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Abstract Regional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km²) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA. 

Elevated groundwater levels drive slope instability through decreased effective stresses and frictional strength. Consequently, landslide mitigation often relies on a variety of stabilizing techniques, often including dewatering and drainage as a primary control on stability. One of the most effective dewatering techniques for landslides are horizontal drain systems, which consist of arrays of perforated pipes drilled into hillslopes for gravity-driven removal of groundwater. One of the few economical solutions for large-magnitude, groundwater-driven landslides, horizontal drain arrays facilitate groundwater drawdown through gravity-driven flow, consequently increasing effective stress and slope stability within its domain of influence. However, design of horizontal drain systems remain largely observational and there is limited insight towards the transient performance of these drainage systems. This study aims to explore relevant theoretical design criteria for horizontal drain systems and their relative importance as related to drawdown mechanism and magnitude, as well as slope stability. 

Abstract Distributions of landslide size are hypothesized to reflect hillslope strength, and consequently weathering patterns. However, the association of weathering and critical zone architecture with mechanical strength properties of parent rock and soil are poorly-constrained. Here we use three-dimensional stability to analyze 7330 landslides in western Oregon to infer combinations of strength - friction angles and cohesion - through analysis of both failed and reconstructed landslide terrain. Under a range of conditions, our results demonstrate that the failure envelope that relates shear strength and normal stress in landslide terrain is nonlinear owing to an exchange in strength with landslide thickness. Despite the variability in material strength at large scales, the observed gradient in proportional cohesive strength with landslide thickness may serve as a proxy for subsurface weathering. We posit that the observed relationships between strength and landslide thickness are associated with the coalescence of zones of low shear strength driven by fractures and weathering, which constitutes a first-order control on the mechanical behavior of underlying soil and rock mass. 

Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches.