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The study concluded that DEMs based on InSAR technology, including SRTM, suffer from radar noise and require prior processing while the NED DEM was remarkably smooth and may overestimate inundation extent. Sanders (2007) examined the suitability of different online DEMs for flood modeling, including the US National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM) data, airborne interferometric synthetic aperture radar (InSAR) data, and the Light Detection and Ranging (LIDAR) data. (2000) assessed the effects of different large-scale DEMs on hydrologic runoff predictions and highlighted that different DEMs can lead to a difference of almost 10% in runoff predictions.
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However, whilst such derived versions are widely used, they still typically exhibit errors in the vertical much larger than those acceptable for many applications.įor instance, current global DEMs cannot resolve the detail of terrain features that control flooding ( Schumann et al., 2014). Over the years, several processing algorithms and approaches for merging with other elevation datasets have been proposed to increase accuracy and remove vegetation biases ( Baugh et al., 2013 Robinson et al., 2014 O'Loughlin et al., 2016 Yamazaki et al., 2017 Yue et al., 2017). Schumann et al., 2008 Patel et al., 2016), the inherent errors in the SRTM-DEM are still notably unacceptable. Even though several studies have found actual errors to be considerably smaller than the requirements (see e.g.
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For instance, SRTM mission requirements defined absolute and relative elevation errors of 16 and 6 m, respectively ( Rabus et al., 2003). Freely available and global scale DEMs, such as that from the Shuttle Radar Topography Mission (SRTM) or from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mission exhibit large vertical errors which are exacerbated over complex topography and they cannot resolve microtopographic variations in relatively flat terrain ( Gallien et al., 2011 Chu and Lindenschmidt, 2017).
#Digital terrain model vs digital surface model free
However, the availability of comprehensive DEMs for water resources studies is rather limited and limitations of current, free or open-access DEMs are well-known. Digital elevation models (DEM) are recognized as a core spatial dataset required for many environmental applications.