Characterising landslides is difficult as these are influenced by a variety of environmental factors that cannot easily be measured in the field. However, process modelling tools provide a method which can complement observations and help us to gain a deeper understanding of the complexities involved in landslide process characterisation.
This project aims to utilise a comprehensive suite of monitoring data of the Hollin Hill landslide observatory to develop a process-driven numerical model of this active landslide site.
The Hollin Hill observatory contains an active landslide on a hillslope farm in North Yorkshire. It was set up in 2006 by the BGS and provides a unique opportunity to monitor an active landslide.
Since its establishment, comprehensive monitoring of geological, geomorphological and geophysical properties of the Hollin Hill site has been undertaken. The subsurface is characterised by four geological units that outcrop on the slope. The Redcar Formation (mudstones) at the base of the slope is overlain by the more porous Staithes Formation (sandstones) which is known to store and discharge groundwater. The Whitby Formation (mudstones) overlies the Staithes sandstones. Most of the landsliding at this site takes place in the Whitby mudstones and involves rotational slides, translational slips and flows. Locally, the top of the sequence is formed by the Dogger Formation (sandstone). Importantly, water stored in the unsaturated zone derived from both groundwater and surface flow infiltration is thought to play a pivotal role in the failure and subsequent movement of the hillslope.
ugh process modelling of the Hollin Hill site. We intend to develop and parameterise a distributed physical process model of the site that couples hillslope hydrology and groundwater flow with mechanical processes representing slope deformation.
Once the physical basis of these models has been established and verified against know historical deformations it will be possible to evaluate the impacts of particular weather event sequences on potential slope deformation. This in turn can be used to develop early warning systems in the future and can also be used to research slope response mechanisms as climate change progresses.
Contact Jonathan Mackay for more information