3D numerical models are increasingly used to simulate flow, sediment transport and morphological changes of rivers. For the simulation of bedload transport, the numerical flow model is generally coupled with an empirical sediment transport model. The application range of the most widely used empirical models is, however, often limited in terms of hydraulic and sedimentological features and therefore the numerical model can hardly be applied to complex situations where different kinds of morphological processes take place at the same time, such as local scouring, bed armoring and aggradation of finer particles. As a possible solution method for this issue, we present the combined application of two bedload transport formulas that widens the application range and thus gives more appropriate simulation results. An example of this technique is presented in the paper by combining two bedload transport formulas. For model validation, the results of a laboratory experiment, where bed armoring, local scouring and local sediment deposition processes occurred, were used. The results showed that the combined application method can improve the reliability of the numerical simulations.

Exploring the effects of bank vegetation on fluvial morphodynamics has long been an essential part of fluvial morphodynamic-related research. In a practical sense, a central question is: does increased vegetation density increase or decrease the channel width? Several aspects concerning the role of vegetation may result in examples of both width decrease and increase. In this study, we examined more than 170 alluvial river sections. Our goal was to detect the phenomena that ultimately determine riparian woody vegetation-induced width variation. We found that bed material is a governing factor. In the case of fine-grained material, i.e. median size D₅₀ < 2 mm, increasingly densely forested riparian vegetation reduces the bankfull Shields number, and destabilizes the banks toward a wider bankfull channel. In the case of coarse-grained material (i.e. median size D₅₀ ≥ 16 mm), the effect is the opposite; increased density is correlated with a higher bankfull Shields number and a narrower bankfull channel. The extent of the role of vegetation varies depending on the ratio of characteristic root zone depth to channel depth and channel width. We present an improved estimator for bankfull Shields number, which considers riparian vegetation density. The bankfull Shields number can be estimated up to 19% more accurately with our corrected estimator.

The aim of this study is to introduce a novel method which can separate sand- or gravel-dominated bed load transport in rivers with mixed-size bed material. When dealing with large rivers with complex hydrodynamics and morphodynamics, the bed load transport modes can indicate strong variation even locally, which requires a suitable approach to estimate the locally unique behavior of the sediment transport. However, the literature offers only few studies regarding this issue, and they are concerned with uniform bed load. In order to partly fill this gap, we suggest here a decision criteria which utilizes the shear Reynolds number. The method was verified with data from field and laboratory measurements, both performed at nonuniform bed material compositions. The comparative assessment of the results show that the shear Reynolds number-based method operates more reliably than the Shields–Parker diagram and it is expected to predict the sand or gravel transport domination with a <5% uncertainty. The results contribute to the improvement of numerical sediment transport modeling as well as to the field implementation of bed load transport measurements.

The composition or bed material plays a crucial role in the physical hydromorphological processes of fluvial systems. However, conventional bed material sampling methods provide only pointwise information, which can be inadequate when investigating large rivers of inhomogeneous bed material characteristics. In this study, novel, image-based approaches are implemented to gain areal information of the bed surface composition using two different techniques: monocular and stereo computer vision. Using underwater videos, captured in shorter reaches of the Hungarian Danube River, a comparison of the bed material grain size distributions from conventional physical samplings and the ones reconstructed from the images is carried out. Moreover, an attempt is made to quantify bed surface roughness, using the so-called Structure from Motion image analysis method. Practical aspects of the applicability of image-based bed material mapping are discussed and future improvements towards an automatized mapping methodology are outlined.

In this study, the field measurement-based validation of a novel sediment transport calculation method is presented. River sections with complex bed topography and inhomogeneous bed material composition highlight the need for an improved sediment transport calculation method. The complexity of the morphodynamic features (spatially and temporally varied bed material) can result in the simultaneous appearance of the gravel and finer sand dominated sediment transport (e.g., parallel bed armoring and siltation) at different regions within a shorter river reach. For the improvement purpose of sediment transport calculation in such complex river beds, a novel sediment transport method was elaborated. The base concept of it was the combined use of two already existing empirical sediment transport models. The method was already validated against laboratory measurements. The major goal of this study was the verification of the novel method with a real river case study. The combining of the two sediment transport models was based on the implementation of a recently presented classification method of the locally dominant sediment transport nature (gravel or sand transport dominates). The results were compared with measured bed change maps. The verification clearly referred to the meaningful improvement in the sediment transport calculation by the novel manner in the case of spatially varying bed content.