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.
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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...
»
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