PhD: Utilising real-time environmental sensing for improved flood forecasting and early warning CASE++

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. Ref: 4787

About the award

Supervisors

Prof Albert Chen (University of Exeter)

Dr Peter Melville-Shreeve (University of Exeter)

Prof Richard Brazier (Director of CREWW, University of Exeter)

Dr Paul Lunt (University of Plymouth)

Tom Dauben (EA)

Project keywords: real-time flood monitoring, real-time flood warning, flood risk management, nature-based solutions, flood risk, catchment management, hydrological monitoring, hydraulic modelling

Introduction
Flood-risks are increasing globally, under the twin pressures of climate and land-use changes. 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. hydrological computer models depicting the response of a river catchment to the forecast rain) of rainfall, water levels or discharge measurements in rivers, or wind, surge and sea levels at the coast.

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 surface water runoff in local areas, especially for capturing the influences caused by local landscapes and structures. Equally there are challenges around forecasting of flooding in small, rural and rapidly responding river catchments which are not currently gauged and are not large enough to warrant a ‘traditional’ flood forecasting model, yet suffer from heightened rates of runoff from the upstream landscape.

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, smart gullies, IoT sensors, soil moisture probes, 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 and improved lead time of early warning. The results will help local authorities and communicates better understand and monitor the evolution of surface water floods and take necessary actions to protect vulnerable populations and assets and mitigate the negative impacts.

The PhD project aims to develop a robust understanding of novel sensing data, and establish transferrable methodologies to utilise monitoring data, modelling and prediction tools, and deliver timely early warnings to support decision makers and vulnerable communities for effective surface water flood risk management.

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 surface water 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, and from practical experience of project partners of the existing systems, 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 surface water flood warning solutions using highly localised data of one or more case study catchments in order to establish a localised surface water flood warning system.

The data from multiple sources, including data from local case studies, the existing forecasting and warning systems, and DRIP pilot projects will be considered. Existing local data and newly acquired data, ranging from IoT sensors to citizen data, can then be used to model and predict the likelihood of surface water flooding in selected catchments. The integration will enable catchment-tailored forecasts, in complementary to the information from the current system, such that local communities can better understand not only the upcoming flood hazards, but also the impacts associated with the disaster to support more effective flood risk prevention and mitigation.

These data will be fused and implemented to predict the evolutions of surface water flooding within a catchment, in the view of feeding them into a pilot local surface water flooding warning that will be issued when the hazards are likely to threaten human safety, damage properties, infrastructure, or environment, or disrupt socio-economic activities. The findings from the case study and pilot projects will help identify gaps in data collection, contribute to creating similar early warning systems in other catchments, and inform the national policy on provision of flood warnings for England.

The next stage, if necessary, will deploy new instruments to collect additional standardised data for filling gaps within the existing monitoring networks or gathering new types of information to support the analysis and enhance the performance of early warning.

Following on from this, data analysis of DRIP pilot projects dealing with surface water flood warning will begin to establish which solutions function well in a given location 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. Finally, the PhD will synthesise understanding of optimal flood warning 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, 17 project partners and two very strong research-led teams of academics at UoE and UoP.    

Objectives and timeline (assuming  September 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 for surface water flood forecasting
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

Background reading and references
(1) 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

(2) 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

(3) 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

(4) 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

(5) 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

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have Master’s degree. Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the project http://www.exeter.ac.uk/postgraduate/apply/english/

How to apply

Apply now

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

CV

Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.

Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.

If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English, please see the entry requirements for details.

Two references

The application deadline is midnight July 10th 2023

Interviews will take place July 21st 2023

Ideally, candidates should be prepared to start this PhD from September 2023.

For information relating to the research project please contact the Lead Supervisor, Prof Richard Brazier (r.e.brazier@exeter.ac.uk)

For information about the application process please contact the Admissions team

Summary