A hydroclimatological approach to predicting regional landslide probability using Landlab
|Authors:||Ronda Strauch · Erkan Istanbulluoglu · Sai Siddhartha Nudurupati · Christina Bandaragoda · Nicole Gasparini · Greg Tucker|
|DOI:||10.4211/hs.27d34fc967be4ee6bc1f1ae92657bf2b How to Cite|
|Created:||Jan 26, 2018 at 8:46 p.m.|
|Last updated:||Jan 31, 2018 at midnight by Ronda Strauch|
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, 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.
Landlab,Landslide,North Cascades,National Park
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/
|Coordinate System/Geographic Projection:||WGS 84 EPSG:4326|
|Coordinate Units:||Decimal degrees|
|Ronda Strauch||University of Washington||Seattle|
|Erkan Istanbulluoglu||University of Washington||Seattle|
|Sai Siddhartha Nudurupati||University of Washington - Seattle||WA, US||4026170098|
|Christina Bandaragoda||University of Washington||Washington, US|
|Nicole Gasparini||Tulane University||New Orleans|
|Greg Tucker||University of Colorado||Boulder|
|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|
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|
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