Climatic Change

, Volume 145, Issue 3–4, pp 431–444 | Cite as

Multi-sectoral, high-resolution assessment of climate change consequences of coastal flooding

  • Alexandra Toimil
  • Iñigo J. Losada
  • Pedro Díaz-Simal
  • Cristina Izaguirre
  • Paula Camus


In the context of growing concern about the threat of flooding posed by climate change in coastal areas, the Spanish plan for coastal adaptation to climate change gave rise to stringent requirements on risk consequence estimates at the regional scale O (100 km). Within this framework, we propose a methodology that combines high space-time resolution climate information (reanalysis databases and projections), local data on exposure that accounts for the most relevant sectors, site-specific vulnerability functions, and flood risk consequence valuation, gridded at 5 m. This approach involves efficient multiple-forcing flood modeling, in which the connection between climate change and potential inundation is primarily established through the definition of a total water level index. This research tackles challenging issues, including the importance of incorporating the effects of existing coastal defenses and local wave effects in port areas, dealing with data at different spatial scales and sectors in an integrated way, and the impact of discounting. The results provide insights into the possible consequences of inaction for a range of future scenarios based on changes in climate and socio-economics over the most relevant sectors. With the goal of prioritizing adaptive action and the efficient assignment of funds, we propose a weight-based integration of the sectoral value-at-risk through the application of Bayesian techniques and expert judgment. The methodology described here was applied to a pilot case study on the coast of Asturias in northern Spain.



This work has been supported by the Spanish Ministry of Agriculture and Fishery, Food and Environment (MAPAMA). The authors would like to acknowledge the technical assistance and data provided by the Government of Asturias. The authors are also grateful to the climate modeling groups for producing and making their model outputs from CMIP5 available.

Supplementary material

10584_2017_2104_MOESM1_ESM.pdf (1.2 mb)
ESM 1 (PDF 1.17 mb)


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Alexandra Toimil
    • 1
  • Iñigo J. Losada
    • 1
  • Pedro Díaz-Simal
    • 1
  • Cristina Izaguirre
    • 1
  • Paula Camus
    • 1
  1. 1.Environmental Hydraulics Institute “IH Cantabria”Universidad de Cantabria, Parque Científico y Tecnológico de CantabriaSantanderSpain

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