Lab-0031

Data Component Use Case for Landslide Susceptibility Calculation

Model
Landlab

Duration
1.5 hrs

Updated
2023-05-03

Download
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Contributor(s)

    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.

Introduction

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 (https://csdms.colorado.edu/wiki/DataComponents) 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: https://www.weather.gov/sju/maria2017)

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 https://www.usgs.gov/supplemental-appropriations-for-disaster-recovery-activities/landslides-triggered-hurricane-maria).

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
Skills

• 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 notebook is temporarily not functional on the OpenEarthscape JupyterHub due to a required package update. In the meantime, you can run it locally by following the instructions at: https://github.com/gantian127/landslide_usecase.

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

References

• 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, https://doi.org/10.5194/esurf-6-49-2018
• 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, https://doi.org/10.1029/93WR02979.
• 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. https://doi.org/10.5194/gmd-17-2165-2024