the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Separating snow and ice melt using water stable isotopes and glacio-hydrological modelling: towards improving the application of isotope analyses in highly glacierized catchments
Abstract. Glacio-hydrological models are widely used for estimating current and future streamflow across spatial scales, utilizing various data sources, notably streamflow and snow/ice observations. However, modeling highly glacierized catchments poses challenges due to data scarcity and complex spatio-temporal meteorological conditions, leading to input data uncertainty and potential misestimation of snow and ice melt proportions. Some studies propose using water stable isotopes to estimate water shares of rain, snow, and ice in streamflow, yet the choice of isotopic composition of these water sources significantly impacts results. This study presents a combined isotopic and glacio-hydrological model to determine seasonal shares of snow and ice melt in streamflow for the Otemma catchment in the Swiss Alps. The model leverages available meteorological station data (air temperature, precipitation, and radiation), ice mass balance data and snow cover maps to model and automatically calibrate the catchment-scale snow and ice mass balances. The isotopic module, building on prior work by Ala-Aho et al. (2017), estimates seasonal isotopic compositions of precipitation, snow, and ice. The runoff generation and transfer model relies on a combined routing and reservoir approach and is calibrated based on measured streamflow and isotopic data.
Results reveal challenges in distinguishing snow and ice melt isotopic values in summer, rendering a reliable separation between the two sources difficult. The modelling of catchment-wide snow melt isotopic composition proves challenging due to uncertainties in precipitation lapse rate, mass exchanges during rain-on-snow events, and snow fractionation. The study delves into these processes, their impact on model results, and suggests guidelines for future models. It concludes that water stable isotopes alone cannot reliably separate snow and ice melt shares for temperate alpine glaciers. However, combining isotopes with glacio-hydrological modeling enhances hydrologic parameter identifiability, in particular those related to runoff transfer to the stream, and improves mass balance estimations.
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RC1: 'Comment on egusphere-2024-631', Anonymous Referee #1, 11 May 2024
This work presents an interesting modeling effort in a Swiss glacierized catchment, which involved a water stable isotope module. Although the authors provided detailed information regarding the data, model structure, and sensitivity analysis, the novelty of this work needs to be further emphasized. Specifically, the authors should explain how the assessments in this study can inform hydrological modeling in glacierized basins. In the introduction, the authors listed a couple of modeling challenges for glacierized basins, which is good. My major suggestion for the authors is to improve their results by providing more evidence on how the built isotope-glacio hydrological model or the assessments conducted in this study can help to address such modeling challenges. Currently, the paper reads more like a description of the model and data. How the model and their findings will contribute to the research community is not well evaluated, which falls below the standard of an original research paper. Additionally, the authors concluded that the isotope-glacio hydrological model didn't guarantee better separation results of the water shares and showed similar performance to a normal glacio-hydrological model. Even though the authors tried to emphasize that combining isotopes with glacio-hydrological modeling enhanced hydrologic parameter identifiability, this aspect was not adequately assessed in the results. Given the added model parameters and model configuration complexity, why an isotope-glacio hydrological model is evaluated in this study? What are the benefits of integrating isotopes into a well-verified glacio-hydrological model in the study area? Perhaps, the authors might consider adding a benchmark model for improved assessments on the influences (or benefits) of combining isotopes. Other comments are as follows:
1. As listed in lines 82-85, some processes were added in the model. However, the impacts of the additional processes on the model simulations were not well assessed. I would suggest adding a reference model in which these processes were not involved for the assessment.
2. Reference literature for many equations was missing. For example, Eqs. 3, 4, 5-7, 9, and 23.
3. Eq. 28, please add calculations for ϕRain and ϕRos.
4. The model calibration procedure should be better described. Is this a stepwise calibration or a simultaneously multiple-objective calibration? How were the competitions between the performance of discharge simulation, isotope simulation, and snow simulation dealt with in the calibration?
5. In Fig. 4, the coefficients of determination of the linear curves appear to be very low. Do the lines make any sense for the modeling? Was any trend-test conducted?
6. In Fig. 6, sub-daily fluctuations of daily discharge can be seen from the plots. Are these daily data (one point per day)?
7. In Section 5, please add discussions on the model's performance in simulating snow, ice, isotopes, and discharge, with respect to comparisons with peer research.
8. In Fig. 10, what are the reasons for the much higher isotopic composition of measured discharge than the pre-event? Is it due to evapotranspiration or contributions from other sources? From the plots, a very low fraction of pre-event can be observed in the discharge. If the fraction of pre-event is low, is this plot good evidence to illustrate the response of discharge to rain events?
9. In lines 703-709, as commented before, what are the benefits of involving isotopes for hydrological modeling in this study area?
