Details of Award
NERC Reference : NE/R008949/1
Improved physical process representation of rivers networks in Global Flood Models
Training Grant Award
- Lead Supervisor:
- Dr MA Trigg, University of Leeds, Civil Engineering
- Grant held at:
- University of Leeds, Civil Engineering
- Science Area:
- Freshwater
- Overall Classification:
- Freshwater
- ENRIs:
- Environmental Risks and Hazards
- Natural Resource Management
- Science Topics:
- Regional & Extreme Weather
- Flood modelling
- Flood Risk Assessment
- Coastal & Waterway Engineering
- Flood models
- Earth Surface Processes
- Floods
- Flood risk assessment
- Environmental Informatics
- Abstract:
- Quantifying flood hazard is an essential component of resilience planning, emergency response, and insurance. Traditionally undertaken at catchment and national scales, efforts have recently intensified to estimate flood hazard with global models. These models are in their infancy and a recent study comparing six models (including one from CASE partner SSBN), identified a number of areas that require improvement. Along with more accurate digital elevation models (DEMs) and more extensive validation, improvements to the representation of global river networks was identified as a key area of improvement. All current global flood models use a simplified drainage network derived automatically from analysis of the satellite measured topography. This simplified network is easy to implement in flood models, but they are too simple, resulting in mismatches with real rivers. Problems occur where centrelines of modelled rivers do not match real rivers, tributaries end up draining into the wrong catchment, and complex multichannel systems can only be represented as a single drainage line. Using remote sensing observations of rivers has been identified as a method of supplementing these DEM derived datasets with real river data, but there remains the question of how best to represent these more complex "real" river networks in the global models, such that the current river network limitations can be overcome. This PhD will aim to answer two questions: How to incorporate the new data and, does this result in model output improvements? The student will begin by reviewing the remotely sensed river datasets that are currently available (many are open access). This will be followed by identifying or developing methods to incorporate this data into the current global river drainage networks used for global flood modelling. With ~10 million km of river channel globally with a width greater than 8 m, this will require automated and consistent methods, while capturing the most relevant details. These will need to be integrated into one rigorously consistent dataset. Developed methods will then be tested under different river types and climates to ensure global applicability and quantify the improvements to output. The researcher will have direct access to a state-of-the-art global flood model framework, developed by SSBN Ltd and work alongside experienced hydrodynamic researchers and code developers to implement and test different methodologies that they devise, with successful approaches being incorporated directly in the official SSBN flood model code during 4 months of secondment. The researcher will also be encouraged to publish all findings in open scientific publications, in line with the SSBN's aim to be at the forefront of advancing the science of global flood modelling. This multidisciplinary project will span diverse research areas including remote sensing, computer data methods, river hydraulics and geomorphology as well as flood management. It will be well supported in all these areas with appropriate training and relevant technical support. The project will lead directly to scientific improvements in the flood risk information used in decision making processes by policy makers and businesses, within the UK and internationally.
- NERC Reference:
- NE/R008949/1
- Grant Stage:
- Completed
- Scheme:
- Doctoral Training
- Grant Status:
- Closed
- Programme:
- NPIF Allocation
This training grant award has a total value of £88,293
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - RTSG | Total - Student Stipend |
|---|---|---|
| £17,295 | £11,000 | £59,998 |
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