the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?
Masihullah Hasanyar
Thomas Romary
Shuaitao Wang
Nicolas Flipo
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: closed
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RC1: 'Comment on bg-2021-333', Anonymous Referee #1, 10 Apr 2022
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.
Citation: https://doi.org/10.5194/bg-2021-333-RC1 -
AC1: 'Reply on RC1', Masihullah Hasanyar, 24 Apr 2022
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
Citation: https://doi.org/10.5194/bg-2021-333-AC1
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RC2: 'Comment on bg-2021-333', Anonymous Referee #2, 17 Jun 2022
This work presents a sensitivity analysis of water quality model where authors include new considerations about organic matter degradation kinetics and the proportion of organic matter fractions.
First of all, I would like to recognize that I am not a big expert in the topic, so my concerns might be associated with the lack of a strong expertise in the methods applied here and the literature on this topic. Still, the methodology applied sounds appropriate to fulfil the objectives of the work. The results are clear and well presented. Even, without a strong expertise in modelling and I could read the whole manuscript getting a good global understanding of it. Therefore, I think authors did a good job to convey their methodological approaches and results. Still, there are several aspects of the writing which could be improved for a better reader flow, but also to highlight better the findings of this study.
First of all, after reading the introduction and the discussion of the manuscript a few times, I still have problems to discern what is the novelty this manuscript brings to the field. The main findings of the manuscript are that bacterial growth rates and yield and the proportion of biodegradable DOM are the most important parameters explaining the variability of dissolved oxygen by the model. The importance of heterotrophic bacteria activity and properties of the dissolved organic matter pool are pinpointed as important parameters to explain uncertainties of water quality models in the introduction (lines 49 to 60). Then, what is this manuscript offering new (or different) from previous studies? This should be more emphasized at the end of the introduction but also by putting findings better into context of previous studies in the discussion.
To be more specific, I suggest to emphasize what research gap or methodological failure authors found and try to solve in the paper. Then, please, specify better what are the research questions or objective of this work. Right now, the last paragraph of the introduction reads more like a list of things authors do along the manuscript more than present their research questions.
About the discussion. In my opinion, the discussion section of any scientific paper should:
- interpret results (why do you find those outputs),
- put findings into the context of previous research (are your findings in agreement with previous studies? Do you find different patterns to what is expected?),
- are there any limitations in the experimental design that should be addressed in future studies? What next steps or how future actions should be?
- expose the implications of those findings (why are they important and, if this particular study case, how should we proceed to solve potential problems).
Authors use the discussion mainly to expose potential implications of their findings and suggest management recommendations. So, I miss a much deeper look into the interpretation of their results, if their findings agree or not with previous research, and the discussion of limitations and future steps needed (for instance, see comments of reviewer 1). The discussion in its current shape is mainly focused on how to design an urban monitoring program which could detect efficiently and quickly problematic changes in water quality. I think this topic is interesting and appropriate, however, I think authors go too deep into their recommendations based on the scope and objectives of the paper. As far as I understand, the main focus of the paper is to evaluate which important biological parameters should be considered in future model for water quality monitoring, not to evaluate and design monitoring programs. For that, a different experimental approach would be necessary (e.g. looking particularly at the effect of pollution sources like waste water treatment plants and so on).
Second, and also related to my concern about the novelty of the study. In this case about the methodological part. Authors state that they incorporate in their analysis new parameters regarding the repartition and degradation of organic matter. What do author mean with new parameters? New regarding what exactly? C-RIVE? Reading section 2.2. of methods, I cannot figure out what parameters are introduced as new, at least, regarding C-RIVE. Section 2.2.1, about OM degradation parameters just state parameters already included in C-RIVE. So, what is new in this regard? In section 2.2.2. about the partitioning of organic matter fractions, I see a change from only considering the concentration of individual fractions (DOM1, DOM2, POM1…), to the author’s version where also the proportion (or ratio) between fractions are considered as well. Another change is that authors pooled DOM1 and DOM2 fractions to create a new fraction called BDOM. Am I missing something? What is really new/different in this approach regarding to previous work in C-RIVE? Is the use of proportions of OM fractions or the pooling of DOM1 and DOM2 into BDOM really making a change from the use of single fractions alone used before?
