the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Jan 2024
Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States
Abstract. The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM, we performed a water isotope (δ18O and δ2H) mass balance using long term mean summer hydrologic fluxes between 2000 and 2019, and gridded precipitation and groundwater isotope ratios. We compared the NWM-flux-estimated (‘model’) river reach isotope ratios to 4503 in-stream water isotope observations in 877 reaches across 5 basins in the western US. A simple regression between observed and mass balance estimated isotope ratios explained 57.9 % (δ18O) and 67.1 % (δ2H) of variance, though observations were 0.5 ‰ (δ18O) and 4.8 ‰ (δ2H) higher, on average, than mass balance estimates. The unexplained variance suggest that the NWM does not include all relevant water fluxes to rivers. To infer possible missing water fluxes, we evaluated patterns in observation-model differences using δ18Odiff (δ18Oobs − δ18Omod) and ddiff (δ2Hdiff −8∗δ18Odiff). We detected evapoconcentration of observations relative to model estimates (negative ddiff and positive δ18Odiff) at lower elevation, higher stream order, arid sites. The catchment actual evaporation to precipitation ratio, the fraction of streamflow estimated to be derived from agricultural irrigation, and whether a site was reservoir-affected were all significant predictors of ddiff in a linear mixed effects model, with up to 15.1 % of variance explained by fixed effects. This finding is supported by patterns in groundwater levels and groundwater isotope ratios, and suggests the importance of including irrigation return flows to rivers, especially in lower elevation, higher stream order, arid rivers of the Western US.
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RC1: 'Comment on hess-2023-308', Anonymous Referee #1, 25 Jan 2024
This paper presents an interesting work on the diagnose of NWM model with the aid of isotope data. The isotope simulation identifies different bias characteristics in high and low elevation regions. The bias of isotope simulation in low regions is attributed to the contributions of irrigation return flows, and some statistical analysis and literature reviews are conducted to support this hypothesis. Overall, the logic is clear, the analysis is solid, and the written is good, making this paper worth publishing in HESS. However, I would like to point out some major concerns related to the isotope dataset and mass balance calculation.
1. The calculation of surface water isotope: From a hydrological viewpoint, the calculation surface water isotope ratio (equation 1) is confusing. The authors determine the isotope ratio through dividing the summed isotope fluxes by the summed runoff and groundwater fluxes. However, the term “runoff” usually refers to the sum of surface runoff and subsurface runoff. I don’t know whether the “runoff” provided by NWM model refers to surface runoff or the total runoff. According to the equation, it seems that the runoff is actually only surface runoff. If this is the case, I suggest the authors to make it clear in the main text. Otherwise, the isotope ratio of surface water should be [Rgw*Fgw+Rp*(Fro-Fgw)]/Fro.
2. Choice of isotope dataset:
The authors adopted the monthly long term average precipitation and groundwater isotope data as the input data. This is okay for groundwater because its isotope composition is rather stable. However, the precipitation isotope usually has very strong temporal variation, especially during wet season. Given that a high-resolution dataset of hydrological fluxes produced by NWM was adopted, it might be better to use a high-resolution precipitation isotope dataset, such as the output of isotope enabled general circulation models (iGCM, such as https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008JD010074). The reliability of the precipitation and groundwater isotope dataset itself also need to be evaluated, at least citing some descriptions about their accuracies in published papers. Otherwise, it would be hard to determine whether the simulation biases come from the NWM data or the isotope input data.
Besides, I find that the authors evaluated the isotope simulation performance by comparing the long term simulated results with the measurement surface water isotope sampled at a specific time. Such kind of measurement data would be highly dependent on the specific precipitation events and the corresponding isotope composition before sampling. So it might be more reasonable to compare the measurement data with the corresponding simulated result at the sampling time. We can observe a smaller range of simulated isotope compared to measurement in Figure 3. I think the aggregation of precipitation input and simulated isotope could be the reason of this.
Nonetheless, I understand that replacing the input data and repeating the whole calculation process is challenging. If this is difficult to achieve, please consider addressing these issues in a discussion section.
Specific issues:
- L142: Provide the full name of HUC2 at the place it appears for the first time.
- L208~L215: How were the 10 random draws generated, and how were they used in specify to evaluate uncertainties? There seems to be results related to uncertainty in the result section.
