the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Influence of Carbon Cycling on Oxygen Depletion in North-Temperate Lakes
Austin Delany
Robert Ladwig
Cal Buelo
Ellen Albright
Paul C Hanson
Abstract. Hypolimnetic oxygen depletion during summer stratification in lakes can lead to hypoxic and anoxic conditions. Hypolimnetic anoxia is a water quality issue with many consequences, including reduced habitat for cold-water fish species, reduced quality of drinking water, and increased nutrient and organic carbon (OC) release from sediments. Both allochthonous and autochthonous OC loads contribute to oxygen depletion by providing substrate for microbial respiration; however, their relative importance in depleting oxygen across diverse lake systems remains uncertain. Lake characteristics, such as trophic state, hydrology, and morphometry are also influential in carbon cycling processes and may impact oxygen depletion dynamics. To investigate the effects of carbon cycling on hypolimnetic oxygen depletion, we used a two-layer process-based lake model to simulate daily metabolism dynamics for six Wisconsin lakes over twenty years (1995–2014). Physical processes and internal metabolic processes were included in the model and were used to predict dissolved oxygen (DO), particulate OC (POC), and dissolved OC (DOC). In our study of oligotrophic, mesotrophic, and eutrophic lakes, we found autochthony to be far more important than allochthony to hypolimnetic oxygen depletion. Autochthonous POC respiration in the water column contributed the most towards hypolimnetic oxygen depletion in the eutrophic study lakes. POC water column respiration and sediment respiration had similar contributions in the mesotrophic and oligotrophic study lakes. Differences in source of respiration are discussed with consideration of lake productivity, hydrology, and morphometry.
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Austin Delany et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2023-22', Anonymous Referee #1, 13 Mar 2023
The authors pair a lake metabolism model with 20-years of observational data on 6 lakes to understand how different sources of organic carbon to a lake (autochthonous versus allochthonous) influence hypolimnetic oxygen dynamics. The manuscript is well written, and the authors make insightful conclusions about the relative importance of autochthonous versus allochthonous OC for DO dynamics across short and long timeframes. While the process model and assumptions are well described, I have a few specific comments about model structure and parameter fitting that should be addressed before publication.
Specific comments:
Line 79: It would be helpful to provide more detail on “external forcings” here. Which variables are you most considering? Hydrology, climate, nutrient inputs?
Line 88-96: Allochthonous OC can also impact DO dynamics through increasing thermal stratification due to its high chromophoricity. Is this process included in the model? Can you disentangle allochthonous OC influences from the effect of increased stratification versus directly fueling microbial respiration?
Figure 1: To improve clarity and simplicity of the model conceptual figure, consider removing the dashed boxes of TP and IC, as they are not state variables tracked in the model. Specifically, since IC is considered non-limiting and not a driver in any equation, the visualization would benefit from its removal. TP could be included by representing primary productivity as a function dependent on TP, ie. primary productivity(TP)
Table 3, eq. 20: Do recalcitrant and labile OC have different chromophoricity? They are treated similarly in their effect on light extinction coefficient in the model. Did you consider weighting recalcitrant OC more due to high aromaticity or light attenuating compounds? Would that change model dynamics?
Lines 375: What about all the other parameters that were manually fit in Table 4. How were those fit and was any sensitivity analysis conducted?
Line 394: Free parameters were fit manually across their ranges, and it seems parameter values chosen were often at the extremes of the ranges. They also are strongly segregated across the northern and southern lakes, which may be driving many of the differences. How much did parameter value influence model fit? A sensitivity analysis here is needed, or atleast reporting the model residuals with different parameter values. Additionally, did you consider extending the ranges since chosen values were often at the extremes?
Lines 473-476: The authors report differences in the contributions of OC fates the overall budget across the study lakes. Are these statistically significant differences? It would help interpretation to determine this, as the error bars on Figure 5 seem to be overlapping across lakes, but it is hard to compare across lake panels.
Lines 580-583: What about anaerobic carbon metabolism? How would anaerobic metabolism influence sediment OC pools? The surplus of auto OC could be a direct reflection of sediment respiration nearing zero when oxygen is depleted. Previous research shows relationships between anaerobic OC degradation and autochthony, such that the OC could be mineralized as CH4, and there is a growing body of work on the importance of anerobic metabolism for overall carbon cycling and OC burial in stratified lakes. I think it is worth noting that this is not considered and considering how it might change model predictions.
