Visualising DEM-related flood-map uncertainties using a disparity-distance equation algorithm

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 empiricalbased 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.

Mapping Flood Risk Uncertainty Zones in Support of Urban Resilience Planning

“…The uncertainty zone produced by the new GIS tool is based solely on the quality of the DEM and the terrain slope characteristics. The previous study by Brandt and Lim (2016) shows, however, that the true flooded area sometimes may be considerably outside or inside this uncertainty zone. To enable capturing some of this additional uncertainty, it seems most relevant to include an estimate of roughness uncertainty.…”

“…Brandt's ( 2016) algorithm (cf. Brandt & Lim, 2016;Lim, 2018) creates uncertainty zones around predicted flood boundaries from 1D hydraulic simulations. Whereas uncertainty zones usually are produced by probabilistic models (Merwade et al, 2008), disregarding the terrain slopes, the uncertainty zones here are based on the characteristics and quality of the used DEM.…”