Project highlights

  • Developing key methodology for new generation of national flood forecasting in the UK.
  • Working along with flood experts from key national organisations and stakeholders, including opportunities for visits and secondments.
  • Developing high employable skillsets in flood risk modelling, statistics, high-performance computing and data analytics.


Flooding is one of the most damaging natural hazards in the UK and worldwide. Flood damage currently costs the UK around £1.3 billion each year. With climate change, it is expected that more extreme rainfall events will happen in the future and flood risk is on the rise. Building resilience to flooding is key to climate adaptation. Flood forecasting is a vital tool that provides information about the magnitude, timing and duration of flooding so that emergency responders, businesses and the public can get prepared to reduce the damage caused by flooding.

This PhD project provides an exciting opportunity for the student to work with a team of internationally leading experts to develop the next generation of flood forecasting methodology. The information being used for predicting flooding can never be certain. Therefore, decisions about flooding needs to incorporate the uncertainty of flood forecasting. One way to incorporate uncertainty into flood forecasting is to use probabilistic forecasts (e.g. ensembles of potential flood outputs, not just a single, deterministic, forecast). These methods provide the probability that certain values of flood depth/discharge would be reached rather than definitive single flood values. The state-of-art probabilistic national-scale flood forecasting gives outputs at river locations that have observations or as broad-scale maps (1 km gridded), but this project aims to develop models that can generate maps of flood probabilities with significantly higher resolutions (1-10 m).

The student will work with models that sits at the heart of UK’s national flood forecasting system and answer the following questions:

1) How to effectively link different types of models (i.e., weather forecasting model, hydrological model, and hydrodynamic flood inundation model) to generate probabilistic flood maps.

2) How to combine uncertainties from different sources and represent them within the probabilistic flood maps.

3) How to verify the probabilistic maps using data from past flood events.

The project is building on substantial efforts in previous projects such as the Met Office funded ‘Flood Hazard Impact Model for India (FHIM-India)’ project, and the outputs will inform the next-generation of the UK’s national flood forecasting capability. The project will help to deliver the much-needed capability for governments and businesses across the UK to become more flood resilient.

set of three images showing the water cycle, maps of the UK for soil moisture and river flow and a Flood Guidance Statement for Tuesday 08 August 2017, as well as logos for the Flood Forecasting Centre, Environment Agency, Met Office, Scottish Environment Protection Agency and Scottish Flood Forecasting Service

Figure 1: Models and outputs of the UK’s national flood forecasting services

CENTA Flagship

This is a CENTA Flagship Project

Case funding

This project is suitable for CASE funding


UK Centre for Ecology & Hydrology


  • Climate and Environmental Sustainability


Project investigator


How to apply


The project will link together the UK Met Office’s Unified Model for numerical weather forecasting in ensemble form, the UKCEH’s G2G model which is sitting at the heart of the Flood Forecasting Centre, and the open-source high-performance flood inundation model ‘HiPIMS’. Building on previous work in the FHIM-India project, new workflows will be created to propagate the ensemble weather forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) through the G2G-HiPIMS model chain to generate ensemble flood maps. The models will be run on high-performance computers supported by BEAR (Birmingham Environment for Academic Research). A Bayesian-based approach will be applied to post-process the model outputs to derive flood probability maps. Hydrological monitoring datasets from the UKCEH, Environment Agency and the Met Office will be utilised to verify the models.

Training and skills

Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.

The supervisory team will equip the candidate with an inter-disciplinary knowledgebase and skillset required for this project. Training will be provided in skills relating to flood modelling, data management, high-performance computing and statistical analysis, either directly by CENTA, or other relevant external training courses, for which funding will be available. The skills developed will equip the candidate with the skills to follow multiple career pathways including academia or industry, such as consultancy, government agencies and charities. The student will be supported to publish the work in peer-reviewed journals and attending academic conferences both nationally and internationally.

Partners and collaboration

This project is in partnership with the UKCEH, the Flood Forecasting Centre (UK Met Office & Environment Agency) and Jacobs (CASE partner). These partners are playing a key role in developing and operating the UK’s current national flood forecasting service. They will support this project by facilitating and providing access to their models and datasets, and hosting visits and secondments. The student and supervisory team will have regular meetings with the partners during the project.

Further details

For further details of the project, please feel free to contact Dr Steven Cole ([email protected]) or Dr Xilin Xia ([email protected]).

If you wish to apply to the project, applications should include:

  • A CV with the names of at least two referees (preferably three and who can comment on your academic abilities)
  • Submit your application and complete the host institution application process via:: and go to Apply Now in the PhD Geography and Environmental Science (CENTA) section.  Please quote CENTA23_CEH2 when completing the application form.

Applications to be received by the end of the day on Wednesday 11th January 2023.

Possible timeline

Year 1

  • Attend DR trainings (e.g. statistics, using R for data processing)
  • Developing a workflow to process the ensemble weather forecasting datasets and feed them into the G2G-HiPIMS model chain.
  • Visits to partners.

Year 2

  • Developing a Bayesian-based framework to postprocess the outputs from the HiPIMS model and derive the uncertainty range.
  • Submitting the first journal paper
  • Attending conferences, e.g., EGU
  • Extended visit to partner.

Year 3

  • Verifying the new flood forecasting workflow using hydrological monitoring datasets
  • Attending conferences.

Further reading

  • Cole, S.J., Moore, R.J., Wells, S.C. and Mattingley, P.S., (2016). Real-time forecasts of flood hazard and impact: some UK experiences. FLOODrisk 2016, 3rd European Conference on Flood Risk Management, E3S Web of Conferences, 7, 18015, 11pp. doi:10.1051/e3sconf/20160718015.
  • Xia, X., Liang, Q., Ming, X., (2019) A full-scale fluvial flood modelling framework based on a High-Performance Integrated hydrodynamic Modelling System (HiPIMS). Advances in Water Resources, doi: 10.1016/j.advwatres.2019.103392
  • Giuntoly, J.P.V., Prudhomme, C., Hannah, D.M., (2015) Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models. Earth System Dynamics 6, 267-285
  • Han, S., Coulibaly, P., (2017) Bayesian flood forecasting methods: A review. Journal of Hydrology, 551:340-351.
  • Ming, X., Liang, Q., Xia, X., Li, D., Fowler, H., (2020), Real-time flood forecasting based on a high-performance 2D hydrodynamic model and numerical weather predictions, Water Resources Research, doi:10.1029/2019WR025583


This project is unlikely to be impacted by any restrictions from COVID-19, since the project is mostly computational and does not involve fieldwork, and all the data required are either publicly available or available through request. Although there are opportunities for national or international exchange study, online meetings and online courses are alternatives in case of local or national lockdown.