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Type: | Resource | |
Storage: | The size of this resource is 19.5 GB | |
Created: | Oct 06, 2022 at 9:50 a.m. | |
Last updated: | Oct 07, 2022 at 6:51 a.m. | |
Citation: | See how to cite this resource | |
Content types: | Multidimensional Content |
Sharing Status: | Public |
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Views: | 711 |
Downloads: | 261 |
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Abstract
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).
Subject Keywords
Coverage
Spatial
Content
Data Services
Related Resources
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 |
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Paleo-Physiography Project | Tristan Salles | Public & Shareable | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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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 CC BY.
http://creativecommons.org/licenses/by/4.0/
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