- L400: Figure S8 should be S10?
- L442-443: This is a strong statement and is the basic of following analysis. Consider providing more explanation on it.
- L449: There is an additional letter “d”
- Please provide the r2 and p values in the scatter figures such as Figure 8 and 10
- Table 3: There is an additional symbol “+” in the last row
Citation: https://doi.org/10.5194/hess-2023-308-RC1 -
AC1: 'Reply on RC1', Annie Putman, 16 Feb 2024
Thank you for your supportive and helpful review. I'll address all comments in the full response, but for now I wanted to continue the discussion on your second major point: choice of isotope dataset, because I think it's an important one.
The authors agree that the differences in the time integrations represented by the model data and the observations are a limitation of this study, and also agree that future studies should consider higher temporal variability in the precipitation input value to help address temporal variability in observations arising from recent precipitation inputs to rivers. Unfortunately, it is outside the capacity of the authors to re-do the study at a finer temporal scale at this point. However, we have plans to address this issue in future regional studies and fully agree that a more accurate treatment of the temporal component of the issue is a critical part of pushing this kind of study forward and improving our ability to evaluate water models using tracers.
To that end, thank you for the reference to the iGCM data – this dataset, or another precipitation isotope-reanalysis approach (maybe with WRF+NADP datasets), or a statistical approach like Finkebeiner et al., 2021 (https://doi.org/10.1175/JHM-D-20-0142.1) could be an excellent method to deploy during a second stage (higher resolution, smaller region studies) of this project. We will take these ideas under consideration as we propose and develop continuations of this project.
Nonetheless, we have taken your suggestion to highlight the accuracies of the isoscapes we did use in the analysis. Considerations have been included in the methods section. Likewise, we have adjusted in the presentation of the evaluation of variability in the observation-model differences, choosing to highlight the strength of our dataset – spatial variability - and present the evaluation of interannual variability as insurance that the spatial variability doesn’t co-vary with temporal variability due to interannual variability in sampling patterns. Hopefully this helps clarify for readers the strengths and weaknesses of this specific approach.
Citation: https://doi.org/10.5194/hess-2023-308-AC1 - AC4: 'Reply on RC1 - full response', Annie Putman, 14 Mar 2024
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RC2: 'Comment on hess-2023-308', Anonymous Referee #2, 02 Feb 2024
Putman et al. use long term mean summer hydrology, gridded precipitation and groundwater isotope ratios, and in-stream water isotope ratio observations to evaluate the accuracy of the National Water Model, which is known to perform poorly at low elevations and in highly managed basins in the western United States. The authors use a water isotope mass balance approach to estimate river reach isotope ratios using National Water Model derived fluxes and compare ‘modeled’ isotope ratios with over 4,000 in-stream isotope observations. The differences between observed and modeled ẟ18O and d-excess are used to evaluate statistical patterns in evapoconcentration of observations relative to modeled isotope values. Putman et al. conclude that offset between modeled and observed water isotope values are diagnostic of the lack of agricultural irrigation practices represented in the National Water Model and test this using 2015 Water Use Census data (annual water use by county) and developing an approach for estimating the amount of streamflow sourced from agricultural irrigation on a coarse catchment scale.
Overall, this manuscript is well written and provides a robust framework for evaluating model accuracy, considering different temporal and spatial patterns in the residuals, representing uncertainty in previously published datasets, and the utility of stable water isotope ratios in diagnosing misrepresentation of physical processes in hydrological models. This study is a solid contribution and worthy of publication in HESS; however, there are a few points I think need some clarification before the manuscript is finalized. Below is a discussion of my major comments, followed by more minor, line specific comments. Thank you for the opportunity to engage with this interesting and useful study.
