Abstract
Abstract. The apparent absoluteness of information presented by crisp-delineated flood boundaries can lead to misconceptions among planners about the inherent uncertainties associated in generated flood maps. Even maps based on hydraulic modelling using the highest-resolution digital elevation models (DEMs), and calibrated with the most optimal Manning's roughness (n) coefficients, are susceptible to errors when compared to actual flood boundaries, specifically in flat areas. Therefore, the inaccuracies in inundation extents, brought about by the characteristics of the slope perpendicular to the flow direction of the river, have to be accounted for. Instead of using the typical Monte Carlo simulation and probabilistic methods for uncertainty quantification, an empirical-based disparity-distance equation that considers the effects of both the DEM resolution and slope was used to create prediction-uncertainty zones around the resulting inundation extents of a one-dimensional (1-D) hydraulic model. The equation was originally derived for the Eskilstuna River where flood maps, based on DEM data of different resolutions, were evaluated for the slope-disparity relationship. To assess whether the equation is applicable to another river with different characteristics, modelled inundation extents from the Testebo River were utilised and tested with the equation. By using the cross-sectional locations, water surface elevations, and DEM, uncertainty zones around the original inundation boundary line can be produced for different confidences. The results show that (1) the proposed method is useful both for estimating and directly visualising model inaccuracies caused by the combined effects of slope and DEM resolution, and (2) the DEM-related uncertainties alone do not account for the total inaccuracy of the derived flood map. Decision-makers can apply it to already existing flood maps, thereby recapitulating and re-analysing the inundation boundaries and the areas that are uncertain. Hence, more comprehensive flood information can be provided when determining locations where extra precautions are needed. Yet, when applied, users must also be aware that there are other factors that can influence the extent of the delineated flood boundary.
Highlights
1.1 BackgroundIn a time of climate change, many countries require production of flood risk maps when planning and managing built-up areas
To evaluate the applicability of the disparity-distance equation and algorithm by Brandt (2016) in deriving uncertainty boundaries that account for both the digital elevation models (DEMs) resolution and the slope characteristics of the area, Lim’s (2011) earlier modelling results for a part of the Testebo River (i.e. Forsby and Varva), north of Gävle, Sweden, were utilised as test cases
The uncertainty zones produced using the modelled results from the 50 m data and the LiDAR data are represented by the red regions in Fig. 4, while those that will most likely be proc-iahs.net/373/153/2016/
Summary
1.1 BackgroundIn a time of climate change, many countries require production of flood risk maps when planning and managing built-up areas. As the maps usually show a potential flood inundation area corresponding to a water discharge so big it has never been experienced before, it is virtually impossible to get an exact, or even near, match between the model output and the future actual flood. This calls for sensitivity and uncertainty modelling (cf for example Merwade et al, 2008; Pappenberger et al, 2008, for general treatises on these issues). The techniques are applied to test the sensitivity of the results, in terms of the produced water surface elevation, discharge and the flood’s spatial extent, to hydraulic model inputs by randomly alter-
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