The frequency and severity of landslides is changing worldwide because of climate change. Landslides are complex phenomena, which involve hydro-mechanical processes in the ground in response to atmosphere-soil interaction. These processes are influenced by temperature, and laboratory experiments indeed show that the main soil parameters (shear strength, compressibility, hydraulic conductivity) are sensitive to even small variations (1–2 °C) in temperature. However, in temperate/warm climates (i.e., in non-freezing conditions), these thermal effects are not usually considered in slope stability models and, if they are, they are only related to hydrological and biological processes and not to soil mechanical behaviours. Consequently, the role of climate change in temperate/warm climates may be underestimated. This could be especially true for shallow (< 10 m) landslides in clay soils – common in Europe (Czech Republic included) – as clays are especially sensitive to thermodynamic disturbances.
In this project, we will explore key aspects of temperature-dependent behaviours in soils and develop modelling strategies accordingly, to take these behaviours into account. We will work at multiple scales, through laboratory experiments under various thermal, hydraulic and mechanical conditions and through modelling at slope and regional scales via both physically-based and data-driven approaches. Finally, we aim to propose a conceptual framework to evaluate whether observed (changes in) landslide patterns can be explained according to temperature-dependent processes observed at small scale.
More specifically, this fellowship is intended for a motivated postdoctoral candidate with expertise in spatial and spatio-temporal modelling of landslides and a solid understanding of physical processes responsible for landslide triggering and propagation. The fellow will be responsible for carrying out data-driven analyses (landslide susceptibility and hazard modelling) at the regional scale using a variety of methods rooted in geostatistics and machine learning. The analysis will focus on selected case studies in different climatic and lithological settings (e.g., in Central, Northern, and Southern Europe), with the goal of exploring the role of thermal forcing at different temporal scales (long-term warming, seasonal oscillations, short-term heatwaves) on different types of landslides (shallow vs. deep-seated, translational vs. rotational landslides, etc.) and learn how this combines with expected changes in precipitation patterns and land use. Special attention will be paid to the interpretability / explainability of the model results, the lack of which is a typical shortcoming in data-driven modelling. The fellow will explore novel AI-powered tools that are currently emerging, which aim at providing physical explainability to patterns identified in a purely data-driven manner. At the same time, the fellow will collaborate with other team members within the project who will be carrying out physically-based modelling at the slope and catchment scales for the same case studies as well as for virtual slope profiles, and will work on understanding whether and why the observed/predicted patterns are or are not consistent, thereby providing guidance on the upscalability/downscalability of landslide modelling approaches.
Salary: co-founding 1000 EUR/month is ensured
Co-founding resources: ERC CZ project THALIS – Thermally induced instability of slopes under climate change (funds from MSMT, waiting for approval)
Department: Institute of Hydrogeology, Engineering Geology and Applied Geophysics
Supervisor: Gianvito Scaringi
Phone: +420 777 546 432
Position available from: January 1, 2024
Deadline date for applications: July 25, 2023