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Bedload Self-Generated Noise (SGN) measurements consist in deploying an underwater microphone (i.e. a hydrophone) in the water course and to record the ambient noise of a river. The use of hydrophones is of interest as it can be easily deployed and can provide a continuous monitoring of bedload transport. However, developments are still required to fully understand how bedload characteristics (e.g. specific flux or granulometry) are related to bedload SGN parameters (e.g. acoustic power and spectrum). Laboratory experiments have shown that central and peak frequencies of bedload noise decrease as the particle size increases, just like in string instruments where the tone frequency decreases from a narrow string to a broader string. In this paper, we propose to test a new inverse method enabling the estimation of bedload grain size distributions from SGN measurements. The inverse method is based on a theoretical modelling of the noise generated by a bedload mixture. SGN and physical sampling measurements have been made in 5 French alpine rivers having several transport conditions (bedload D50 from 1 to 40 mm) and varying slopes (0.05 to 1%). Measurements were made for specific bedload flux varying from 10 to 150 g.m-1s-1. The proposed inverse method was used to estimate the bedload grain size distributions. SGN results are compared to bedload samples and are found to largely overestimate sampled granulometries. Finally, it is observed that the spectral characteristics of bedload SGN are not related to bedload GSD but rather to the roughness of the river bed, acting as a source of attenuation and shaping bedload SGN spectra.

Bedload Self-Generated Noise (SGN) measurements consist in deploying an underwater microphone (i.e. a hydrophone) in the river and to record the ambient noise. The use of hydrophones to measure bedload characteristics (flux, spatial distribution, granulometry) could be of interest as it can be more easily and rapidly deployed than physical samplers in rivers. Several measurement campaigns where conducted during spring and summer 2017 in 5 alpine rivers with contrasted transport conditions (bedload D50 between 1 and 40 mm) and varying slopes (0.05 to 1 %). Physical sampling measurements were done from a bridge along the river cross section for specific bedload flux varying between 10 and 150 g.m-1s-1. Bedload SGN measurements were obtained with a small board equipped with a hydrophone and deriving downstream the bridge within a 10 to 50 m long river section. For 2 of the 5 rivers, acoustic Doppler current profilers (ADCP) were also deployed along the river cross-section to provide a surrogate measurement of apparent bedload velocity. As a result, we have been able to draw an acoustic 1D-map of the river bottom, derived from the SGN sub-surface measurements obtained with the deriving board. The results show a coherent relation between the riverbed acoustic maps and the physical samplings for 3 rivers over 5. Bedload profile were less consistent with SGN measurements when bedload transport was localized in a narrow channel. The apparent bedload velocities obtained with ADCP for 2 rivers are consitent with the physical samplings (bedload location and flux distribution) but a slight bias was observed and is attributed to grain-size sorting effects along the cross-section. Finally, when plotting together 4 over 5 rivers, an almost linear relation can be established between bedload discharge (computed with physical samplings data) and the average acoustic response (i.e acoustic power averaged over the cross-section). This result suggests that a generalized calibration curve could exist between bedload SGN and bedload discharge. The existence of an outsider is interpreted as a problem related to propagation effects. Further researches should therefore concentrate their effort on deconvoluting SGN signals from propagation effects to give a better confident proxy for bedload discharge measurement in different rivers types.