|Ackerer et al. coupled the catchment hydrology model NIHM with the geochemical model KIRMAT, enabling the much-needed connection between hydrological processes and geochemical reactions. Such connection is particularly important as the hydrology and biogeochemistry fields advance to resolve pressing issues at the interface of water quantity and quality. In addition to the model development, the authors also validated the model with field data, and explored the connections between transit time distribution and reaction rates, another important missing link. While I applaud these novel aspects of the work, there are issues that need to be addressed before publication. |
First, with the advanced modeling tool and particle tracking technique that quantifies the travel time, the scientific results from this work appear weak. In particular, the conclusion that “durations of water rock interactions exert a first order control on the chemical composition of waters and that the acquisition of the water chemistry can be explained by weathering processes that are spatially fairly homogeneous over the catchment.” This seems a fairly obvious conclusion that does not need a coupled watershed modeling tool. In fact, the dependence on residence time and the closely related concept of Damholker number have been discussed extensively in existing literature. See, for example, (Maher, 2010; Wen & Li, 2018) and the literature therein.
The authors also concluded that “… the chemostatic behavior of the water chemistry is a direct consequence of the strong control exerted by hydrological processes on water transit times.” This very general statement does not provide specific insights on how water transit time control water chemistry. We all know that transit time controls water chemistry. But how and via what mechanism it leads to chemostatic behavior? Transit time influences dissolution rates but that do not necessarily would have chemostatic behavior. This needs to be better explained and discussed from view point of how processes occurred. The real strength of process-based model like the one presented here is its power in linking observations to processes and mechanisms.
Second, despite of the comments from previous reviewers about earlier work, the authors are still not up to speed about literature. For example, the authors still state that (in Line 186 – 187) “To the best of our knowledge, this is the first Time that such a coupling between hydrological and hydrogeochemical modeling approaches has been attempted at the watershed scale.” This work is obviously NOT the “first time” such coupling has been done, as the previous reviewers have pointed out. Although Beisman et al (2015)’s model does not consider the surface hydrology processes and temporal dynamics and therefore is not strictly a watershed scale hydrological and biogeochemical model, RT-Flux-PIHM (Bao et al., WRR, 2017; Li et al., WRR, 2017) is a coupled hydro-biogeochemical model with the relevant surface hydrology processes. They in fact have a string of new papers out based on this model, including (Wen et al., 2019; Zhi et al., 2019). In that context, the authors really cannot claim the “first time”. Even not being the first time doing this, this paper is still valuable and publishable. It is better not to claim “the first time” when it is not. The series papers from RT-Flux-PIHM did not link water transit time with geochemical reactions, which may be the angel that the authors CAN claim as the major novelty of this work.
Third, although a major focus is on concentration discharge (CQ) literature, the authors seem not aware of the most recent CQ literature. For example, (Musolff et al., 2017) explored relationships between travel time and emergent CQ patterns, although they used a different approach for quantification of travel time. Zhi et al (2019) showed that the contrasts between shallow and deeper water composition governed CQ patterns, which in fact can explain the chemostatic behavior observed in this work, as the dissolving minerals are homogeneously distributed here (if I understand correctly). It would be meaningful and increase the readership of the paper if the authors can discuss results from this work in the context of previous topics on similar topics. Other relevant papers include, for example, (Diamond & Cohen, 2018; Herndon et al., 2018; Musolff et al., 2015).
I generally believe that more insights can be gained via more detailed analysis. For example, when and where the dissolution rates are highest in the watershed at dry and at wet times? In addition to the spatial patters of conductivity, can you show rates or concentration spatial patterns over the entire watershed?
I also think the writing of the manuscript can be improved by being more specific and concise. Some of the discussion appears lengthy and diffusive. For example, 6.1 and 6.2.
title: what is "elementary" watershed? it seems an unusual name.
Introduction: Motivation for coupling is still not strong. It can use literature review of CQ to motivate the need of coupling, as previous reviewers have pointed out.
Line 62, Ameli et al 2017 is not at the watershed scale. it is hillslope scale.
Line 85-87: please define “depth-integrated models”. Do you mean there is no resolution in the vertical direction and there is only one grid in the vertical direction?
Line 103 – 107: do you need “if” at the beginning of the sentence. Reads awkward. Can be separated into 2 sentences.
Line 114 – 115: please define “dimensionally-reduced” here and later in approaches. What specific did you do? again related to the “depth-integrated”. Are these two terms equivalent here? if so, maybe stick to one term? Do you mean you only have two grids in vertical direction with unsaturated and saturated zones?
