Creating a Coastal National Elevation Database (CoNED) for Science and Conservation Applications

Thatcher, Cindy A.; Brock, John C.; Danielson, Jeffrey J.; Poppenga, Sandra K.; Gesch, Dean B.; Palaseanu‐Lovejoy, Monica; Barras, John A.; Evans, Gayla A.; Gibbs, Ann E.

doi:10.2112/si76-007

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…Finally, cross‐shore elevation profiles were extracted from a combined topographic and bathymetric DEM at each transect (Danielson et al, ; Thatcher et al, ). Using the elevation profiles, the remaining model inputs were estimated, which included average foreshore beach slope ( β ), cliff height ( h c ), nearshore slope from 3 to 10 m depth ( α ), and cliff toe elevation ( E j ).…”

Section: Methodsmentioning

confidence: 99%

A Model Ensemble for Projecting Multidecadal Coastal Cliff Retreat During the 21st Century

et al. 2018

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Sea cliff retreat rates are expected to accelerate with rising sea levels during the 21st century. Here we develop an approach for a multimodel ensemble that efficiently projects time‐averaged sea cliff retreat over multidecadal time scales and large (>50 km) spatial scales. The ensemble consists of five simple 1‐D models adapted from the literature that relate sea cliff retreat to wave impacts, sea level rise (SLR), historical cliff behavior, and cross‐shore profile geometry. Ensemble predictions are based on Monte Carlo simulations of each individual model, which account for the uncertainty of model parameters. The consensus of the individual models also weights uncertainty, such that uncertainty is greater when predictions from different models do not agree. A calibrated, but unvalidated, ensemble was applied to the 475‐km‐long coastline of Southern California (USA), with four SLR scenarios of 0.5, 0.93, 1.5, and 2 m by 2100. Results suggest that future retreat rates could increase relative to mean historical rates by more than twofold for the higher SLR scenarios, causing an average total land loss of 19–41 m by 2100. However, model uncertainty ranges from ±5 to 15 m, reflecting the inherent difficulties of projecting cliff retreat over multiple decades. To enhance ensemble performance, future work could include weighting each model by its skill in matching observations in different morphological settings.

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Section: Methodsmentioning

confidence: 99%

A Model Ensemble for Projecting Multidecadal Coastal Cliff Retreat During the 21st Century

et al. 2018

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“…To better understand the coastal zone, consistently integrated topographic and bathymetric (topobathymetric) data are needed for many coastal applications (Danielson et al, 2013;Eakins and Grothe, 2014;Gesch and Wilson, 2001;Gesch, Gutierrez, and Gill, 2009;Leon et al, 2013;Poppenga et al, 2014;Stoker et al, 2009;Turnipseed et al, 2007;Tyler et al, 2007;Zhang et al, 2015). For example, near-shore topography and bathymetry reflect physical processes that are controlled by the geomorphology of both land elevation and water depth; therefore, onshore-offshore, cross-ecosystem characteristics are useful for understanding the likely impacts of global natural hazards (Gesch, Gutierrez, and Gill, 2009;Thatcher et al, 2016) and support scientific research assessing the impacts of various climate change scenarios on coastal regions (Buxton et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Topobathymetric Elevation Model Development using a New Methodology: Coastal National Elevation Database

Danielson

Poppenga

Brock

et al. 2016

Journal of Coastal Research

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During the coming decades, coastlines will respond to widely predicted sea-level rise, storm surge, and coastal inundation flooding from disastrous events. Because physical processes in coastal environments are controlled by the geomorphology of over-the-land topography and underwater bathymetry, many applications of geospatial data in coastal environments require detailed knowledge of the near-shore topography and bathymetry. In this paper, an updated methodology used by the U.S. Geological Survey Coastal National Elevation Database (CoNED) Applications Project is presented for developing coastal topobathymetric elevation models (TBDEMs) from multiple topographic data sources with adjacent intertidal topobathymetric and offshore bathymetric sources to generate seamlessly integrated TBDEMs. This repeatable, updatable, and logically consistent methodology assimilates topographic data (land elevation) and bathymetry (water depth) into a seamless coastal elevation model. Within the overarching framework, vertical datum transformations are standardized in a workflow that interweaves spatially consistent interpolation (gridding) techniques with a land/water boundary mask delineation approach. Output gridded raster TBDEMs are stacked into a file storage system of mosaic datasets within an Esri ArcGIS geodatabase for efficient updating while maintaining current and updated spatially referenced metadata. Topobathymetric data provide a required seamless elevation product for several science application studies, such as shoreline delineation, coastal inundation mapping, sediment-transport, sea-level rise, storm surge models, and tsunami impact assessment. These detailed coastal elevation data are critical to depict regions prone to climate change impacts and are essential to planners and managers responsible for mitigating the associated risks and costs to both human communities and ecosystems. The CoNED methodology approach has been used to construct integrated TBDEM models in Mobile Bay, the northern Gulf of Mexico, San Francisco Bay, the Hurricane Sandy region, and southern California.

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“…The contemporary bathymetric data for much of the nearshore area were from a 20 m DEM developed from single-beam sonar surveys by the USGS in 2015 [39]. Bathymetric data for the remainder of the study area were from the 3 m USGS Coastal National Elevation Database (CoNED) TBDEM [40] for the northern Gulf of Mexico, which included historical data from various periods between 1920 through 1988. We developed a seamless 10 m TBDEM by converting the rasters to points and using inverse distance-weighted interpolation to combine the datasets.…”

Section: Remote Sensing Data and In Situ Datamentioning

confidence: 99%

Modeling Barrier Island Habitats Using Landscape Position Information

Enwright

Wang

et al. 2019

Remote Sensing

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Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

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Creating a Coastal National Elevation Database (CoNED) for Science and Conservation Applications

Cited by 15 publications

References 13 publications

A Model Ensemble for Projecting Multidecadal Coastal Cliff Retreat During the 21st Century

A Model Ensemble for Projecting Multidecadal Coastal Cliff Retreat During the 21st Century

Topobathymetric Elevation Model Development using a New Methodology: Coastal National Elevation Database

Modeling Barrier Island Habitats Using Landscape Position Information

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