The role of organic matter and bacterial physiology on river metabolism at low flow
- 1MINES ParisTech, PSL Research University, Geosciences and Geoengineering Department, Fontainebleau, France (masihullah.hasanyar@mines-paristech.fr)
- 2UMR 7619 METIS - Sorbonne Université, Paris, France
Development of accurate water quality modelling tools is necessary for integrated water quality management of river systems. The existing water quality models can simulate dissolved oxygen (DO) concentration quite well in rivers, however, there are discrepancies during summer low flow season which are assumed to be due to the heterotrophic bacterial decomposition of organic matter (OM) (Wang, 2019). Therefore, we used the C-RIVE biogeochemical model in order to evaluate the influence of controlling parameters on the DO simulations at low flow.
Four Sobol’ sensitivity analyses (SA) were carried out based on an evolving strategy of reduction in the number of parameters and hiding the inter-parameter interactions. The studied parameters are bacterial (such as growth rate of bacteria), OM-related (repartition and degradation of OM into constituent fractions) and physical (for instance reaeration of river due to navigation and wind) whose variation ranges are selected based on a detailed literature review.
Bacterial growth and mortality rates are by far the two most influential parameters followed by bacterial yield and the share of biodegradable dissolved organic matter (BDOM). More refined SA results indicate that depending on the net bacterial growth (=growth – mortality) being low or high, the bacterial yield and BDOM concentration are the most influential parameters, respectively. Reaeration constant due to navigation and the bacterial uptake of substrate are the other two influential parameters identified in this work.
The results of this study highlight the importance of accurate in-situ sampling and measurement of these influential parameters in order to reduce modelling uncertainties, as well as the necessity for a suitable sampling frequency in order to characterize potential bacterial community switch during transient events such as combined sewer overflows.
References:
Wang, S. (2019). Simulation Du Métabolisme de La Seine Par Assimilation de Données En Continu. These de doctorat, Paris Sciences et Lettres
How to cite: Hasanyar, M., Flipo, N., Romary, T., and Wang, S.: The role of organic matter and bacterial physiology on river metabolism at low flow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3031, https://doi.org/10.5194/egusphere-egu21-3031, 2021.