Major Comments
1. I’d like some additional discussion or clarity on why the interpretive framework in Figure 2 is applicable to the results of the d-excess(diff) and δ18O(diff) calculations presented. Typically, d-excess is calculated from the isotope ratios of a single/discrete water sample, but here d(diff) is calculated from isotope values that are a mix of spatially and temporally averaged modeled isotope ratios and point observations of in-stream isotope values. The framework in Figure 2 is based on dual isotope fractionation processes as units of water moves through the hydrological system. Given that the d-excess(diff) calculation here is based on spatially and temporally averaged modeled values, then d-excess(diff) and δ18O(diff), used as indices of evapoconcentration, are likely masking or missing a lot of variability in climate conditions over time and space. Some additional reasoning would be useful for the reader. The discussion section describes how interannual variability is considered by looking at the regressions between the modeled values and the entire observational dataset versus the averaged observations, but I didn’t clearly understand the lines being drawn between that interannual variability (due to variable climates?) and the consideration of how much variance is missing in the NWM that can be attributed to agricultural return flows.
2. The diagnosis of the National Water Model inaccurately representing agricultural return flows is well reasoned in the study and a conclusion that makes sense given the difficulty of many hydrological models in representing irrigation practices given that water use data is difficult to obtain (water users are often reluctant to share this information freely in highly managed areas). One concern I have is the practicality of reasonably incorporating agricultural return fluxes into the model and the approach for estimating this contribution taken in the manuscript. The simplified way of calculating ratios of water use contributions to stream flow in this study seems like a reasonable first pass, but there are many unknowns. For one thing, water use can vary widely between water year types – so including some level of uncertainty or variability in the 2015 Water Use Census data would be helpful. For example, 2015 was a critically dry year in California, so water use data is likely reduced during that year compared to a wetter year on record and the ratio of groundwater to surface water use in the Central Valley is likely inflated for that year. Applying water use data that is from a specific year as a point of understanding contributions to streamflow should likely consider the isotope data from that specific year to match, since it’s not representative of long-term mean conditions. I’d like to see some explicit discussion of what the water use data represents in the main text and whether it's representative of long-term conditions. One thing that could be considered (it may or may not be appropriate here) is the EPA’s EnviroAtlas’ dataset of different types of water use. They have estimated longer term datasets for agricultural water use, industrial, domestic, etc. https://www.epa.gov/enviroatlas
Line Specific Comments
Line 89-92: d-excess is typically calculated for corresponding δ2H and δ18O values for a specific sample/observation. In this study, d(diff) is calculated from estimated average isotope values. Is d-excess still a reliable metric for evaporation when you are calculating from long term averages/values calculated using mass balance? It seems like the mass balance calculation step would not include all the non-equilibrium fractionation processes that could impact the d-excess value. Some additional reasoning somewhere in the text would be helpful for the reader.
Line 108-110: remove “associated with irrigation”
Figure 1 Caption: in text citation format typo for (Bowen 2022b)
Line 191: “Where available, we filled these data gaps using method outlined in Text S2.” I would briefly explain that the authors used the gridded DJF precipitation isotope products to help fill the gaps, since they are listed in Figure 1, but not mentioned anywhere in the main text.
Line 220: typo, should be “This decision was made…”
Line 245: “We evaluated the results with all unaveraged observations and mean isotope ratio at river reaches with multiple observations.” I’m not clear on what this means. The correlation/regression analyses would need to be done between monthly average isotope ratios for an apples-to-apples comparison, rather than mixing discrete observations with monthly average model values.
Line 250: “( Text S3)” has an extra space after first parenthesis.
Figures 6: This is a really nice figure illustrating the different temporal evolution of δ18O(diff) and d(diff) throughout the different major basins in the western US! Please list the distribution statistics in the caption (i.e., box represents what percentiles, what are the smaller, shaded boxes in the Great Basin boxes, diamonds are outliers?).
Line 454: typo, remove “a” after due to
Line 458: p<0.001 is listed for significance, whereas it has been p<0.1 or p<0.01 in other parts of the manuscript. I suggest staying consistent with listing the p-value in the text.
Figure 8: y-axis label I believe should be listed as ‰ instead of %.
Line 468: The meaning of the first sentence is unclear. The δ18O(diff) and d(diff) are statistically significant relative to what?
Figure 9: Please explain the statistics of what the boxplots represent in the caption. Also, is not all data shown? Every land cover type looks like it has groundwater d less than -20 but that’s where the plot stops.
Line 556: typo, should be “stream”
Citation: https://doi.org/10.5194/hess-2023-308-RC2 -
AC2: 'Reply on RC2', Annie Putman, 16 Feb 2024
Thank you for your well reasoned and helpful review. I will post a full response to reviewer after the comment period is over. However, for the moment I'd like to continue the discussion of the major points raised by the reviewer.
