EGU24-19965, updated on 11 Mar 2024
https://doi.org/10.5194/egusphere-egu24-19965
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modelling the impact from cascading geohazards using hypergraphs

Alexandre Dunant1, Alexander Densmore1, Thomas Robinson2, Sihan Li3, Mark Kincey4, Nick Rosser1, Ramesh Guragain5, Ragindra Man Rajbhandari6, Max Van Wyk de Vries7, Sweata Sijapati5, Katherine Arrell8, Erin Harvey1, and Simon Dadson9
Alexandre Dunant et al.
  • 1Durham University, Department of Geography, United Kingdom of Great Britain – England, Scotland, Wales (alexandre.dunant@durham.ac.uk)
  • 2University of Canterbury, School of Earth and Environment, New Zealand
  • 3Sheffield University, United Kingdom of Great Britain – England, Scotland, Wales
  • 4Newcastle University, United Kingdom of Great Britain – England, Scotland, Wales
  • 5NSET, Nepal
  • 6UN, Nepal
  • 7University of Cambridge , United Kingdom of Great Britain – England, Scotland, Wales
  • 8Northumbria University, United Kingdom of Great Britain – England, Scotland, Wales
  • 9University of Oxford , United Kingdom of Great Britain – England, Scotland, Wales

Modelling risk systems, in which natural hazards and exposure elements are intricately intertwined, poses a significant challenge, especially over large spatial and temporal scales. To address this issue, this study introduces the use of hypergraphs as a modelling framework for dynamic multi-hazard systems. Hypergraphs have found applications across disciplines for effectively capturing complexities in various systems.

The study demonstrates the suitability of hypergraphs to multihazard risk assessment through a case study of  the 2015 Gorkha earthquake in Nepal and its subsequent coseismic landslides. The initial test case is followed by the generation of cascading scenarios initiated by thirty high-magnitude simulated earthquakes across Nepal and analysis of the subsequent cascading impacts arising from landsliding on buildings and roads. The modelling is being developed to provide scientific evidence to inform preparedness planning at a range of scales.

Our results show that this approach is effective, offering several key advantages. First, the easy compatibility with spatial data enables a more accurate representation of real-world scenarios. Second, the proposed method is hazard-agnostic, allowing it to accommodate various types of natural hazards. Third, the high computational efficiency of the hypergraph-based model enables the use of large scenario ensembles. Finally, the capability to handle complex interactions between hazard processes and exposure elements streamlines the risk assessment process.

We emphasise that the adoption of hypergraphs as a modelling framework has the potential to substantially enhance multi-hazard risk assessment in natural systems. By providing a comprehensive and flexible approach, this method offers a promising avenue for improving risk management strategies and bolstering preparedness measures to mitigate the impacts of environmental disasters.

 

How to cite: Dunant, A., Densmore, A., Robinson, T., Li, S., Kincey, M., Rosser, N., Guragain, R., Man Rajbhandari, R., Van Wyk de Vries, M., Sijapati, S., Arrell, K., Harvey, E., and Dadson, S.: Modelling the impact from cascading geohazards using hypergraphs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19965, https://doi.org/10.5194/egusphere-egu24-19965, 2024.