I’m trying to develop teaching materials using Jupyter notebook (mainly inspired by my colleague Fabien Maussian) and its super interesting but quite hard work for me as a python beginner. I was looking for other things people have been doing with this format and I came across the teaching tools that accompany Landlab, which aims to create an environment in which scientists can build a numerical landscape model without having to code all of the individual components (Hobley et al., 2017). Landscape models have a number of commonalities, such as operating on a grid of points and routing material across the grid. Scientists who want to use a landscape model often build their own unique model from the ground up, re-coding the basic building blocks of their landscape model rather than taking advantage of codes that have already been written. Landlab offers python coded building blocks for developing your own model. Cool huh? Landlab is described in an open source paper.
The Landlab team currently shares teaching resources appropriate for geomorphology and surface water hydrology classes. The exercises use numerical models to illustrate physical processes. These exercises were designed as homework or laboratory assignments, but they could also be used to illustrate concepts in the classroom related to:
- Hillslopes evolving according to the linear diffusion equation.
- Drainage density sensitivity to the strength of hillslope and fluvial processes.
- Fluvial channel morphology (steepness and chi-elevation relationships) sensitivity to rock uplift and rock erodibility.
- Hydrograph sensitivity to watershed shape and storm characteristics.
These exercises do not require coding knowledge. They are written in Jupyter notebooks, which combine text and code, and are easy for students to use. The exercises include directed exploration exercises (i.e. students are told exactly how to change the code and run it) and thought and interpretation questions based on the resulting plots. The exercises can be tailored for your class.
Everything is open source so FREE! You can even run them online without any software installation.
For more information: https://github.
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