Maybe my doubts are associated with my lack of expertise in the topic. But still, even if this is the case, I think authors should clarify the mentioned aspects and pinpoint the novelties of their approach but also their findings, if they want to reach a wider audience of readers.
Finally, I appreciate that the methods and result are explained with a lot of detail. That really facilitates the understanding of the work by non-expert reader as me. However, there are parts of the manuscripts which are over-explained or are repetitive (e.g. compare last paragraph of introduction and first paragraph methods, or the legend of figure 4 with lines 254-259). Also, authors abuse a bit of introductory sentences or openings to state what is coming next (e.g. lines 388-389). Removing those parts will facilitate the reading without losing any level of explanation.
Citation: https://doi.org/10.5194/bg-2021-333-RC2 -
AC2: 'Reply on RC2', Masihullah Hasanyar, 21 Jun 2022
Dear sir, madam,
First of all, I would like to thank the reviewer for their positive comments on our paper and valuable propositions for improvement. Here are our subsequent responses, clarifications and the modifications that we would add in the original manuscript in case of acceptance for publication :
In response to their comment:
“What is the novelty this manuscript brings to the field?”
[R1] Our article provides the following novelties compared to the previous works:
- In the previous works, Wang et al., (2018) have focused on both high & low flow under different conditions of bloom, non-bloom and post-bloom, whereas here we have explored further the summer post-bloom low flow condition where significant discrepancies are observed between water quality model results and dissolved oxygen (DO) observations (Wang et al., 2022). We already knew that bacterial physiological parameters control DO evolution during low flow and that was already quantified in Wang et al., (2018). However, what we didn’t know is the extent to which the characteristics of the organic matter (OM), particularly its dissolved biodegradable fraction, control the oxygen dynamics at low water levels, and whether these characteristics are important with respect to the physiological properties of heterotrophic bacteria;
- To explore this question through a sensitivity analysis, we had to explicitly add the parameters of OM model inside the software CRIVE itself, and especially in the boundary conditions of the model, where CRIVE was used to read the six pools of OM (DOM1, DOM2, DOM3, POM1, POM2, POM3) as state variables defined by the user. We therefore added an OM repartition model inside CRIVE so that the DOMi and POMi state variables are now calculated by the model based on only one value provided by the user, i.e. the total organic carbon (TOC), and 5 new model parameters (t, b1, b2, s1, s2). This repartition model not only distributes TOC among the six CRIVE pools using the 5 parameters whose variation ranges were found using a bibliography review (Table 1) , but it also gives us the possibility to do a sensitivity analysis for evaluating their role and influence on DO variation. The main difference is that instead of reading the 6 pools (not varying) directly, now it reads TOC and uses the 5 parameters to convert it into the 6 pools (now varying because the 5 parameters have a range).
- Compared to the previous work (Wang et al., 2018) that has studied only the direct impact of each parameter, we went further and looked into intra-parameter interactions (higher order Sobol indices) and we found that certain parameters hide the influence of other parameters due to their interactions. Thanks to that, we designed the 2nd and 3rd Sobol’ sensitivity analysis which allowed us to better quantify how the share of OM influences DO in river systems with regards to the physiological parameters of heterotrophic bacteria. We believe that this methodology may also be of interest for future sensitivity analysis where parameter interactions may hide the effect of other parameters.
- In the previous study, the sensitivity analysis was conducted under a constant OM load of TOC =3.2 mgC/L whereas in this work, we evaluated the evolution of sensitivity indices for various TOC loadings ranging from 1 up to 10 mgC/L which represents the OM load from river, treatment plants and combined sewage overflows (Fig. 7b and Fig 8).
- Conducting long-term sensitivity analysis: In the previous work, the influence of model parameters are usually studied over a short period of time, for instance a 4 day period in Wang et al. (2018). Here we looked deeper inside the system dynamics by extending the period up to 45 days. Such strategy led to a better understanding of the mid-term effect of slowly biodegradable OM. Even though those effects appear rather negligible, this result is important for improving and simplifying water quality models.
