- Timely project: a game changing approach to improve understanding, model and forecast landslides in mountainous areas is urgently needed.
- Significance: Key aspects of landslide science are not fully understood yet, including the mechanisms behind a series of cascade events as a result of global warming, permafrost degradation, progressive degradation of the slope and the transition from slow-moving landslides to catastrophic failures.
- Teamwork effort: This PhD project will benefit from a wider group collaboration, with a team of researchers with interest in Engineering Geology, Risk Management, Cryo-Hazards, Hydrology and Computer science. A wealth of data is available from our project partner in Norway (Landslide Unit | Norwegian Water and Energy directorate) and from different Earth Observation platforms.
PROPOSED RESEARCH | Aims and objectives: We propose an exciting and truly multi-disciplinary PhD opportunity to create a paradigm shift in methodologies for modelling and forecasting the temporal occurrence of a catastrophic landslides in High Mountain areas, where the instabilities are controlled both by a progressive strength reduction -associated with freeze/thaw cycles and permafrost degradation- and a seasonally intermittent water flow through deep fractures.
You will start investigating a recent collapse of Mannen mountain in Norway (figure 1) using a series of high quality datasets including in-situ extensometers, remote sensing techniques (GB-Radar, drones, time-lapse cameras), environmental forcing (precipitation, snow melting, solar radiation, air temperature, rock temperature at different depths), etc.  provided by the project partner in Norway (NVE). This exceptional data of an active slope failure that was captured in real-time by NVE, and a series of time-dependent models of brittle failure, which importantly apply to all brittle slope failures worldwide (, ) will be used to back analyse the slope during the first stage of this PhD project. Accessing to these fundamental observations, you will unpick and model the highly non-linear landslide response to both the environmental forcing and the progressive movement of the slope, using a new physically-based time-invariant model for forecasting slope kinematics (fig. 2) . Back and forward analysis of tipping point behaviours will be carried out in order to model better extreme and often unexpected events (the so called black swan events ).
According to background education and to your particular research interests, this research will involve one or several of the following elements:
- Advance data analytics + artificial Intelligence: You will be analysing big-data in the form of time-series (see above) either using data-driven approaches (e.g. support vector machine, neural networks systems, multivariate regression analysis, Bayesian theories, etc.) or physically-based models –or both- in order to improve landslide forecasting. The complex links between external forcing (rainfall, temperature), infiltration and slope kinematics will be investigated using state of the art computational techniques in order to propose a new generation of Early Warning Systems, that will be developed and implemented in close collaboration with project partners in Switzerland and Norway (CREALP, NVE).
- Remote Sensing: using state of-the-art Earth Observation techniques (GB-Radar, drones, time-lapse cameras, satellite imagery, etc.) and in-situ sensors you will investigate both slope kinematics and gradual slope damage of the slope by looking at freeze-thaw cycles + permafrost degradation, including the links with changing climate. A series of recent landslide events at regional scale will be investigated in high mountain areas (Swiss Alps / Norway) in close collaboration with our project partners (CREALP, NVE) that will be providing logistical support and facilitating fieldwork.
- Laboratory-based experiments: you will be using the facilities in our rock mechanics and engineering geology laboratories –and partner facilities- to better parametrize and model the gradual slope damage by studying progressive strength degradation of geologic materials through time (crack growth, freeze-thaw cycles, rock bridges, etc.) using a series of experimental methods, which has key implications for slope stability.
ENHANCED TRAINING OPPORTUNITIES: This project will be carried out in collaboration with key partners outside academia, including the “Research Center in Alpine Environment (CREALP)” in Switzerland and the “Norwegian Water and Energy Directorate (NVE)” in Norway, facilitating a unique opportunity for accessing to training, facilities and expertise not available in an academic setting alone. Priceless experimental observations, together with profound knowledge of the topic and areas under investigation will be shared to the prospective PhD student. You will be invited to have a real-world experience outside the academic environment with the project partners, where you will have the opportunity to visit their facilities / study areas of interest for this project, present your scientific advances, and discuss further ideas for a potential implementation of your findings in existing EWS for alerting the population over different levels of danger (6) and even do a placement in the CREALP-Switzerland or NVE-Norway (to be agreed with project supervisors).
FURTHER INFORMATION: Please contact the lead supervisor (A.Abellan@leeds.ac.uk) for further information. We encourage interested applicants to get in touch and arrange an informal skype meeting to discuss details of the project.
FURTHER READING:  Blikra and Christiansen (2014). A field-based model of permafrost-controlled rockslide deformation in northern Norway. Geomorphology, 208, 34-49;  Krautblatter et al. (2013). Why permafrost rocks become unstable: a rock–ice‐mechanical model in time and space. Earth Surface Processes and Landforms, 38(8), 876-887;  Petley et al. (2005). Development of progressive landslide failure in cohesive materials. Geology, 33(3), 201-204;  Abellan et al (under review). Real-time modelling of time-dependent landslide response to precipitation. Landslides;  Taleb, N. (2007). The black swan: The impact of the highly improbable. Vol. 2. Random house