This study presents a reliable methodology for monitoring streamflow in a dynamic river of the Alps prone to bathymetric changes using non-contact instruments. The method relies on water level and surface velocity radar monitoring, discharge measurements by Large-Scale Particle Image Velocimetry (LSPIV), and topographic surveys. A single proportional relation, resistant to bathymetric changes, is established between maximum surface velocity (Vs,max) and bulk velocity (Umean). Different methods are used to build this relation: (i) an empirical approach calibrated with the LSPIV measurements; (ii) the Isovel model; (iii) the Q-Commander software developed by the Sommer company. The applicability of the method is tested over a 2.5-year dataset. Compared to the empirical approach, both models, which require minimal input data, predict well the Vs,max-Umean relation. The location of the maximum surface velocity, which reveals to be resistant to bathymetric changes, is also well predicted by these models. Discharge is calculated at a time step of 10 min by multiplying the bulk velocity and the wetted area. The results are compared to the discharge series at the historical station located 2.5 km further upstream, which has a stage-discharge rating curve. Good agreement is observed when surface velocity is above 0.7 m/s, but accuracy decreases for lower velocities. A simplified uncertainty analysis estimates a 20% relative error on discharge calculated with the presented method.

This study represents a pioneering endeavor in estimating the biodegradability of dissolved organic carbon (DOC) in river systems thanks to high-frequency dissolved oxygen (DO) data. Indeed, the first implementation of a particle filter algorithm in a hydro-biogeochemical model had improved DO simulation in river systems. Yet, mismatches between observed and simulated oxygen remain during summer low-flow periods. To address this issue, a sensitivity analysis was conducted, revealing the influence of biodegradable dissolved organic carbon (BDOC) during non-bloom low-flow periods in summer when bacterial net growth activity is high. Therefore, in this study, BDOC is parameterized in ProSe-PA data assimilation software by integrating an organic carbon partitioning model. As a proof of concept, several case studies are developed which demonstrate that the incorporation of the parameter representing BDOC in the data assimilation scheme of ProSe-PA i) improves DO simulation during low-flow periods, ii) helps identify the posterior distribution of bacterial parameters, and iii) for the first time quantifies the biodegradability of DOC in a river given oxygen data. Next, it is shown that at least two DO monitoring stations are necessary to identify model parameters whose locations are controlled by BDOC and bacterial activity. Finally, ProSe-PA is configured to detect changes in bacteria physiology and DOC biodegradability by evaluating the role of data assimilation configuration parameters such as the assimilation time step and the amount of perturbation of model parameter values. The study also verifies that ProSe-PA is capable of detecting abrupt and gradual changes in the biodegradability of DOC.