For the first major point, concerning the interpretive framework, I had some challenges parsing the question. I'm not totally sure if I understand what is being asked, so forgive me if the explanation begins a little too simplistically. I don’t want to make any logic leaps. Please let me know if I correctly understood the comment and if the response is adequate to address your concerns.
The idea behind this framework is that it helps us interpret the meaning of the deviations of the observations from the estimates based on the mass balance approach. If the model and data inputs correctly capture all isotope-influencing sources and processes, then all points would cluster around (0,0). Instead, we see a spread along an 8:1 line as well as deviations from that 8:1 line. The structure of the deviations from the 8:1 line indicates that negative d18O(diff) tends to be associated with positive d(diff) if d(diff) is non-zero, whereas positive d18O(diff) tends to be associated with negative d(diff) if d(diff) is non-zero. This structure arises in this case because the mass balance approach tends to produce estimates with d-excess values of close to 10 (see Figure 3, gray dots), indicating no evidence of non-equilibrium processes influencing river processes (e.g., evapoconcentration (- d), mixed phase cloud processes (+d), snowmelt fractionation (+d)). On the other hand, observations have a wide range of d-excess values, though most tend to be close to or less than 10 (see Figure 3, blue dots), which are characteristic of evapoconcentration, though some plot above the GMWL, indicating potential for condensation-oriented non-equilibrium processes (e.g., snow processes). So, when the two datasets are compared / differenced, the non-equilibrium signals in the observations are highlighted.
So, the interpretation framework is predicated on the (unintentional) fulfillment of an equilibrium assumption by the mass balance approach and the deviation from that assumption by the observations. If the mass balance approach yielded some evidence of non-equilibrium signals (due to input data or process), then the interpretation framework would likely have different implications. This logic is already largely in place (in brief) in the methods:
“We can interpret combinations of d18O(diff) and d(diff) together, as well as d(diff) independently to infer the uncharacterized sources responsible for the observation-model difference. This framework is useful because the ratios of d2H to d18O of the isotopic inputs to the isotope mass balance tend to be close to 8, whereas those from the observations more often differ from 8. This means that all non-zero d(diff) values can be used to identify omitted water sources and where they are important to streamflow.” However, I have added a caveat in the methods and in the figure caption that the interpretations of the framework would change if the characteristics of the null hypothesis change (i.e., don’t represent equilibrium conditions/an equilibrium assumption).
As for attribution of the source of the evapoconcentration signal, the reviewer is correct in that there are many factors that can influence the deviation of the observation from the model. We attempted to evaluate the potential influence of interannual and seasonal variability as explanations for the signal. Certainly, both of those modes of variability are responsible for some scatter in the results, as we demonstrate in the different sections of the manuscript. However, among the modes of variability we evaluated, the spatial variability was the most consistent across spatial domains and remained even when using average values. Unfortunately, due to the nature of our approach, it was not possible to evaluate all three modes of variability simultaneously, especially because of the sometimes small number of high leverage points, so the evaluation of the spatial mode certainly includes scatter from the interannual and seasonal modes of variability as well as other, unevaluated sources of variability. This likelihood of scatter from other sources of variability probably accounts for the lower predictive power of the statistical approach. However, to avoid overfitting our model, particularly in basins with fewer observations, we did not attempt to statistically evaluate all identified modes of variability simultaneously. I added a caveat specifically calling out the inability to directly address temporal aspects of variability in the methods section, and a nod to the contribution of temporal variability as a cause for scatter / low variance explained in the discussion section.
Evaluating all modes of variability at once might be possible in future studies that are able to resolve the temporal aspect of variability (i.e., producing estimates for each month and year) to match observations, and for smaller scale studies with higher temporal resolution sampling and input data. We hope that we, or others may be able to pursue this approach as an improvement to what we’ve put forth in this initial study (for more on this, see reply to Reviewer 1).
* note that the information contained in this reply is preliminary, intended to promote academic discussion, and should not be cited.
