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

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lentz, E. E., Plant, N. G., & Thieler, E. R. Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model. Earth Surface Dynamics, 7(2), (2019):429-438, doi:10.5194/esurf-7-429-2019. ; Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US 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. ; This research was funded by the U.S. Geological Survey Coastal and Marine Geology Program. We thank P. Soupy Dalyander for early reviews and discussion of this paper. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.