Abstract
Elevation data are critical for assessments of sea-level rise and coastal flooding exposure. Previous research has demonstrated that the quality of data used in elevation-based assessments must be well understood and applied to properly model potential impacts. The cumulative vertical uncertainty of the input elevation data substantially controls the minimum increments of sea-level rise and the minimum planning horizons that can be effectively used in assessments. For regional, continental, or global assessments, several digital elevation models (DEMs) are available for the required topographic information to project potential impacts of increased coastal water levels, whether a simple inundation model is used or a more complex process-based or probabilistic model is employed. When properly characterized, the vertical accuracy of the DEM can be used to report assessment results with the uncertainty stated in terms of a specific confidence level or likelihood category. An accuracy evaluation has been conducted of global DEMs to quantify their inherent vertical uncertainty to demonstrate how accuracy information should be considered when planning and implementing a sea-level rise or coastal flooding assessment. The evaluation approach includes comparison of the DEMs with high-accuracy geodetic control points as the independent reference data over a variety of coastal relief settings. The global DEMs evaluated include SRTM, ASTER GDEM, ALOS World 3D, TanDEM-X, NASADEM, and MERIT. High-resolution, high-accuracy DEM sources, such as airborne lidar and stereo imagery, are also included to give context to the results from the global DEMs. The accuracy characterization results show that current global DEMs are not adequate for high confidence mapping of exposure to fine increments (< 1 m) of sea-level rise or with shorter planning horizons (< 100 years), but they are suitable for general delineation of low elevation coastal zones. In addition to the best practice of rigorous accounting for vertical uncertainty, other recommended procedures are presented for delineation of different types of impact areas (marine and groundwater inundation) and use of regional relative sea-level rise scenarios. The requirement remains for a freely available, high-accuracy, high-resolution global elevation model that supports quantitative sea-level rise and coastal inundation assessments at high confidence levels.
Highlights
The effects of sea-level rise (SLR) and other sources of increased water levels along the world’s coastlines are pervasive and varied (Williams, 2013)
The initial accuracy assessment was conducted on the digital elevation models (DEMs) for which coverage was available for all of conterminous United States (CONUS): Shuttle Radar Topography Mission (SRTM), ASTER Global Digital Elevation Model (GDEM), National Elevation Dataset (NED), and NED derived from lidar DEM source data (Gesch et al, 2014)
Because the primary interest for coastal assessments is in the low-lying areas subject to inundation and other adverse effects of increased water levels, the remainder of the results presented are for the low elevation coastal zone (LECZ)
Summary
The effects of sea-level rise (SLR) and other sources of increased water levels along the world’s coastlines are pervasive and varied (Williams, 2013). Elevation data, most often in the form of DEMs, have been used to identify low-lying coastal lands over broad areas to conduct assessments of the effects of rising sea levels (Schneider and Chen, 1980; Titus et al, 1991; Titus and Richman, 2001; Small and Nicholls, 2003; Ericson et al, 2006; Rowley et al, 2007; Dasgupta et al, 2008, 2010; Weiss et al, 2011; Haer et al, 2013; Blankespoor et al, 2014; Neumann et al, 2015; Hardy and Nuse, 2016; Kulp and Strauss, 2017; Small et al, 2018; Wolff et al, 2018). Previous research has demonstrated that the quality of data, and associated transformations, used for elevation-based assessments must be well understood and applied to properly model potential impacts (Gesch, 2009; Coveney and Fotheringham, 2011; Cooper and Chen, 2013; Cooper et al, 2013, 2015; Gesch, 2013; Schmid et al, 2014; Dahl et al, 2017; Jones et al, 2017; West et al, 2018)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
