The dynamic effects of sea level rise on low‐gradient coastal landscapes: A review

Passeri, Davina L.; Hagen, Scott C.; Medeiros, Stephen C.; Bilskie, Matthew V.; Alizad, K.; Wang, Dingbao

doi:10.1002/2015ef000298

Cited by 272 publications

(173 citation statements)

References 153 publications

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“…Sediment accretion is a natural response that contributes to changes in elevation of a marsh surface relative to sea level. For a marsh to sustain its ecosystem function, its accretion rate must be at least equal to the pace of the rslr at its location as the vertical elevation of the marsh relative to mean sea level is critical for its productivity and stability [12]. Therefore, it is important to accurately characterize salt marsh surface elevation, landcover, and their changes to be able to predict whether salt marsh wetlands and the ecosystem function they support can withstand an accelerated rise in sea level.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the "k" clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point-and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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“…The resulting impacts from these events require a range of human and natural resource considerations, from threats to buildings and infrastructure to changes in habitat availability (Schlacher et al, 2007;Weinstein et al, 2007;FitzGerald et al, 2008;Gutierrez et al, 2009;Moser et al, 2014;Passeri et al, 2015). Often in the days and weeks following an event there is significant pressure to restore beaches and dunes through replenishment projects, wherein the potential for natural recovery is either overlooked (Lazarus, 2014), considered spatially and temporally insufficient, and/or poorly understood and as a consequence not factored into decision-making.…”

Section: Introductionmentioning

confidence: 99%

Characterizing storm response and recovery using the beach change envelope: Fire Island, New York

et al. 2018

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Hurricane Sandy at Fire Island, New York presented unique challenges in the quantification of storm impacts using traditional metrics of coastal change, wherein measured changes (shoreline, dune crest, and volume change) did not fully reflect the substantial changes in sediment redistribution following the storm. We used a time series of beach profile data at Fire Island, New York to define a new contour-based morphologic change metric, the Beach Change Envelope (BCE). The BCE quantifies changes to the upper portion of the beach likely to sustain measurable impacts from storm waves and capture a variety of storm and post-storm beach states. We evaluated the ability of the BCE to characterize cycles of beach change by relating it to a conceptual beach recovery regime, and demonstrated that BCE width and BCE height from the profile time series correlate well with established stages of recovery. We also investigated additional applications of this metric to capture impacts from storms and human modification by applying it to several post-storm historical datasets in which impacts varied considerably; Nor'Ida (2009), Hurricane Irene (2011), Hurricane Sandy (2012), and a 2009 community replenishment. In each case, the BCE captured distinctive upper beach morphologic change characteristic of these different beach building and erosional events. Analysis of the beach state at multiple profile locations showed spatial trends in recovery consistent with recent morphologic island evolution, which other studies have linked with sediment availability and the geologic framework. Ultimately we demonstrate a new way of more effectively characterizing beach response and recovery cycles to evaluate change along sandy coasts.Published by Elsevier B.V.

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“…This will have an impact on the location of maximum tidal range within the estuary and tidal asymmetry (Robins et al 2014). In addition to this, rising sea level may alter channel depths or alter tidal prism (Passeri et al 2015), therefore fundamentally altering the feedback between estuarine form and water level. In some coastal regions, sea-level rise will increase the magnitude and frequency of extreme storm events, leading to increased flood hazard (Nicholls et al 2011;Woodworth et al 2009).…”

Section: Implications For Local Management Needs In the Severn Estuarmentioning

confidence: 99%

Flood Hazard Assessment for a Hyper-Tidal Estuary as a Function of Tide-Surge-Morphology Interaction

Lyddon

Brown

Leonardi

et al. 2018

Estuaries and Coasts

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Astronomical high tides and meteorological storm surges present a combined flood hazard to communities and infrastructure. There is a need to incorporate the impact of tide-surge interaction and the spatial and temporal variability of the combined flood hazard in flood risk assessments, especially in hyper-tidal estuaries where the consequences of tide and storm surge concurrence can be catastrophic. Delft3D-FLOW is used to assess up-estuary variability in extreme water levels for a range of historical events of different severity within the Severn Estuary, southwest England as an example. The influence of the following on flood hazard is investigated: (i) event severity, (ii) timing of the peak of a storm surge relative to tidal high water and (iii) the temporal distribution of the storm surge component (here in termed the surge skewness). Results show when modelling a local area event severity is most important control on flood hazard. Tide-surge concurrence increases flood hazard throughout the estuary. Positive surge skewness can result in a greater variability of extreme water levels and residual surge component, the effects of which are magnified up-estuary by estuarine geometry to exacerbate flood hazard. The concepts and methodology shown here can be applied to other estuaries worldwide.

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The dynamic effects of sea level rise on low‐gradient coastal landscapes: A review

Cited by 272 publications

References 153 publications

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Characterizing storm response and recovery using the beach change envelope: Fire Island, New York

Flood Hazard Assessment for a Hyper-Tidal Estuary as a Function of Tide-Surge-Morphology Interaction

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