Technical corrections:
Line 176-192: Where was hydrology data from Lake MO obtained from? This lake is missing from the description.
Table 3, Eq. 1: Missing open parentheses in front of NPP.
Citation: https://doi.org/10.5194/egusphere-2023-22-RC1 -
RC2: 'Comment on egusphere-2023-22', Anonymous Referee #2, 29 Mar 2023
This paper investigates the links between the sources and fate of organic carbon and the hypolimnetic oxygen dynamics in 6 Wisconsin lakes. The authors built a 2-box C and O model calibrated and tested independently for all six lakes.
I find the topic very interesting. As indeed, C and O cycles and transport in lakes are naturally coupled, they are rarely studied together in an integrated way. The methodological approach is rich and powerful and conducted wisely. The results are original and provide a new perspective. This manuscript has then great potential.
Yet, at this stage, the writing does not serve the quality of the work done. While I'll go into further details below, I find that it is difficult for the reader to really extract and get to the value of the work done in its present form. The introduction is messy, poorly informative, and lacks an explicit conceptual background. The M&M is also messy and could definitely be improved.
The introduction struggles to highlight to need to connect the C and O cycles in lakes. Many sentences are vague and poorly informative, leaving the reader to check by themselves what is really said in the cited references. For instance, at different places, trophic status, hydrology, and morphometry are cited as all acting/influencing deep hypoxia (l74,l92,l100, l107, l109). But how they are mechanistically acting upon hypoxia remains very vague, and readers should go and check the references. Instead of mentioning that factors 'influence" hypoxia, I would find it much more informative to explain how they do, providing more informative facts. I would suggest rewriting and refocusing the introduction, enriching the conceptual background. The main achievement of this paper is to show that heterotrophy does not necessarily generate greater hypoxia. There is a body of research relating terrestrial organic matter inputs to lake respiration; then, an easy set of hypotheses would be to drive from this. With a greater allochthony and heterotrophy, greater respiration could shift the oxygen balance towards depletion. Then, I suggest focusing on the processes acting upon hypolimnetic oxygen, namely, those by which oxygen is consumed and then relates to carbon sources, and those by which oxygen is renewed. It would be finally easier, from processes, to introduce how lake hydrology and morphometry can affect both consumption and renewal and finally modulate what to expect regarding oxygen depletion.
The M & M is dense, but this is necessary. I appreciate the transparency in the modeling approach. Yet, it is difficult to follow as it sometimes lacks structure. For instance, some data (for external forcings) were measured, other extracted from other models. At this stage, I still have not understood how allochthotnous loadings were computed. Discharges come essentially from a hydrological model, but what about concentrations? There is also sometimes confusion between what is a flux and what is a rate. For instance, l303, "sediment respiration for the hypolimnion [...] is a constant daily rate", rate should be included after respiration.but l 332, "the respiration rates are also scaled by oxygen availability", here rate should be replaced by "flux", BTW, r_rate is not defined, and l390, acronyms for r_sed and R_DOCL are different (Resp_DOCL and Resp_sed).
Specific comments
l69-74. It is surprising to start the introduction by mentioning that hypoxia can lead to increased OC release from the sediment. At the same time, this process is not included in the very model aimed at linking the O and C cycle. The study of drivers of hypoxia in lakes could be better motivated.
Table ": r_rate not defined
Results: RMSE is the only metric used to assess the goodness of fit. However, it poorly captures the model's ability to reproduce the seasonal dynamics, while this is the critical requirement for the model (as it is reinitialized each Jan 1st). Does it make sense to add another metric?
L615- "It has been shown that POC respiration contributes substantially to hypolimnetic DO depletion (Jenny et al. 2016)," This quote needs to be checked. I do not recall Jenny et al quantifying the respiration of POC.
L658-662. Is the long-term effect of allochthony testable from a model that uses annual reinitialization?
Citation: https://doi.org/10.5194/egusphere-2023-22-RC2 -
RC3: 'Comment on egusphere-2023-22', Anonymous Referee #3, 04 Apr 2023
Review: The influence of carbon cycling on oxygen depletion in north-temperate lakes
This manuscript describes the results of a modeling exercise that examines sources of organic carbon and the rates of oxygen depletion in a set of lakes. The main goal of this analysis is to characterize the relative contributions of autochthonous and allochthonous carbon to oxygen depletion across the different lakes in the data set.
