THESEUS decision support system for coastal risk management
THESEUS decision support system for coastal risk management
While planning coastal risk management strategies, coastal managers need to assess risk across a range of spatial and temporal scales. GIS-based tools are one efficient way to support them in the decision making process through a scenarios analysis starting from social, economic and environmental information integrated into a common platform. However, this integration process requires a significant effort from a team of scientists in terms of a) identifying the appropriate scales and data resolution for analysing social, environmental and economic issues; b) selecting and linking an appropriate set of tools to build a coupled model; c) representing key emerging (and hence challenging) research issues, such as risk perception and social resilience in the model; d) developing multi-criteria analysis to integrate social, environmental, economic impacts; and e) accounting for the expectations of the stakeholders and therefore optimizing the opportunity for them to interact with the tool development and with the final tool itself.
In this spirit, this paper presents an open-source Spatial Decision Support System developed within the THESEUS Project to help decision makers to scopeg optimal strategies to minimise coastal risks. The exploratory tool allows the users to perform an integrated coastal risk assessment, to analyse the effects of different combinations of engineering, social, economic and ecologically based mitigation options, across short (2020s), medium (2050s) and long-term (2080s) scenarios, taking into account physical and non-physical drivers, such as climate change, subsidence, population and economic growth
218-239
Zanuttigh, B.
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Simcic, D.
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Bagli, S.
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Bozzeda, F.
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Pietrantoni, L.
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Zagonari, F.
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Hoggart, S.
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Nicholls, R.J.
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Zanuttigh, B.
ff9e26ff-1751-4502-a1fe-2cddcd2b7add
Simcic, D.
bcfe85d9-53be-4495-8994-a8e42ceb61c3
Bagli, S.
57d33f61-2be2-4b6f-a002-49fd5abfb815
Bozzeda, F.
e7282d03-ee7a-4044-8c81-648f7f8dd17b
Pietrantoni, L.
a85998ea-95b0-4c7b-a21e-3019d3657e31
Zagonari, F.
70bf3021-2a87-4a55-a09c-c28a009fb5d5
Hoggart, S.
2c0a1bf6-966e-4f46-a87c-340995796b44
Nicholls, R.J.
4ce1e355-cc5d-4702-8124-820932c57076
Zanuttigh, B., Simcic, D., Bagli, S., Bozzeda, F., Pietrantoni, L., Zagonari, F., Hoggart, S. and Nicholls, R.J.
(2014)
THESEUS decision support system for coastal risk management.
Coastal Engineering, 87, .
(doi:10.1016/j.coastaleng.2013.11.013).
Abstract
While planning coastal risk management strategies, coastal managers need to assess risk across a range of spatial and temporal scales. GIS-based tools are one efficient way to support them in the decision making process through a scenarios analysis starting from social, economic and environmental information integrated into a common platform. However, this integration process requires a significant effort from a team of scientists in terms of a) identifying the appropriate scales and data resolution for analysing social, environmental and economic issues; b) selecting and linking an appropriate set of tools to build a coupled model; c) representing key emerging (and hence challenging) research issues, such as risk perception and social resilience in the model; d) developing multi-criteria analysis to integrate social, environmental, economic impacts; and e) accounting for the expectations of the stakeholders and therefore optimizing the opportunity for them to interact with the tool development and with the final tool itself.
In this spirit, this paper presents an open-source Spatial Decision Support System developed within the THESEUS Project to help decision makers to scopeg optimal strategies to minimise coastal risks. The exploratory tool allows the users to perform an integrated coastal risk assessment, to analyse the effects of different combinations of engineering, social, economic and ecologically based mitigation options, across short (2020s), medium (2050s) and long-term (2080s) scenarios, taking into account physical and non-physical drivers, such as climate change, subsidence, population and economic growth
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e-pub ahead of print date: 1 May 2014
Organisations:
Energy & Climate Change Group
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Local EPrints ID: 364213
URI: http://eprints.soton.ac.uk/id/eprint/364213
ISSN: 0378-3839
PURE UUID: f230f0d4-3e9a-4dd1-b7bf-1af027183353
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Date deposited: 10 Apr 2014 08:16
Last modified: 15 Mar 2024 03:18
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Author:
B. Zanuttigh
Author:
D. Simcic
Author:
S. Bagli
Author:
F. Bozzeda
Author:
L. Pietrantoni
Author:
F. Zagonari
Author:
S. Hoggart
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