A Coastal Risk Assessment Framework Tool to Identify Hotspots at the Regional Scale

Christophe Viavattene1, Jose A. Jimenez2, Oscar Ferreira3, Annelies Bolle4, Damon Owen1, Sally Priest1 and Ap Van Dongeren5, (1)Middlesex University, FHRC, London, United Kingdom, (2)2. Polytechnic University of Catalonia, (3)U. Algarve, Portugal, (4)4. International Marine and Dredging Consultants, (5)Deltares, Delft, Netherlands
Abstract:
Extreme events in combination with an increasing population on the coast, future sea level rise and the deterioration of coastal defences can lead to catastrophic consequences for coastal communities and their activities. The Resilience-Increasing Strategies for Coasts – toolkit (RISC-KIT) FP7 EU project is producing a set of EU-coherent open-source and open-access tools in support of coastal managers and decision-makers.

This paper presents one of these tools, the Coastal Risk Assessment Framework (CRAF) which assesses coastal risk at a regional scale to identify potential impact hotspots for more detailed assessment. Applying a suite of complex models at a full and detailed regional scale remains difficult and may not be efficient, therefore a 2-phase approach is adopted. CRAF Phase 1 is a screening process based on a coastal-index approach delimiting several hotspots in alongshore length by assessing the potential exposure for every kilometre along the coast. CRAF Phase 2 uses a suite of more complex modelling process (including X-beach 1D, inundation model, impact assessment and Multi-Criteria Analysis approach) to analyse and compare the risks between the aforementioned identified hotspots.

Results of its application are compared on 3 European Case Studies, the Flemish highly protected low-lying coastal plain with important urbanization and harbors, a Portuguese coastal lagoon protected by a multi-inlet barrier system, the highly urbanized Catalonian coast with touristic activities at threat. The flexibility of the tool allows tailoring the comparative analysis to these different contexts and to adapt to the quality of resources and data available. Key lessons will be presented.