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Title: Allowances for evolving coastal flood risk under uncertain local sea-level rise

Estimates of future flood hazards made under the assumption of stationary mean sea level are biased low due to sea-level rise (SLR). However, adjustments to flood return levels made assuming fixed increases of sea level are also inadequate when applied to sea level that is rising over time at an uncertain rate. SLR allowances—the height adjustment from historic flood levels that maintain under uncertainty the annual expected probability of flooding—are typically estimated independently of individual decision-makers’ preferences, such as time horizon, risk tolerance, and confidence in SLR projections.We provide a framework of SLR allowances that employs complete probability distributions of local SLR and a range of user-defined flood risk management preferences. Given non-stationary and uncertain sea-level rise, these metrics provide estimates of flood protection heights and offsets for different planning horizons in coastal areas. In conclusion, we illustrate the calculation of various allowance types for a set of long-duration tide gauges along U.S. coastlines.
Authors:
 [1] ;  [2] ;  [1] ;  [3]
  1. Princeton Univ., Princeton, NJ (United States)
  2. Rutgers Univ., New Brunswick, NJ (United States)
  3. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
Publication Date:
Grant/Contract Number:
FC02-97ER62402
Type:
Accepted Manuscript
Journal Name:
Climatic Change
Additional Journal Information:
Journal Volume: 137; Journal Issue: 3-4; Journal ID: ISSN 0165-0009
Publisher:
Springer
Research Org:
Univ. Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Flood Risk; Generalize Extreme Value; Flood Hazard; Generalize Pareto Distribution; Flood Risk Management
OSTI Identifier:
1438454

Buchanan, Maya K., Kopp, Robert E., Oppenheimer, Michael, and Tebaldi, Claudia. Allowances for evolving coastal flood risk under uncertain local sea-level rise. United States: N. p., Web. doi:10.1007/s10584-016-1664-7.
Buchanan, Maya K., Kopp, Robert E., Oppenheimer, Michael, & Tebaldi, Claudia. Allowances for evolving coastal flood risk under uncertain local sea-level rise. United States. doi:10.1007/s10584-016-1664-7.
Buchanan, Maya K., Kopp, Robert E., Oppenheimer, Michael, and Tebaldi, Claudia. 2016. "Allowances for evolving coastal flood risk under uncertain local sea-level rise". United States. doi:10.1007/s10584-016-1664-7. https://www.osti.gov/servlets/purl/1438454.
@article{osti_1438454,
title = {Allowances for evolving coastal flood risk under uncertain local sea-level rise},
author = {Buchanan, Maya K. and Kopp, Robert E. and Oppenheimer, Michael and Tebaldi, Claudia},
abstractNote = {Estimates of future flood hazards made under the assumption of stationary mean sea level are biased low due to sea-level rise (SLR). However, adjustments to flood return levels made assuming fixed increases of sea level are also inadequate when applied to sea level that is rising over time at an uncertain rate. SLR allowances—the height adjustment from historic flood levels that maintain under uncertainty the annual expected probability of flooding—are typically estimated independently of individual decision-makers’ preferences, such as time horizon, risk tolerance, and confidence in SLR projections.We provide a framework of SLR allowances that employs complete probability distributions of local SLR and a range of user-defined flood risk management preferences. Given non-stationary and uncertain sea-level rise, these metrics provide estimates of flood protection heights and offsets for different planning horizons in coastal areas. In conclusion, we illustrate the calculation of various allowance types for a set of long-duration tide gauges along U.S. coastlines.},
doi = {10.1007/s10584-016-1664-7},
journal = {Climatic Change},
number = 3-4,
volume = 137,
place = {United States},
year = {2016},
month = {6}
}