Data Component Use Case for Landslide Susceptibility Calculation

1.5 hrs
Run online using:
  1. Jupyter
  2. Lab
     Jupyter logo.png

    Tian Gan at INSTAAR - University of Colorado Boulder..
    Benjamin Campforts at VU University Amsterdam.
    Greg Tucker at Geological Sciences - University of Colorado Boulder.
    Irina Overeem at Geological Sciences - University of Colorado Boulder.

Landslide susceptibility is the likelihood of a landslide occurring in an area on the basis of local terrain condition to estimate “where” landslides are likely to occur. This lab demonstrates how to use several CSDMS Data Components ( to download topography and soil datasets to calculate the landslide susceptibility for a study area in Puerto Rico when Hurricane Maria hit the island in 2017. (Picture source:

Classroom organization
Landslides are frequent hazards in Puerto Rico which are mainly caused by the steep terrain and heavy rainfall from hurricanes and other tropical weather systems. For example, Hurricane Maria hit the island of Puerto Rico on September 20th, 2017 and triggered more than 40,000 landslides in Puerto Rico (see details at

In this lab, we will calculate the hourly landslide susceptibility for the area that has high concentration of landslide of Puerto Rico during Hurricane Maria. We will use the Topography and ERA5 Data Components as well as Landlab to prepare several datasets to calculate the landslide susceptibility.

Learning objectives
  • Learn to use Data Components to download research datasets
  • Learn to create landslide susceptibility map
Key concepts
  • Landslide Susceptibility
  • CSDMS Data Component
  • Landlab

Lab notes
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel. Note that the notebook uses a little over 3 GB of memory.

If you don't already have a JupyterHub account, follow the instructions to sign up at If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk:

If run locally, please follow the instruction at

This work was supported by the National Science Foundation under collaborative grants 1831623, 2026951, 2140831, 2104102, and 2148762.

  • Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E. (2018), A hydroclimatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam., 6, 49–75,
  • Montgomery, D. R., and Dietrich, W. E. (1994), A physically based model for the topographic control on shallow landsliding, Water Resour. Res., 30( 4), 1153– 1171,
  • Gan, T., Tucker, G.E., Hutton, E.W.H., Piper, M.D., Overeem, I., Kettner, A.J., Campforts, B., Moriarty, J.M., Undzis, B., Pierce, E., McCready, L., 2024: CSDMS Data Components: data–model integration tools for Earth surface processes modeling. Geosci. Model Dev., 17, 2165–2185.