the Bristol Research Initiative for the Dynamic Global Environment. Model ref: https://www.paleo.bristol.ac.uk/ummodel/scripts/html_bridge/scotese_02.html).
|The content of this resource references||Scotese & Wright (2018) | PALEOMAP Project - https://doi.org/10.5281/zenodo.5460860|
|The content of this resource references||Mean precipitation grids from Valdes et al. (2021) - https://doi.org/10.5194/cp-17-1483-2021 | data available from the Bristol Research Initiative for the Dynamic Global Environment | Model ref: https://www.paleo.bristol.ac.uk/ummodel/scripts/html_bridge/scotese_02.html|
|Title||Owners||Sharing Status||My Permission|
|Paleo-Physiography Project||Tristan Salles||Public & Shareable||Open Access|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|Australian Research Council||ARC Training Centre in Data Analytics for Resources and Environments (DARE)||GRANT_NUMBER: IC190100031|
|University of Sydney||Modelling the deep Earth to predict climate change||Artemis HPC Grand Challenge|
How to Cite
This resource is shared under the Creative Commons Attribution-NoCommercial-ShareAlike CC BY-NC-SA.http://creativecommons.org/licenses/by-nc-sa/4.0/