the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jul 2025
High resolution monthly precipitation isotope estimates across Australia from machine learning
Abstract. The stable isotopic composition of precipitation (δ2HP, δ18OP; 'water isotopes') is a powerful tool for tracking water through the atmosphere, as well as fingerprinting land-surface water masses and identifying water cycle biases in isotope-enabled climate models. Water isotopes also underpin our understanding of multi-decadal to multi-centennial water cycle variability via their retrieval from palaeoclimate archives. Water isotopes thereby increase our understanding of past and present – and hence future – water cycle variability. Understanding the drivers of spatial and temporal water isotope variability is a critical first step in applying these tracers for a better understanding of the water cycle. However, water isotope observations are sparse in both space and time. Here we develop and apply a machine learning (random forest) approach to predict spatially continuous monthly δ2HP and δ18OP across the Australian continent at 0.25° resolution from 1962–2023. We train the random forest models on monthly δ2HP (n = 5199) and δ18OP (n = 5217) observations from 60 sites across Australia. We also predict the deuterium excess of precipitation (dxsP, defined as δ2HP − 8*δ18OP). Out-of-sample δ2HP and δ18OP prediction skill is high both geographically and temporally. Skill is slightly lower for the secondary parameter dxsP, likely reflecting the larger reliance of spatio-temporal dxsP variability on moisture source conditions. The random forest models accurately capture both the seasonal cycle of precipitation isotopic variability and long-term annual-mean precipitation isotopic variability across the continent, and outperform estimates from an isotope-enabled atmosphere general circulation model over an equivalent time period. We show that spatio-temporal variability in precipitation amount, precipitation intensity, and surface temperature are particularly important for monthly δ2HP and δ18OP variations across the continent, with local surface pressure also important for dxsP. Drivers of site-level δ2HP, δ18OP, and dxsP are more varied. Overall, the new random forest modelled dataset reveals clear spatial and temporal variability in δ2HP, δ18OP, and dxsP across the Australian continent over the past decades – providing a robust foundation for hydrology, ecology, and palaeoclimate research, as well as an accessible framework for predicting water isotope values in other locations.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2458', Gabriel Bowen, 12 Aug 2025
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AC2: 'Reply on RC1', Georgina M. Falster, 11 Nov 2025
In this response, we reply directly to each of the Reviewer's comments. Original Reviewer comments are in italicised text, our responses are in regular text following. New figures are provided an an attached pdf (including new figures made in response to both reviewers' comments).
- Falster et al. model a large precipitation isotope dataset from Australia using Random Forest, compare the results to those from two other methods, and present and interpret a set of historical monthly average precipitation isoscapes. This is an excellent study, and very well presented…in many ways it’s the precipitation ML isoscape study I’ve been hoping to see for several years now! It’s thorough, really uses the power of the method and an expanded suite of features to go well beyond what’s been done with other statistical methods and learn more from our isotope data. In so doing it represents one of the first successful attempts at a data-driven, time-explicit analysis of historical precipitation isotope patterns. Kudos to the authors, and I’m excited to see this published.
Many thanks for this!! We spent a lot of time trying to do this as well as possible with the tools to hand, so it was really great to read this assessment from someone who has done a lot of work on this topic over many years.
- Below are a handful of comments and suggestions that I hope might be useful and help the authors tie up a few loose ends.
Thanks—these are all super-helpful suggestions and we have addressed each below.
- I have mixed feelings about the choice to model D-excess directly. I’ve done this, too, and am not fundamentally opposed to this approach. But it does lead to a fundamental inconsistency…because D-excess is not and independent parameter you have 3 independent models that are describing a system with only two degrees of freedom. In an ideal world, this would be modeled as a multivariate system, since H and O isotopes have a lot of shared information. A single self-consistent model could be fit to simultaneously predict δ2H, δ18O, and from them D-excess. Maybe a next step, but in the current manuscript it would at least be interesting to see how strongly the D-excess values implied by the separate δ2H and δ18O models deviate from the predictions of the D-excess model. Areas w/ large differences imply inconsistency in the models, which could be due to the influence of specific (poorly represented) forcing factors, incomplete or inconsistent data, or other factors that might motivate future work.
Agreed—I did waver a bit on this point before, in the end, separately modelling the precipitation deuterium excess. In fact, I did already perform this comparison, but didn’t include it in the original submission. This was in part for fear of overwhelming the readers with too enormous a supplement, but also because I suspect there is quite a lot of ‘science’ in that comparison that could potentially merit a longer discussion. But ultimately yes—it will be good for users of these isoscapes to have that assessment to hand. And that’s a fun suggestion to simultaneously model δ2H and δ18O—something for future work. It is indeed clear across all the results that when using the random forest approach, as expected from the isotope physics, H and O have a very high amount of shared information across predictors.
