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Estimating Coastal Digital Elevation Model Uncertainty



Details

  • Journal Title:
    Journal of Coastal Research
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Integrated bathymetric-topographic digital elevation models (DEMs) are representations of Earth's solid surface that extend across the coastal land-water interface. DEMs are fundamental to the modeling of coastal processes, including tsunami, storm-surge, and sea-level-rise inundation. Vertical errors in coastal DEMs are deviations in elevation values from the actual seabed or land surface, which originate from the (1) elevation measurements, (2) datum transformation that converts bathymetric and topographic measurements to a common vertical reference system, (3) spatial resolution of the DEM, and (4) interpolative gridding technique that estimates elevations in areas unconstrained by measurements. The magnitude and spatial distribution of the vertical errors are typically unknown, and a DEM uncertainty surface is a statistical assessment of the likely magnitude of these errors. The National Oceanic and Atmospheric Administration National Centers for Environmental Information develops DEMs for United States' coastal communities. This study describes a methodology to derive uncertainty surfaces that estimate coastal DEM vertical errors at the DEM cell-level. A coastal DEM south of Sarasota, Florida is the case study for deriving uncertainty surfaces. Results indicate that large vertical uncertainty exists in deeper waters offshore with sparse echo-sounder measurements, and small vertical uncertainty exists on flat terrains with dense light detection and ranging measurements. The estimated uncertainty can be propagated into the modeling of coastal processes that utilize DEMs by deriving numerous plausible DEM realizations within the uncertainty bounds. The numerous DEMs realizations can then produce an ensemble of coastal modeling results, and in turn, better-informed coastal management decisions.
  • Source:
    Journal of Coastal Research, 34(6), 1382-1397.
  • DOI:
  • Document Type:
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:b689a5949a0e29140bd97a01f52e1ef828fdfe0396ba10b19e16d8128b4084fce70bd8823362a0a509d8e6cf56acdb71aad1856bcbdb3bbc6ca51a26dd96e4c7
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  • File Type:
    Filetype[PDF - 1.55 MB ]
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