General comments:
Model documentation. The description of the metabolism model is incomplete. I could not determine whether the metabolism model was specifically developed for this analysis or whether it was based on some previously published model, as no citations were offered. If the model is specific to this analysis, citations should be provided that justify different modeling choices. It seems that much of the model is similar to that described in Hanson et al. (2004) but then there are some major differences. For example, I couldn’t find a citation for the model linking TP to NPP (Equation 12). Where did this come from? What is Pmax? What is the uncertainty inherent in this relationship? Allochthonous DOC loading is estimated using models from Hanson et al. (2014) but as far as I can tell, this only provides total DOC. How was this DOC allocated to labile and recalcitrant fractions? I’m guessing that it was assigned entirely to the recalcitrant fraction, but no information is provided. Also, the presence of a INDOCL term in Equation (2) suggests that at least some allochthonous load is designated as labile. Overall, the metabolism model needs sufficient documentation so that the reader can decide whether it is a valid approach.
Recalcitrant vs. labile OC. The focus of this paper is to determine the relative contributions of allochthonous and autochthonous OC to oxygen depletion, and the lability of OC is a crucial determinant of these contributions. As stated by the authors, autochthonous OC tends to be more labile than allochthonous OC, but as far as I can tell, all allochthonous OC is assigned to the recalcitrant fraction and all autochthonous OC is assigned to the labile fraction. If this is so, then isn’t the conclusion that most oxygen depletion is driven by autochthonous OC self-evident? Why bother running the model?
Model uncertainty. More work is needed to quantify the uncertainty in the model. The uncertainty in the key components of the model (estimating NPP and external OC loads) is likely large, but no information is offered as to how this uncertainty would affect model results. Assuming that the uncertainty of all model parameters is 20% of the mean value is also a gross simplification.
Line-specific comments
Table 1: The authors states that the sampled lakes cover a broad range of allochthonous loads, but the range of DOC concentrations is narrow relative to concentrations observed at the continental scale (see for example, values reported in EPA’s National Lakes Assessment. It would be good to place the DOC concentrations in a broader context.
Line 190-192: The citation here points to documentation for the data but how exactly were nutrient concentrations used to computed PP?
Line 204: The Hippsey et al. 2019 paper is cited repeatedly but in my reading, this paper only covers the hydrodynamic part of the lake model and says nothing about metabolism models. Is the citation incorrect, or is there a different paper that should be cited?
Line 257: What does the sentence here mean? It seems to state the same information as the previous sentence.
Line 266 - 267: How is the calibration performed? The citation again just documents the source of the data.
Line 319: So, all OC from NPP is assumed to be labile, right?
Line 350: Is inorganic suspended sediment negligible in these lakes? Most Secchi computations would include a contribution from inorganic sediment.
Line 396 – 401: The uncertainty distribution for each parameter is assumed, with no real effort to estimate true uncertainty. So, I’m not sure what added value the calculated uncertainty bounds provide.
Line 406: This approach for Secchi seems very ad hoc. Why are Secchi’s in northern lakes so variable? Higher sampling error? More temporal variability?
Line 416: I think validation RMSE is the most important statistic to report in the main text, and calibration RMSE can be reported in the Supplements.
Line 442: The differences between model predictions of DOC and observed values are so great that it’s hard to argue that the model is accurately reproducing temporal changes in DOC. Only predictions TR and SP are close to observations and that seems more due to the fact that the magnitude of DOC temporal variations is small.
Line 561: Good point. I’m struggling to determine whether the model described in this manuscript is accurate enough to support this statement. A more realistic sensitivity and uncertainty analysis would help, particularly if that uncertainty was carried through to examine the effects on the budgets depicted in Figure 5.
Citation: https://doi.org/10.5194/egusphere-2023-22-RC3
Austin Delany et al.
Data sets
Modeled Organic Carbon, Dissolved Oxygen, and Secchi for six Wisconsin Lakes, 1995-2014 Austin Delany https://doi.org/10.6073/PASTA/1B5B947999AA2F9E0E95C91782B36EE9
Model code and software
Modeled Organic Carbon, Dissolved Oxygen, and Secchi for six Wisconsin Lakes, 1995-2014 Austin Delany https://doi.org/10.6073/PASTA/1B5B947999AA2F9E0E95C91782B36EE9
Austin Delany et al.
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