Line 202-245: this is a rather long paragraph. I suggest separate the particle track part as a separate paragraph and with its own subtitle (starting from line 222). this would help give attention to this important section.
Line 222: are there existing references for backtracking approach? does figure 3 indicate that the backtracking only track through flowing water? what about the areas that were dry and disconnected at dry times and reconnected to the stream at a later time.
Are mineral phases homogeneously distributed? I think they are. Please be explicit.
Section 3.2: it sounds like the concentrations are calculated based on TST rate law. but the description also sounds like the calculation is based on travel time. The TST itself does not have a time component to take into account of travel time. so I am confused about how exactly the rates were calculated. Please clarify.
Figure 7, 11: these bar figures are unnecessary and not effective. Why not plot lines for conc vs time for different solutes? you can still add the measurement data for comparison. It would be nice to also include conc. vs. mean travel time, as they may reveal different trend as conc vs discharge.
518-523: “give weight to” were used for a few times. Suggest rephrasing
The discussion about geometric surface area is applicable together with lab-measured reaction constants may need further consideration. The text describing Table 3 emphasizes similar geometric surface area and BET surface area. But the geometric surface area has a large range, often by orders of magnitude. if one takes log average instead of arithmetic, the geometric surface area is in fact much lower than the BET surface area, which mean a much lower surface area is needed to reproduce the concentration data. This in fact is consistent with many previous studies showing that lower surface area needs to be used in order to directly use TST rate law at the field scale, see for example (Heidari et al., 2017; Moore et al., 2012).
657-659: “the study of concentration discharge relationships has been intensively used to assess the chemostatic behavior of waters (Godsey et al., 2009; Kim et al., 2017; Ameli et al., 2017).” Oddly phrased sentence. Please rephrase.
Diamond, J. S., & Cohen, M. J. (2018). Complex patterns of catchment solute–discharge relationships for coastal plain rivers. Hydrological Processes, n/a-n/a. doi: 10.1002/hyp.11424
Heidari, P., Li, L., Jin, L., Williams, J. Z., & Brantley, S. L. (2017). A reactive transport model for Marcellus shale weathering. Geochimica et Cosmochimica Acta, 217(Supplement C), 421-440. doi: https://doi.org/10.1016/j.gca.2017.08.011
Herndon, E. M., Steinhoefel, G., Dere, A. L. D., & Sullivan, P. L. (2018). Perennial flow through convergent hillslopes explains chemodynamic solute behavior in a shale headwater catchment. Chemical Geology, 493, 413-425. doi: https://doi.org/10.1016/j.chemgeo.2018.06.019
Maher, K. (2010). The dependence of chemical weathering rates on fluid residence time. Earth and Planetary Science Letters, 294(1-2), 101-110. doi: 10.1016/j.epsl.2010.03.010
Moore, J., Lichtner, P. C., White, A. F., & Brantley, S. L. (2012). Using a reactive transport model to elucidate differences between laboratory and field dissolution rates in regolith. Geochimica et Cosmochimica Acta, 93, 235-261. doi: 10.1016/j.gca.2012.03.021
Musolff, A., Fleckenstein, J. H., Rao, P. S. C., & Jawitz, J. W. (2017). Emergent archetype patterns of coupled hydrologic and biogeochemical responses in catchments. Geophysical Research Letters, 44(9), 4143-4151. doi: 10.1002/2017GL072630
Musolff, A., Schmidt, C., Selle, B., & Fleckenstein, J. H. (2015). Catchment controls on solute export. Advances in Water Resources, 86, 133-146. doi: 10.1016/j.advwatres.2015.09.026
Wen, H., & Li, L. (2018). An upscaled rate law for mineral dissolution in heterogeneous media: The role of time and length scales. Geochimica et Cosmochimica Acta, 235, 1-20. doi: https://doi.org/10.1016/j.gca.2018.04.024
Wen, H., Perdrial, J., Bernal, S., Abbott, B. W., Dupas, R., Godsey, S. E., et al. (2019). Temperature controls production but hydrology controls export of dissolved organic carbon at the catchment scale. Hydrol. Earth Syst. Sci. Discuss., 2019, 1-35. doi: 10.5194/hess-2019-310
Zhi, W., Li, L., Dong, W., Brown, W., Kaye, J., Steefel, C., & Williams, K. H. (2019). Distinct Source Water Chemistry Shapes Contrasting Concentration-Discharge Patterns. Water Resources Research, 0(0). doi: 10.1029/2018wr024257