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Utilising real-time environmental sensing for improved flood forecasting and early warning CASE++ Fully-funded PhD with University of Exeter, Devon County Council, EA and DRIP partners.

   College of Engineering, Mathematics and Physical Sciences

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  Dr A Chen, Dr P Melville-Shreeve, Dr R Brazier  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project


Flood-risks are increasing globally, under the twin pressures of climate and land-use change. Whilst conventional solutions to mitigate flooding are proven, they are also costly and often do not deliver environmental resilience in a holistic sense or in the long-term. Various interventions also require a clear understanding of upcoming flood threats such that adequate actions can be implemented to protect vulnerable communities. To identify the locations that are under imminent flood risk, real-time flood modelling and early warning systems will provide critical information for decision making. Most of the existing flood forecasting approaches are based on the observations (e.g. rain gauge or weather radar) or forecast (e.g. numerical weather prediction) of rainfall, water levels or discharge measurements in rivers or sea.

Despite approaches of this type have provided reliable and timely assessment of flood hazards at catchment or national scales, they are limited to describe the propagations of runoffs in local areas, especially for urban setting, due to complicated landscapes and structures influencing the process significantly, which are not captured by the above-mentioned monitoring measures.

The recent rapid development of Internet of Things (IoT) provides an excellent opportunity to fill the current gaps and gather high resolution data to improve environment configurations for real-time assessment to support informed decision making. Data streams from car fleets, building sensors and targeted deployment of environmental sensing represent a key element of the fourth industrial revolution. As the “Digital 4.0” revolution continues apace, transformative change in the way we gather data, produce insight, and take action will see autonomous cars providing valuable nowcast data from their windscreen mounted rainfall sensors. Meanwhile, water level sensors in water butts can monitor rainfall gathered at houses across a city and function as a rain gauge.

The real-time information collected from different sources of measurements (e.g. rain gauge, radar, river gauge) or monitoring/sensing (e.g. CCTV, IoT sensors, crowd-sourcing) will enhance the representations of environmental conditions and assimilate variables in flood modelling to provide more accurate predictions of flood dynamics in near future. The results will help local authorities and communicates better understand and monitor the evolution of floods and take necessary actions to protect vulnerable populations and assets and mitigate the negative impacts.

Project Description

The research will be designed, in the first instance, to draw together the state-of-the-art methodologies and technologies adopted in existing flood warning monitoring projects worldwide. This review of understanding will both contextualise what we know, allowing the PhD candidate to build a conceptual model of how different flood warning interventions work within a standard literature review framework, but will also yield data that can begin to be used to analyse differences between such solutions. Of course, strong datasets describing flood warning system deployments tend to be national scale, so the next stage of the PhD will be to design a monitoring program that collects the empirical data that are needed to characterise the effectiveness of flood warning solutions using highly localised data for one or more case study catchment.

The data from multiple sources will be fused and implemented to predict the evolutions of floodings within a catchment that early warnings will be issued when the hazards are likely to threat human safety, damage properties or environment, or disrupt socio-economic activities. The findings from the case study will further strengthen the monitoring program for supporting wider applications in other catchments. The next stage will therefore be to instrument multiple sites to collect standardised data which can allow new data sets to be acquired. Following on from this, data analysis will begin to establish which solutions function well where and these data will also be shared with the partner-PhD’s on this program of research, to evaluate models or multiple benefits of flood warning solutions, for example. Finally, the PhD will synthesise understanding of optimal flood waring approaches and share this information widely across all partner organisations to build an evidence-base for decision-making around their further value and deployment.

In common with all PhD’s in this program, the PhD project will benefit from lateral support within the team of 5 PhD students, 22 project partners and two very strong research-led teams of academics at UoE and UoP.    

Objectives and timeline (assuming 1st January 2023 start)

Year 1.

- Objective 1. (months 0 - 6)

- Objective 2. (months 6 - 9)

- Objective 3. (months 9 – 12)

1.1 Literature review on existing flood early warning methodologies and technologies

1.2 Build conceptual model of flood warning understanding

2 Design sensor deployment program

3 Install monitoring equipment across multiple sites

Year 2.

- Objective 4. (months 13 – 36)

- Objective 5. (months 13 – 18)

- Objective 6. (months 16 – 24)

- Objective 7. (months 16 – 24)

4 Begin monitoring at new sites, continue monitoring at existing sites

5 Development of data extraction and data fusion methodologies

6 Development of flood forecasting methodology using real-time observations

7 Peer-reviewed journal publication on environmental sensing data integration for flood early warning

Year 3.

- Objective 8. (months 25 – 30)

- Objective 9. (months 31 – 36)

- Objective 10. (months 31 – 36)

8 Flood forecasting model calibration

9 Development of flood early warning and decision support tools

10 Peer-reviewed journal publication on flood forecasting model

Year 4.

- Objective 11. (months 37 – 40)

- Objective 12. (months 37 – 42)

- Objective 13. (months 40 – 42)

11 Collate all data and synthesise to compare/contrast between sites and value of flood warning interventions

12 Write-up findings for thesis and peer-reviewed journals

13 Final reporting for DRIP program

The application deadline is midnight Saturday 31st December 2022

Interviews will take place in the week commencing 9th January 2022

Ideally, candidates should be prepared to start this PhD from 1st February 2023

For information relating to the research project please contact the Lead Supervisor, Prof Richard Brazier ([Email Address Removed])

For information about the application process please contact the Admissions team via [Email Address Removed]

For further information and to submit an application please visit - https://www.exeter.ac.uk/study/funding/award/?id=4645

Funding Notes

The studentship will provide funding of fees and a stipend which is currently £17,668 per annum for 2022-23 plus RTG of £6,141.46. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD


(9) Webber JL, Fletcher T, Farmani R, Butler D, Melville-Shreeve P. (2022) Moving to a future of smart stormwater management: A review and framework for terminology, research, and future perspectives, Water Research, volume 218, DOI:10.1016/j.watres.2022.118409
(10) Rapant, P.; Kolejka, J. Dynamic Pluvial Flash Flooding Hazard Forecast Using Weather Radar Data. Remote Sens. 2021, 13, 2943. https:// Ellis et al., (2021) Mainstreaming natural flood management: A proposed research framework derived from a critical evaluation of current knowledge https://doi.org/10.1177/0309133321997299
(11) Riede, H., Acevedo-Valencia, J. W., Bouras, A., Paschalidi, Z., Hellweg, M., Helmert, K.,& Nachtigall, J. (2019). Passenger car data–a new source of real-time weather information for nowcasting, forecasting, and road safety. https://repositorio.aemet.es/bitstream/20.500.11765/10651/1/OBN1_Riede_3ENC2019.pdf
(12) Piadeh, F., Behzadian, K. Alani, M. A., (2022) A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology. 607 (127476). DOI:10.1016/j.jhydrol.2022.127476
(13) Wallbank, J.R., Dufton, D., Neely, R.R., Bennett, L., Cole, S. J., Moore, R.J. (2022) Assessing precipitation from a dual-polarisation X-band radar campaign using the Grid-to-Grid hydrological model. Journal of Hydrology. 613A (128311). DOI: 10.1016/j.jhydrol.2022.128311
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