A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information
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This paper studies the dynamic application of the minimax regret (MR) decision criterion to identify robust flood risk management strategies under climate change uncertainty and emerging information. An MR method is developed that uses multiple learning scenarios, for example about sea level rise or river peak flow development, to analyse effects of changes in information on optimal investment in flood protection. To illustrate the method, optimal dike height and floodplain development are studied in a conceptual model, and conventional and adaptive MR solutions are compared. A dynamic application of the MR decision criterion allows investments to be changed after new information on climate change impacts, which has an effect on today’s optimal investments. The results suggest that adaptive MR solutions are more robust than the solutions obtained from a conventional MR analysis of investments in flood protection. Moreover, adaptive MR analysis with multiple learning scenarios is more general and contains conventional MR analysis as a special case.
KeywordsMinimax regret Flood risk Climate change Adaptive management Flexibility Robust optimisation Learning
We thank two anonymous reviewers for helpful comments and suggestions. This research project has been supported by the Knowledge for Climate programme, the Netherlands, the Spanish state (Project TIN2015-66680-c2-2-R) and Junta de Andalucía (P11-TIC-7176), in part financed by the European Regional Development Fund (ERDF).
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