Application Deadline: 30 June 2024
Details
Hydrological modelling forms the backbone of crucial operational water management and planning such as design of flood defences, flood forecasts, water resources planning, and more. Understanding changes to catchment hydrology is essential as it is a key factor in multiple climate hazards. To adapt well to climate change, engineers require robust estimates of climate impacts on river flows, overland flows, soil moisture and groundwater levels which can come from hydrological modelling studies.
Hydrological modelling has a long legacy of development and as such there are a huge range of models to choose from for practical use. One of the key differences between hydrological models is the level of physical representation of catchment processes within the model code. Machine learning (ML) models are entirely data-driven and contain no pre-conceived representation of catchment processes. Conceptual models represent processes in a simplified way that are parameterised and calibrated to observational data. Physically based (PB) models codify known physical laws into a single modelling framework. Each model structure has strengths and weaknesses. Recent studies have highlighted the superior performance of Machine Learning (ML) models over conceptual and PB models in replicating historical river flows, indicating their potential for more effective operational use, yet water managers remain wary of ML models due to their opaque nature, raising concerns about process visibility. Conversely, PB models offer explicit representation of known physical processes, and have been shown to simulate more robust projections of future river flows under climate change. Current operational methodologies predominantly rely on conceptual models, lacking the sophistication of more advanced techniques.
Emerging research suggests that hybrid ML and PB models hold promise for achieving even better historical simulations, with the added bonus of improved process understanding and robustness for use in climate impact studies. However, this area remains largely unexplored in the hydrological domain. This PhD opportunity therefore aims to explore the area of PB+ML hydrological modelling in the UK context and address several key research objectives:
– Investigate the reasons behind the superior performance of ML models over physically based models and how they leverage data more effectively.
– Explore whether ML models emulate physical processes absent in physically based models and if ML models can identify expressions of these processes.
– Assess the applicability of existing Physics-Informed Neural Network (PINN) and hybrid ML+PB models in hydrological contexts, determining the most appropriate framework.
– Evaluate whether a PB+ML model outperforms existing national models in the UK.
– Develop a national-scale PB+ML model for the UK and assess its confidence in predicting flows in a variety of catchments.
– Examine the ability of a PB+ML model to robustly project future changes in floods and droughts.
– Embark on this exciting journey to push the boundaries of hydrological modelling and contribute to solutions for pressing water management challenges. Apply now to be at the forefront of cutting-edge research in the field.
Eligibility
Applicants should have, or expect to achieve, an excellent academic record (UK First-class or 2.1 honours or international equivalent depending on the funding source) in Engineering, Earth Sciences, Computing or another related physical science discipline (MSc, MSci or BSc). You should have appropriate experience in hydrology, modelling or machine learning and an interest in developing your modelling skills. You must be able to code in Python. Some knowledge or previous experience in flood and drought management or computational modelling would be helpful, but is not an essential since you will receive training in all the relevant techniques. You will be encouraged to attend national and international conferences to share your research.
How to apply
Apply online through our website: https://uom.link/pgr-apply-fap
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
After you have applied you will be asked to upload the following supporting documents:
- Final Transcript and certificates of all awarded university level qualifications
- Interim Transcript of any university level qualifications in progress
- CV
- Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
- Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
- English Language certificate (if applicable)
If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Funding Notes
This 3.5 year PhD is funded by the School of Engineering. Tuition fees will be paid and you will receive a tax free stipend set at the UKRI rate (ÂŁ19,237 for 2024/25). The funding is for home students and EU students with settled status. The start date is 1st October 2024.
References
1. CCRA https://www.theccc.org.uk/uk-climate-change-risk-assessment-2017/
2. Flood Hydrology Roadmap https://assets.publishing.service.gov.uk/media/62335ac2e90e070a54e18185/FRS18196_Flood_hydrology_roadmap_-_report.pdf
3. https://www.nature.com/articles/s41586-024-07145-1
4. https://doi.org/10.5194/hess-26-3377-2022
5. https://hess.copernicus.org/articles/26/3079/2022/
6. https://doi.org/10.1029/2020WR028091
7. https://doi.org/10.1029/2019WR026065
8. https://doi.org/10.1038/s41467-020-14688-0
9. https://doi.org/10.1002/hyp.14288
10. https://doi.org/10.5194/hess-27-2357-2023
11. https://doi.org/10.1029/2021GL092999
12. https://doi.org/10.5194/hess-2023-258
13. https://doi.org/10.5194/hess-28-479-2024
14. https://doi.org/10.102
