Risk-based optimization of adaptable protection measures against natural hazards

Risk protection measures against natural hazards are typically costly structures with a long lifespan. Their design should therefore take into account possible future changes in risk, e.g. due to socio-economic development and climate change. These future changes are uncertain, and one possibility for coping with these uncertainties is building adaptable risk protection systems, which allow later alterations with low cost. The challenge is to quantitatively evaluate how cost-effective such systems are. This paper proposes a formal quantitative measure of adaptability and it introduces a general decision model using Bayesian decision analysis for quantification and optimization of the risk protection systems taking into account their adaptability. The decision model is applied on a numerical example of risk-based optimization of flood protection measures under different scenarios of climate change. The numerical investigations show that for non-adaptable measures, a conservative design is recommendable, while for adaptable systems, the optimal initial capacity is lower because their potential future adjustments are not costly. Furthermore, the value of adaptability is evaluated, and it is found that building adaptable measures is not significantly more cost-effective. It is concluded that in most situations, a conservative design is preferable, as the additional risk reduction due to the conservative design is beneficial under all possible future scenarios.