Abstract In this study, a new predictive framework is introduced to calculate equilibrium scour depth for waves, currents, and combined wave current conditions. A common way of modeling the impact of waves on scour depth is to assume that wave-induced scour converges to a steady-current scour depth associated with similar forcing conditions. This convergence is dependent on the resemblance of the flow structure around the foundation to a fully developed flow structure associated with a steady current. The present paper introduces a new parameter to express in the form of a modified Keulegan-Carpenter (KC) number that combines (1) the wave velocity plus current velocity and (2) the classic KC number. It is shown that the aforementioned modified KC number may be obtained via the Navier-Stokes equations. Furthermore, instead of assuming a constant value for the steady-current related scour depth, as is commonly done, this scour depth is explicitly calculated. The newly introduced predictive equations show good performance on a large set of experimental data for scour due to only waves, combined waves and currents and steady currents, outperforming commonly used other methods. By eliminating the assumption of a constant value for steady-current scour, the model especially yields better performance in predicting deepest scour depths compared to predictive equations that do assume a constant value. No residual trend with the most important nondimensional parameters was observed.
Riparian vegetation provides crucial ecosystem services but the reliable estimation of its impacts on the hydrodynamic and transport processes is limited by the oversimplified description of plants in numerical models. The aim of this paper is to improve the representation of the interactions between flow and flexible woody vegetation in the 2-dimensional hydrodynamic model D-Flow FM. We generalized the existing total resistance approach, implementing a two-way flow-vegetation interaction process allowing to correctly describe effective bed shear stress. The novelties compared to other numerical models are the realistic representation of woody plants by considering the leaf and stem area indices, the full reconfiguration process including the streamlining of leaves and bending of the stems under flow forcing, and the flexibility-generated modifications specifically for submerged conditions. 1D simulations representative of floodplain shrubs and young trees demonstrated the 3-7 times higher accuracy of the new model compared to the original model describing plants as rigid cylinders. The consideration of the foliation process supports reliable simulations from leafless to densely foliated conditions. As one of the first validation studies for submerged vegetation, we found that the proposed von K & aacute;rm & aacute;n scaling factor improved the predictions. Further, we preliminarily validated the new bed shear stress computation method for the rarely measured case of complex reconfiguring vegetation. We support the mainstreaming of these developments by integrating the described numerical scheme into the open-source Delft3D FM software, making it accessible to end-users. Finally, the developed model decreased the uncertainty in flow hydraulics, indicating an improved description of the physical system. Thus, the advances serve for improving understanding of the impacts of varying vegetation conditions on broader hydrological, transport and water quality processes in rivers and floodplains. As the developed model has constant vegetative parameter values under variable conditions, it is expected to improve predictions particularly under non-calibrated conditions.
The active-layer model used to account for mixed-size sediment morphodynamic processes may be ill-posed under certain circumstances. Well-posedness guarantees the existence of a unique solution continuously depending on the problem data. When a model becomes ill-posed, infinitesimal perturbations to a solution grow infinitely fast. Apart from the fact that this behaviour cannot represent a physical process, numerical simulations of an ill-posed model continue to change as the grid is refined. For this reason, ill-posed models cannot be used as predictive tools. There exists a regularisation strategy based on a preconditioning method that guarantees that the one-dimensional active-layer model is well-posed. Here, we show that the extension of this strategy to two dimensions does not regularise the model and we propose a different regularisation strategy based on diffusion that guarantees that both the one-dimensional and two-dimensional active-layer models are well-posed. We implement the strategy in Delft3D Flexible Mesh and show an application.
Field surveys for monitoring river morphology are resource-intensive, especially in large river systems. Earth observation (EO) satellites provide valuable alternatives, yet the integrated use of multiple missions remains underexplored. This study develops a framework that combines Landsat, Sentinel-1, Sentinel-2 and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) data to estimate periodically inundated bed topography and morphological change in the large alluvial Jamuna River. The method achieved an accuracy of similar to 1.5 m of RMSE in topography estimation, with a tendency to overestimate elevations with a mean bias of 1.24 m while preserving slope. By relating water occurrence probability to elevation, morphological changes were quantified for elevation bands, enabling the estimation of area and volume dynamics while accounting for interannual hydrological variability. Compared with the earlier EO-based morphological monitoring tool-which tends to overestimate erosion and accretion areas-the proposed framework reduced false detections (similar to 65%) by accounting for hydrological variability and refining change-classification thresholds. Estimated volumes were comparable in magnitude to EO-based estimates reported in the literature. However, validation with field data revealed that erosion and accretion volumes were underestimated primarily due to the inability to detect changes in aperiodic bed and an inconsistent temporal scale of field and satellite data. Despite these limitations, the method successfully captured dominant reach-scale morphological processes and is suitable for deriving first-order estimates of morphological changes in large rivers with seasonally fluctuating water levels. While further research is needed to better quantify errors/uncertainties and validate the SWOT-based water surface elevation profile correction, this work lays a foundation for using multi-satellite EO data for large-scale river morphological assessments.
