Discharge and location dependency of calibrated main channel roughness: Case study on the River Waal

Abstract

To accurately predict water levels, river models require an appropriate description of the hydraulic roughness. The bed roughness increases as river dunes grow with increasing discharge and the roughness depends on differences in channel width, bed level and bed sediment. Therefore, we hypothesize that the calibrated main channel roughness coefficient is most sensitive to the discharge and location in longitudinal direction of the river. The roughness is determined by calibrating the Manning coefficient of the main channel in a 1D hydrodynamic model. The River Waal in the Netherlands is used as a case study. Results show that the calibrated roughness is mainly sensitive to discharge. Especially the transition from bankfull to flood stage and effects of floodplain compartmentation are important features to consider in the calibration as these produce more accurate water level predictions. Moreover, the downstream boundary condition also has a large effect on the calibrated roughness values near the boundary.

Hydraulic modelling approaches to decrease uncertainty in flood frequency relations

“…Although many research has been done on the main channel friction due to river dune dynamics during ood events (e.g. Warmink 2014 adapted such that simulated water levels are close to measurements (Domhof et al, 2018). As a result, the calibrated main channel friction values capture the following features: the physical friction of the main channel caused by e.g.…”

“…simplications in the model set-up (Warmink et al, 2013) and discretization of the model domain (Bomers et al, 2019d); and an articial friction to compensate for errors in the remaining input parameter (Warmink et al, 2013). The main channel friction is thus treated as a garbage bin to capture both the physical phenomena and model errors (Domhof et al, 2018). As a result, the calibrated friction value do not describe the physical friction as encountered in the eld anymore (Bomers et al, 2019d).…”

Historic Flood Reconstruction With the Use of an Artificial Neural Network

“…Hydraulic models with a discharge wave as upstream boundary condition are commonly calibrated on measured water levels with the main channel friction as calibration parameter (Bomers et al, 2019b;Caviedes-Voullième et al, 2012;Domhof et al, 2018). Generally, for many historic flood events, one or multiple maximum water levels at several locations are known.…”

“…As a result, the calibrated main channel friction values capture the following features: the physical friction of the main channel caused by, for example, dune growth (Paarlberg and Schielen, 2012), channel irregularity, and vegetation (Herget and Meurs, 2010); a model-generated friction caused by, for example, simplifications in the model setup (Warmink et al, 2013) and discretization of the model domain (Bomers et al, 2019b); and an artificial friction to compensate for errors in the remaining input parameter (Warmink et al, 2013). The main channel friction is thus treated as a garbage bin to capture both the physical phenomena and model errors (Domhof et al, 2018). As a result, the calibrated friction value does not describe the physical friction as encountered in the field anymore (Bomers et al, 2019b).…”

Novel approaches for large-scale two-dimensional hydrodynamic modelling of rivers

“…When there is wealth of data, and the demands on the model are as high as in our case, the selection of calibration and validation periods becomes important. Domhof et al [11] shows that the selection of calibration discharge stages affects the model result. Calibration discharge levels can be selected based on: return period, geometry, available data, and largest error in model results.…”