Paleo-physiography analysis

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).

Jupyter workflows to extract information from the resources using Hydroshare THREDDS Data Service: https://github.com/Geodels/paleoPhysiography

该数据集包含全球尺度景观演化模型输出的 netcdf 格式数据，该模型整合了过去5.41亿年的古高程和古气候重建数据。结果以全球0.05度分辨率的网格形式提供，包括高分辨率古地貌图、水沙通量、长期侵蚀/沉积速率以及与景观动力学相关的多个形态度量（例如，流域标识、地形位置指数、地貌多样性）。模拟采用 goSPL 模型（全球可扩展的古景观演化模型 - https://gospl.readthedocs.io）进行，并依赖于 Scotese & Wright (2018)（PALEOMAP 项目 - https://doi.org/10.5281/zenodo.5460860）的古高程重建数据和 Valdes 等人（2021）的降水网格（https://doi.org/10.5194/cp-17-1483-2021 | 数据可从布里斯托尔动态全球环境研究倡议获取。模型参考：https://www.paleo.bristol.ac.uk/ummodel/scripts/html_bridge/scotese_02.html）。利用 Hydroshare THREDDS 数据服务通过 Jupyter 工作流程从资源中提取信息：https://github.com/Geodels/paleoPhysiography