Optimal life-cycle adaptation of coastal infrastructure under climate change

Area of Science:

• Environmental Science
• Climate Science
• Decision Science

Background:

• Current climate risk mitigation relies on static cost-benefit analysis, which is suboptimal due to climate projection uncertainties.
• Existing methods fail to incorporate evolving conditions and real-time data for adaptive management.

Purpose of the Study:

• To develop a dynamic decision-making framework for climate risk management using Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs).
• To ensure optimal life-cycle actions for coastal adaptation by integrating real-time data and accounting for model uncertainties.

Main Methods:

• Formulation of climate risk management as a dynamic decision-making problem using MDPs and POMDPs.
• Incorporation of real-time data for evaluating evolving climate conditions and model uncertainties.
• Development of a framework for coastal adaptation applications, including nature-based solutions.

Main Results:

• The proposed dynamic framework guarantees global optimality for climate risk management decisions.
• The approach allows for adaptive selection of life-cycle actions based on real-time observations.
• The framework is applicable to coastal adaptation, considering diverse action types and environmental impacts.

Conclusions:

• Dynamic decision-making, incorporating real-time data, offers a superior approach to static policies for climate risk mitigation.
• The MDP/POMDP framework provides a robust method for optimizing coastal adaptation strategies under uncertainty.
• Future work can expand the framework to include more complex environmental and socioeconomic factors.