How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

Hasanyar, Masihullah; Romary, Thomas; Wang, Shuaitao; Flipo, Nicolas

doi:https://doi.org/10.5194/bg-2021-333

Preprints

https://doi.org/10.5194/bg-2021-333

Preprints

17 Feb 2022

Status: this preprint is currently under review for the journal BG.

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

Masihullah Hasanyar¹, Thomas Romary¹, Shuaitao Wang², and Nicolas Flipo¹ Masihullah Hasanyar et al.

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¹Geosciences and Geoengineering Department, MINES ParisTech, PSL University, 35 Rue Saint-Honoré, 77300 Fontainebleau, France
²UMR 7619 METIS - Sorbonne Université - 4 place Jussieu - 75252 Paris, France

Received: 16 Dec 2021 – Discussion started: 17 Feb 2022

Abstract. Development of accurate water quality modeling tools is necessary for integrated water quality management of river systems. The existing water quality models can simulate dissolved oxygen (DO) concentration quite well during high flow and phytoplankton blooms in rivers; however, there are discrepancies during the summer low-flow season that are assumed to be due to the uncertainties related to the organic matter contribution of the model boundary conditions. Therefore, we used the C-RIVE biogeochemical model to evaluate the influence of controlling parameters on DO simulations at low flow. Three Sobol sensitivity analyses (SA) were carried out based on a coarse model pre-analysis whose target was to develop SA scenarios providing a reduction in the number of model parameters and computation cost as well as hiding inter-parameter interactions. The parameters studied are related to bacterial (e.g., bacterial growth rate), organic matter (OM; repartition and degradation of OM into constituent fractions), and physical factors (e.g., reoxygenation of the river due to navigation and wind), whose variation ranges are selected based on a detailed literature review. Bacterial growth and mortality rates are found to be by far the two most influential parameters, followed by bacterial growth yield. More refined SA results indicate that the biodegradable fraction of dissolved organic matter (BDOM) and the bacterial growth yield are the most influential parameters under conditions of a high net bacterial growth rate (= growth rate – mortality rate), while bacterial growth yield is independently dominant in low net growth situations. Based on the results of this study, proposals are made for in situ measurement of BDOM under a dense and well-equipped urban area water quality monitoring network that could provide high-frequency data. The results also indicate the need for bacterial community monitoring in order to detect potential bacterial community shifts after transient events such as combined sewer overflows and post-infrastructure improvement in treatment plants. Furthermore, we discuss the integration of BDOM in data assimilation software for better estimation of BDOM contribution from boundary conditions, which would result in improved water quality modeling.

Masihullah Hasanyar et al.

Status: open (until 16 Jun 2022)

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RC1:
'Comment on bg-2021-333', Anonymous Referee #1, 10 Apr 2022 reply
The manuscript provides the sensitivity analysis for a water quality submodel that is amended by introducing constant repartitioning rations for dissolved organic matter fractions. A synthetic dataset is used in simulations The work is technically correct. However, the realism of the results and recommendations is questionable since general substantial factors are ignored. The following processes and factors may make their results not necessarily applicable in some situations. Specific reasons for that are as follows.

It is not clear whether the sensitivity results will hold if there will be source-sink terms for organic matter.

No attention is paid to the radiation effects of the bacteria population that is most pronounced at low flows

The role of the hyporheic exchange in bacteria population dynamics at low flows is ignored and can be substantial

The introduction of constant parameters to simulate the repartitioning is a gross simplification. Monitoring data show that the ratios vary.

These and similar limitations of the work should be acknowledged, evaluated, and discussed before the work can be considered for publication.

Reply
Citation: https://doi.org/10.5194/bg-2021-333-RC1
- AC1: 'Reply on RC1', Masihullah Hasanyar, 24 Apr 2022 reply
  
  Dear sir, madame,
  First of all, I would like to thank the anonymous reviewer for its kind attention to our article and the time that they allocated to read it and provided us with comments and suggestions for enhancing our paper. Hereafter are our answers to the 4 comments raised by the reviewer.
  