Citation: https://doi.org/10.5194/hess-2023-308-AC2 - AC5: 'Reply on RC2 - full response', Annie Putman, 14 Mar 2024
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AC2: 'Reply on RC2', Annie Putman, 16 Feb 2024
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RC3: 'Comment on hess-2023-308', Anonymous Referee #3, 06 Feb 2024
This is a well-written, comprehensive study that applies a novel evaluation framework using isotopes to diagnose potential error sources in hydrologic models. The study offers useful analyses and conclusions for hydrologic model evaluation, with a focus on processes, which are supported by multiple lines of evidence. I believe the study merits publication, but I have several main comments that I offer for the authors to consider:
- Isotope tracer expertise is assumed. I suspect that many HESS readers are not experts in isotope tracers, and there is a lot of assumed expertise and jargon. The manuscript would benefit from more explanations up front and throughout so that readers unfamiliar can benefit from this novel approach. I point out examples in my specific comments.
- The main conceptual method is hard to pick out from all the details. The authors do an excellent job of explaining their methods in great detail, but I found myself missing the forest for the trees in my first read through. Having never read a tracer study, the main approach – comparing the observed isotopes with the NWM derived estimates (which comes from both NWM fluxes AND gridded datasets) was hard to follow. I think this should be clarified earlier, and I offer a few suggestions to improve the Method section organization in my minor comments. For example, Figure 1 is very detailed, but hard to follow all that’s going on at the outset of the Methods, so I suggest adding a general conceptual overview and/or simple flow chart to guide the reader (including what’s in Equation 1, otherwise it appears much too late for the reader to follow what’s happening). I recognize that this is a subjective suggestion, but I think it would help to increase the reach of your paper.
- The key results are hard to pick out from the supporting results. I appreciate the comprehensive results and multiple lines of evidence presented, but will caution the authors that it can make it difficult for readers to focus on the key results on which the main conclusions are based. The authors may want to review all the results and see if there are any that they might like to include in the Supplemental (I make a few suggestions). I recognize that this is a subjective suggestion, and that there is a tradeoff to including too few versus too many results, but I think a slightly more curated results section would help to increase the reach of your paper. (Also, I will note that the authors do a nice job of summarizing the key results in the Abstract and Conclusions, so this is just a suggestion for the Results themselves).
Specific comments:
Abstract:
Line 3: Is “fidelity” the right work here?
Line 4: In parenthesis, I am not familiar with the delta 18 Oxygen and delta 2 hydrogen notation. Is there a way to define them here for non-experts? Maybe just remove the parenthetical all together from this sentence? You may need to define them later (line 8, etc).
Line 4-7: I had a hard time understanding generally what you did from these sentences until I read the manuscript and thoroughly studied Figure 1. Here’s a suggestion of what might help a reader like me (at least in terms of laying out the general conceptual framework of what was done – please fix details if I have them wrong):
“In this study, we compare observational river isotope data with estimates of river isotopes derived from the NWM. The evaluation is done in 5 basins in the western US in summer from 2000 and 2019. In terms of observations, we use 4503 in-stream water isotope observations in 877 reaches. In terms of the corresponding estimates of river isotopes, these are calculate using a mass balance equation based on NWM-fluxes and estimates of isotope ratios from long term mean gridded precipitation and groundwater datasets.”
Introduction
Line 18-26. This paragraph is quite general in scope and I don’t think is needed. I think you could delete and start with paragraph 2 (i.e., line 27), using something like the first line of your abstract, i.e., : “Hydrologic models, such as the National Oceanic and Atmospheric Administration’s National Water Model (NWM), provides critical analyses and predictions of streamflow that support water management decisions. The NWM is an application of the WRF-Hydro model (Gochis et al., 2018), and is fully routed with high spatial and temporal resolution, providing short and medium term streamflow … “ etc.
Line 27: The operational NWM is based on the WRF-Hydro model (not its data); the NWM is an application of the WRF-Hydro model. Perhaps you are confusing WRF-Hydro (a hydro model) with WRF (a meteorological model). Suggest saying, “…like the National Oceanic and Atmospheric Administration’s National Water Model (NWM) which is an application of the WRF-Hydro model (Gochis et al., 2018)” or you could say “is based on the WRF-Hydro model (Gochis et al., 2018).”
Line 34: “fidelity” does not seem like the right word here.
