The relevance of different land-based microplastic inputs to marine pollution is still largely unclear. Nevertheless, the contamination of the terrestrial environment has gained considerable public interest. In particular, the high media presence of this environmental issue reinforces calls for policy regulation of microplastic inputs to the environment. In the public perception, agriculture plays a predominant role as a polluter. However, in order to create a factual basis, it is necessary to record all sources and input pathways as well as their quantitative relevance for microplastic pollution in a system-overarching view. With the aim of an ecosystem-wide quantification of microplastic inputs and migration pathways from the land to the marginal sea, the model network MOMENTUM was developed within the PLAWES project. By linking three independent models adapted for microplastics, RAUMIS-MP, mGROWA+TeMBa, and FSK-MPTP, a model system prototype was developed, which for the first time models relevant sources and migration pathways in a river basin and the estuary. Emissions to agricultural land and diffuse discharge to surface waters, atmospheric deposition to land surfaces, point source inputs to the river system and estuary, and three-dimensional particle movement and their interaction with microorganisms and fine sediment in the coastal zone were considered. The results of the modeling are to be considered exemplary in large parts, since they are subject to large uncertainties due to still very incomplete input data sets. Nevertheless, the application of the MOMENTUM model system in the Weser river system proves the functional capability of the model to estimate the total microplastic mass flux in the catchment and estuary. The model chain was designed to be easily transferable to other regions and to integrate new data sets. By providing initial estimates of microplastic inputs and mass fluxes, the models support targeted data collection, which is highly relevant, especially given the complexity of microplastics analytics. The possibility to include all input sources, pathways and processes over a wide spatial extent contributes – complementary to sample analysis – to the improvement of the overall system understanding and forms the basis for policy advice and development of mitigation measures.