Don't miss our weekly PhD newsletter | Sign up now Don't miss our weekly PhD newsletter | Sign up now

  A Physically-Based Early Warning System for Landslide Hazards in the UK (RDF23/EE/MCE/QI)

   Faculty of Engineering and Environment

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr Xiaohui Qi  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

The United Kingdom is confronting heightened and more frequent extreme weather events due to climate change, resulting in an alarming increase in landslide hazards that pose significant threats to both the economy and human life. Notably, the extended precipitation in 2012 caused a five-fold surge in landslide occurrences. Urgent measures are required to implement a proactive approach in mitigating these destructive events by establishing early warning systems for regional-scale landslide hazards. However, slope stability analyses necessitate geologic, geotechnical, meteorological, and geomorphological data, which are often scarce and limited in engineering practice. Presently, rainfall serves as the primary driver of landslide hazards in the UK, but existing network infrastructure operators rely on static rainfall thresholds based on localized rainfall intensity, duration, or antecedent rainfall amounts, giving no strategic information on where the landslide hazard is the largest or evolves the quickest.

This research project proposes a pioneering, physically-based early warning procedure to assess landslide hazards in near real-time over extensive areas. The new approach will yield dynamic hazard maps that enhance the comprehension of slope responses under extreme weather conditions. Moreover, it will facilitate the strategic placement of field monitoring instruments, ultimately leading to cost-effective loss reductions through proactive preventative or mitigative efforts. To address the existing knowledge gap, high-resolution rainfall radar data will be harnessed to provide slope-scale rainfall delivery details at five-minute intervals. Historical slope events, encompassing both failures and non-failures, will be utilized to refine and constrain soil and geological properties of slopes through an innovative probabilistic back-analysis method, effectively addressing the challenge of sparse data. Additionally, a machine learning approach will be employed to develop a computationally efficient slope response model based on physically-based numerical analyses of rainfall-induced landslides. This model will enable rapid evaluation of slope responses for thousands of locations. Furthermore, new rainfall thresholds will be established using future climate scenarios from the UK climate projection, generated by the Met Office, to enhance the accuracy and efficacy of the early warning protocols.

By advancing this research, we can significantly improve the UK's ability to predict and manage landslide hazards, safeguarding communities, infrastructure, and valuable resources. The results of this study will pave the way for more proactive and sustainable measures in dealing with the challenges posed by increasing extreme weather events and their impact on the landscape.

Academic Enquiries

This project is supervised by Dr Xiaohui Qi. For informal queries, please contact [Email Address Removed]. For all other enquiries relating to eligibility or application process please use the email form below to contact Admissions.

Eligibility Requirements:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.

Please note: to be classed as a Home student, candidates must meet the following criteria:

  • Be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.
  • If a candidate does not meet the criteria above, they would be classed as an International student. Applicants will need to be in the UK and fully enrolled before stipend payments can commence.

How to Apply

For further details of how to apply, entry requirements and the application form, see  

For applications to be considered for interview, please include a research proposal of approximately 1,000 words and the advert reference (e.g. RDF23/…).

Deadline for applications: 31 August 2023

Start date of course: 1 October 2023

Computer Science (8) Engineering (12) Environmental Sciences (13) Geography (17) Geology (18)

Funding Notes

The studentship is available to Home students and includes a full stipend at UKRI rates (for 2023/24 full-time study this is £18,622 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £11,173 per year and full tuition fees) in combination with work or personal responsibilities).


Apip, Takara, K., Yamashiki, Y., Sassa, K., Ibrahim, A. B., & Fukuoka, H. (2010). A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale. Landslides, 7, 237-258.
Kirschbaum, D., & Stanley, T. (2018). Satellite‐based assessment of rainfall‐triggered landslide hazard for situational awareness. Earth's Future, 6(3), 505-523.
Liu, X., & Wang, Y. (2021). Reliability analysis of an existing slope at a specific site considering rainfall triggering mechanism and its past performance records. Engineering Geology, 288, 106144.
Liu, X., Wang, Y., & Leung, A. K. (2023). Probabilistic back analysis of rainfall-induced slope failure considering slope survival records from past rainfall events. Computers and Geotechnics, 159, 105436.

How good is research at Northumbria University in Engineering?

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

Click here to see the results for all UK universities

Where will I study?

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.