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• W4297887783 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> In urban areas, topography data without above-ground objects are typically preferred in wide-area flood simulation but are not yet available for many locations globally. High-resolution satellite photogrammetric DEMs, like ArcticDEM, are now emerging and could prove extremely useful for global urban flood modelling; however, approaches to generate bare-earth DEMs from them have not yet been fully investigated. In this paper, we test the use of two morphological filters (simple morphological filter – SMRF – and progressive morphological filter – PMF) to remove surface artefacts from ArcticDEM using the city of Helsinki (192 <span class=inline-formula>km²</span>) as a case study. The optimal filter is selected and used to generate a bare-earth version of ArcticDEM. Using a lidar digital terrain model (DTM) as a benchmark, the elevation error and flooding simulation performance for a pluvial scenario were then evaluated at 2 and 10 <span class=inline-formula>m</span> spatial resolution, respectively. The SMRF was found to be more effective at removing artefacts than PMF over a broad parameter range. For the optimal ArcticDEM-SMRF the elevation RMSE was reduced by up to 70 % over the uncorrected DEM, achieving a final value of 1.02 <span class=inline-formula>m</span>. The simulated water depth error was reduced to 0.3 <span class=inline-formula>m</span>, which is comparable to typical model errors using lidar DTM data. This paper indicates that the SMRF can be directly applied to generate a bare-earth version of ArcticDEM in urban environments, although caution should be exercised for areas with densely packed buildings or vegetation. The results imply that where lidar DTMs do not exist, widely available high-resolution satellite photogrammetric DEMs could be used instead." @default.
• W4297887783 created "2022-10-01" @default.
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• W4297887783 date "2022-09-21" @default.
• W4297887783 modified "2023-10-01" @default.
• W4297887783 title "Comment on nhess-2022-210" @default.
• W4297887783 doi "https://doi.org/10.5194/nhess-2022-210-rc1" @default.
• W4297887783 hasPublicationYear "2022" @default.
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