The reviewer advises to make those findings clearer in the paper. We agree with this comment and propose to add a first section to the discussion to wrap up those findings as stated here before.
In response to their comment:
“The importance of heterotrophic bacteria activity and properties of the dissolved organic matter pool are pinpointed as important parameters to explain uncertainties of water quality models in the introduction (lines 49 to 60). Then, what is this manuscript offering new (or different) from previous studies? “
[R2] The reviewer was misled by a flawn formulation of this section of the introduction. We propose to rephrase it following those clarifications : Formerly, Wang et al., (2022) simply assumed from their study that the OM degradation and OM repartition are playing a role in the model discrepancies during low flow, without explicitly quantifying their relative influences. In our paper we tested those hypotheses and therefore extended the parameters of interest to include 3 parameters representing OM kinetics and 5 representing OM repartition to quantify the sensitivity of DO variation with respect to those with a Sobol sensitivity analysis. We found that b1, the share of BDOM, has a significant influence on DO variations in certain circumstances, such as the presence of fast growing heterotrophic bacteria. In that case, a low b1 value may lead to a depletion of BDOM by heterotrophic bacteria, while high b1 allows the micro-organism to grow without limit, leading to significant oxygen depletions.
In response to their comment:
“Please, specify better what are the research questions or objectives of this work?”
[R3] We will add the following main research questions in the introduction of the article:
- What are the influential parameters controlling DO during a post-bloom summer low flow period where discrepancies are observed in different water quality models? Is a model that includes bacteria physiological parameters only sufficient to describe DO variation ?
- To what extent is the knowledge of the quantity of OM share, especially that of BDOM influential for water quality modeling?
- What is the hierarchy among the influential parameters ?
In response to their proposal on the discussion part:
[R4] We will restructure the discussion section by first answering the research questions merging our R1 reply with the current section 3.4. This new section will be called “Hierarchy of the most influential parameters during low flow period”.
The second subsection of the discussion will be dedicated to data assimilation, as is in the current version of the paper.
Finally, we will restructure the sub section “Consequences of the results on water quality monitoring in urban areas” by reformulating how important are our results in the context of water quality monitoring and what information or experimental data is required to be supplied to the water quality models in order to provide better estimates of the river water quality. Indeed, we will first show how important it is to have better identification of bacterial parameters in any water quality monitoring network and second what we can do to get more information on b1 or BDOM.
In addition, we will also discuss the limitations and assumptions of this study as indicated in the response to the comments of the first reviewer. We will provide recommendations for future studies in order to incorporate these limitations.
In response to their second question regarding the incorporation of new parameters:
[R5] We will restructure the material and method section 2.2 to display how we have incorporated the organic matter repartition model consisting of 5 new parameters inside CRIVE instead of using the 6 forced user inputs that are not model parameters.
[R6] Finally, regarding the use of repetitive and introductory paragraphs, we think that it is a good habit to brief the reader regarding what they are going to expect in different sections of an article. This will give them the chance to fast access to their intended sections or subsections. However, we will find the annoying ones and will remove them from the article.
[R7] We reply hereafter to technical questions of the reviewer. Considering the short timeline before the closure of the discussion, we prefered to reply as exhaustively as possible to the reviewer and therefore may introduce some redundancy with our former answers [R1-6]. In case of acceptance of our paper, we would pay attention to avoid any repetition in our arguments.
In response to their specific questions on the addition of new parameter like:
What do authors mean with new parameters? New regarding what exactly? C-RIVE?
[RS7.1] Here, we have two types of new parameters. First, OM degradation kinetic parameters that already exist in CRIVE but whose influence was not studied in any other research. Secondly, OM repartitioning parameters (section 2.2.2) that as I have explained in the point #2 of R1. This is a novelty that did not exist in CRIVE before. Indeed, CRIVE used to read the share of each one of the 6 OM pools directly as an input (that was not variable, they were created in a form of database by multiplying TOC with certain assumed values), however, what we did as a novelty was that we gave CRIVE the possibility to read directly TOC (which comes from experimental data) and convert it into the above 6 OM pools using 5 parameters for which we did an extensive bibliography to find their variation range (t,b1,b2,s1,s2). Thereby, we created these 5 new parameters whose influence on DO could be studied and thanks to which we can now have varying 6 OM pools. This is something which was not possible before.