In any case, the comparison is in the attached file (Fig. A), and will be included in the revised manuscript. Pleasingly, Fig. Aa shows that there is minimal difference in long-term annual mean precipitation dxs when modelled directly versus when calculated from modelled δ2HP and δ18OP. Broadly, the independent random forest model predicts slightly higher dxsP across inland western Australia than implied by the δ2HP and δ18OP models. The reverse is true for the southern and northern coastline, excepting the far-northern tips of the continent. However, the magnitudes of the difference are small in the long-term annual mean.
The differences are slightly larger for the monthly climatologies, where there is a seasonal cycle in the difference between directly-modelled dxsP versus dxsP calculated from δ2HP and δ18OP (Fig. Ab). The difference is biggest in January, where the independent random forest model predicts higher dxsP across most of the continent than the δ2HP and δ18OP models. The difference is less for most other months, except around the south-western coastline in December where the reverse is true.
Our sense is that digging more deeply into this would be beyond the scope of the current manuscript, however we are very grateful for the opportunity to provide this extra information for the readers, and look forward to investigating the reason for the discrepancy in more detail. We would most likely include Fig. Aa in a revised version of the manuscript, with the extra detail provided by Fig. Ab available here for interested readers.
- Methods: Did you make any attempt at feature selection? I realize this is less important for RF than for many other methods but can still be beneficial. The very smooth decline in feature importance in Fig. 7 is interesting to me and could reflect some influence of highly correlated features. I think it would be work checking/reporting on this, at least.
We did not perform feature selection, instead relying on 1) a careful initial choice of predictors; then 2) the random forest’s predictor importance algorithm to determine if any predictors were detrimental to model skill (not the case here). In fact, we chose the random forest method because precipitation isotopic variability is influenced by so many highly correlated variables, and we were hoping to capture as much of the nuance across those relationships as possible. We will add a statement to this effect to the Methods section (2.3 Predictor Variables) of the revised manuscript.
- In several places you refer to D-excess as an ‘isotope system’ (e.g., line 349, 398, others)…which isn’t quite correct, it’s a derived parameter that integrates information from two isotope systems. I suggest adjusting your terminology for correctness. For example you could refer to ‘three isotopic metrics’ instead of ‘three isotope systems’.
Yes we dithered for quite a while on this terminology! And couldn’t find anything consistent across the literature. But we will change the wording to ‘isotope metrics’ in all instances.
- L 350-354: this is an important point given RF’s inherent inability to meaningfully extrapolate beyond the training data’s feature space. Thank you for including this information.
No worries (: we agree that this is a very important point, and something that is probably not widely known about the Australian continent’s climatic feature space—that actually it can be captured reasonably well from a small subset of locations.
- L 428, also previously: The text implies that the term ‘isoscape’ refers specifically to climatology (long-term average models), which is not the case – the term has been applied to space- and/or time-explicit models of isotopic variation since its inception (e.g., Bowen, West, & Hoogewerff, 2009; Bowen, West, Vaughn, et al., 2009; West et al., 2010).
Thanks for the clarification and references—we will correct the terminology to simply differentiate between space-and-time-explicit isoscapes and their climatologies.
- L 580-581: This line in the data availability statement is unclear – are the data themselves also available in the Zenodo archive referenced in the previous section? If so, please clarify, if not, please indicate where they are available.
The new random forest isoscapes are all now publicly available from the Zenodo archive (i.e., the link is now live - here it is again for reference https://zenodo.org/records/15486278). Things are more complicated for the underlying observational data used to train the models. The availability of those data is outlined in Table S1, and we will add that reference to the Data Availability statement to avoid confusion. But In brief: I used a combination of published data (e.g., GNIP), unpublished datasets obtained directly from the authors (who are co-authors in all cases), and one dataset that is published but not freely available (Yarrangobilly, Tadros et al. 2022 QSR).
In summary: some of the underlying observational data are available from online repositories (outlined in Table S1), but some are as yet unpublished or not available in online archives. The new isoscapes presented in this study are all freely available from the linked Zenodo repository (and will also be hosted on a user-friendly web app for those less familiar with the netcdf format).
