About the Project
This PhD will draw on world-leading datasets from the Eddleston Water Project, the UK’s flagship natural flood management living laboratory. The project combines large-scale river and catchment restoration with one of the most comprehensive long-term monitoring programmes of its kind. Since 2010, interventions such as river re-meandering, wetland creation, floodplain reconnection, and tree planting (Figure 1) have been monitored through a detailed network of meteorological and hydrological, sensors. These datasets provide a great opportunity to research the effectiveness of Natural Flood Management interventions on catchment scale floods, droughts and hydrology.
Furthermore, the project’s close collaboration with the Tweed Forum, Scottish Government, Borders Local Authority, SEPA and other agencies, ensures direct impact on flood risk policy, climate adaptation, and nature restoration strategies. We are seeking an enthusiastic and motivated candidate with strong quantitative and analytical skills, and a keen interest in catchment hydrology, natural flood management, and research that informs environmental policy and practice.
This PhD will address the lack of conclusive evidence on the effectiveness of NFM, particularly at larger catchment scales (like the 69km2 Eddleston Water). We have limited knowledge of how the effect of the interventions combine together and propagate through the river system. The importance of how sub-catchments interact in terms of tributary synchronicity is essential to understand in terms of larger scale scheme design (Pattison et al., 2014).
To achieve this, two specific research gaps will be addressed: first, the collection of robust meteorological and hydrological data. You will be responsible for maintaining and collecting data from the extensive gauging network (12 river gauges and 8 weather/rain gauges), as well as developing innovative flow gauging approaches using salt dilution and drone/fixed camera-based particle image velocimetry, for rating curve development. This will involve extensive fieldwork. Second, this PhD will develop novel Deep/Machine Learning approaches for data mining the big data which has been collected through the gauged network. Importantly, this data was collected before and after NFM interventions. These methods will be used to understand how the catchment functions hydrologically i.e. what generates floods, and also to quantify the impact of the NFM interventions at different spatial scales and for floods of differing magnitudes. There also possibilities to combine data-based techniques with the outputs generated from physically based hydrological-hydraulic modelling to assess the benefits of hybrid approaches for NFM impact.
Training and skills
The project will provide opportunity for the student to gain knowledge and skills relating to catchment hydrology and nature-based solutions. Fieldwork skills will be developed to collect a range of hydrological and meteorological data. There is also opportunity for the student to develop machine learning and numerical modelling skills to assess catchment scale impacts. Most importantly, the project will provide the relaxed environment for the student to be creative, show initiative and build confidence in their ability to lead a research project.
You will join a vibrant research community within the Institute of the Sustainable Built Environment at Heriot Watt University in Edinburgh. You will be a member of the Nature Based Solutions research group, which run a series of seminars and training workshops.
The successful applicant will have opportunities to attend relevant training courses and to present their research at national and international conferences to build communication and networking skills. Furthermore, you will work closely with the project partners, providing frequent updates and attending meetings with stakeholders in the catchment. All costs will be covered for these activities.
Eligibility
This project is available to home applicants only. The successful candidate should have a strong background in hydrology and river science, have good mathematical and computer skills and possess at minimum a masters and undergraduate degree in related disciplines (Geography, Civil Engineering, Environmental Science, Mathematics, Computer Science).
As the PhD involves extensive fieldwork, it is essential that you interested and happy to work in the beautiful Scottish Uplands in any weather, and have a valid UK driving licence
We recognise that not every talented researcher will have had the same opportunities to advance their careers. We therefore will account for any particular circumstances that applicants disclose (e.g. parental leave, caring duties, part-time jobs to support studies, disabilities etc.) to ensure an inclusive and fair recruitment process. For any informal enquiry regarding project,
How to Apply
To apply you must complete our online application form.
Please select PhD Environment as the programme and include the full project title, reference number (EGIS2025-IP) and supervisor name on your application form. Ensure that all fields marked as ‘required’ are complete.
Once have entered your personal details, click submit. You will be asked to upload your supporting documents. You must complete the section marked project proposal; provide a supporting statement (1-2 A4 pages) documenting your reasons for applying to this particular project, outlining your suitability and how you would approach the project. You must also upload your CV, a copy of your degree certificate and relevant transcripts and an academic reference in the relevant section of the application form.
Please contact Dr Ian Pattison (i.pattison@hw.ac.uk ) for further information or an informal discussion.
Please contact egis-pgr-apps@hw.ac.uk for technical support with your application.
Timelines
The closing date for applications is 5pm on Friday 17th October, and applicants must be available to start in January 2026.
Funding Notes
This scholarship will cover tuition fees at the home rate and provide an annual stipend (paid in line with UKRI recommended rates, £20,780 in 2025-25) for 42 months. Thereafter, candidates will be expected to pay a continuing affiliation fee (currently £130) whilst they complete writing up their thesis.
References
Pattison I, Lane SN, Hardy RJ, Reaney SM , (2014), The role of tributary relative timing and sequencing in controlling large floods, Water Resources Research, 50, 5444-5458.
Black A, Peskett L, MacDonald A, et al. Natural flood management, lag time and catchment scale: Results from an empirical nested catchment study. J Flood Risk Management. 2021; 14:e12717. https://doi.org/10.1111/jfr3.12717
