Landslide Scaling: A Review

Abstract

This paper is a review of landslide and rockfall studies of hilly and mountainous regions worldwide. Repositories of landslide inventories are available online (e.g., Tanyaş et al., 2017; https://doi:10.1002/2017JF004236). The landslide inventories predominantly record the surface area of deep-seated, fast-moving, landslides, generally triggered by an earthquake or rainfall event, and such landslides are the primary focus of this review. The size-frequency distributions of landslides and rockfalls are well described by a power function for larger (generally for the largest 2 orders of magnitude) of event sizes (e.g., Malamud et al., 2004; https://doi:10.1002/esp.1064; Tanyaş et al., 2018; https://doi:10.1002/esp.4359). Smaller event sizes are under-represented by the power function that describes the larger events (e.g., Stark & Hovius, 2001; https://doi.org/10.1029/2000GL008527). The deviation from a power function at smaller sizes is arguably not a simple detection issue and possible explanations include lack of temporal resolution in sampling, and amalgamation of smaller events into larger events when mapping (e.g., Tanyaş et al., 2019; https://doi:10.1002/esp.4543). Self-organized criticality models and cellular automata models have been developed that replicate the power scaling behavior (e.g., Hergarten, 2013). The self-organized criticality models are alluring in their simplicity but have shortcomings such as failing to recreate the same scaling exponent as observed in nature (e.g., Hergarten, 2002). Parameterized cellular automata models include one or more relevant variables that affect shear stress in the surface materials and come closer to replicating the scaling exponents observed for natural systems (e.g., D'Ambrosio et al., 2003; https://doi:10.5194/nhess-3-545-2003). Mechanical models have also successfully replicated the observed power scaling (e.g., Jeandet et al., 2019; https://doi:10.1029/2019GL082351).