Geotechnical Engineering 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.

Precipitation-triggered geohazards, including landslides, debris flows, flooding, erosion, and scour, present growing challenges as hydroclimatic extremes intensify. This presentation describes an integrated framework that combines physics-based probabilistic hazard modeling with holistic, multimodal monitoring to support detection, forecasting, prioritization, and management of geohazard risk. The framework incorporates InSAR, optical imagery, lidar, and field observations within a structured escalation pathway that links regional-scale screening with targeted investigation and decision support. Particular emphasis is placed on integrating monitoring and modeling into a self-improving system in which observations inform model refinement and models guide monitoring priorities. Current applications, implementation lessons, and future directions are discussed, including climate-informed forecasting and the development of scalable approaches that can be transferred across a wide range of geohazards and infrastructure settings. Towards an Integrated and Holistic Framework for Precipitation-Triggered Geohazard Risk Management, Kozacı (2026), 12 August 2026.

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.

Volcanic edifice flank collapses are low-frequency, high-magnitude events that can rapidly mobilize tens of km3 of material and transport it large distances (>10 km) from the edifice. In ocean basins, these failures induce secondary hazards through the generation of tsunamis that can impact both neighboring and distal coastlines. These high-risk, far-reaching events have historically generated significant damage and loss of life, such as the 1792 collapse of the Mayuyama dome (Japan) that led to ~15,000 fatalities, and recently the 2018 collapse of Anak Krakatau (Indonesia) that resulted in ~440 deaths. Globally, there are 400–800 oceanic and coastal volcanic edifices with the potential for future collapses and associated tsunamis. Although previous studies have evaluated edifice collapse potential at both single volcanoes and over entire arcs, a global assessment of the edifices that are most capable of producing a tsunami-generating collapse is incomplete, in part because of remote locations and lack of detailed investigations. Building on previous studies, we develop a multi-component weighting procedure to rank the potential for edifice flank collapse and tsunami generation using three criteria: 1) edifice morphology; 2) satellite-derived estimations on the extent of hydrothermal alteration; and 3) 3D slope-stability analysis of potential collapses. We test this approach on oceanic and coastal volcanic edifices from the Marianas, Aleutians, Indonesia, and Philippines. After describing the results and relative ranking of edifices with the highest potential for collapse, we discuss implications for future tsunami hazard assessment.

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.