In process-based numerical models, reducing the amount of input parameters, known as input reduction (IR), is often required to reduce the computational effort of these models and to enable long-term, ensemble predictions. Currently, a comprehensive performance assessment of IR-methods is lacking, which hampers guidance on selecting suitable methods and settings in practice. In this study, we investigated the performance of 10 IR-methods and 36 subvariants for wave climate reduction to model the inter-annual evolution of nearshore bars. The performance of reduced wave climates is evaluated by means of a brute force simulation based on the full climate. Additionally, we tested how the performance is affected by the number of wave conditions, sequencing, and duration of the reduced wave climate. We found that the Sediment Transport Bins method is the most promising method. Furthermore, we found that the resolution in directional space is more important for the performance than the resolution in wave height. The results show that a reduced wave climate with fewer conditions applied on a smaller timescale performs better in terms of morphology than a climate with more conditions applied on a longer timescale. The findings of this study can be applied as initial guidelines for selecting input reduction methods at other locations, in other models, or for other domains.

No abstract

No abstract

A variety of uncertainty sources are inherent in process-based morphodynamic modelling applications. There is an increasing demand for the quantification of these uncertainties. This contribution introduces a probabilisticmorphodynamic (PM) modelling framework that enables this quantification. The PM modelling framework provides a systematic approach, while also lowering the required effort for inclusion of uncertainty quantification in morphodynamic model studies. Applicability and added value is shown using a pilot application to the Holland coast.

Tidal basins, as found in the Dutch Wadden Sea, are characterized by strong spatial variations in bathymetry and sediment distribution. In this contribution, the aim is at simulating the spatial sand-mud distribution of a tidal basin. Predicting this spatial distribution is however complicated, due to the non-linear interactions between tides, waves, sediment transport, morphology and biology. To reduce complexity, while increasing physical understanding, an idealized schematization of the Amelander inlet system is considered. Delft3D is applied with a recently developed bed module, containing various sediment layers, combined with formulations for both cohesive and non-cohesive sediment mixtures. Starting with uniform mud content in the spatial domain, the development of the sediment distribution is simulated. Realistic sand-mud patterns are found, with accumulation of mud on the tidal flats. The schematization is further used to determine the sensitivity of the sand-mud patterns to changes in tide, while assessing the influence of tidal dominance on the large-scale sand-mud patterns. The patterns are enhanced/diminished under the influence of higher/lower tides.