Seattle City Light is interested in improving understanding of landslide hazard and sediment transport to ensure reliable and cost-effective hydropower generation.
Landslide Hazard Modeling in the Skagit Basin contains the working folder of data and code and all GIS files
Launch Notebooks and code from this resource: Predicting future regional landslide probability using soil saturation
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).
Earlier publications on HydroShare:
Strauch, R., E. Istanbulluoglu, S. S. Nudurupati, C. Bandaragoda (2018). Regional landslide hazard using Landlab - NOCA Observatory, HydroShare, http://www.hydroshare.org/resource/3a925bd4a5784a38944b1e8b51224de1
Strauch, R., E. Istanbulluoglu, S. S. Nudurupati, C. Bandaragoda (2017). Regional landslide hazard using Landlab - NOCA Data, HydroShare, https://doi.org/10.4211/hs.a5b52c0e1493401a815f4e77b09d352b