Abstract
For morphodynamic modelling, riverbed survey data are essential as the basis for the evaluation of temporal riverbed development, mesh creation, and model calibration. To study the effects of uncertain geometry input on these issues, datasets of different spatial resolutions were analysed. As a result, cross-profile data were derived from high-resolution survey data, which are available for a river reach in the Upper Danube in Bavaria for several periods. Finally, the prediction quality of simulations based on cross-profile and high-resolution spatial data was assessed. The analysis of both datasets shows continuous riverbed erosion but of different magnitudes. However, complex riverbed geometry due to, e.g., scours, is depicted poorly by cross-profile data. In more homogenously characterised reaches, cross-profile data significantly more closely represents the riverbed geometry than the high-resolution spatial data base. Local misinterpretation of riverbed geometry by cross-profile data leads to deviations of calibration parameters in the entire study area. Consequently, these deviations in calibration outcome effect the model predictions. In this case, cross-profile calibration generally induces higher transport capacities, leading to more erosion in the study area compared to the model based on high-resolution spatial calibration. The general shape of predicted riverbed geometries is found to be similar but with local deviations, which are not limited to areas with complex river geometry.
Highlights
Two-dimensional morphodynamic numerical modelling is dependent on various input parameters that are subject to several sources of uncertainty
Two different types of datasets are commonly used: (i) Cross-profile data, which can be obtained with either traditional survey equipment in wadable water, or with hydroacoustic instruments mounted to a watercraft in deep water. (ii) Spatial data, which can be acquired with boat-mounted sonar or a LIDAR (Light Detection And Ranging) system
The availability of high-resolution bathymetry data is constantly increasing, raising questions of whether morphodynamic predictions can be improved and how they differ in comparison to predictions based on traditional cross-profile data
Summary
Two-dimensional morphodynamic numerical modelling is dependent on various input parameters that are subject to several sources of uncertainty. The acquisition of bathymetric data is expensive, and difficult to execute due to the dynamic nature of riverine environments (e.g., changing water levels, continuous changing bed morphology, or difficult approachability) [3]. This results in strong temporal and spatial limitations of geometric data, increasing the uncertainty of numerical models [4]. Approaches to fill these data gaps are essential to avoid bias Combined, these individual drawbacks lead to increased uncertainty of the obtained river bathymetry depending on survey techniques, data processing, and the riverine environment
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