10. In Section 5.4, why not give an estimate for the fractional contribution of groundwater to discharge? Without a contribution number, it is a very weak discussion on the role of groundwater.
11. Sensitivity of model parameters to the assumptions should be added. Also, please add a sensitivity analysis of model parameters to the model configuration of isotopes, as well as the influences of added processes on model parameter identifiability.
12. Perhaps the conclusions can be further improved by emphasizing their novel findings better. "(iii) assess how water isotopes can be used to estimate the proportions of different water sources at the outlet." This might need to be better interpreted.
13. In Table A1, why are there multiple temperature lapse rates?
Citation: https://doi.org/10.5194/egusphere-2024-631-RC1 -
RC2: 'Comment on egusphere-2024-631', Anonymous Referee #2, 15 May 2024
The work presents glacio-hydrological model incorporating stable water isotope data in the simulations. The work is done at a glacierized catchment in the Swiss Apls, and uses a multitude of data sources for model calibration, which is performed in different steps. They found out that the time-variable snowmelt isotope values converge with ice melt isotope values late in the season, brining about the typical problem in isotope-based mixing model analysis in glacial catchments. However, the tracer-aided model can provide additional insight to the runoff generation processes and snow vs glacial melt contributions.
I find that the work is a interesting and ambitious case study leveraging of multiple data sources for model calibration. I liked the fresh approach of using transfer module approach, circumventing the need for explicit conceptual storages for soil and GW most often used in hydrological modeling. Though also that approach resulted in distribution parameters to calibrate, and the stepwise calibration approach was a bit difficult to follow. Because of the explicit model description and multitude of data sources, I found the paper fairly long, and suggest below some parts that could be considered leaving out. I recommend the authors address my comments below publishing the work.
L135: specify how measured? average snow depth reduction in the ablations stakes? Where the density 900 assumption comes from?
L161: Shaded location may not be enough, if average is 7, highs can get much more than that. Did you examine the potential evaporation effects? No paraffine oil of other fine tubes to prevent evaporation?
L209: where is this Fig. D1d?
L221: is the snow redistribution scheme based on some existing work? I find no refs here. Wind speed is not a factor?
L278: how were the other two field datasets used in the PEST inversion?
L302: how good is the regression goodness? Typically you would miss the extreme values, the lows in particular
L328: I’m not convinced by the assumption. Why would a think snowpack be less ripe by assumption? It can be equally isothermal and retain liquid water as a smaller pack. Do you have any data to back up this assumption? See my later comment on this
L333-338 :The rules seem a bit subjective. Why did you not compare your simulated i_sp with the depth (or SWE even better) averaged snow pit d2H? Or use the snowmelt samples you had? Instead of this more “soft” calibration.
L338 – L 345: this is very difficult to understand without seeing the timeseries data
L365: not sure I understand this, not familiar with the kinematic subsurface saturated flow concept: do you mean that all water delivery from the hillslope is darcian-type GW flow, with instantaneous recharge. No overland flow?
L447: do you propose that snow surface samples are representative of the snowfall precipitation over winter? That would be the logic behind the Beria et al statements, where the isotope variability in snowfall events over winter is higher than variability in snowmelt.
L452: Interesting data, sublimation can be causing this. There is pretty high variability in your snow surface d-excess values, considering your sampling strategy. If the surface snow is deposited from the same storm, and experiences the same atmospheric conditions after deposition, the d-excess values should be pretty similar. I recommend to discuss what pre and post depositional processes might cause this.
L453: to me it looks like there is a positive correlation between H2 and elevation, contrary to what one would expect.
L473: why did you not calibrate one parameter set for both years? This would be the typical approach where parameters are assumed invariant in time.
L484: can you justify how SWE simulations were deemed good? RMSE of ~100 mm w.e. seems pretty high? What was your catchment average SWE, so you could put the error to perspective
Fig 8a: add in the legend “snow melt composition” not to confuse with snowpack
Fig8: what is the physical process that would explain the snowmelt isotope values to become gradually more depleted at times your 2021 simulations?
L534: this seems counter intuitive, that a bigger snowpack would mix less with rain, ie. deliver more rain water throught the snowpack, that a smaller pack. I think liquid water retained in the snowpack would be important to consider here. Snowpack with 2000mm w.e. is massive, and conceptually difficult to see that storms of your magnitude (mostly 10 mm/day) would seep through the snowpack. One modeling Rule of thumb for snow water retention is 5% of w.e., which in your snowpack would be 100m of water. Is there any other explanation why you get less enrichment in thick snowpacks?
L565-575: good discussion about the uncertainties in sublimation.
L588: including the objective functions, and in particular how the snow extent maps were numerically compared with simulations, would be an important addition to the methodology.
Chapter 5.3 This is interesting discussion, but for the benefit of shortening the paper, could be left out without compromising the main findings in the paper.
