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

Journal metrics

  • IF value: 2.649 IF 2.649
  • IF 5-year<br/> value: 2.688 IF 5-year
  • CiteScore<br/> value: 2.64 CiteScore
  • SNIP value: 0.628 SNIP 0.628
  • SJR value: indexed SJR
  • IPP value: 1.689 IPP 1.689
  • h5-index value: 6 h5-index 6
Earth Surf. Dynam., 6, 49-75, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
07 Feb 2018
A hydroclimatological approach to predicting regional landslide probability using Landlab
Ronda Strauch1, Erkan Istanbulluoglu1, Sai Siddhartha Nudurupati1, Christina Bandaragoda1, Nicole M. Gasparini2, and Gregory E. Tucker3,4 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), Boulder, CO, USA
4Department of Geological Sciences, University of Colorado Boulder, Boulder, CO, USA
Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 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 the 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 at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. 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 hydroclimatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam., 6, 49-75,, 2018.
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, USA, 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...