2025 CSDMS meeting-074

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From Earthquakes to Landslides: Predictive Modeling and Applications

Suryodoy Ghoshal, (He/Him),University of Plymouth Plymouth , United Kingdom. suryodoy.ghoshal@plymouth.ac.uk

Sarah Boulton, University of Plymouth PLYMOUTH , United Kingdom. sarah.boulton@plymouth.ac.uk

Benjamin Campforts, Vrije Universiteit Amsterdam Amsterdam , Netherlands. b.campforts@vu.nl

Tristram Hales, Cardiff University Cardiff, Wales , United Kingdom. HalesT@cardiff.ac.uk

Georgie Bennett, University of Exeter Exeter, Devon , United Kingdom. G.L.Bennett@exeter.ac.uk

Landslides represent a significant natural hazard that often cascade off earthquakes and other major disaster events, further impacting landscapes and human infrastructure globally. Accurate modelling of landslide-prone areas is crucial for risk assessment and mitigation strategies, especially in seismically active regions. This study introduces a novel simulation component designed to predict the spatial distribution of seismically-triggered landslides.

Seamlessly integrating within the broader Landlab modelling framework, the new component leverages high-resolution topographic data and incorporates key factors such as slope stability, soil thickness, and various hydrological conditions to predict locations that are particularly sensitive to failure in the event of an earthquake, mapping their probable extents. A key aspect of this component is the incorporation of the Newmark method, a classical mechanical model used in seismic landslide hazard analysis. Proposed by Newmark in 1965, it models a landslide as a rigid block sliding on an inclined plane. It calculates the critical acceleration needed to overcome the friction of the sliding surface, allowing the block to move when seismic intensity exceeds the slope’s stability. The model is currently being tested and validated against a detailed landslide inventory from central Nepal, with further plans to validate it against additional inventories from Papua New Guinea and New Zealand. Preliminary results demonstrate the component’s capability to accurately replicate observed shallow landslide patterns, and its potential for application in real-time hazard forecasting.

Here, we present the methodological framework, the challenges encountered during development, and the validation process. Additionally, we will showcase case studies highlighting the component’s practical applications, especially in seismically active regions, and discuss future enhancements to improve its predictive accuracy and computational efficiency.

Click on the poster to enlarge