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CSDMS 2025: Exploring Earth's Surface with Models, Data & AI


Landlab’s NetworkSedimentTransporter: A Lagrangian model for riverbed material transport dynamics



Allison Pfeiffer

Western Washington University, Geology, United States
pfeiffa@wwu.edu
Jon Czuba Virginia Tech, Biological Systems Engineering United States


Abstract
This hands-on clinic will introduce Landlab’s NetworkSedimentTransporter (NST) component, giving participants an overview of the workings of the model and a chance to explore a simple use case.

The NST is a 1D morphodynamic model for gravel riverbed evolution that allows for the Lagrangian tracking of sediment. The NST operates on a network model grid, tracking the transport of individual ‘parcels’ of sediment as they pass downstream according to sediment transport equations. As these parcels move from one link (reach) to another, the grid topographic elevation and bed surface grain size distributions evolve.

By the end of the clinic, participants should be able to: - Explain how sediment is represented as discrete parcels that are tracked as they transport through the network model grid - Explain how sediment parcels are transported within a link and how the model selects which parcels to transport or store - Explain how grid topographic elevations evolve as a function of the volume of parcels present on the neighboring linksUnderstand the purpose of each of the essential sections of the code needed to run the NST - Make minor edits to an example NST model script to explore model sensitivity to input parameters - Propose several possible use cases of the NST

This clinic is appropriate for beginners with little to no experience using the Landlab library. Participants will run the model on the OpenEarthscape JupyterHub (on the cloud), so there are no specialized software requirements. Prior experience with Python programming and some knowledge of sediment transport equations is helpful but not necessary.




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Of interest for:
  • Terrestrial Working Group
  • River Network Modeling Initiative