- Fig 5: Symbology could be adjusted to make it a little easier to distinguish the different series…the differences are quite subtle and hard to pick out on the small panels.
Thanks for this suggestion—we will update Fig. 5 and its equivalent supplementary figures to differentiate the timeseries with symbols as well as the line colours.
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AC2: 'Reply on RC1', Georgina M. Falster, 11 Nov 2025
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RC2: 'Comment on egusphere-2025-2458', Anonymous Referee #2, 27 Oct 2025
Review for "High resolution monthly precipitation isotope estimates across Australia from machine learning" Falster et al. (2025):
This is a well-executed paper demonstrating a rigorous application of machine learning method to expand the Australian stable water isotope record. The results are clear and consistent. The combination of model testing, predictor diagnostics and spatial coverage makes it a valuable dataset to the community. My comments below are intended to strengthen the interpretation and presentation, however, the paper is already very strong overall. Below are my comments:
- The authors say that each isotope model was trained 50 times with random seeds. Figures 1-3 already present site-level uncertainties across the 50 random-forest runs. These quantities show how stable these model skill parameters are relative to observations. However, understanding the uncertainties within the predicted isotope fields themselves would be useful too. For this, the authors could include a map of ensemble spread in the supplement. For example, the standard deviation of dD, d18O and dxs predictions across all the 50 runs at each grid cell would be useful to highlight where model confidence is low or high.
- The manuscript relies on ERA5 for meteorological predictors, including precipitation amount and intensity. While the authors mention that ERA5 and AGCDv2 precipitation show “very similar” results, this statement could benefit from quantitative support. Because precipitation amount and intensity are among the dominant predictors in the isotope models, any biases in ERA5 are likely to propagate into the isotope estimates. I recommend that the authors quantify ERA5 against AGCDv2 using a scatterplot or bias map of monthly precipitation at isotope sites (or even a table would do).
- The lowest model skill occurs for dxs, due to its linkage to moisture-source humidity and temperature. The authors acknowledge that these source conditions are not directly represented and that when “weather objects” are introduced, there is some partial improvement in the model. While a full trajectory modeling would indeed by very computationally expensive, the paper could still test or discuss some simpler methods that could capture the source-region variabilities (e.g., ERA5 based upwind SST or column-integrated humidity gradients, etc.). Even a short comparison between the dxs skill improvements with and without the introduction of weather objects would be useful to clarify how users should be aware of the missing source information for the data usage.
- In section 2.2.1, the authors explain that they test temporal transitivity by randomly leaving out 10% of all observations and using the rest to train the model. This random sampling is effective in checking how well the model predicts data that look similar to what it has already seen. However, it does not give much information on how the model will perform for changes over time. Since Australia’s climate has shifted over the last several decades, it would be helpful to see how stable the model’s performance has been over time. For this, I suggest that the authors add a simple figure showing the model error or correlation changes per decade, or simply plot residuals in a time series. These tests will help users understand whether the model’s relationship between isotope and climate can stay consistent over the entire record.
- The RF models are trained on 60 sites located in coastal and near populated regions. The predictive skills show no relationship with distance to the nearest site and that more than 99% of predictor values fall within the training range. While this confirms good coverage in predictor space, the manuscript will gain from a quantification over the unobserved inland regions. The authors could provide a difference map between RF and ECHAM6-wiso climatology across inland Australia.
- Section 4.3 presents useful examples showing how predictor importance varies by region, but the discussion could be expanded by mentioning why specific predictors dominate in each climate regime and what they imply isotopically. For example, how the high influence of precipitation amount and intensity in the Australian tropics reflects the stronger rainout or amount effect behavior and how to interpret it isotopically. Extending each regional examples in this way would help show how RF predictors reproduce physically meaningful isotope-climate linkage that were introduced in the Introduction.
Citation: https://doi.org/10.5194/egusphere-2025-2458-RC2 -
AC1: 'Reply on RC2', Georgina M. Falster, 11 Nov 2025
In this response, we reply directly to each of the Reviewer's comments. Original Reviewer comments are in italicised text, our responses in regular text following.
- This is a well-executed paper demonstrating a rigorous application of machine learning method to expand the Australian stable water isotope record. The results are clear and consistent. The combination of model testing, predictor diagnostics and spatial coverage makes it a valuable dataset to the community. My comments below are intended to strengthen the interpretation and presentation, however, the paper is already very strong overall. Below are my comments:
Many thanks for the positive review, and we too hope for this to be a valuable community asset. Thanks also for the great suggestions—we have addressed each below, and provide all new analyses in the attached document.
