Details of Award
NERC Reference : NE/N012984/1
Delivering resilient power, road and rail networks by translating a tree failure risk model for multi-sector applications.
Grant Award
- Principal Investigator:
- Professor GA Blackburn, Lancaster University, Lancaster Environment Centre
- Co-Investigator:
- Dr C Edwards, Lancaster University, Computing & Communications
- Co-Investigator:
- Professor JD Whyatt, Lancaster University, Lancaster Environment Centre
- Grant held at:
- Lancaster University, Lancaster Environment Centre
- Science Area:
- Atmospheric
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Environmental Risks and Hazards
- Natural Resource Management
- Science Topics:
- Environmental Planning
- Spatial Planning
- Plant responses to environment
- Earth Surface Processes
- Land - Atmosphere Interactions
- Abstract:
- When storms cause trees to fall onto power lines, roads and railways this can pose serious threats to human life and disruption to electricity supplies and transport leading to large financial costs to both operators and users of these networks. There is growing evidence that climate change will lead to an increase in storminess in the UK so the problems associated with tree failure are likely to grow. This project aims to use a computerised system for predicting which trees are likely to fall onto powerlines, roads and railways during the types of storms that typically occur in the UK. This will allow the operators of these infrastructure networks to fell those trees that are most likely to fail and cause disruption. The project will make use of newly-developed techniques which employ airborne laser scanners to map trees and measure their key properties, which will improve our ability to estimate the susceptibility of trees to failure. So the outputs of the system will enable the operators of infrastructure networks to pro-actively manage trees in order to improve the resilience of their infrastructure to future storms. The intensity and impacts of storms vary considerably over space and time so it is not possible to manage trees for all possible conditions. Therefore we will develop the tree failure prediction system so that it is able to use short-range weather forecasts (up to 5 days) which are the most reliable predictions of impending storm events. This will enable the system to predict which trees are likely to fail and cause disruptions to infrastructure networks during the forthcoming storm conditions. This information will help the network operators to draw up effective plans for responding to and recovering from storms, e.g. by organising field teams to be in the locations where greatest tree damage is likely to occur so they can remove fallen debris and repair the infrastructure. To be effective our tree failure prediction system will need to operate quickly and repeatedly so it can respond to regular updates in weather forecasts as storms develop. Also it needs to incorporate assessments of the large number of trees which surround the power, road and rail networks in the UK. Therefore, to achieve this, we will make use of the very powerful cloud-based computing technology that is now rapidly developing. The outputs of our system will be conveyed to users via an interactive web page which will support strategic decision-making and a mobile app that will support field teams. Keywords: tree failure, storm, prediction, power supply, road, rail, decision-making, resilience. The following organisations are stakeholders in the project and will form an advisory board to oversee our work: UK Power Networks, Scottish Power, Transport Scotland, Scottish Water, Bluesky International, Atkins Global.
- NERC Reference:
- NE/N012984/1
- Grant Stage:
- Completed
- Scheme:
- Innovation
- Grant Status:
- Closed
- Programme:
- Innovation - Risk
This grant award has a total value of £65,936
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DI - T&S |
|---|---|---|---|---|---|
| £14,244 | £17,166 | £3,742 | £8,726 | £20,538 | £1,518 |
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