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Exploring Surface Processes using CSDMS Modeling Tools: How to Build Coupled Models

A hands-on short course on model coupling at the 2020 GSA Annual Meeting

Abstract

Predicting long-term Earth surface change or the impacts of short-term natural hazards 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. The Community Surface Dynamics Modeling System (CSDMS) is an NSF-funded initiative that supports the open software efforts of the surface processes community. CSDMS distributes >300 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. 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, and Landlab is a library for building 2D numerical models. The full-day 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.

Instructors

Requirements

  • Computer
  • Web browser
  • Internet access
  • Coffee (optional, but recommended)

Agenda

This short course is broken into four 80-minute interactive sections, with a 20-minute break between each.

Time (ET) Topic Presenter
10:00 - 10:20 Welcome & Introduction Tucker
10:20 - 11:40 Landlab Tucker
11:40 - 12:00 Break
12:00 - 1:20 Landlab Campforts
1:20 - 1:40 Break
1:40 - 3:00 Basic Model Interface (BMI) Piper
3:00 - 3:20 Break
3:20 - 4:40 Python Modeling Tool (pymt) Hutton
4:40 - 5:00 Discussion & Closing Piper

==> Run the course tutorials on our JupyterHub

Links

Thank you!

A big thanks to those that contributed notebooks to this clinic.

  • Jordan Adams
  • Katy Barnhart
  • Nicole Gasparini
  • Albert Kettner
  • Nathan Lyons
  • Margaux Mouchene
  • Irina Overeem
  • Katherine Ratliff
  • Kang Wang

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