In response to the question: Another change is that authors pooled DOM1 and DOM2 fractions to create a new fraction called BDOM. Am I missing something? What is really new/different in this approach regarding to previous work in C-RIVE?
[RS7.2] If we look at equations 12 & 13, the variation range of BDOM is found using the variation range of b1, therefore it is not pooled by addition of DOM1 and DOM2. On the contrary, DOM1 and DOM2 are now derived from BDOM using the parameter s1. But why did we do this? In the first sensitivity analysis, we found b1 as an influential parameter, however for the second and third Sobol and in order to decrease the computation cost, we used BDOM as a parameter to get rid of the 5 initial parameters (as shown in Table 4, we went from 17 parameters to 12 parameters). BDOM is the equivalent of b1 (b1 = BDOM/DOM). DOM is constant (DOM = t x TOC) because t was found to be non-influential in the first experiment and fixed here, therefore, having b1 or BDOM does not make any difference technically.
References:
Wang, S., Flipo, N., and Romary, T.: Time-Dependent Global Sensitivity Analysis of the C-RIVE Biogeochemical Model in Contrasted Hydrological and Trophic Contexts, Water Research, 144, 341–355, https://doi.org/10.1016/j.watres.2018.07.033, 2018.
Wang, S., Flipo, N., and Romary, T.: Oxygen Data Assimilation for Estimating Micro-Organism Communities’ Parameters in River Systems, Water Research, 165, 115 021, https://doi.org/10.1016/j.watres.2019.115021, 2019.
Wang, S., Flipo, N., Romary, T., and Hasanyar, M.: Particle Filter for High Frequency Oxygen Data Assimilation in River Systems, Environmental Modelling & Software, 151, 105382, 2022
https://doi.org/10.1016/j.envsoft.2022.105382Citation: https://doi.org/10.5194/bg-2021-333-AC2
Status: closed
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RC1: 'Comment on bg-2021-333', Anonymous Referee #1, 10 Apr 2022
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.
Citation: https://doi.org/10.5194/bg-2021-333-RC1 -
AC1: 'Reply on RC1', Masihullah Hasanyar, 24 Apr 2022
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
Citation: https://doi.org/10.5194/bg-2021-333-AC1
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RC2: 'Comment on bg-2021-333', Anonymous Referee #2, 17 Jun 2022
This work presents a sensitivity analysis of water quality model where authors include new considerations about organic matter degradation kinetics and the proportion of organic matter fractions.
First of all, I would like to recognize that I am not a big expert in the topic, so my concerns might be associated with the lack of a strong expertise in the methods applied here and the literature on this topic. Still, the methodology applied sounds appropriate to fulfil the objectives of the work. The results are clear and well presented. Even, without a strong expertise in modelling and I could read the whole manuscript getting a good global understanding of it. Therefore, I think authors did a good job to convey their methodological approaches and results. Still, there are several aspects of the writing which could be improved for a better reader flow, but also to highlight better the findings of this study.
First of all, after reading the introduction and the discussion of the manuscript a few times, I still have problems to discern what is the novelty this manuscript brings to the field. The main findings of the manuscript are that bacterial growth rates and yield and the proportion of biodegradable DOM are the most important parameters explaining the variability of dissolved oxygen by the model. The importance of heterotrophic bacteria activity and properties of the dissolved organic matter pool are pinpointed as important parameters to explain uncertainties of water quality models in the introduction (lines 49 to 60). Then, what is this manuscript offering new (or different) from previous studies? This should be more emphasized at the end of the introduction but also by putting findings better into context of previous studies in the discussion.
To be more specific, I suggest to emphasize what research gap or methodological failure authors found and try to solve in the paper. Then, please, specify better what are the research questions or objective of this work. Right now, the last paragraph of the introduction reads more like a list of things authors do along the manuscript more than present their research questions.
