Civil 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.