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.

A reliable flow surface is necessary for deriving a consistently connected drainage network, which is important in understanding surface water movement and developing applications for surface water runoff, flood inundation, and erosion. Improved methods are needed to extract continuous surface flow features from high-resolution elevation data based on lidar.

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The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.