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

Abstract

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.

Numerical Modeling of Historical Storm Tides and Waves and Their Interactions Along the U.S. East and Gulf Coasts

“…In the center of Bulls Bay, located near the city of Charleston SC, the maximum storm tide was about 5.3 m, which was induced by Hurricane Hugo (1989). Our results satisfactorily agree with those from Dietsche et al (2007) who found that Hurricane Hugo generated a water level of between 4.3 and 5.1 m in the middle of Bulls Bay. HWMs surveyed by the U.S. Army Corps of Engineers (USACE) show that the water level on the bay's floodplain exceeded 5.5 m above MSL (Schuck- Kolben, 1990).…”

“…The bottom friction is parametrized using the hybrid nonlinear bottom friction law so that the friction coefficient is constant in deep waters whereas, in shallow waters, it increases as the water depth decreases. The friction coefficient in deep waters C F (i.e., the minimum friction coefficient) is set to a constant of 0.0025, a widely used value in ocean circulation models (e.g., Akbar et al, 2017;Dietsche et al, 2007). Our sensitivity analysis also showed that the model with C F 5 0.0025 outperforms the model with C F 5 0.0015 or 0.0035.…”

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

“…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 ).…”

Interdisciplinary knowledge exchange across scales in a globally changing marine environment

“…MARXAN; Watts et al., ), to direct regional and larger scale management plans, and to parameterize models with baseline conditions from which predictions can be made and scenarios tested (Chaumillon et al., ; Roscoe, ). The iterative development of models also requires the continued input of interdisciplinary knowledge (Danielson et al., ; Munoz, Marquez, & Real, ). For example, ecosystem models are only just beginning to include dynamic representation of genetic adaptations that improve a species or individuals’ resilience to climate change—previously adaptations had to be identified by genetic sequences or other direct observations and the information then integrated into model parameterizations or user defined scenarios (Munday, Donelson, & Domingos, ).…”