Accurate and efficient prediction of spatiotemporal variations in the distribution of substances in fluids (SIFs) is crucial for various aspects of fluid mechanics related research and applications, involving for instance, material transport quantification, water quality assessment, and engineering condition analysis. This study proposes a framework for resolving the spatiotemporal distribution of SIFs such as salt and suspended sediment based on water levels and flow velocities. The framework incorporates a deep learning model based on a classic neural operator (DeepONet) architecture, which consists of a feature network and a position network to encode the characteristics of input variables and the problem domain. Numerical simulations were performed to generate the needed datasets. The framework was well-validated by predicting salinity and suspended sediment concentration (SSC) distributions in two idealized cases and a real-word case, demonstrating its efficacy and robustness. Time-series validation further demonstrated the prediction accuracy of the framework. The deep learning model is also capable of enhanced-resolution predictions, enabling the generation of high-resolution spatial distributions of SIFs from low-resolution hydrodynamic data. Both bottom and surface layers of the water column were analyzed, revealing that the mapping relationships between hydrodynamics and SIF distributions can be accurately captured throughout the water column, despite variations in correlation coefficients. Due to these capabilities and advantages, additional data sources can be integrated into the framework in the future, highlighting its considerable potential for broader applications in aquatic environments.

Future sediment transport from the North Sea coasts to the Dutch Wadden Sea for various future sea level scenarios has been studied because it influences the future sand nourishment demand for the maintenance of the coastline and because it determines bio-geomorphological development of the Wadden Sea. The present study focuses on two questions which have not yet been considered in the previous modelling studies using ASMITA: How will the transport develop around drowning of the intertidal flats in the Wadden Sea? How will tidal range change influence the future sediment exchange? By using SLR scenarios with faster acceleration and running the simulations for longer periods of time some inlets exhibited drowning, i.e., where the tidal flat volume vanishes. When drowning occurs, the sediment import rate approaches a maximum or a minimum, depending on the initial morphological state of the tidal inlet system. This maximum or minimum rate for a certain tidal inlet system depends on the SLR scenario. Theoretical analysis as well as modelling results show that tidal range change will influence the sediment import to the Wadden Sea. A tidal range increase will cause a decrease of the sediment demand in the Wadden Sea resulting into less sediment import to the Wadden Sea. It is thus important to study the tidal range development in the Wadden Sea by considering the interaction between SLR, tidal range change and morphological development in the system. It is further concluded that the empirical relation used in the previous studies is not representative of conditions in a tidal basin with fixed basin area, even though this relation has been derived from field observations in many tidal inlet systems worldwide. The equilibrium channel volume should be proportional to the tidal prism instead of to its 1.5^th power.