Data to support modeling of the 2015 Tyndall Glacier landslide, Alaska

• Main Authors: Cannon, Charles M., George, David L., Iverson, Richard M
• Format: Dataset
• Language: unknown
• Published: U.S. Geological Survey 2017
• Subjects: glacier; Alaska
• Online Access: https://dx.doi.org/10.5066/f73r0rr3; https://www.sciencebase.gov/catalog/item/5963e612e4b0d1f9f059d94b

Landslide-generated tsunamis pose significant hazards, but developing models to assess these hazards presents unique challenges. George and others (2017) present a new methodology in which a depth-averaged two-phase landslide model (D-Claw) is used to simulate all stages of landslide dynamics and subsequent tsunami generation, propagation, and inundation. Because the model describes the evolution of solid and fluid volume fractions, it treats both landslides and tsunamis as special cases of a more general class of phenomena. Therefore, the landslide and tsunami can be seamlessly and efficiently simulated as a single-layer continuum with evolving solid-grain concentrations, and with wave generation via mass displacement and direct longitudinal momentum transfer: dominant physical mechanisms that are unresolved with traditional modeling approaches. To test their methodology, George and others (2017) used D-Claw to model a large subaerial landslide and resulting tsunami that occurred on October, 17, 2015, in Taan Fiord near the terminus of Tyndall Glacier, Alaska. Modeled shoreline inundation patterns compare well with observations derived from satellite imagery. This data release contains topographic datasets used to model the landslide and a Normalized difference vegetation index (NDVI) change image used to assess the model results. These data are intended to accompany the journal article by George and others (2017): George, D.L., Iverson, R.M., and Cannon, C.M., 2017, New methodology for computing tsunami generation by subaerial landslides: application to the 2015 Tyndall Glacier Landslide, Alaska, Geophysical Research Letters, 44, doi:10.1002/2017GL074341.