Real-time flood inundation forecasting and mapping for key railway infrastructure: a UK case study

Murphy, Alexandra T.; Gouldby, Ben; Cole, Steven J.; Moore, Robert J.; Kendall, Helen. 2016 Real-time flood inundation forecasting and mapping for key railway infrastructure: a UK case study [in special issue: 3rd European Conference on Flood Risk Management (FLOODrisk 2016)] E3S Web of Conferences, 7, 18020. 8, pp.

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Flooding events that impede railway infrastructure can cause severe travel delays for the general public and large fines in delayed minutes for the rail industry. Early warnings of flood inundation can give more time to implement mitigation measures which help reduce cancellations, delays and fines. Initial work is reported on the development of a real-time flood inundation forecasting and mapping system for the Cowley Bridge track area near Exeter, UK. This location is on one of the main access routes to South West England and has suffered major floods in the past resulting in significant transport impacts. Flood forecasting systems in the UK mainly forecast river level/flow rather than extent and depth of flood inundation. Here, the development of a chain of coupled models is discussed that link rainfall to river flow, river level and flood extent for the rail track area relating to Cowley Bridge. Historical events are identified to test model performance in predicting inundation of railway infrastructure. The modelling system will operate alongside a series of in-situ sensors chosen to enhance the flood mapping forecasting system. Sensor data will support offline model calibration/verification and real-time data assimilation as well as monitoring flood conditions to inform track closure decisions.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Reynard
ISSN: 2267-1242
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
NORA Subject Terms: Hydrology
Data and Information
Date made live: 02 Nov 2016 17:08 +0 (UTC)

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