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

Erika E Lentz,E Robert Thieler,Nathaniel G Plant

doi:10.5194/esurf-7-429-2019

Erika E Lentz, E Robert Thieler + Show 1 more

Open Access

https://doi.org/10.5194/esurf-7-429-2019

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Abstract

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 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.

Highlights

In addition to having the lowest number of pixels of all the land cover classes, user error and producer accuracy were lowest for the bare land category (49 % and 21 %, respectively); the lowest number of correctly classified pixels was in the bare land class when compared with the ground truth (CCAP) class
We cannot confirm that this error resides solely with the Coastal Change Analysis Program (CCAP) data, the updated and more detailed information in the DSL data, as well as the similarity in error rate with the published CCAP error, suggests that entering the DSL data as if they are known with certainty is an appropriate assumption for most of our land cover (LC) categories
The greatest error in the comparison – the bare land category – is in part explained by the substantial underrepresentation of beaches in both datasets when compared with other LC types

Summary

Introduction

Estimates of global sea-level rise (SLR) predict increases between 0.3 and 1.2 m by 2100 (Church et al, 2013; Kopp et al, 2014), while northeastern and mid-Atlantic US SLR projections are higher than the global average due to a variety of factors including subsidence, static equilibrium effects, and changing ocean dynamics (Goddard et al, 2015; Mitrovica et al, 2011; Kopp, et al, 2014; Sella et al, 2007; Slangen et al, 2014; Sweet et al, 2017a, b; Yin and Goddard, 2013; Yin et al, 2009; Zervas et al, 2013). SLR impacts such as hightide flooding, barrier island narrowing, and salt marsh degradation have been increasingly observed along the US East Coast (e.g., Cahoon et al, 2009; Ezer and Atkinson, 2014; Kirwan and Megonigal, 2013; Sweet and Park, 2014). The northeastern US coast (from Maine southward through Virginia) is a diverse landscape, with major shipping ports (e.g., New York City, Boston, Norfolk), heavily populated cities (e.g., Washington, D.C., New York City, Boston), and extensive natural areas that provide a variety of habitat and ecosystem services. Understanding and assessing how coastal landscapes such as this respond to SLR is central to refining adaptive management strategies (Fishman et al, 2014) and identifying areas that provide buffering or mitigation to support long-term management targets (Pelletier et al, 2015). Because coastal land elevation is primarily governed by the substrate and/or underlying geology of the landscape, as well as being a prod-

Results

Discussion

Conclusion