Accounting for deep uncertainties in sea-level rise and storm surge projections is critical to understanding risk trade-offs

Open Access
Oddo, Perry Charles
Graduate Program:
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 27, 2016
Committee Members:
  • Klaus Keller, Thesis Advisor
  • Chris Eliot Forest, Committee Member
  • James Kasting, Committee Member
  • storm surge
  • flood adaptation
  • global sensitivity analysis
  • decision-making
  • deep uncertainty
Anthropogenic climate change is currently affecting global flood risks. Atmospheric greenhouse gas concentrations are increasing, causing increased land and ocean temperatures. Sea-level rise and changes in storm surge patterns are posing risks for coastal communities and infrastructures. Strategies to manage these flood risks are often designed using decision analytical tools that combine key geophysical, economic, and infrastructure models. Previous studies analyzing sea-level rise adaptation have broken important new ground, but are often silent on the effects of potentially important uncertainties and the trade-offs between diverse objectives. Here, we implement and improve on a classic decision-analytical model (van Dantzig 1956) by: (i) capturing trade-offs across conflicting stakeholder objectives, (ii) determining the effects of structural uncertainties in the sea-level rise and storm surge models, and (iii) using global sensitivity analysis to determine which parameters matter the most for a given objective. We find that the flood adaptation model produces myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multi-objective trade-offs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and which could have critical implications for flood adaptation strategies.