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- Presenters-0468 + (Predicting long-term Earth surface change, … Predicting long-term Earth surface change, the impacts of short-term natural hazards and biosphere/geosphere dynamics requires computational models. Many existing numerical models quantitatively describe sediment transport processes, predicting terrestrial and coastal change at a great variety of scales. However, these models often address a single process or component of the earth surface system. </br></br>The Community Surface Dynamics Modeling System is an NSF-funded initiative that supports the open software efforts of the surface processes community. CSDMS distributes >200 models and tools, and provides cyberinfrastructure to simulate lithosphere, hydrosphere, atmosphere, or cryosphere dynamics. Many of the most exciting problems in these fields arise at the interfaces of different environments and through complex interactions of processes.</br></br>This workshop presents recent cyberinfrastructure tools for hypothesis-driven modeling— the Python Modeling Tool (PyMT) and LandLab. PyMT allows users to interactively run and couple numerical models contributed by the community. There are already tools for coastal & permafrost modeling, stratigraphic and subsidence modeling, and terrestrial landscape evolution modeling (including hillslope, overflow, landslide processes, and a suite of erosion processes with vegetation interactions), and these are easy to run and further develop in a Python environment. </br></br>This 2-part tutorial aims to provide a short overview of the PyMT and Landlab, a demonstration of running a coupled model, and hands-on exercises using Jupyter notebooks in small groups of attendees. The organizers will facilitate break-out groups for discussion of pressing research needs and then have a plenary discussion with reports of each of the breakouts on future frontier applications of coupled landscape/bioscape process modeling.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020 be found at: https://github.com/csdms/csdms-2020)
- Presenters-0485 + (Predicting long-term Earth surface change, … Predicting long-term Earth surface change, the impacts of short-term natural hazards and biosphere/geosphere dynamics requires computational models. Many existing numerical models quantitatively describe sediment transport processes, predicting terrestrial and coastal change at a great variety of scales. However, these models often address a single process or component of the earth surface system. </br></br>The Community Surface Dynamics Modeling System is an NSF-funded initiative that supports the open software efforts of the surface processes community. CSDMS distributes >200 models and tools, and provides cyberinfrastructure to simulate lithosphere, hydrosphere, atmosphere, or cryosphere dynamics. Many of the most exciting problems in these fields arise at the interfaces of different environments and through complex interactions of processes.</br></br>This workshop presents recent cyberinfrastructure tools for hypothesis-driven modeling— the Python Modeling Tool (PyMT) and LandLab. PyMT allows users to interactively run and couple numerical models contributed by the community. There are already tools for coastal & permafrost modeling, stratigraphic and subsidence modeling, and terrestrial landscape evolution modeling (including hillslope, overflow, landslide processes, and a suite of erosion processes with vegetation interactions), and these are easy to run and further develop in a Python environment. </br></br>This 2-part tutorial aims to provide a short overview of the PyMT and Landlab, a demonstration of running a coupled model, and hands-on exercises using Jupyter notebooks in small groups of attendees. The organizers will facilitate break-out groups for discussion of pressing research needs and then have a plenary discussion with reports of each of the breakouts on future frontier applications of coupled landscape/bioscape process modeling.</br></br>Materials for this clinic can be found at: https://github.com/csdms/csdms-2020 be found at: https://github.com/csdms/csdms-2020)