Line 84: Sentence that starts with “tracers” – can you add a general sentence on tracers and isotopes (before getting into the 16O, etc), for the non-expert? When you get to the parenthetical 16 oxygen, etc, please define these. I start catching your drift a bit later when you define 2H/18O as “heavy” and 1H and 16O as “light”, though as a non-expert I’m not sure to what it is compared.
Line 89: The expression: “The secondary parameter, deuterium excess..” is not clear to a non-expert.
This paragraph (lines 84-92) is very dense, especially for a non-expert (of isotopes), try rereading as someone who doesn’t know about isotopes and generalizing a bit as possible, to help the reader understand this powerful evaluation tool.
Line 93-94. What is isotopic fractionation? Can you say this another way for the non-expert?
Line 110: What is evopoconcentrated and evaporative enrichment?
Line 115-116: Same as in the Abstract, I wasn’t really sure what you did until I read through and studied Figure 1, although this is easier for me to understand than the Abstract. Could you start most generally, saying: “In this study, we compared stream water isotope observations with estimates of water isotopes derived from an isotope mass balance. The isotope mass balance is from xyz NWM and lmnop gridded long term, etc”.
Methods
127-128. Same as previous comments, I initially had trouble understanding what was done here. Even though I like Figure 1, it is very dense. I’m wondering if you could have a very simple conceptual figure first to ground the reader before Figure 1, where you just show the 3 main pieces: (1) Direct obs, (2a) NWM, (2b) gridded ratios, as well as equation 1 and where the pieces fit in on the model side. This could be in a section called “2.1 Conceptual Framework”, and could include the general figure, which would introduce sections 2.4 and 2.5, and it would absorb the current specs listed in “2.1 Temporal domain”, “2.2 Spatial…” and “2.3 Data assim”. Then Figure 1, with all the details, could come later.
Figure 1: This is a very nice figure, but dense… See previous comments. One note: For NWM data feeding into Equation 1: maybe have the notation (Fgw) and (Fro) and for obs data have (Rgw) and (Rro) there to link with Equation 1. This might be better suited to a more general conceptual figure though (see previous comment).
2.3. Data assimilation: This seems minor to be a full section, and I wasn’t sure what this was related to. Can this be part of 2.2 Spatial domain or just Supplemental? Or if you decide to have a Conceptual Framework section, it could be absorbed in that.
Line 160. Correction, the NWM is based on the WRF-Hydro model – which is an open source, community hydrologic model, it is not based on inputs from it (I think you might be confusing WRF-Hydro with WRF, where WRF would provide inputs): “The operational hydrologic model is based on the open-source, community hydrologic model, WRF-Hydro (Gochis et al., 2020b, a)…”
Line 170: Do you mean Figure 1 here?
Equation 1: Seeing this equation helped me to see how the pieces fit together. If you decide to include a conceptual model, I suggest having this equation in it to see how each piece fits in (you could do it generally, for just one reach, as a demonstration, so it was a simpler equation without the subscripts).
Figure 2 – I like this figure and how it showed the way to interpret. I often had to look back at this figure to interpret later results.
Line 371: What is a meteoric water line? What is a surface water line?
Line 380: Is Table 1 needed for the main text or could it go into the supplement?
Line 386. What is an isotopologue?
Line 405: “The strongest signal in our data is that of evaporation, evidenced by combinations of positive δ18Odiff and negative ddiff in arid regions.” <- This is an important conclusion, but there are so many results it’s hard to quickly see what evidence this is from – I had to really go back and study all the figures and tables to realize I needed to imagine all the points from Figure 4 as if they were on Figure 2. Can you add something to that effect to guide the reader? Or add the colored quadrants to remind the reader? Or maybe just say in the caption of Figure 4, “see Figure 2 for what the different locations on the x- y- axis mean”?
Table 2: Is this need for the main text or could it go into the supplement?
Section 3.3. This section and Figure 5 did not seem particularly important to the results/conclusions, and one suggestion would be to put it in the Supplemental (so that the other key results are less buried). If so, you could just have one sentence at the end of the previous section saying something like “ There was little interannual variability, which we interpret to mean there was pervasive presence of eval… etc.. see Supplemental xxx”.
