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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: Apr 04, 2020 at 1:24 a.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


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End Date:




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
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 4/4/2020, 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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