Details of Award
NERC Reference : NE/Z503411/1
FLOODTWIN
Grant Award
- Principal Investigator:
- Dr M Mansour, British Geological Survey, Environmental Modelling
- Co-Investigator:
- Mr M Nayembil, British Geological Survey, Geo-Information & Systems
- Co-Investigator:
- Dr A Barkwith, British Geological Survey, Groundwater
- Co-Investigator:
- Dr J M Scheidegger, British Geological Survey, Groundwater
- Co-Investigator:
- Dr A A Novellino, British Geological Survey, Earth Hazards & Observatories
- Grant held at:
- British Geological Survey, Environmental Modelling
- Science Area:
- None
- Overall Classification:
- Unknown
- ENRIs:
- None
- Science Topics:
- None
- Abstract:
- Water is an important driver for many environmental processes and can be key in the timing, scale and impact of natural hazards. Floods, landslides, storms and droughts are becoming more frequent and intense under climate and land use change. These water-related hazards pose direct impacts (e.g., damage to buildings, crops and infrastructure, and loss of life and property), and indirect impacts (e.g., losses in productivity and livelihoods, increased investment risk, indebtedness and human health impacts) to society, both acutely and at longer timescales. Traditional methods for assessing, monitoring, analysing, forecasting and disseminating water-related hazards are poorly connected or standalone and, this combined with the complex interactions between rain, river flow, groundwater and tides, make it difficult to predict and understand flooding. Recent advancements in digital technology, communications, numerical modelling and Earth Observations (E.G. remote sensed satellite data) allow many of the limitations of traditional methods to be overcome through the integrated, holistic approach of a Digital Twin (DT). A digital twin is dynamic virtual copy of a physical asset, process, system or environment that looks like?and behaves in real time identically to its real-world partner. For flooding, a digital twin approach is timely, as we can now leverage Earth Observation and large telemetered datasets to inform numerical models of catchment properties in real-time, allowing scenario analysis, forecasting and interpolation to be undertaken with less delay. This also allows the rapid 'gaming' of management scenarios/solutions where practitioners or users of the DT can try out different management methods or scenarios to see if this helps flooding before the events themselves. Through a programme of Earth Observation, sensor and network integration, modelling, data infrastructure development and stakeholder engagement, our project will build a digital twin for water-related hazard forecasting and decision-making for Hull and the East Riding of Yorkshire, a region heavily impacted by complex hydrometeorological hazards. The novelty of our approach is the co-production of the DT in collaboration with multi sectoral end users, as well as our engagement with one of the more complex environmental ecosystems (hydrology and flooding) in terms of data integration and the physical processes involved. This is a 15 month #700k project for the NERC TWINE (developing pilots for environmental digital twin) call. It involves interdisciplinary collaboration between natural and social science researchers from The University of Hull (Modelling, SUDs), Imperial College, London (Remote sensing and surface hydrology), British Geological Survey (Groundwater modelling and digital twin integration) and the University of Western England (investigating stakeholder end user interactions with DT's).
- Period of Award:
- 2 Jan 2024 - 1 Apr 2025
- Value:
- £254,984 Split Award
Authorised funds only
- NERC Reference:
- NE/Z503411/1
- Grant Stage:
- Awaiting Event/Action
- Scheme:
- Research Grants
- Grant Status:
- Active
- Programme:
- TWINE
This grant award has a total value of £254,984
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DI - Staff | DA - Estate Costs | DI - T&S |
|---|---|---|---|---|
| £10,299 | £75,405 | £115,102 | £43,621 | £10,556 |
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