Citation: https://doi.org/10.5194/hess-2023-308-RC3 -
AC3: 'Reply on RC3', Annie Putman, 16 Feb 2024
Thank you for your helpful and supportive review. I will post a full response after the comment period closes. For the purposes of academic discussion, I've included discussion and response to a few of the major comments raised by the reviewer below.
Per your suggestions, I have expanded the section that introduces background on stable water isotopes to the attached text. This is in addition to including clarification in other parts of the text, and removing some of the more discipline specific jargon throughout. I am wondering if the reviewer finds the updated introductory material sufficient to address many of the concerns about requirement of subject matter expertise brought up in the review.
"Elemental or isotope ratios in media associated with hydrologic processes (i.e., water, dissolved gasses, suspended sediments, dissolved ions) are used used to track the contributions of specific water sources (e.g., groundwater fluxes, runoff fluxes) to rivers or other surface waters (Cook and Solomon, 1995; Hall et al., 2016; Gabor et al., 2017). Tracers are useful because they
provide information that is otherwise impossible to disentangle from direct, physical measurements of streamflow or other media. For example, stable water isotopes have been used to extract hydrologic process information (Jasechko et al., 2014; Evaristo et al., 2015) and diagnose process limitations in other modeling contexts (Nusbaumer et al., 2017; Putman et al., 2019). Water comprises three commonly measured stable isotopologues: the most abundant, light atom-bearing 1H216O, as well as a heavy hydrogen bearing (1H2H16O) and a heavy oxygen bearing (1H218O) isotopologues. Measurements of stable water isotopes use ratios of the heavy to light isotopologue for each atom (R = 18O / 16O or 2H / 1H) and are expressed in deltanotation (δ18O and δ2H), where δ = 1000 ∗ ( Rsample−Rstandard / Rstandard) ). The utility of tracers comes from their spatial and temporal variability. In the case of water isotopes, these arise from isotopic fractionation, a physically-governed ‘sorting’ of heavy-atom bearing water molecules (1H2H16O and 1H218O) from those bearing only light atoms (1H216O) that occurs during
phase changes (i.e., evaporation, condensation, sublimation, deposition) (Bowen et al., 2019). Spatial and temporal patterns of δ18O and δ2H are very similar, as evidenced by the strong correlations between δ18O and δ2H in precipitation (Craig, 1961; Putman et al., 2019) and in other waters, including those in the ground, surface, and soil (Evaristo et al., 2015; Tulley-Cordova et al., 2021). These linear relationships are the basis for the ubiquitous water line (WL) data interpretation framework, in which the best fit lines of the form δ2H = βδ18O + I are calculated for different water types (e.g., meteoric (MWL), ground (GWL), surface (SWL)) and are defined either for specific points (local, e.g., LMWL) or for regional or global datasets (e.g.,
GMWL) comprising multiple points. Slopes and intercepts of these lines have useful physical interpretations (Putman et al., 2019), particularly as they relate to the global average conditions as defined by the Global Meteoric Water Line (GMWL), which has a slope of 8 and intercept of 10. Differences between δ18O and δ2H, relative to an expected, global average
relationship are calculated using a secondary parameter called deuterium excess (defined as d = δ2H − 8 ∗ δ18O). Deuterium excess (d) is used to detect evaporation of precipitation and surface waters, evaporation under a vapor pressure gradient or non-equilibrium condensation processes, like snow formation in mixed phase clouds or isotopic fractionation during the melting of snow (Putman et al., 2019; Bowen et al., 2018; Ala-aho et al., 2017)."In terms of the comments about challenges understanding the approach, the suggestions of the reviewer throughout seemed to boil down to "present the obs-model comparison first, then dive into the details of the "modeling" / mass balance approach". I agree that the framing is more straightforward have applied it throughout, and hope that it helps clear up the big picture understanding of the study.
* this author comment is for the purposes of rapid academic communication and should not be cited.
Citation: https://doi.org/10.5194/hess-2023-308-AC3 - AC6: 'Reply on RC3 - full', Annie Putman, 14 Mar 2024
Data sets
Hydrogen and oxygen stable isotope mass balance evaluation of the National Water Model (v2.1) streamflow, runoff and groundwater flows: U.S. Geological Survey data release J. E. Reddy et al. https://doi.org/10.5066/P9NOD5ES
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