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

May 15, 2019

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.

Citation Information

Publication Year 2019
Title Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model
DOI 10.5194/esurf-7-429-2019
Authors Erika E. Lentz, Nathaniel G. Plant, E. Robert Thieler
Publication Type Article
Publication Subtype Journal Article
Series Title
Series Number
Index ID 70203552
Record Source USGS Publications Warehouse
USGS Organization Woods Hole Coastal and Marine Science Center