2018
DOI: 10.2112/jcoastres-d-17-00211.1
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Estimating Coastal Digital Elevation Model Uncertainty

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Cited by 32 publications
(26 citation statements)
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“…Nevertheless, this limited spatial coverage results in an incomplete representation of topographic spatial patterns and evolving features, especially in the case of complex topographies such as steep and unconsolidated slopes. In such cases, interpolation methods are typically required, introducing additional uncertainty into the DEM [3].Remote sensing techniques, such as airborne LiDAR (Light Detection and Ranging) and Unmanned Aerial Vehicle (UAV), emerge in this context as a solution to overcome the limited spatial coverage of the RTK-GPS method [4][5][6][7][8][9]. The use of airborne LiDAR to measure geomorphological changes in coastal areas is relatively new.…”
mentioning
confidence: 99%
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“…Nevertheless, this limited spatial coverage results in an incomplete representation of topographic spatial patterns and evolving features, especially in the case of complex topographies such as steep and unconsolidated slopes. In such cases, interpolation methods are typically required, introducing additional uncertainty into the DEM [3].Remote sensing techniques, such as airborne LiDAR (Light Detection and Ranging) and Unmanned Aerial Vehicle (UAV), emerge in this context as a solution to overcome the limited spatial coverage of the RTK-GPS method [4][5][6][7][8][9]. The use of airborne LiDAR to measure geomorphological changes in coastal areas is relatively new.…”
mentioning
confidence: 99%
“…Nevertheless, this limited spatial coverage results in an incomplete representation of topographic spatial patterns and evolving features, especially in the case of complex topographies such as steep and unconsolidated slopes. In such cases, interpolation methods are typically required, introducing additional uncertainty into the DEM [3].…”
mentioning
confidence: 99%
“…RMSE. When accounting for vertical uncertainty in inundation assessments, combining DEM error and datum transformation error into cumulative vertical uncertainty is a recognized best practice that has been successfully employed in numerous recent studies [22][23][24]46,[65][66][67][68].…”
Section: Cumulative Vertical Uncertainty and Corresponding Minimum Inmentioning
confidence: 99%
“…Coastal hazard uncertainty at the boundary of LISFLOOD-FP is approximated by a sensitivity analysis from eight model simulations (Table 1) completed for the January 2014 and December 2012 events in Delft3D across the entire Severn Estuary model domain (Figure 1b). The Delft3D model for the Severn Estuary uses a two-dimensional, horizontal curvilinear grid which has been previously validated and successfully used in [5,25]. Each model simulation was forced with a combination of time-varying, spatially uniform WL from Ilfracombe tide gauge and time-varying, space-varying H s from the UK Met Office WAVEWATCH III hindcast [55,56] Uncoupled model scenarios represent standalone water level simulations in FLOW, which use a time-varying, spatially uniform WL boundary, or wave simulations in WAVE, which superimpose timeand spatially-varying H s on a constant total water level at mean high water spring tide, taken from Ilfracombe tide gauge.…”
Section: Coastal Hazard Uncertaintymentioning
confidence: 99%
“…Outputs from numerical modelling tools can be used to design crest levels of flood defence structures [17], or force the model boundary of process-based and shoreline response models to predict the pathway, maximum velocities and extent of floodwater, and timing of peak discharge, [18][19][20], wave overtopping [21], morphological change [22,23] and hazard rating [24] arising from the combined effect of these hazards. However, uncertainty can arise in modelled HWL and HWH s predictions due to bathymetric and topographic resolution [25] model coupling processes, local forcing processes and coastal geometry [26]. Therefore an additional key component of flood hazard assessment is to identify sources of uncertainty in HWL and HWH s predictions, and the main objective of this study is to quantify how these uncertainties can propagate through the modelling chain from regional models to generate uncertainty in the impacts of flooding events simulated by process-based and shoreline response models.…”
Section: Introductionmentioning
confidence: 99%