News Release

Review: New framework needed to assess complex “cascading” natural hazards

Summary author: Walter Beckwith

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

In a Review, Brian Yanites and colleagues argue the need for a unified, interdisciplinary approach to studying cascading land surface hazards. Earth’s surface is continually shaped by a range of natural processes, from slow erosion to sudden disasters like earthquakes and floods. Notably, one hazardous event can trigger a series of subsequent, interrelated disasters, or ”cascading hazards,” that unfold over timescales ranging from seconds to centuries. However, despite their growing impact on human populations, a comprehensive mechanistic framework from which to understand, predict, and manage these interconnected threats remains lacking. Here, Yanites et al. review current research on how Earth systems and the resulting land surface processes can interact in complex, sequential ways that intensify hazard risk. Unlike compound hazards, where multiple events occur independently but simultaneously, cascading hazards involve a direct causal link – one event alters the physical state of the landscape in a way that increases the likelihood of subsequent hazards. For example, earthquakes can destabilize hillslopes, raising landslide risk for years, while wildfires can transform vegetation and soil properties, amplifying the potential for debris flows during post-fire storms. According to the authors, the dynamic nature of these hazards challenges current risk assessment tools. To address this critical gap, Yanites et al. present a collaborative, cross-disciplinary framework that leverages recent technological advances and brings together atmospheric scientists, geologists, geomorphologists, engineers, and others to refine theory, models, and hazard monitoring. The authors argue that the development of a cascading hazards index could offer a promising tool by serving as an integrative, location-specific metric that synthesizes process-based models, observational data, and knowledge of hazard evolution to help communities assess these chains of evolving, interlinked hazards.


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