About the discussion. In my opinion, the discussion section of any scientific paper should:
- interpret results (why do you find those outputs),
- put findings into the context of previous research (are your findings in agreement with previous studies? Do you find different patterns to what is expected?),
- are there any limitations in the experimental design that should be addressed in future studies? What next steps or how future actions should be?
- expose the implications of those findings (why are they important and, if this particular study case, how should we proceed to solve potential problems).
Authors use the discussion mainly to expose potential implications of their findings and suggest management recommendations. So, I miss a much deeper look into the interpretation of their results, if their findings agree or not with previous research, and the discussion of limitations and future steps needed (for instance, see comments of reviewer 1). The discussion in its current shape is mainly focused on how to design an urban monitoring program which could detect efficiently and quickly problematic changes in water quality. I think this topic is interesting and appropriate, however, I think authors go too deep into their recommendations based on the scope and objectives of the paper. As far as I understand, the main focus of the paper is to evaluate which important biological parameters should be considered in future model for water quality monitoring, not to evaluate and design monitoring programs. For that, a different experimental approach would be necessary (e.g. looking particularly at the effect of pollution sources like waste water treatment plants and so on).
Second, and also related to my concern about the novelty of the study. In this case about the methodological part. Authors state that they incorporate in their analysis new parameters regarding the repartition and degradation of organic matter. What do author mean with new parameters? New regarding what exactly? C-RIVE? Reading section 2.2. of methods, I cannot figure out what parameters are introduced as new, at least, regarding C-RIVE. Section 2.2.1, about OM degradation parameters just state parameters already included in C-RIVE. So, what is new in this regard? In section 2.2.2. about the partitioning of organic matter fractions, I see a change from only considering the concentration of individual fractions (DOM1, DOM2, POM1…), to the author’s version where also the proportion (or ratio) between fractions are considered as well. Another change is that authors pooled DOM1 and DOM2 fractions to create a new fraction called BDOM. Am I missing something? What is really new/different in this approach regarding to previous work in C-RIVE? Is the use of proportions of OM fractions or the pooling of DOM1 and DOM2 into BDOM really making a change from the use of single fractions alone used before?
Maybe my doubts are associated with my lack of expertise in the topic. But still, even if this is the case, I think authors should clarify the mentioned aspects and pinpoint the novelties of their approach but also their findings, if they want to reach a wider audience of readers.
Finally, I appreciate that the methods and result are explained with a lot of detail. That really facilitates the understanding of the work by non-expert reader as me. However, there are parts of the manuscripts which are over-explained or are repetitive (e.g. compare last paragraph of introduction and first paragraph methods, or the legend of figure 4 with lines 254-259). Also, authors abuse a bit of introductory sentences or openings to state what is coming next (e.g. lines 388-389). Removing those parts will facilitate the reading without losing any level of explanation.
Citation: https://doi.org/10.5194/bg-2021-333-RC2 -
AC2: 'Reply on RC2', Masihullah Hasanyar, 21 Jun 2022
Dear sir, madam,
First of all, I would like to thank the reviewer for their positive comments on our paper and valuable propositions for improvement. Here are our subsequent responses, clarifications and the modifications that we would add in the original manuscript in case of acceptance for publication :
In response to their comment:
“What is the novelty this manuscript brings to the field?”
[R1] Our article provides the following novelties compared to the previous works:
- In the previous works, Wang et al., (2018) have focused on both high & low flow under different conditions of bloom, non-bloom and post-bloom, whereas here we have explored further the summer post-bloom low flow condition where significant discrepancies are observed between water quality model results and dissolved oxygen (DO) observations (Wang et al., 2022). We already knew that bacterial physiological parameters control DO evolution during low flow and that was already quantified in Wang et al., (2018). However, what we didn’t know is the extent to which the characteristics of the organic matter (OM), particularly its dissolved biodegradable fraction, control the oxygen dynamics at low water levels, and whether these characteristics are important with respect to the physiological properties of heterotrophic bacteria;
- To explore this question through a sensitivity analysis, we had to explicitly add the parameters of OM model inside the software CRIVE itself, and especially in the boundary conditions of the model, where CRIVE was used to read the six pools of OM (DOM1, DOM2, DOM3, POM1, POM2, POM3) as state variables defined by the user. We therefore added an OM repartition model inside CRIVE so that the DOMi and POMi state variables are now calculated by the model based on only one value provided by the user, i.e. the total organic carbon (TOC), and 5 new model parameters (t, b1, b2, s1, s2). This repartition model not only distributes TOC among the six CRIVE pools using the 5 parameters whose variation ranges were found using a bibliography review (Table 1) , but it also gives us the possibility to do a sensitivity analysis for evaluating their role and influence on DO variation. The main difference is that instead of reading the 6 pools (not varying) directly, now it reads TOC and uses the 5 parameters to convert it into the 6 pools (now varying because the 5 parameters have a range).
