Effect of uncertainty in Digital Surface Models on the extent of inundated areas

Abstract

The impact of uncertainty in ground elevation on the extent of areas that are inundated due to flooding is investigated. Land surface is represented through a Digital Surface Model (DSM). The effect of uncertainty in DSM is compared to that of the uncertainty due to rainfall. The Monte Carlo method is used to quantify the uncertainty. A typical photogrammetric procedure and conventional maps are used to obtain a reference DSM, later altered to provide DSMs of lower accuracy. Also, data from the Shuttle Radar Topography Mission are used. Floods are simulated in two stages. In the first stage, flood hydrographs for typical return periods are synthesized using generated storm hyetographs, the Soil Conservation Service–Curve Number method for effective rainfall, and the Soil Conservation Service synthetic unit hydrograph. In the second stage, hydrographs are routed via a one‐dimensional hydraulic model. Uncertainty in DSM is considered only in the second stage. Data from two real‐world basins in Greece are used. To characterize the inundated area, we employ the 90% quantile of the inundation extent and inundation topwidth for peak water level at specific river cross‐sections. For topwidths, apart from point estimates, also interval estimates are acquired using the bootstrap method. The effect of DSM uncertainty is compared to that for rainfall. Low uncertainty in DSM is found to widen the inundated area; whereas, the opposite occurred with high uncertainty. SRTM data proved unsuitable for our test basins and modelling context.

Friction Modeling of Flood Flow Simulations

“…However, in this work, our analysis is limited to point estimates because of the computational budget limitations, for both Manning and the proposed friction model. Needless to say, approaches that are less demanding in computing time can afford the assessment of uncertainty of model parameters (Nalbantis and Lymperopoylos 2012;Nalbantis et al 2017).…”

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

“…It is sometimes necessary to also include uncertainty in the mean ground elevation variable, $$\overline{z}(x,y)$$, with respect to which the range of variation σ z ( x , y ) can be generated from the analysis of the Digital Elevation Model (DEM) data. The range of variation σ z ( x , y ) is often assigned a measurement error uncertainty as high as 10% depending on the quality of the DEM data (Hu et al., 2009; Liu et al., 2015; Nalbantis et al., 2017; Shaw et al., 2020; West et al., 2018). Therefore, the uncertainty in $$\overline{z}(x,y)$$ may be significant in some locations, informed by local estimates of σ z ( x , y ).…”