L695-702: the degrees of freedom caused by the model complexity are apparent in this discussion. The stream isotope response can be explained by many independent processes. This is not a critical comment as such, but you system is very complex to conceptualize even with isotope data and models.
L704: in the objective function?
L751: what do you mean by outlet snowmelt d2H? The stream value, of d2H leaving the snowpack (the meltwater)?
L791: also the model would be good to validate with either snowpack or snowmelt samples.
L801: not clear why you did not collect the bulk snowpack samples in this case: they are easier to get as a by product of SWE measurements than a snow pit profile.
Citation: https://doi.org/10.5194/egusphere-2024-631-RC2 -
RC3: 'Comment on egusphere-2024-631', Anonymous Referee #3, 17 Jun 2024
General comment
This manuscript reports an interesting combination of a glacio-hydrological and isotopic model to estimate the seasonal shares of snowmelt and ice melt in the stream waters of a high-elevation catchment in the Swiss Alps. The authors well described their dataset, the model with the various modules and the sensitivity analysis, and provided some useful recommendations for future studies including the collection of samples for isotopic analysis from different water sources in glacierized catchments. Overall, I think this is a valuable research paper that deserves to be published. However, some modifications are needed (please see the specific comments) before the acceptance of this paper. Among these changes, in agreement with reviewer 1, I think the authors should better emphasize the advantages of integrating isotopic observations and an isotopic model into the glacio-hydrological model that was used. Indeed, some results and the discussion highlight more the challenges of using the isotopic tracers than the usefulness of their integration into the glacio-hydrological model.
Furthermore, since the manuscript is quite long, I recommend to the authors to better emphasize the novelty of the manuscript (compared to other research papers) in the abstract and the conclusions, to shorten some paragraphs in the conclusions and better highlight the key findings.
Specific comments
- Lines 50-53: These basic sentences about fractionation can be skipped because they are not meaningful for the introduction. Given the topic of the manuscript, if the authors want to define isotopic fractionation, I think they should provide an example regarding the snowpack instead of vapour masses and precipitation.
- Equation 1: The sentence at Lines 54-55 is enough and does not require Equation 1.
- Lines 288-289: Is there any consideration based on the uncertainty in the isotopic analysis or is it just a simple preference for δ2H?
- Lines 443-444: ‘likely due to the preferential elution of solutes in the snowpack (Costa et al., 2020)’ belongs to the discussion.
- Line 447: ‘As suggested in other studies (Beria et al., 2018)…’ belongs to the discussion.
- Lines 450-452: This sentence also belongs to the discussion.
- Lines 461-462: This sentence also belongs to the discussion.
- Lines 497-499: This sentence also belongs to the discussion.
- Section 5.2.1 and Figure C2: It is interesting to note that ice melt had a relatively small spatio-temporal variability, despite the larger variability observed in ice surface samples. This is in agreement with the isotopic composition of ice and meltwater samples collected over a glacier surface in the Italian Alps (Zuecco et al., 2019). In Figure C2, I wonder whether the first ice melt samples collected in July 2019 were affected by mixing with snowmelt or recent rain water.
- Section 5.6 and Conclusions: By reading these two sections, I wonder whether stables isotopes of hydrogen and oxygen represent a real added value for the model application and for improving our understanding of hydrological processes in glacierized catchments. It looks like that the huge effort and the still-present challenges make the application of isotopes not that appealing compared to other tracers (e.g., major ions, trace elements, other isotopes, artificial tracers) that could better help discriminating the end members in stream runoff.
- Lines 787-820: Given the length of the manuscript and of the conclusions (quite long), I suggest organizing this text using bullet points and reducing the paragraph starting at Line 799. Bullet points and a shorter text should help the reader to understand the novelty and the take home messages of this manuscript.
Technical corrections
- Line 145: ‘Snow profiles for isotopic analysis’ instead of ‘isotopic snow profiles’. Please change the term at Line 147, as well.
- Line 245: It should be Walter et al. (2005) instead of Todd Walter et al. (2005).
- Line 265: It should be Walter et al. (2005).
- Line 288: Please remove ‘water’ before ‘stable’.
- Line 334: Please replace ‘remain below’ with ‘more depleted than’.
Citation: https://doi.org/10.5194/egusphere-2024-631-RC3
Data sets
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021). Tom Müller https://doi.org/10.5281/zenodo.7529792
Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021) T. Müller and F. Miesen https://doi.org/10.5281/zenodo.6202732
Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021) Tom Müller https://doi.org/10.5281/zenodo.6106778
Model code and software
Combined isotopic and glacio-hydrological model developped for the Otemma glacierized catchment. T. Müller https://doi.org/10.5281/zenodo.10736126
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