- Compared to the previous work (Wang et al., 2018) that has studied only the direct impact of each parameter, we went further and looked into intra-parameter interactions (higher order Sobol indices) and we found that certain parameters hide the influence of other parameters due to their interactions. Thanks to that, we designed the 2nd and 3rd Sobol’ sensitivity analysis which allowed us to better quantify how the share of OM influences DO in river systems with regards to the physiological parameters of heterotrophic bacteria. We believe that this methodology may also be of interest for future sensitivity analysis where parameter interactions may hide the effect of other parameters.
- In the previous study, the sensitivity analysis was conducted under a constant OM load of TOC =3.2 mgC/L whereas in this work, we evaluated the evolution of sensitivity indices for various TOC loadings ranging from 1 up to 10 mgC/L which represents the OM load from river, treatment plants and combined sewage overflows (Fig. 7b and Fig 8).
- Conducting long-term sensitivity analysis: In the previous work, the influence of model parameters are usually studied over a short period of time, for instance a 4 day period in Wang et al. (2018). Here we looked deeper inside the system dynamics by extending the period up to 45 days. Such strategy led to a better understanding of the mid-term effect of slowly biodegradable OM. Even though those effects appear rather negligible, this result is important for improving and simplifying water quality models.
The reviewer advises to make those findings clearer in the paper. We agree with this comment and propose to add a first section to the discussion to wrap up those findings as stated here before.
In response to their comment:
“The importance of heterotrophic bacteria activity and properties of the dissolved organic matter pool are pinpointed as important parameters to explain uncertainties of water quality models in the introduction (lines 49 to 60). Then, what is this manuscript offering new (or different) from previous studies? “
[R2] The reviewer was misled by a flawn formulation of this section of the introduction. We propose to rephrase it following those clarifications : Formerly, Wang et al., (2022) simply assumed from their study that the OM degradation and OM repartition are playing a role in the model discrepancies during low flow, without explicitly quantifying their relative influences. In our paper we tested those hypotheses and therefore extended the parameters of interest to include 3 parameters representing OM kinetics and 5 representing OM repartition to quantify the sensitivity of DO variation with respect to those with a Sobol sensitivity analysis. We found that b1, the share of BDOM, has a significant influence on DO variations in certain circumstances, such as the presence of fast growing heterotrophic bacteria. In that case, a low b1 value may lead to a depletion of BDOM by heterotrophic bacteria, while high b1 allows the micro-organism to grow without limit, leading to significant oxygen depletions.
In response to their comment:
“Please, specify better what are the research questions or objectives of this work?”
[R3] We will add the following main research questions in the introduction of the article:
- What are the influential parameters controlling DO during a post-bloom summer low flow period where discrepancies are observed in different water quality models? Is a model that includes bacteria physiological parameters only sufficient to describe DO variation ?
- To what extent is the knowledge of the quantity of OM share, especially that of BDOM influential for water quality modeling?
- What is the hierarchy among the influential parameters ?
In response to their proposal on the discussion part:
[R4] We will restructure the discussion section by first answering the research questions merging our R1 reply with the current section 3.4. This new section will be called “Hierarchy of the most influential parameters during low flow period”.
The second subsection of the discussion will be dedicated to data assimilation, as is in the current version of the paper.
