DEVELOPING LANDSLIDE MODELING CAPABILITIES WITHIN LANDLAB TO EVALUATE LANDSCAPE RESPONSE AND RECOVERY TO MEGA-LANDSLIDES

Mega-landslides reside on the surface and in the subsurface throughout the Basin and Range province of the western USA. The landslides play an important role in the denudation of the ranges, but most of the landslide deposits are probably buried in the basins. Basins within the province are filled with several km of poorly consolidated and unconsolidated clastic material shed from the ranges, lacustrine sediments, and volcanic ash and flows, making seismic imaging in this region very difficult and expensive. Therefore, the purpose of this study is to use landscape evolution modeling to identify critical characteristics in rangefront morphology (such as relative drainage network density, slope aspect ratio, or range crest sinuosity) that will help identify buried mega-landslide deposits.

We developed a module in LandLab to build numerical landscape models of a developing horst range affected by mega-landslides. LandLab is a Python-based open source landscape evolution modeling software that is designed to quantify surface processes and evolution of landscapes through time. The landslide module allows for modeling uplift of a horst block, failure(s) of a portion of the block, and landscape response following modeled landslides. This module was then used to conduct idealized modeling exercises to evaluate the scope of potential rangefront morphologies resulting from mega-landslide processes.

This idealized landscape modeling has provided information on (i) the general evolution of rangefront stratigraphy as it relates to landsliding and landslide deposits; (ii) estimating the time scale for relaxation of landslide source areas following an event, and what lingering impacts on the fluvial network may remain to be extracted from the topography; and (iii) disruptions to the pattern of alluviation on the fans and other basin fill by emplacement of a mega-landslide. These results will be compared to exposed and buried mega-landslides throughout the province. Key geomorphic features from these models will be used to guide analysis of rangefronts for potential buried mega-landslide deposits.