- The authors say that each isotope model was trained 50 times with random seeds. Figures 1-3 already present site-level uncertainties across the 50 random-forest runs. These quantities show how stable these model skill parameters are relative to observations. However, understanding the uncertainties within the predicted isotope fields themselves would be useful too. For this, the authors could include a map of ensemble spread in the supplement. For example, the standard deviation of dD, d18O and dxs predictions across all the 50 runs at each grid cell would be useful to highlight where model confidence is low or high.
We had in fact performed this analysis, but did not include it in the original submission for fear of overwhelming readers with too many supplementary figures(!) However, we agree that it is useful information for isoscape users, and will include the maps of ensemble spread in a revised manuscript. Specifically, for each precipitation isotope metric (δ2HP, δ18OP, dxsP) we show the standard deviation across the 50-member ensemble, for the long-term annual mean as well as the centre months of each season (see Fig. B in the attached file, which will be included in the supplement of a revised manuscript). As shown on Fig. B, the magnitude of variability across the 50-member ensemble is very small—highlighting the stability of the models.
- The manuscript relies on ERA5 for meteorological predictors, including precipitation amount and intensity. While the authors mention that ERA5 and AGCDv2 precipitation show “very similar” results, this statement could benefit from quantitative support. Because precipitation amount and intensity are among the dominant predictors in the isotope models, any biases in ERA5 are likely to propagate into the isotope estimates. I recommend that the authors quantify ERA5 against AGCDv2 using a scatterplot or bias map of monthly precipitation at isotope sites (or even a table would do).
This is another analysis that we had already performed but weren’t sure whether or not to include given the already-long supplement. But again, we agree that readers may be interested in this point, especially given there is a bit of nuance to it. We therefore provide more information here so it is accessible to readers (as well as an additional figure and table), and will also add this discussion to the revised manuscript if requested by the Editor.
In the attached file, Fig. C compares monthly precipitation from the AGCDv2 and ERA5, across every month from 1962–2023 (the time span of the long isoscapes) at each monitoring site. The thin black line shows a 1:1 relationship. At some sites, precipitation estimates from the two products are extremely similar. At others, the slope is flatter than 1, implying that ERA5 produces slightly too little precipitation at the high end.
However, there is some nuance to this. First (and most importantly), the sites with the ERA5~AGCDv2 slope closest to 1 are not necessarily the same sites where the modelled precipitation δ2H/δ18O/dxs is the best match for observed precipitation δ2H/δ18O/dxs—including with respect to extremes (e.g., Fig. 4 in the manuscript). Second, precipitation estimates from neither ERA5 nor AGCDv2 are a perfect match for the precipitation amount observations recorded alongside the precipitation isotopes. Table A shows all precipitation isotope monitoring sites that also reported precipitation amount. For each, we show a) the correlation, and b) the regression coefficient (slope) with respect to precipitation from AGCDv2 and ERA5. The average correlation of observed precipitation with AGCDv2 precipitation is higher than that with ERA5 precipitation, however the regression coefficients for AGCDv2 ~Obs tend to be >1, whilst the regression coefficients for ERA5~Obs tend to be <1. This discrepancy increases when using only sites with >100 observations.
This is a point that, in fact, tends to be glossed over in many studies relying on interpolated data or reanalysis products—there are uncertainties even in observational products. We acknowledge that this is an uncertainty we did not explicitly account for (e.g., by obtaining all predictor variables from multiple sources and doing all other method steps several times accordingly), but we considered that with all the other uncertainties incorporated into the method, this would make the modelled and uncertainty quantification processes quite unwieldy. We also considered that this would not make a major difference to the results, although we acknowledge that this could be a contributing factor to the under-estimated extreme precipitation δ2H/δ18O/dxs values (as already stated at L474-475 in the original manuscript). In any case, we hope that the new plots here will be of interest to some readers (for reasons beyond just this study, too!) and thank the Reviewer for the opportunity to include them (:
- The lowest model skill occurs for dxs, due to its linkage to moisture-source humidity and temperature. The authors acknowledge that these source conditions are not directly represented and that when “weather objects” are introduced, there is some partial improvement in the model. While a full trajectory modeling would indeed by very computationally expensive, the paper could still test or discuss some simpler methods that could capture the source-region variabilities (e.g., ERA5 based upwind SST or column-integrated humidity gradients, etc.). Even a short comparison between the dxs skill improvements with and without the introduction of weather objects would be useful to clarify how users should be aware of the missing source information for the data usage.
