Flood map boundary sensitivity due to combined effects of DEM resolution and roughness in relation to model performance

Abstract

In comprehending flood model results, we performed sensitivity analyses and evaluated how different combinations of digital elevation model (DEM) resolution and Manning's roughness affect flood maps produced from a 2D hydraulic model. Moreover, we analysed how the estimation of accuracy can further be influenced by the performance measure and the area's topography. Various combinations of DEM and Manning's n produced different results, in terms of quantified performance in relation to actual flood extent and the generated flood boundaries. High-resolution DEMs performed better with higher Manning's n; while lower n values were better for lower resolution DEMs. Furthermore, although lower resolution DEMs (25 and 50 m) received higher quantified performances, there are more discrepancies in the flood maps and water surface elevations (WSE) produced by them. The current statistical estimators of model performance do not necessarily provide an accurate estimate of which combination of DEM resolution and roughness are more suitable for application to modelling. Different statistical estimates have different assumptions, which can affect the model selection. Therefore, a more holistic approach towards model selection should be adopted that gives equal importance to statistical estimators, as well as the quality of flood inundation extents.

Sensitivity of habitat hydraulic model outputs to DTM and computational mesh resolution

“…Several studies that examined the effect of the mesh resolution in flood inundation modelling, and mapping support the fact that the use of smaller mesh elements reduces the terrain truncation errors and flow truncation errors (Begnudelli & Sanders, ; Begnudelli, Sanders, & Bradford, ; Horritt, Bates, & Mattinson, ; Schubert, Sanders, Smith, & Wright, ). Moreover, numerous studies that examined the effect of the DTM accuracy and resolution in flood inundation modelling and mapping support the fact that detailed and accurate representation of the river and riverine area has significant impact on the hydraulic‐hydrodynamic modelling results, not only concerning the flood extent but the water depth as well (Courty, Soriano‐Monzalvo, & Pedrozo‐Acuña, ; Lim & Brandt, ; Md Ali et al, ; Papaioannou, ; Papaioannou et al, ; Papaioannou, Loukas, & Georgiadis, ; Vozinaki, Morianou, Alexakis, & Tsanis, ). Nevertheless, a limited number of studies in fish habitat hydraulic modelling have examined the DTM and/or mesh resolution (Boavida et al, ; Crowder & Diplas, ; Grantham, ; Kolden, Fox, Bledsoe, & Kondratieff, ; Lin, Lin, & Wu, ).…”

Investigating sea‐state effects on flash flood hydrograph and inundation forecasting

“…The evaluation of the hydraulic‐hydrodynamic model's performance achieved using typical indices based on flood extent (Di Baldassarre, Giuliano, Bates, Freer, & Beven, 2010; Horritt, Di Baldassarre, Bates, & Brath, 2007; Lim & Brandt, 2019). Therefore, the Critical Success Index (CSI) or threat score (TS) (Horritt et al, 2007; Lim & Brandt, 2019; Sampson et al, 2015; Shastry et al, 2020) and the penalize CSI (Di Baldassarre et al, 2010; Horritt et al, 2007; Lim & Brandt, 2019) are selected for the evaluation of the simulated flooded areas against the validation polygon. CSI and penalizing CSI will be mentioned from now on as F1 and F2 respectively.…”

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

“…To determine and compare how well the raster layers of the flood prone area obtained by SAR images fit with the raster of flooded areas simulated by HEC‐RAS 2D, two scales, feature agreement statistics F 1 and F 2 are used as shown in Equations (1) and (2) (Bates & De Roo, 2000; Lim & Brandt, 2019b; Mason, Bates, & Dall'Amico, 2009): where A is the total number of pixels showing a perfect match between SAR and the simulated model, B is false or overestimated matches, and C is the missing or underestimated pixels. More details about different verification scales for raster and vector data can found in Hunter (2005)and Lim and Brandt (2019a).…”

“…To determine and compare how well the raster layers of the flood prone area obtained by SAR images fit with the raster of flooded areas simulated by HEC-RAS 2D, two scales, feature agreement statistics F 1 and F 2 are used as shown in Equations ( 1) and ( 2) (Bates & De Roo, 2000;Lim & Brandt, 2019b;Mason, Bates, & Dall'Amico, 2009):…”