New Study Reveals Potential to Forecast Coastal Cliff Collapses

In a groundbreaking study spanning four years, researchers at UC San Diego’s Scripps Institution of Oceanography have demonstrated the feasibility of an early warning system for coastal landslides. By deploying an advanced network of in-ground sensors along California’s erosion-prone cliffs, the team successfully detected movement signaling imminent cliff collapses days before they occurred, offering a critical window for hazard mitigation.

Approximately 70% of California’s coastline consists of unstable cliffs, posing persistent risks to infrastructure and public safety. Tragic incidents, such as the 2019 fatal collapse at Grandview Beach in Encinitas, have heightened urgency for reliable monitoring and predictive technologies. Responding to this need, the Scripps team, led by coastal geomorphologist Dr. Adam Young and geophysicist Dr. Mark Zumberge, instrumented several high-risk sites including Beacon’s Beach, Del Mar’s Railway Corridor, and San Elijo State Beach. These locations were chosen for their known susceptibility to landslides impacting public access and transportation corridors.

The sensor array included high-precision tiltmeters capable of detecting ground tilts as subtle as 10 micrometers, extensometers that monitor nanometer-scale ground deformation, seismometers, GPS stations, and wave pressure sensors. This multidisciplinary instrumentation tracked five cliff collapses over the study period. In every case, sensor data revealed measurable precursory movements hours to days before failure, effectively marking a tipping point in monitoring cliff stability.

One particularly notable event occurred in Del Mar in April 2024. The team observed a fissure widening at a rate imperceptible to the naked eye, with tiltmeters showing accelerating ground deformation triggered by intermittent rainfall. These real-time measurements prompted early alerts to coastal managers before approximately 200 tons of cliff material catastrophically slid onto the beach below.

Complementing the sensor data, researchers utilized weekly mobile lidar surveys and drone photogrammetry to generate detailed 3D topographic models of the cliff faces. This laser mapping combined with rainfall analytics is paving the way toward a probabilistic warning framework that can issue alerts based on environmental thresholds such as rainfall intensity or cliff movement rates.

While the scientific evidence now supports predictive monitoring, the study underscores the necessity of developing coordinated emergency protocols for timely response. Potential interventions range from temporary beach closures and structural reinforcements to public awareness initiatives to reduce risk exposure.

This pioneering effort not only illuminates a path to enhancing coastal safety amid a changing climate but also bridges earthquake geophysics instrumentation with coastal hazard science. The next steps include expanding monitoring networks along other vulnerable California coastlines and refining the balance between sensor sensitivity, cost, and coverage for scalable implementation.

As the researchers continue to analyze longer-term data, this early warning system heralds a transformative approach to protecting lives and vital infrastructure from the unpredictable forces reshaping our shorelines.

• California Coastal Landslide Early Warning Research report: https://siocpg.ucsd.edu/California%20Coastal%20Landslide%20Early%20Warning%20Research.pdf
• Journal of Geophysical Research: Earth Surface study: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JF008562
  Image Credits: Adam Young/Coastal Processes Group at Scripps Institution of Oceanography
  Keywords: Coastal erosion, landslide prediction, tiltmeters, lidar mapping, California coast, hazard monitoring