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Slope stability and climate change in high-mountain regions: Understanding and predicting geo-hazards.


Project Description

HIGHLIGHTS:
• 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 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/rock temperature), etc. [1] 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 ([2], [3]) 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) [4]. Back and forward analysis of tipping point behaviours will be carried out in order to model better extreme and often unexpected events ([5]).

According to background education and to your particular research interests, this research will involve one or several of the following elements:
• 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…) 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.
• Remote Sensing: using state of-the-art Earth Observation techniques (GB-Radar, drones, time-lapse cameras, satellite imagery…) 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. A series of recent landslide events at regional scale will be investigated in high mountain areas (Norway, Nepal, Alps) including the links with changing climate.
• 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...) using experimental methods.

SCIENTIFIC BACKGROUND AND METHODS
The complexities that obstruct a straightforward forecasting of slope failures in alpine areas include complex model parameterization and a series of epistemic and parametric uncertainties. Well-established strain-rate failure models, such as the power-law acceleration rate [7] and inverse failure models that were built under the assumption of constant stress conditions may explain the lack of agreement between these simple models and field observations, questioning the ability to predict landsliding. In addition, current models for progressive slope degradation in permafrost do not yet take into account the time-varying behaviour of the slope [8]. At present, critical strains, detectable limits and the transition from secondary to tertiary creep, are maturing conceptually in this field, but we will test in this PhD whether the availability of Big Data together with the use of modern data-driven techniques will shed light in predicting slope accelerations as response to rainfall.

Funding Notes

Only highly motivated individuals with the ability to solve complex problems, the patience to deal with multifaceted data, a talent for data analytics / interest in computers and high academic achievement (first-class honours or 2:1) will be considered.

Areas: Engineering Geology | Geohazards | Remote Sensing | Geo-Computing | Civil Engineering | Geophysics | Geomatics | Rock Engineering | Cryosphere | Earth Surface Processes | Hydrogeology | etc.

Importantly, the PhD project will evolve according to your background, research interests and motivation, so it is not expected that you will arrive with all the skillset!

References

[1] Kos et al (2016). Contemporary glacier retreat triggers a rapid landslide response, Great Aletsch Glacier, Switzerland. Geophysical Research Letters 43 (24), 12,466–12,474.
[2] Wirz et al (2016). Short-term velocity variations at three rock glaciers and their relationship with meteorological conditions. Earth Surface Dynamics, 4(1), 103.
[3] Oppikofer et al. (2008). Collapse at the eastern Eiger flank in the Swiss Alps. Nature Geoscience, 1(8), 531.
[4] Huss et al. (2017). Toward mountains without permanent snow and ice. Earth's Future, 5(5), 418-435.
[5] PERMOS (2013). Glaciological Report (Permafrost) No. 10/11 of the Cryospheric Commission of the Swiss Academy of Sciences.
[9] Blikra and Christiansen (2014). A field-based model of permafrost-controlled rockslide deformation in northern Norway. Geomorphology, 208, 34-49.
[7] 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.
[8] Petley et al. (2005). Development of progressive landslide failure in cohesive materials. Geology, 33(3), 201-204.
[9] Abellan et al (under review). Real-time modelling of time-dependent landslide response to precipitation. Landslides
[10] Taleb, N. (2007). The black swan: The impact of the highly improbable. Vol. 2. Random house
[11] Gruber and Haeberli (2007). Permafrost in steep bedrock slopes and its temperature‐related destabilization following climate change. Journal of Geophysical Research: Earth Surface 112.F2.
[12] Voight, B. (1988). Nature, 332(6160), 125.
[13] Phillips et al (2016). Seasonally intermittent water flow through deep fractures in an Alpine Rock Ridge: Gemsstock, Central Swiss Alps. Cold Regions Science and Technology, 125, 117-127.
[14] Bernardie et al., 2015. Prediction of changes in landslide rates induced by rainfall. Landslides. 1;12(3):481-94.

How good is research at University of Leeds in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 79.20

Research output data provided by the Research Excellence Framework (REF)

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