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A hydroclimatological approach to predicting regional landslide probability using Landlab

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Created: Jan 26, 2018 at 8:46 p.m.
Last updated: Jan 30, 2018 at 7:08 p.m.
DOI: 10.4211/hs.27d34fc967be4ee6bc1f1ae92657bf2b
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Sharing Status: Published
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This resource supports 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, 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:

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.

Subject Keywords



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



This repository contains example code and documentation related to the following manuscript:

Strauch RL, Istanbulluoglu E, Nudurupati SS, Bandaragoda C, Gasparina N, and Tucker G. (2018). Hydro-climatological approach to predicting regional landslide probability. Earth Surface Dynamics 6:1-26.

This manuscript is based on Landlab version 1.1.0.

Installation instructions and documentation for Landlab are provided at:


(Last updated June 2017)


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation CBET Environmental Sustainability Program 1336725
National Science Foundation OAC 1450412
National Science Foundation 1450409
National Science Foundation 1450338
USGS Northwest Climate Science Center Graduate Fellowship


People or Organizations 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
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

Strauch, R., E. Istanbulluoglu, S. S. Nudurupati, C. Bandaragoda, N. Gasparini, G. Tucker (2018). A hydroclimatological approach to predicting regional landslide probability using Landlab, HydroShare,

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


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