2026-09-20T09:41:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/784962026-06-06T01:06:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Henry D. Jacoby.
Lickley, Megan Jeramaz
Massachusetts Institute of Technology. Technology and Policy Program.
Massachusetts Institute of Technology. Engineering Systems Division
n-us---
2013-04-12T19:29:40Z
2013-04-12T19:29:40Z
2012
2012
http://hdl.handle.net/1721.1/78496
836772188
Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 75-77).
The 2005 hurricane season was particularly damaging to the United States, contributing to significant losses to energy infrastructure -much of it a result of flooding from storm surges during hurricanes Katrina and Rita. Previous research suggests that these events are not isolated, but rather foreshadow a risk that is to continue and likely increase with a changing climate (17). Since extensive energy infrastructure exists along the U.S. Atlantic and Gulf coasts, these facilities are exposed to an increasing risk of flooding. We study the combined impacts of anticipated sea level rise, hurricane activity, and subsidence on energy infrastructure in these regions with a first application to Galveston Bay. Using future climate conditions as projected by four different Global Circulation Models (GCMs), we model the change in hurricane activity from present day climate conditions in response to a climate projected in 2100 under the IPCC A l B emissions scenario using hurricane analysis developed by Emanuel (5). We apply the results from hurricane runs from each model to the SLOSH model (Sea, Lake and Overland Surges from Hurricanes) (19) to investigate the change in frequency and distribution of surge heights across climates. Further, we incorporate uncertainty surrounding the magnitude of sea level rise and subsidence, resulting in more detailed projections of risk levels for energy infrastructure over the next century. With a detailed understanding of energy facilities' changing risk exposure, we conclude with a dynamic programming cost-benefit analysis to optimize decision making over time as it pertains to adaptation.
by Megan Jeramaz Lickley.
S.M. in Technology and Policy
77 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Technology and Policy Program.
The vulnerability of U.S. coastal energy infrastructure under climate change
Thesis
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false
Publication
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<Abstract>The 2005 hurricane season was particularly damaging to the United States, contributing to significant losses to energy infrastructure -much of it a result of flooding from storm surges during hurricanes Katrina and Rita. Previous research suggests that these events are not isolated, but rather foreshadow a risk that is to continue and likely increase with a changing climate (17). Since extensive energy infrastructure exists along the U.S. Atlantic and Gulf coasts, these facilities are exposed to an increasing risk of flooding. We study the combined impacts of anticipated sea level rise, hurricane activity, and subsidence on energy infrastructure in these regions with a first application to Galveston Bay. Using future climate conditions as projected by four different Global Circulation Models (GCMs), we model the change in hurricane activity from present day climate conditions in response to a climate projected in 2100 under the IPCC A l B emissions scenario using hurricane analysis developed by Emanuel (5). We apply the results from hurricane runs from each model to the SLOSH model (Sea, Lake and Overland Surges from Hurricanes) (19) to investigate the change in frequency and distribution of surge heights across climates. Further, we incorporate uncertainty surrounding the magnitude of sea level rise and subsidence, resulting in more detailed projections of risk levels for energy infrastructure over the next century. With a detailed understanding of energy facilities' changing risk exposure, we conclude with a dynamic programming cost-benefit analysis to optimize decision making over time as it pertains to adaptation.</Abstract>
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