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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esurf-2018-79
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-2018-79
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Nov 2018

Research article | 27 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth Surface Dynamics (ESurf).

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Erika E. Lentz1, Nathaniel G. Plant2, and E. Robert Thieler1 Erika E. Lentz et al.
  • 1U.S. Geological Survey, Woods Hole Coastal and Marine Science Center, Woods Hole, MA 02543, USA
  • 2U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL 33701, USA

Abstract. Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern U.S. by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.

Erika E. Lentz et al.
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Erika E. Lentz et al.
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Coastal landscape response to sea-level rise assessment for the northeastern United States (ver. 2.0., December 2015): U.S. Geological Survey data release E. E. Lentz, S. R. Stippa, E. R. Thieler, N. G. Plant, D. B. Gesch, R. M. and Horton https://doi.org/10.5066/F73J3B0B

Erika E. Lentz et al.
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Latest update: 12 Dec 2018
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Short summary
Our findings show regional sea-level rise (SLR) impact predictions can be made using relatively coarse data. To predict the coastal response to SLR, detailed information on the landscape, including elevation, vegetation, and/or level of development are needed. However, we find the inherent relationship between elevation and land cover datasets (e.g. beaches tend to be low lying) is used to reduce error in a coastal response to SLR model, suggesting new applications for areas of limited data.
Our findings show regional sea-level rise (SLR) impact predictions can be made using relatively...
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