Regarding ‘Even a short comparison…’: At L345-347 we state that dxsP is the only isotope metric for which the addition of the weather objects results in a major increase in a particular skill metric (the density estimate overlap proportion). We also state at L486-490 “The shorter models—incorporating the weather object data—predicted dxsP more skillfully than the longer models without the weather objects. Further, the skill increase resulting from inclusion of the weather objects was larger for dxsP than for δ2HP and δ18OPP suggesting that the moisture source and transport information inherent in the weather objects is particularly important for dxsP”.
Regarding the suggestion that we could discuss some simpler methods for capturing the moisture-source conditions: the Reviewer accurately summarised that we tried to do this as comprehensively as possible with the inclusion of the weather objects—which in itself is a major step forward in isoscape calculation as this data type has not been used in previous isoscape studies. However, considering this same point we did also test the effect of including the vertically-integrated water vapour flux as a predictor. The addition of this variable did not result in any skill increase (or indeed any change in the results), which suggests that the information was likely captured intrinsically in the other meteorological variables.
The reviewer’s suggestion to use ERA5-based upwind SST would run into the same problems as the back-trajectory modelling (outlined in the manuscript at L491-494). That is, it would be extremely computationally expensive to identify the relevant regions for the upwind SST for all grid cells for all months. However, it would be a very interesting avenue of future research to use this information to model dxsP at a single location (or small geographical region), and we plan to do this in the coming years.
- In section 2.2.1, the authors explain that they test temporal transitivity by randomly leaving out 10% of all observations and using the rest to train the model. This random sampling is effective in checking how well the model predicts data that look similar to what it has already seen. However, it does not give much information on how the model will perform for changes over time. Since Australia’s climate has shifted over the last several decades, it would be helpful to see how stable the model’s performance has been over time. For this, I suggest that the authors add a simple figure showing the model error or correlation changes per decade, or simply plot residuals in a time series. These tests will help users understand whether the model’s relationship between isotope and climate can stay consistent over the entire record.
Thanks for this suggestion—we have performed both of these additional skill tests. The results are shown in the attached document, and we will add these to the supplement of a revised manuscript along with a brief discussion in the Results (Section 3.1).
Fig. D shows, for all sites, the difference (total difference rather than residuals from a model) between observed and modelled δ2HP for all months that have observations. The black line shows a perfect match between the observed and modelled values. A positive offset means the modelled δ2HP value is too high relative to the observed value and vice versa for a negative offset. Figs. E and F show the same for δ18OP and dxsP, respectively. The plots suggest that model performance is fairly stable though time.
The same is evident from Fig. G, which summarises the bias in each isotope metric by decade (voxplot widths are scaled by the number of observations in that decade). Again, there is no major change through time in the model bias relative to observations (accounting for data density).
Finally, for anyone looking for more information on temporal variability in model skill, we have also included Fig. H, which shows model bias by season, plotted in 5° latitude bins.
- The RF models are trained on 60 sites located in coastal and near populated regions. The predictive skills show no relationship with distance to the nearest site and that more than 99% of predictor values fall within the training range. While this confirms good coverage in predictor space, the manuscript will gain from a quantification over the unobserved inland regions. The authors could provide a difference map between RF and ECHAM6-wiso climatology across inland Australia.
We agree that this is a tricky point, and a lack of inland monitoring data is not just an issue for precipitation isotopes in Australia, but for many climate variables—even including precipitation amount. The lack of observational data across much of the continent was a main motivator for this study, although I acknowledge that it does make both verification and accurate uncertainty quantification very difficult. Unfortunately there is currently no more data available for further model verification inland than we have already used in this study.
In any case, we have created the difference maps as suggested (Fig. I). Broadly, the random forest isoscapes predict higher inland climatological δ2HP/δ18OP values than ECHAM6-wiso; the reverse is true for dxsP. It is difficult to say which is more accurate—Fig. S17 shows that the random forests tend to overestimate inland δ2HP/δ18OP values with respect to observations, but ECHAM6-wiso tends to underestimate them. These differences may be influenced by bias in the observational training data. Inland Australia is climatologically hot and dry, and is sparsely populated. Because of this sparse population, in many cases data are collected via composite samplers rather than daily rainfall collection, despite the dry climate. Composite samplers tend to be associated with isotopic bias in low-rainfall months, resulting in a positive (but not systematic) bias in δ2HP/δ18OP values. Accordingly, ECHAM6-wiso—which directly simulates processes important for inland precipitation δ2H/δ18O/dxs, such as sub-cloud evaporation (Crawford et al., 2017)—underestimates inland δ2HP/δ18OP with respect to observations, but may in fact be closer to the true values. We will add a statement to this effect to a revised manuscript.
