A Quasi-2D Bayesian network model for assessments of coastal inundation pathways and probabilities
A Quasi-2D Bayesian network model for assessments of coastal inundation pathways and probabilities
Coastal flood assessments often require the analysis of a complex system of flood sources, pathways and receptors. This can be challenging for traditional numerical modelling approaches. In this paper we use a Bayesian networks approach to assess coastal floodplains as networks of inter-linked elements. A Bayesian network (Bn) model is built to describe flood pathways and estimate flood extents for different extreme events. The network of the Bn model is constructed from a quasi-2D Source – Pathway – Receptor (SPR) systems diagram. The Bn model is applied in Teignmouth in the UK, a coastal floodplain of typical complexity. It identifies two key flood pathways and assesses their sensitivity to changes in sea levels, beach widths and coastal defences. The advantages, utility and limitations of the Teignmouth Bn model are discussed. The process of 2D SPR and Bn model construction helps identify gaps in floodplain understanding and description. The Bn model quantifies inundation probabilities and facilitates the rapid identification of critical pathways and elements, before committing resources to further detailed analysis. The approach is transferable and can be readily applied in local-scale coastal floodplains to obtain a systems-level understanding and inform numerical modelling assumptions.
Bayesian networks, coastal, flood pathways, flood risk, inundation, network model, sea level rise, source-pathway-receptor
1-28
Narayan, S.
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Simmonds, Dave
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Nicholls, Robert J.
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Clarke, Derek
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Narayan, S.
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Simmonds, Dave
446d879b-44f6-4e3e-a10d-3c47ff9bafa8
Nicholls, Robert J.
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Clarke, Derek
9746f367-1df2-4e0e-8d71-5ecfc9ddd000
Narayan, S., Simmonds, Dave, Nicholls, Robert J. and Clarke, Derek
(2015)
A Quasi-2D Bayesian network model for assessments of coastal inundation pathways and probabilities.
Journal of Flood Risk Management, .
(doi:10.1111/jfr3.12200).
Abstract
Coastal flood assessments often require the analysis of a complex system of flood sources, pathways and receptors. This can be challenging for traditional numerical modelling approaches. In this paper we use a Bayesian networks approach to assess coastal floodplains as networks of inter-linked elements. A Bayesian network (Bn) model is built to describe flood pathways and estimate flood extents for different extreme events. The network of the Bn model is constructed from a quasi-2D Source – Pathway – Receptor (SPR) systems diagram. The Bn model is applied in Teignmouth in the UK, a coastal floodplain of typical complexity. It identifies two key flood pathways and assesses their sensitivity to changes in sea levels, beach widths and coastal defences. The advantages, utility and limitations of the Teignmouth Bn model are discussed. The process of 2D SPR and Bn model construction helps identify gaps in floodplain understanding and description. The Bn model quantifies inundation probabilities and facilitates the rapid identification of critical pathways and elements, before committing resources to further detailed analysis. The approach is transferable and can be readily applied in local-scale coastal floodplains to obtain a systems-level understanding and inform numerical modelling assumptions.
Text
JFRM_Paper_Submission_Final.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 12 May 2015
e-pub ahead of print date: 20 July 2015
Keywords:
Bayesian networks, coastal, flood pathways, flood risk, inundation, network model, sea level rise, source-pathway-receptor
Organisations:
Energy & Climate Change Group
Identifiers
Local EPrints ID: 378944
URI: http://eprints.soton.ac.uk/id/eprint/378944
PURE UUID: 87c23790-b525-4e34-ad6b-6d8c7a4ed3a5
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Date deposited: 21 Jul 2015 13:05
Last modified: 15 Mar 2024 03:18
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Contributors
Author:
S. Narayan
Author:
Dave Simmonds
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