AbstractIn recent decades, fluvial geomorphology and ecohydraulic research have extensively used field observations, remote sensing or hydrodynamic modelling to understand river systems. This study presents an innovative approach that combines field surveys, Light Detection and Ranging (LiDAR)‐based topographical and biomass analyses and model‐derived hydro‐morphodynamic geostatistics to examine short‐term biogeomorphological changes in the wandering gravel‐bed Orco River in Italy. Our primary hypothesis is that hydro‐morphological variables can be robust descriptors for riparian vegetation distribution. From a geomorphological perspective, our study confirms the prevalent wandering behaviour of the Orco River. Moreover, we identified a widening trend in braiding and anabranching sections, particularly downstream. This is evident because of hotspots of flood‐induced morphological reactivation and the redistribution of sediments from the riverbed to lateral bars, resulting in a multi‐thread pattern. Our analysis reveals a net increase in biomass during the observation period despite frequent flood disturbances. We attributed it to two opposing biogeomorphological dynamics: the reduced flow disturbance in some regions due to flood‐induced geomorphological changes and the self‐healing of lateral connectivity through river wandering. Such a net increase indicates that transitional rivers store carbon in the form of vegetation biomass due to their short‐term morphological instability and the different timescales between vegetation and morphological adjustments. Finally, we supported our initial hypothesis with three key findings: (i) a signature of vegetation not just on topography but also on hydro‐morphological conditions, summarised by inundation probability; (ii) the lower variance in vertical topographical changes in vegetated areas compared with bare ones; and (iii) the introduction of a new parameter, named inundation viscosity, derived from the product of mean bed shear stress and average inundation duration, as a discriminating factor for colonisation conditions. These results underscore the value of our comprehensive approach.
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