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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union

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© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
21 Jun 2017
Review status
This discussion paper is under review for the journal Earth Surface Dynamics (ESurf).
A hydro-climatological approach to predicting regional landslide probability using Landlab
Ronda Strauch1, Erkan Istanbulluoglu1, Sai Siddhartha Nudurupati1, Christina Bandaragoda1, Nicole M. Gasparini2, and Gregory E. Tucker3 1Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
2Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA
3Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of Geological Sciences, University of Colorado Boulder, Boulder, CO, USA
Abstract. We develop a hydro-climatological approach to modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation. The physically-based model couples the infinite slope stability model with a steady-state subsurface flow representation and operates on a digital elevation model. Spatially distributed raster data for soil properties and a soil evolution model and vegetation classification from National Land Cover Data are used to derive parameters for probability distributions to represent input uncertainty. Hydrologic forcing to the model is through annual maximum recharge to subsurface flow obtained from a macroscale hydrologic model, routed on raster grid to develop subsurface flow. A Monte Carlo approach is used to generate model parameters at each grid cell and calculate probability of shallow landsliding. We demonstrate the model in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting uncertainty of soil depth and its potential long-term variability. We found elevation dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests in low elevations, an increased landslide probability with forest decline at mid elevations (1,400 to 2,400 m), and soil limitation and steep topographic controls at high alpine elevations and post-glacial landscapes. These dominant controls manifest in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similar model confidence for the three hazard maps, suggesting suitable use as relative hazard products. Validation of the model with observed landslides is hindered by the completeness and accuracy of the inventory, estimation of source areas, and unmapped landslides. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

Citation: Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. Discuss.,, in review, 2017.
Ronda Strauch et al.
Ronda Strauch et al.

Data sets

Regional landslide hazard using Landlab – NOCA Data
R. Strauch, E. Istanbulluoglu, S. S. Nudurupati, and C. Bandaragoda
Ronda Strauch et al.


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Publications Copernicus
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, U.S.A., but it is designed to run elsewhere with available data for risk planning using the Landlab.
We develop a model of annual probability of shallow landslide initiation triggered by soil water...