Soil Mechanics seminars

The leaky barrel hillside hydrology metaphor—a straightforward “water in – water out” linear reservoir idealization originally applied to landslide studies more than 30 years ago by USGS scientists Ray Wilson and Gerry Wieczorek—remains useful despite (or maybe because of) its age and simplicity, especially for regional hazard forecasting applications potentially covering exceptionally large areas. The presentation will include a contextual overview of hindcasting (i.e., back-calculating) vs forecasting and the value of simple models for decision support, offer a new analytical solution for finite-duration rainfall pulses applied to examples from the Santa Cruz Mountains and Ohio River Valley, propose a rational expression for the originally empirical leaky barrel drainage coefficient, show how a leaky barrel model can be integrated into a factor of safety equation in a computationally efficient probabilistic approach amenable to spatially distributed (e.g., GIS-ish) applications, and finally show how the solutions used as input debris flow runout models.

Landslides remain a major global hazard, with thousands of events each year posing significant impacts to human life, infrastructure, and ecosystems. While advanced constitutive models enable detailed slope-stability analyses at the single-hillslope scale, a critical gap persists in assessing landslide hazards at the community scale. Existing regional-scale models often struggle to balance physical rigor, predictive accuracy, and computational efficiency. They rely either on empirical approaches with limited physical basis or on mechanistic formulations that rely on major simplifying assumptions. This study presents CRISIS (Coupled Regional Rainfall-Induced and Seismic Slope Instability Simulations), a physics-based regional landslide prediction framework. It couples a pseudo-3D slope-stability formulation with a transient three-dimensional groundwater flow model. CRISIS operates in back-analysis and forward-prediction modes, integrating multi-scale topographic, geotechnical, hydraulic, and hydrological data. Back-analysis of both failed and stable slopes from prior events generates high-resolution spatial estimates of shear strength. These estimates are iteratively refined and used for forward prediction of landslide location, size, depth, and timing under future triggering events. Results reveal a strong geospatial connection between hillslope location and dominant failure mechanisms, emphasizing that landslide initiation is inherently geospatial and governed by coupled surface–subsurface processes. Application of the framework to Hurricane Maria in Puerto Rico, where over 70,000 landslides were triggered, demonstrates close agreement between predicted and observed landslide locations, sizes, and timing. The broader vision of this work is to advance regional characterization, assessment, and mitigation of cascading hazards through data-model integration.

Shallow landslides typically involving just the soil mantle constitute both a primary process driving landscape evolution and a significant geologic hazard. Theory for and empirical documentation of shallow landslides generally assumes that the elevated pore pressures that destabilize soils arise from runoff just within the soil. In many landscapes, however, it has been recognized that groundwater emerging from underlying weathered bedrock can generate destabilizing pore pressures. Here, we develop a coupled hydrologic and slope stability model that explicitly explores how a conductive bedrock weathering zone guides storm runoff and localizes shallow landslides across a watershed. Our findings suggest that first-order variations of deep CZ can provide physical explanations for variations observed in the susceptibility, magnitude, and timing of shallow landslides, and that CZ structure may be inferred from patterns and timing of landsliding.

In the past decade, several fatal landslides have impacted Southeast Alaska, highlighting the need to advance our understanding of regional geomorphic and atmospheric controls on triggering events and runout behavior. A large and long runout landslide on Wrangell Island, with area in the top 0.5% of >14,760 slides mapped in the Tongass National Forest, initiated during an atmospheric river event in November 2023 and traveled >1 km downslope, causing six fatalities. We used field observations, sequential airborne lidar, geotechnical analyses, and climate data to characterize the geomorphic, hydrologic, and atmospheric conditions contributing to the landslide. Rainfall intensities recorded at the Wrangell airport were modest (~1-yr recurrence interval), but rapid snowmelt and drainage from a ridgetop wetland may have contributed to rapid saturation of the landslide. Although strong winds were recorded, we did not observe extensive windthrow, which may downgrade its contribution to slope failure. The landslide mobilized a steep, thick (>4 m) wedge of colluvium that accumulated below a resistant bedrock ledge and entrained additional colluvial deposits as it traveled downslope across cliff-bench topography. The substantial entrainment resulted in an unusually large width, extensive runout, and low depositional slope as the landslide terminated in the coastal environment. Our results suggest that the sequencing of rain- and snow-dominated storms, geologic controls on post-glacial colluvium production and accumulation, and ridgetop hydrology contributed to landslide initiation and mobility. Advances in post-glacial landscape evolution models, frequent lidar acquisition, and additional climate data are needed to inform regional landslide hazard assessment.

On February 6, 2023, a Mw 7.8 and Mw 7.5 earthquake doublet struck along the East Anatolian Fault Zone in Türkiye, triggering over 3,600 coseismic landslides. Roughly a month later, a rare atmospheric river event delivered up to 183 mm of rainfall in just 20 hours to the earthquake impacted region. This extreme precipitation induced additional landslides, debris flows, and flooding. In our research, we conduct a probabilistic landslide hazard assessment using the open-source Landlab earth surface modeling library, integrating both seismic and climatic drivers. The model incorporates earthquake legacy effects, representing post-seismic slope weakening, and simulates shallow landslide susceptibility using a Monte Carlo implementation of the infinite slope stability theorem informed by remote sensing datasets. I will present three key analyses: (1) modeling hydrologic response to the AR event and mapping landslide probability with and without earthquake legacy effects; (2) examining how the sequence and combination of extreme events alter landslide hazard; and (3) demonstrating how using historic climate data can effectively replicate post-seismic landslide hazard maps that use real-time data, offering an approach for seasonal hazard forecasting in tectonically active and climate-sensitive regions.