Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model
<p>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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-05-01
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| Series: | Earth Surface Dynamics |
| Online Access: | https://www.earth-surf-dynam.net/7/429/2019/esurf-7-429-2019.pdf |
| Summary: | <p>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.</p> |
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| ISSN: | 2196-6311 2196-632X |