Prediction of Impact on Transport System due to Coastal Flooding under Climate Change
Global warming and climate change are reshaping our world in many ways. One of their most obvious evidences is sea level rise due to melting of polar glaciers and arctic ice. It is estimated that global sea level rise is at an alarming rate of 0.18cm/yr during 1961-2003, it is even higher at 0.3cm/yr during 1993-2003, and research estimates that sea level could range from 0.8 to 2 m by 2100 under glaciological conditions. Another important consequence of global warming is pattern change and increased variance of precipitation around the world; precipitation increasing and hurricanes becoming stronger and more frequent in high latitudes (Northern Hemisphere). The Northeast region is projected to see an increase in winter precipitation on the order of 20 to 30 percent. Combined effects from sea level rise and increase of variance of precipitation could result in catastrophic coastal flooding under hurricanes and winter storms, putting many major transportation infrastructures, including highways, bridges, and railroads at great risk. The Executive Committee of the Transportation Research Board (TRB) initiated a national study on potential impacts of climate change on U.S. transportation infrastructures. Coastal flood evolution involves multi-physics/multi-scale phenomena and currently there is no appropriate modeling tool to predict impact of coastal flooding on transportation systems. In prediction of flooding at transportation systems, two crucial issues have to be addressed: 1) desired accuracy and resolution in time and space for flooding at transportation systems, 2) modeling of storm surges with sharp fronts (such as the Tsunami in Japan on March, 2011). In view of current status of conditions, the most promising and feasible approach is hybrid method that couples different well-tested models designed for individual water flows. Recently developed were brand new hybrid methods implemented into a framework that couples different models and also applied it to coastal flooding at Cape May, NJ under projected climate change conditions. The framework provides attention to actual transportation systems that will also be examined. It is expected that the results will be published in a prestigious journal. unprecedented platform to predict and resolve flooding at transportation systems. This project is to study the hybrid methods and validate the developed modeling framework previously proposed in [6-9] on rigorous foundations. In particular, theoretical analysis such as order of accuracy and stability of the framework and systematical numerical experiments on its performance in aspect of solution quality will be made. Feasibility and strategies for its application to actual transportation systems will also be examined. It is expected that the results will be published in a prestigious journal.
Language
- English
Project
- Status: Active
- Funding: $6465.00
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Contract Numbers:
49111-15-23
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Sponsor Organizations:
University Transportation Research Center
City College of New York
Marshak Hall, Suite 910, 160 Convent Avenue
New York, NY United States 10031Research and Innovative Technology Administration
University Transportation Centers Program
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Project Managers:
Thorson, Ellen
Mooney, Deborah
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Performing Organizations:
City College of New York
Civil Engineering, Steinman T-127
140th Street and Convent Avenue
New York, NY United States 10031 -
Principal Investigators:
Tang, Hansong
- Start Date: 20110901
- Expected Completion Date: 0
- Actual Completion Date: 20130731
- Source Data: RiP Project 29273
Subject/Index Terms
- TRT Terms: Climate change; Floods; Storm surges
- Uncontrolled Terms: Flow capacity (Water flow); Impact assessment; Transportation infrastructure
- Geographic Terms: Cape May County (New Jersey)
- Subject Areas: Data and Information Technology; Transportation (General); I15: Environment;
Filing Info
- Accession Number: 01467852
- Record Type: Research project
- Source Agency: University Transportation Research Center
- Contract Numbers: 49111-15-23
- Files: UTC, RIP, USDOT
- Created Date: Jan 3 2013 3:40PM