Using Direct Policy Search to Identify Robust Strategies in Adapting to Uncertain Sea Level Rise and Storm Surge

TL;DR

This paper demonstrates that direct policy search improves the quality and computational efficiency of coastal adaptation strategies under uncertain sea-level rise and storm surge conditions, compared to traditional methods.

Contribution

It reformulates a sea-level rise adaptation model to incorporate multi-objective adaptive strategies using direct policy search, enhancing solution quality and computational efficiency.

Findings

Improved Pareto-dominance of solutions using direct policy search

Less computational demand compared to intertemporal optimization

Effective in addressing uncertainty and tail-area events

Abstract

Sea-level rise poses considerable risks to coastal communities, ecosystems, and infrastructure. Decision makers are faced with uncertain sea-level projections when designing a strategy for coastal adaptation. The traditional methods are often silent on tradeoffs as well as the effects of tail-area events and of potential future learning. Here we reformulate a simple sea-level rise adaptation model to address these concerns. We show that Direct Policy Search yields improved solution quality, with respect to Pareto-dominance in the objectives, over the traditional approach under uncertain sea-level rise projections and storm surge. Additionally, the new formulation produces high quality solutions with less computational demands than an intertemporal optimization approach. Our results illustrate the utility of multi-objective adaptive formulations for the example of coastal adaptation and point to wider-ranging application in climate change adaptation decision problems.