  Comment #1:
  It is not clear whether the sensitivity results will hold if there will be source-sink terms for organic matter.
  Response #1:
  Each of the three sensitivity analyses is conducted with varying total organic matter (TOC) sources between 1 to 10 mgC/L (Fig. 7-8). Therefore, the source term is well-considered, and adding a new source will not change the results.
  
  As for the organic matter sink, since we had designed the study under a situation where the system shall not be depleted of organic matter and dissolved oxygen at any moment as it is our goal to see how the parameters behave in their presence, this study cannot stand under a sink term that would lead to depletion, especially of organic matter. Indeed, in a depleted environment, the influence of the organic parameters cannot be studied.
  To summarize, the fact that we conducted many numerical experiments under various organic matter conditions already answers the reviewer’s concern. The answer to the effect of a poor or rich environment on organic matter is already largely discussed throughout the paper.
  
  Comment #2:
  No attention is paid to the radiation effects of the bacteria population that is most pronounced at low flows
  Response #2:
  We would like to thank you for bringing up this point. Indeed, our model currently lacks this process and it is something that needs to be incorporated into the model source code. However, its implementation and reanalysis of the work will take far more time than the review period of this article. Moreover, experimental data on the subject needs to be found in order to conduct a sensitivity analysis. This specific question may be the subject of further research. For now, we will mention this limitation of our approach in the discussion section of the paper.
  
  Comment #3:
  The role of the hyporheic exchange in bacteria population dynamics at low flows is ignored and can be substantial.
  
  Response #3:
  The contribution of groundwater to downstream rivers is well known to be negligible with respect to the discharge of those rivers (Strahler order > 6). Our case study mimics such rivers, especially the Seine river crossing the Paris urban area. For such a system, Pryet et al. (2015) provide estimates of aquifer contribution to the Seine River. More specifically, the hyporheic exchange rate is very limited in this area, with a maximum value of 0.005 m3.s-1.km-1, which corresponds to a maximum of 1 m3.s-1 over a 200 km stretch of downstream river. With respect to the actual discharge of the Seine river of 80 m3/s, it is quite clear that this process is not relevant to account for in our study. However, the reviewer is correct that if studying headwater streams functioning, the hyporheic exchanges would be of primary importance.
  
  Comment #4:
  The introduction of constant parameters to simulate the repartitioning is a gross simplification. Monitoring data show that the ratios vary.
  Response #4:
  Without any doubt, the organic matter partitioning parameters vary with time. However, from a sensitivity analysis point of view, we need to keep them constant during the simulation period so that the influence of their increment could be studied on the model total variance and then ranked using the Sobol criteria. That is why we have 360,000 (N(2D+2)) parameter combinations where each parameter value is changed within its variation range given in Table 1 but unchanged during the simulation period to study their impact on model output. Fortunately, the concept of time-varying parameters has been addressed in our data assimilation study (https://doi.org/10.1016/j.envsoft.2022.105382) which is the second step after the sensitivity analysis as I have explained in the discussion subsection “Consequences of the results on data assimilation (DA) strategy”. In that work, we had estimated the time evolution of parameters thanks to observation data and new work is underway to test an automatic detection of boundary conditions organic matter content using data assimilation. To our knowledge, this would be a major step forward for the modeling of the water quality of downstream river systems.
  
  Reference:
  Pryet, A., Labarthe, B., Saleh, F., Akopian, M., and Flipo, N. (2015) Reporting of stream-aquifer flow distribution at the regional scale with a distributed process-based model, Water Resour. Manage., 29, 139-159. doi: 10.1007/s11269-014-0832-7
  
  Reply
  
  Citation: https://doi.org/10.5194/bg-2021-333-AC1

Masihullah Hasanyar et al.

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Short summary

The results of this study indicate that the biodegradable dissolved organic matter is responsible for oxygen depletion at low flow during summer seasons when heterotrophic bacterial activity is so intense. Therefore, the dissolved organic matter must be well measured in the water monitoring networks in order to have more accurate water quality models. It also advocates for high frequency data collection for better quantification of the uncertainties related to organic matter.


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