A data-driven framework for calibrating 2D morpho-sedimentary models in gravel-bed meandering rivers

Lateral channel mobility is a key process controlling the morphodynamic evolution of large gravel-bed rivers, yet it remains difficult to represent numerically because of the strong coupling between flow hydraulics, sediment transport and bank erosion. This communication presents a data-driven approach for morpho-sedimentary model calibration, based on several complementary research articles derived from extensive field measurements and combining dense field instrumentation with a rich multi-sensor dataset to constrain detailed numerical models.

The proposed framework is applied to the morphodynamic modelling of an actively migrating meander of the Moselle River (north-eastern France), located within the Wild Moselle regional nature reserve. A multi-year field monitoring program included topo-bathymetric LiDAR surveys, water level records, ADCP velocity measurements, photogrammetric monitoring of bank erosion, direct (in situ) and indirect (hydrophone) bedload measurements, and particle mobility analyses using painted bed patches and RFID tracers.

A high-resolution 2D hydro-sedimentary model was implemented using TELEMAC-2D coupled with SISYPHE to investigate the processes governing meander dynamics. Model parameterization was constrained by in situ measurements, enabling the reproduction of observed flow patterns, sediment transport and bank erosion processes. Particular attention was paid to the contribution of bank-derived sediments to bedload transport and their role in channel morphodynamic.

Results show that the morpho-sedimentary model is capable of reproducing observed bank retreat, highlighting the potential of data-constrained two-dimensional approaches to represent inherently three-dimensional erosion processes. This modelling framework provides a robust basis for assessing future channel evolution and for exploring river management scenarios addressing lateral channel mobility and associated risks to infrastructure destabilization.