Articles | Volume 6, issue 1
https://doi.org/10.5194/esurf-6-49-2018
https://doi.org/10.5194/esurf-6-49-2018
Research article
 | 
07 Feb 2018
Research article |  | 07 Feb 2018

A hydroclimatological approach to predicting regional landslide probability using Landlab

Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker

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Cited articles

Abbaszadeh, M., Shahriar K., Sharifzadeh M., and Heydari M.: Uncertainty and re-liability analysis applied to slope stability: a case study from Sungun copper mine, Geotechnical and Geological Engineering, 29, 581–596, 2011.
Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017.
Alvioli, M., Guzzetti, F., and Rossi, M.: Scaling properties of rainfall induced landslides predicted by a physically based model, Geomorphology, 213, 38–47, 2014.
Anagnostopoulos, G. G., Fatichi, S., and Burlando, P.: An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions, Water Resour. Res., 51, 7501–7523, 2015.
Arnone, E., Dialynas, Y. G., Noto, L. V., and Bras, R. L.: Parameter Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale: A Probabilistic Approach, Proced. Earth Plan. Sc., 9, 101–111, 2014.
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Short summary
We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
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