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 The impacts of future sea level rise (SLR) are challenging to predict because they are not the same everywhere.  Coastal environments and the amount of development vary—from marshes, beaches, and rocky headlands to cities, towns and beach communities—and so does how the coast responds to SLR. 

 To capture this variability, detailed information on features of the coastal landscape, such as its elevation, the vegetation, and/or level of development are needed.  Large datasets that provide this information have been used to predict where the Northeastern U.S. coast is likely to adapt or change in response SLR, rather than simply become inundated.  However, these regional datasets likely contain some level of error that must be assessed for its impact on likelihood predictions.

In this paper, published by Earth Surface Dynamics we evaluate potential dataset error to determine where it is most likely to occur, and compare it to how different environment types such as marshes, beaches, and forests  are likely to change or adapt to SLR.  We find our SLR impact model uses the relationship between elevation and land cover datasets (e.g. beaches tend to be low lying) to reduce error in each, suggesting that our approach can be extended beyond the study region to areas where such datasets may be lacking in either coverage or quality.  We also show that in contrast to large sources of uncertainty factored into our model, such as how people may change the coast to adapt to SLR impacts, data error is small and has minimal effect on our likelihood predictions. These findings increase the ability to conduct SLR impact assessments using relatively coarse data with greater understanding of its limitations and with increased confidence in predicted outcomes. 

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