Publication Date:
2022-05-26
Description:
Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Climate Change 6 (2016): 696–700, doi:10.1038/nclimate2957.
Description:
Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making
assessments of SLR-induced hazards essential for informed decision-making3. We develop a
probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically
respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by
producing 30x30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in
the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical
land movement are used to estimate likely future inundation levels. Then, conditioned on future
inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response
vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond
dynamically to SLR, and we show that inundation models over-predict land likely to submerge.
This approach is well-suited to guiding coastal resource management decisions that weigh future
SLR impacts and uncertainty against ecological targets and economic constraints.
Description:
This research was funded by the U.S. Geological Survey Coastal and Marine Geology Program, the
Department of the Interior Northeast Climate Science Center, and the U.S. Army Corps of Engineers
Institute for Water Resources under the Responses to Climate Change Program.
Description:
2016-09-14
Repository Name:
Woods Hole Open Access Server
Type:
Preprint
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