Nevertheless, as stated in Table 1 (and apparent visually from Fig. S17 for the inland sites), the overall magnitude of the (apparent) bias in the random forest isoscapes is less than that of ECHAM6-wiso, lending confidence to our results. This is essentially impossible to test further without new observational data—which we strongly advocate for whilst recognising the expense and difficulty of long-running observational campaigns.
Crawford, J., Hollins, S., Meredith, K., Hughes, C.: Precipitation stable isotope variability and subcloud evaporation processes in a semi-arid region, Hydrol. Proc., 31, 20–34, 2017.
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Falster et al. model a large precipitation isotope dataset from Australia using Random Forest, compare the results to those from two other methods, and present and interpret a set of historical monthly average precipitation isoscapes. This is an excellent study, and very well presented…in many ways it’s the precipitation ML isoscape study I’ve been hoping to see for several years now! It’s thorough, really uses the power of the method and an expanded suite of features to go well beyond what’s been done with other statistical methods and learn more from our isotope data. In so doing it represents one of the first successful attempts at a data-driven, time-explicit analysis of historical precipitation isotope patterns. Kudos to the authors, and I’m excited to see this published.
Below are a handful of comments and suggestions that I hope might be useful and help the authors tie up a few loose ends.
I have mixed feelings about the choice to model D-excess directly. I’ve done this, too, and am not fundamentally opposed to this approach. But it does lead to a fundamental inconsistency…because D-excess is not and independent parameter you have 3 independent models that are describing a system with only two degrees of freedom. In an ideal world, this would be modeled as a multivariate system, since H and O isotopes have a lot of shared information. A single self-consistent model could be fit to simultaneously predict δ2H, δ18O, and from them D-excess. Maybe a next step, but in the current manuscript it would at least be interesting to see how strongly the D-excess values implied by the separate δ2H and δ18O models deviate from the predictions of the D-excess model. Areas w/ large differences imply inconsistency in the models, which could be due to the influence of specific (poorly represented) forcing factors, incomplete or inconsistent data, or other factors that might motivate future work.
Methods: Did you make any attempt at feature selection? I realize this is less important for RF than for many other methods but can still be beneficial. The very smooth decline in feature importance in Fig. 7 is interesting to me and could reflect some influence of highly correlated features. I think it would be work checking/reporting on this, at least.
In several places you refer to D-excess as an ‘isotope system’ (e.g., line 349, 398, others)…which isn’t quite correct, it’s a derived parameter that integrates information from two isotope systems. I suggest adjusting your terminology for correctness. For example you could refer to ‘three isotopic metrics’ instead of ‘three isotope systems’.
L 350-354: this is an important point given RF’s inherent inability to meaningfully extrapolate beyond the training data’s feature space. Thank you for including this information.
L 428, also previously: The text implies that the term ‘isoscape’ refers specifically to climatology (long-term average models), which is not the case – the term has been applied to space- and/or time-explicit models of isotopic variation since its inception (e.g., Bowen, West, & Hoogewerff, 2009; Bowen, West, Vaughn, et al., 2009; West et al., 2010).
L 580-581: This line in the data availability statement is unclear – are the data themselves also available in the Zenodo archive referenced in the previous section? If so, please clarify, if not, please indicate where they are available.
Fig 5: Symbology could be adjusted to make it a little easier to distinguish the different series…the differences are quite subtle and hard to pick out on the small panels.
Bowen, G. J., West, J. B., & Hoogewerff, J. (2009). Isoscapes: Isotope mapping and its applications. Journal of Geochemical Exploration, 102(3), v–vii. https://doi.org/10.1016/j.gexplo.2009.05.001
Bowen, G. J., West, J. B., Vaughn, B. H., Dawson, T. E., Ehleringer, J. R., Fogel, M. L., Hobson, K. A., Hoogewerff, J., Kendall, C., Lai, C. T., Miller, C. C., Noone, D., Schwarcz, H. P., & Still, C. J. (2009). Isoscapes to address large-scale Earth science challenges. Eos, 90(13), 109–110.
West, J. B., Bowen, G. J., Dawson, T. E., & Tu, K. P. (Eds.). (2010). Isoscapes: Understanding Movement, Pattern, and Process on Earth Through Isotope Mapping. Springer.