Characterizing uncertain sea-level rise projections to support investment decisions.

Ryan L Sriver,Klaus Keller,Robert J Lempert,Per Wikman-Svahn,Nanette H Bishopric

doi:10.1371/journal.pone.0190641

Abstract

Many institutions worldwide are considering how to include uncertainty about future changes in sea-levels and storm surges into their investment decisions regarding large capital infrastructures. Here we examine how to characterize deeply uncertain climate change projections to support such decisions using Robust Decision Making analysis. We address questions regarding how to confront the potential for future changes in low probability but large impact flooding events due to changes in sea-levels and storm surges. Such extreme events can affect investments in infrastructure but have proved difficult to consider in such decisions because of the deep uncertainty surrounding them. This study utilizes Robust Decision Making methods to address two questions applied to investment decisions at the Port of Los Angeles: (1) Under what future conditions would a Port of Los Angeles decision to harden its facilities against extreme flood scenarios at the next upgrade pass a cost-benefit test, and (2) Do sea-level rise projections and other information suggest such conditions are sufficiently likely to justify such an investment? We also compare and contrast the Robust Decision Making methods with a full probabilistic analysis. These two analysis frameworks result in similar investment recommendations for different idealized future sea-level projections, but provide different information to decision makers and envision different types of engagement with stakeholders. In particular, the full probabilistic analysis begins by aggregating the best scientific information into a single set of joint probability distributions, while the Robust Decision Making analysis identifies scenarios where a decision to invest in near-term response to extreme sea-level rise passes a cost-benefit test, and then assembles scientific information of differing levels of confidence to help decision makers judge whether or not these scenarios are sufficiently likely to justify making such investments. Results highlight the highly-localized and context dependent nature of applying Robust Decision Making methods to inform investment decisions.

Highlights

Decision-makers planning for potential changes in future flood hazards grapple with the challenge of uncertain changes in future sea-levels and storm surges
Our initial analysis considers a decision where Port of Los Angeles (LA) upgrades a terminal in 2020; the costs of hardening are small, Charden/Cupgrade = 1 percent; and Port of LA uses a discount rate of 5 percent per year. As this analysis will show, this low hardening cost is considered because it is at the high end of near-term investments Port of LA might reasonably make to protect its terminals against extreme sea-level rise
We have described how decision analytic approaches such as Robust Decision Making that begin with policies, identify vulnerabilities, and suggest potential responses can lead to more productive engagement with decision makers than do approaches that rank options based on specified probabilities [48,99,109]

Summary

Introduction

Decision-makers planning for potential changes in future flood hazards grapple with the challenge of uncertain changes in future sea-levels and storm surges. One common approach to managing this uncertainty is based on defining deterministic scenarios of future changes in sea-levels [1,2] and choosing one or more scenarios, typically called the ‘best-estimate’, ‘worst case’ or ‘plausible upper bound’, as a basis for decision-making [3,4]. An alternative approach to manage uncertainty in sea-level changes is based on characterizing well-defined probability density functions and use this information in a risk-based decision framework, for example a cost-benefit analysis based on maximizing expected utility [4] This approach, which we call the probabilistic approach, has the advantage of providing guidance on how to incorporate extreme cases into decisions, typically by weighing the impacts of such cases by their probability. The highest projections or scenarios of sea-level rise in 2100 documented in previous assessments have varied greatly (Fig 1 and S1 Table), which illustrates the difficulty in constraining the projected upper bound

Methods

Results

Conclusion