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.