River meanders are one of the most recurrent and varied patterns in fluvial systems. Multiple attempts have been made to detect and categorise patterns in meandering rivers to understand their shape and evolution. A novel data-driven approach was used to classify single-bend meanders. A dataset containing approximately 10 million single-lobe meander bends was generated using the Kinoshita curve. A neural network autoencoder was trained over the curvature energy spectra of Kinoshita-generated meanders. Then, the trained network was then tested on real meander bends extracted from satellite images, and the energy spectrum in the meander curvature was reconstructed accurately thanks to the autoencoder architecture. The meander spectrum reconstruction was clustered, and three main bend shapes were found associated with the meander datasets, namely symmetric, upstream-skewed, and downstream-skewed. The autoencoder-based classification framework allowed bend shape detection along rivers, finding the dominant pattern with implications on migration trends. By studying the shift in the prevailing bend shape over time, cutoff events were approximately forecast along the Ucayali River, whose migration was remotely sensed for 32 years. Overall, the method proposed opens the venue to data-driven classifications to understand and manage meandering rivers. Bend shape classification can thus inform restoration and flood control practices and contribute to predicting meander evolution from satellite images or sedimentary records.

The formation and development of tidal channels and salt marshes are controlled by complex interactions between hydrodynamics, sediment transport, and vegetation dynamics. Tidal channels affect and, at the same time, are affected by the growth of salt marshes fringing them. The coupled evolution of these morphological units is thus a key ingredient for simulating the typical behaviour of tidal environments. We developed a mathematical model accounting for vegetation-induced flow resistance and wetting-drying processes typical of tidal environments, to investigate the eco-morphodynamic evolution of intertidal areas fringing a main channel and of the tidal creeks cutting through them. Model results indicate that vegetation promotes the development of channel networks, leading to more complex channel structures and higher drainage efficiency. Vegetation encroachment influences sediment deposition patterns by trapping sediment in the seaward and middle intertidal areas, while reducing the amount of sediment delivered to landward areas. In the presence of sea level rise, this deficit of sediment enhances the landward-decreasing trend of the intertidal platform and leads to more isolated vegetation patches. Overall, sea level rise restricts the extension of salt marshes and consequently reduces the effect of vegetation on channel development.

Confined meandering rivers are bounded between valleys and they are limited in their lateral migration compared to free meandering ones. In this study, we designed a model-based analysis to investigate types of confined meandering rivers. To simulate cases, we used the model developed by (Bogoni et al. 2017) based on a semi-analytical solution for flow and bed morphology ((Zolezzi and Seminara 2001). According to the result of (Lewin and Brindle 1977) the categorization of this type of meandering river should be based on the potential of evolution. We defined a confinement ratio as the ratio between the confined valley width and the meander belt width that the river would theoretically reach without geological confinement. Such unconfined meander belt width has been computed through long-term morphodynamic model simulations. This differs from previous work (Camporeale et al. 2005), semi-empirically suggesting an unconfined meander belt being nearly 3 times the modeled, linearly most unstable, intrinsic meander length. Our results indicate the existence of four different types of confined meandering rivers, which represents the most important point of this work. Compared to previous work, our classification adds a new class and reclassifies one of the previous types. Our proposed classes are; “Moderately confined condition” for confinement ratio in the range 0.1-1, “Strongly confined condition” for confinement ratio in the range 0.03-0.1. For a smaller confinement ratio than 0.03 we obtain a low sinuosity, single thread river, and for a confinement ratio greater than 1, we retrieve a free meandering river. Preliminary testing of our model-based classification with data from real confined meandering rivers shows promising results.