Finally, we will restructure the sub section “Consequences of the results on water quality monitoring in urban areas” by reformulating how important are our results in the context of water quality monitoring and what information or experimental data is required to be supplied to the water quality models in order to provide better estimates of the river water quality. Indeed, we will first show how important it is to have better identification of bacterial parameters in any water quality monitoring network and second what we can do to get more information on b1 or BDOM.
In addition, we will also discuss the limitations and assumptions of this study as indicated in the response to the comments of the first reviewer. We will provide recommendations for future studies in order to incorporate these limitations.
In response to their second question regarding the incorporation of new parameters:
[R5] We will restructure the material and method section 2.2 to display how we have incorporated the organic matter repartition model consisting of 5 new parameters inside CRIVE instead of using the 6 forced user inputs that are not model parameters.
[R6] Finally, regarding the use of repetitive and introductory paragraphs, we think that it is a good habit to brief the reader regarding what they are going to expect in different sections of an article. This will give them the chance to fast access to their intended sections or subsections. However, we will find the annoying ones and will remove them from the article.
[R7] We reply hereafter to technical questions of the reviewer. Considering the short timeline before the closure of the discussion, we prefered to reply as exhaustively as possible to the reviewer and therefore may introduce some redundancy with our former answers [R1-6]. In case of acceptance of our paper, we would pay attention to avoid any repetition in our arguments.
In response to their specific questions on the addition of new parameter like:
What do authors mean with new parameters? New regarding what exactly? C-RIVE?
[RS7.1] Here, we have two types of new parameters. First, OM degradation kinetic parameters that already exist in CRIVE but whose influence was not studied in any other research. Secondly, OM repartitioning parameters (section 2.2.2) that as I have explained in the point #2 of R1. This is a novelty that did not exist in CRIVE before. Indeed, CRIVE used to read the share of each one of the 6 OM pools directly as an input (that was not variable, they were created in a form of database by multiplying TOC with certain assumed values), however, what we did as a novelty was that we gave CRIVE the possibility to read directly TOC (which comes from experimental data) and convert it into the above 6 OM pools using 5 parameters for which we did an extensive bibliography to find their variation range (t,b1,b2,s1,s2). Thereby, we created these 5 new parameters whose influence on DO could be studied and thanks to which we can now have varying 6 OM pools. This is something which was not possible before.
In response to the question: Another change is that authors pooled DOM1 and DOM2 fractions to create a new fraction called BDOM. Am I missing something? What is really new/different in this approach regarding to previous work in C-RIVE?
[RS7.2] If we look at equations 12 & 13, the variation range of BDOM is found using the variation range of b1, therefore it is not pooled by addition of DOM1 and DOM2. On the contrary, DOM1 and DOM2 are now derived from BDOM using the parameter s1. But why did we do this? In the first sensitivity analysis, we found b1 as an influential parameter, however for the second and third Sobol and in order to decrease the computation cost, we used BDOM as a parameter to get rid of the 5 initial parameters (as shown in Table 4, we went from 17 parameters to 12 parameters). BDOM is the equivalent of b1 (b1 = BDOM/DOM). DOM is constant (DOM = t x TOC) because t was found to be non-influential in the first experiment and fixed here, therefore, having b1 or BDOM does not make any difference technically.
References:
Wang, S., Flipo, N., and Romary, T.: Time-Dependent Global Sensitivity Analysis of the C-RIVE Biogeochemical Model in Contrasted Hydrological and Trophic Contexts, Water Research, 144, 341–355, https://doi.org/10.1016/j.watres.2018.07.033, 2018.
Wang, S., Flipo, N., and Romary, T.: Oxygen Data Assimilation for Estimating Micro-Organism Communities’ Parameters in River Systems, Water Research, 165, 115 021, https://doi.org/10.1016/j.watres.2019.115021, 2019.
Wang, S., Flipo, N., Romary, T., and Hasanyar, M.: Particle Filter for High Frequency Oxygen Data Assimilation in River Systems, Environmental Modelling & Software, 151, 105382, 2022
https://doi.org/10.1016/j.envsoft.2022.105382Citation: https://doi.org/10.5194/bg-2021-333-AC2
Masihullah Hasanyar et al.
Masihullah Hasanyar et al.
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