Sediment transport in Alpine rivers plays a crucial role in shaping river morphology, sustaining ecosystems, and influencing human activities such as hydropower management and hazard mitigation. However, predicting sediment fluxes in these environments remains challenging due to the complex interactions of hydrological processes, sediment availability, and connectivity within river networks. Additionally, the inherent difficulty of measuring transported volumes in the field further complicates efforts to quantify and model sediment dynamics. Climate change introduces another layer of complexity, as shifting precipitation patterns, glacier retreat, and the resulting transition of runoff regimes directly impact sediment transport processes.

In this study, we apply D-CASCADE, a network-scale sediment transport and connectivity model, to the Sulden/Solda catchment (Italian Alps) – a glaciated basin with a nivo-glacial hydrological regime and a well-documented history of sediment monitoring. The model offers the flexibility to analyse bedload transport dynamics across a range of temporal scales, from years to centuries and from daily to hourly timesteps, depending on available input data. By integrating hydrological data and sediment entrainment processes adapted to mountain streams into D-CASCADE, this research aims to assess the model’s capabilities in reconstructing past bedload fluxes while also providing a robust tool for simulating future scenarios.

Beyond quantifying bedload transport, this research explores the potential of D-CASCADE to enhance our understanding of sediment connectivity within Alpine catchments, identifying key zones of sediment production, transfer, and deposition. A better understanding of these dynamics is critical for improving hazard assessments, informing sediment management strategies, and evaluating potential impacts on river and riparian ecosystems. The insights gained will contribute to the broader discourse on sustainable sediment management in mountain environments, demonstrating the value of network-scale modelling in bridging the gap between observations and predictions in a changing climate.