Landslides are a persistent and widespread geohazard in Indonesia, causing tens to hundreds of deaths and widespread infrastructure damage on a yearly basis (e.g. Froude and Petley 2018). Landslide triggers are multiple and complex, ranging from seismic shaking and volcanic activity to intense rainstorms. Rainfall-triggered landslides are the most frequent and widely distributed. The intense rainfall typically associated with slope failure can, in theory, be forecast. Indeed, a precipitation-threshold-based approach is currently used in Indonesia in an operational capacity to highlight periods of elevated landslide hazard. However, precipitation thresholds are known to oversimplify the mechanistic processes of landslide triggering – including the importance of slope preconditioning – and require rigorous testing to ensure their reliability.
This PhD project aims to:
- Explore statistical patterns in the occurrence and distribution of rainfall-triggered landslides in Indonesia. This will involve building an inventory of the spatial and temporal occurrences of landslides in specific regions of Indonesia using a combination of remote sensing techniques and field investigations.
- Examine the performance of existing landslide precipitation threshold models across geographically and climatologically distinct regions of Indonesia using existing and newly compiled fatal and non-fatal landslide databases.
- Evaluate possible alternatives to threshold-based landslide forecast approaches, including machine-learning techniques, and better representation of local and preconditioning factors.
The successful applicant will become part of a highly motivated group of researchers at the Nottingham Geospatial Institute at the University of Nottingham and be closely integrated with and co-supervised by the British Geological Survey and local Indonesian hazard experts. The necessary training will be provided will be provided both ‘in-house’ and externally, and the successful applicant will be required to undertake multiple fieldwork trips to Indonesia.
Students should hold a 2:1 degree or above in an appropriate physical science discipline including physics, geography, geology or a related subject. Knowledge of basic computational techniques and the ability to process large datasets, including climate and remotely sensed data would be an advantage.
Email address for enquiries.