About the Project
This project is one of a number that are in competition for funding from the Red-ALERT CDT, hosted by the University of Bath for entry in September 2026.
Overview of the Research:
River catchments are complex systems where natural hydrological processes and anthropogenic activities interact to determine the mobilisation, transport and fate of pollutants. Increasing pressures from climate change and urbanisation will result in deterioration of water quality, unless cost-effective solutions can be identified. Thus, effective catchment management strategies require the development of new decision-making support systems capable of representing the governing processes, while also making best use of the emerging data sources such as citizen science and remote sensing, alongside insitu environmental monitoring programmes. To address these challenges, this project will develop, validate, and implement a digital twin for the River Cam and Wellow catchments in Somerset. The digital twin will enable decision-makers to simulate, predict, and optimise management of water quality across the catchments, including assessing the effectiveness of interventions. Pollution from diffuse sources, treated wastewater and intermittent untreated discharges will be represented. The system will combine data from regulators, real-time sensor networks, existing citizen science data available from the project partners with hydrological and biogeochemical models, and machine learning. We will focus on environmental and human health indicators (e.g. nutrients, eutrophication, dissolved oxygen and pathogens). The key objectives of the project include:
(1) building a modular data-driven architecture, integrating data assimilation methods, (2) Enable modelling impacts of urbanisation, climate change, and pollution control, and (3) assess effectiveness of proposed intervention strategies, including nature-based solutions. Using existing hydrological and water quality models such as URMOD (University of Bath) and QUESTOR (UKCEH), the project will deliver a scalable decision-support tool for stakeholders, enhancing proactive catchment management and evidence-based policy. The successful students will work with the supervisor team, and the Bristol Avon Rivers Trust to improve water quality management in the catchment areas.
Training Provided:
Training will be provided in the key technical aspects of the use of advanced environmental modelling systems, data management and uncertainty analysis. We are expecting a secondment to UKCEH and the project partner, providing an exciting opportunity to be involved in real-world projects and catchment management.
Interdisciplinary:
This project integrating hydrology, biogeochemistry, data science, and environmental engineering with social science and policy. It combines process-based modelling, machine learning, and citizen science to develop a digital twin that unites environmental monitoring, computational methods, and stakeholder engagement, supporting evidence-based catchment management and water quality improvement.
Project Keywords:
Industrial Partner: Simon Hunter (CEO), Bristol Avon Rivers Trust, Simon@bristolavonriverstrust.org
Candidate Requirements:
Applicants should hold, or expect to receive, a First Class or good Upper Second-Class UK Honours degree (or the equivalent)in a relevant subject – e.g. civil and environmental engineering, physical geography, computer science etc. Academic qualifications are considered alongside significant relevant non-academic experience. A master’s level qualification would also be advantageous.
Equality, Diversity, and Inclusion:
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Enquiries and Applications:
Formal applications should be submitted via the Red-ALERT CDT online application form prior to the closing date of this advert.
Funding Notes
Candidates may be considered for a NERC Red-ALERT studentship tenable for 3.5 years. Funding covers tuition fees, a stipend (£20,780 p/a in 2025/6) and access to a training support budget.
