Climate Change Adaptation Planning and Decision Making for Transit Infrastructure

Sea level rise (SLR) and associated increases in the frequency and intensity of coastal flooding pose a significant threat to coastal communities and the critical infrastructure upon which they rely. In coastal cities, rail transit systems are particularly sensitive to such increases in coastal flood exposure. Transit agencies, infrastructure managers, and planners are increasingly cognizant of these risks, yet current literature and practice lack methods for quantifying the costs of flooding for rail transit infrastructure. Consequently, current literature lacks methods for valuing transit-specific investments in climate change adaptation and provides little guidance on how transit agencies can decide between potential adaptation strategies, given prevailing uncertainties and their institutional priorities. This thesis aims to address these gaps in literature and practice by first developing a coupled hydraulic and hydrodynamic model to assess the severity of coastal flood exposure (i.e., flood depths) for rail rapid transit systems (inclusive of their underground spaces) under present and future SLR conditions. Relying on novel transit-specific depth-damage curves collected via structured expert judgement, we translate event-specific flood depth estimates into direct damage costs via a novel flood damage cost estimation framework, considering uncertainty and variability in damage outcomes. We next estimate how expected annualized losses (EAL) increase over time with uncertain future SLR. Recognizing that future flood damage costs are less consequential than present damage costs, we propose a novel fair market value (FMV) discounting approach and demonstrate flood risk-related cash flows exhibit low correlation to the market and can therefore be discounted near the risk-free rate. Applying this finding in conjunction with the flood damage cost estimation model, we value investments in climate adaptation projects and explore the benefits of flexibly implementing proposed projects via real options analysis (ROA). Lastly, moving beyond valuation, we explore the tradeoffs inherent in planning adaptation investment across a rail rapid transit system given institutional priorities and objectives via a multi-criteria decision analysis (MCDA) framework. Applying these methods to the Massachusetts Bay Transportation Authority (MBTA) rail rapid transit network, we find unprotected tunnel portals responsible for a disproportionate volume of tunnel inflow. Hydraulic connectivity of the central tunnel network allows flooding from one ingress location to propagate across the network. Consequently, we find effective adaptation measures protecting the central tunnel must function cohesively as a system to ensure a uniform level of protection. Absent adaptation, we observe coastal flood risk (measured by EAL) has more than doubled from 2008 to 2022 and is expected to grow 16% annually over the next decade under all SLR scenarios. Through a case study considering a regional adaptation pathway proposed by Climate Ready Boston, we demonstrate significant benefits to MBTA rail transit infrastructure, particularly when investments follow a flexible implementation pathway. Lastly, through a Blue Line case study, we find implementation of shore-based measures to be more favorable than transit-specific adaptation alternatives, when considering sampled institutional priorities.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Martello, Michael Vincent

Advisor dc:contributor.advisor

• Whittle, Andrew J.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151212

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151212