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Slippery Future Code: Predicting future regional landslide probability using soil saturation

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Created: May 07, 2019 at 6:22 p.m.
Last updated: Jun 18, 2020 at 11:40 p.m.
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Landslide probability modeling can be used to better understand landslides in the watersheds containing the electrical transmission lines and facilities. A recently published landslide model (Strauch et al. 2018) updated to use spatially distributed saturation (depth to water table) derived from a basin calibrated hydrologic model (Distributed Hydrology Soil and Vegetation Model - DHSVM) at 150-m grid resolution. Contemporary and future probability of landslide initiation is used to create landslide hazard maps at a 30-m resolution. Our case study of the Skagit Hydroelectric Project evaluates the sensitivity of the landslide model to subsurface saturation and reduced cohesion of a simulated a fire. We compare historic landslide probability to two future time periods using two scenarios (RCP 4.5 and RCP 8.5) and a representative distribution of global climate models (GCMs).

This resource is an updated copy of the work published in Strauch et al., (2018) "A hydroclimatological approach to predicting regional landslide probability using Landlab", Earth Surf. Dynam., 6, 1-26 . It demonstrates a hydroclimatological approach to modeling of regional shallow landslide initiation based on the infinite slope stability model coupled with a steady-state subsurface flow representation. The model component is available as the LandslideProbability component in Landlab, an open-source, Python-based landscape earth systems modeling environment described in Hobley et al. (2017, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017). The model operates on a digital elevation model (DEM) grid to which local field parameters, such as cohesion and soil depth, are attached. A Monte Carlo approach is used to account for parameter uncertainty and calculate probability of shallow landsliding as well as the probability of soil saturation based on annual maximum recharge. The model is demonstrated in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2.

This resource contains a 1) User Manual that describes the Landlab LandslideProbability Component design, parameters, and step-by-step guidance on using the component in a model, and 2) two Landlab driver codes (notebooks) and customized component code to run Landlab's LandslideProbability component for 2a) synthetic recharge and 2b) modeled recharge published in Strauch et al., (2018). The Jupyter Notebooks use HydroShare code libraries to import data located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/.

The Synthetic Recharge Jupyter Notebook <Synthetic_recharge_LandlabLandslide.ipynb> demonstrates the use of the Landlab LandslideProbability Component on a synthetic grid with synthetic data with four options for parameterizing recharge. This notebook was used to verify and validated the theoretical application and digital representation of Landslide processes.

The Modeled Recharge Jupyter Notebook <NOCA_runPaper_LandlabLandslide.ipynb> models annual landslide probability in the North Cascades National Park Complex, and was used to verify that model results in Strauch et al., (2018) could be reproduced online.

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Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
North Cascades National Park Complex
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:



Software Instructions: Online and Personal Computer

To open interactive Jupyter Notebooks with Binder JupyterHub server. You will be connected to a virtual machine with the software environment required to execute the models.


Online Modeling Instructions

To open interactive Jupyter Notebooks with the CUAHSI JupyterHub server, go to the upper right corner of the resource page and click on 'Open With'. Select CUAHSI JupyterHub. You will be connected to a virtual machine with the software environment required to execute the models.

Notebook 1: Learn about Landlab functions with a synthetic grid and recharge forcings

Notebook 2: Learn about Version 2 Landslide Component comparing depth to water table and recharge forcings on a synthetic grid

Notebook 3: Replicate an experiment on a watershed subset within regional Landlab landslide model to explore fire impacts. The resource was originally derived from a reproducible demonstration of the landslide modeling results from: Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: (2018) A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-6-49-2018: Replicate_landslide_model_for_fire.ipynb

Personal Computer Installation Instructions


The notebooks included in this resource require the following Python 3 packages:

``` hs_restclient==1.3.5 landlab==1.10.0


To ensure that you have the correct packages and versions, run the following command(s) inside a Python terminal:

$ conda list


$ pip list

Creating a Working Environment

We recommend using Anaconda to create a fresh Python environment with all dependencies installed. After installing Anaconda, simply run the commands below with your desired environment name in place of MY_ENVIRONMENT_NAME:

conda create -n MY_ENVIRONMENT_NAME --file requirements.txt

activate the environment and start a jupyter server

source activate MY_ENVIRONMENT_NAME jupyter notebook

Debugging a Working Environment

Are you getting errors? Here are some suggested steps. If you still have issues, email help@cuahsi.org or reach out to us (comment on this resource or see emails in HydroShare profiles) and we will invite you to the HydroShare Slack #landlab channel.

Bug: PackagesNotFound

PackagesNotFoundError: The following packages are not available from current channels.

Reduce the number of packages that were not available by running the following command

conda config --append channels conda-forge

Bug: Conda vs.Pip Install

If you get errors for a few packages, remove them from the requirements.txt file until you successfully created the conda environment.

conda create -n MY_ENVIRONMENT_NAME --file requirements.txt

Any packages that didn't get installed during creation of conda environment can be pip installed separately in the newly created conda environment. for example:

pip install hs-restclient==1.3.5

Reproducible Quote of the Day:

"The product of mental labor - science - always stands far below its value, because the labor-time necessary to reproduce it has no relation at all to the labor-time required for its original production." Karl Marx

Additional Metadata

Name Value
appkey MyBinder



Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Seattle City Light, City of Seattle Climate Change Adaptation Program
National Science Foundation PREEVENTS TRACK 2: Integrated Modeling of Hydro-Geomorphic Hazards: Floods, Landslides and Sediment 1663859


People that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Scott Black USU
Anthony Michael Castronova CUAHSI MA, US 3399334127 ORCID , ResearchGateID , GoogleScholarID
Daniel Miller TerrainWorks Seattle, WA
Regina Rochefort National Park Service North Cascades National Park Complex, Sedro-Woolley, WA
Jon Riedel National Park Service North Cascades National Park Complex, Sedro-Woolley, WA

How to Cite

Bandaragoda, C., R. Strauch, E. Istanbulluoglu (2020). Slippery Future Code: Predicting future regional landslide probability using soil saturation, https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/, accessed 6/18/2020, replicated in HydroShare at: http://www.hydroshare.org/resource/4cac25933f6448409cab97b293129b4f

This resource is shared under the Creative Commons Attribution CC BY.



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