stTwin: A digital twin framework for catchment-scale sediments transport

Sediment transport is a fundamental process shaping landscapes and posing significant hazards in mountainous regions. However, traditional field monitoring and simulation approaches, such as grain size sampling and numerical modeling, are often costly and time consuming. Recent advances in physics-based models and machine learning have substantially improved spatial and temporal resolution. These achievements enable the development of digital twins to explore what-if scenarios and to better understand the dynamic processes involved.

In this work, we combine the probabilistic sediment cascade model (SedCas) with the machine learning–based event detection model (Flow-Alert) to develop a digital twin of a catchment. The former relies solely on climate forcing to simulate sediment dynamics, whereas the latter uses seismic signals to identify extreme sediment transport events, such as debris flows. We address three key questions. First, how to design a digital twin framework that captures the physical components of sediment transport, including erosion on hillslopes, hillslope to channel transfer, and channel transport to the catchment outlet, at hourly and even sub hourly temporal resolution. Second, how to fuse predictions from the physics-based model SedCas and the machine learning based model Flow-Alert to merge and balance the strengths of these two modeling approaches. Third, how to reduce uncertainty when translating insights from the virtual entity back to the physical entity. We demonstrate that the digital twin framework enables potential users, such as governmental agencies and local stakeholders, to explore what if scenarios and better understand how climate change and human interventions influence sediment transport dynamics.