the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Aug 2026
Isotope-enabled modeling of fast streamflow dynamics in a steep Alpine catchment
Abstract. Alpine headwater catchments often produce streamflow peaks anywhere from minutes to hours after a rainfall event, quickly mobilizing previously stored subsurface water. Understanding and describing this response to rainfall events and its variations via modeling remains challenging, despite the growing availability of data, including tracer data such as stable isotopes of water, to inform internal flow partitioning.
Here, we present a novel flux partitioning routine integrated into an existing hydrological model based on the Geomorphological Instantaneous Unit Hydrograph and multiple subsurface reservoirs. The model structure is based on a detailed perceptual model derived from available field data, and it simulates streamflow magnitude and streamwater isotopic ratios at high temporal resolution (10-minute intervals) in a snow-dominated headwater catchment in the western Swiss Alps (Vallon de Nant) over two consecutive summers. By jointly calibrating the model with both streamflow quantity and streamwater isotopic ratios, we obtain a reliable representation of catchment-scale partitioning of streamflow between groundwater, snowmelt, and direct runoff. The simulations showed that despite the catchment’s high level of flashiness, with peak streamflow occurring within an hour of peak rainfall, most streamflow originates from subsurface flow pathways with limited contributions from direct runoff. Even during short periods of intense convective rainfall events, peak flows mostly comprise water released by subsurface storage, which are mobilized by rainfall.
Our results show that incorporating stable water isotopes into a relatively simple catchment-scale model can refine model structure, reduce parameter uncertainty, and improve process attribution of short-timescale streamflow generation dynamics. We discuss the advantages and limitations of the proposed isotope-enabled modeling approach and its potential extension to other catchments and year-round modeling.
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Status: open (until 21 Oct 2026)
- RC1: 'Comment on egusphere-2026-4249', Anonymous Referee #1, 12 Oct 2026 reply
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RC2: 'Comment on egusphere-2026-4249', Anonymous Referee #2, 14 Oct 2026
reply
In the manuscript “Isotope-enabled modeling of fast streamflow dynamics in a steep Alpine catchment” by Beria et al., the authors aim to disentangle conceptual sources of stream flow and the temporal evolution thereof using a detailed data set of stable isotopes together with a simple rainfall-runoff model conceptual model. The overall topic is of interest and fits well into the scope of HESS. The manuscript follows a logical set-up and is well-written.
I particularly appreciate the authors’ effort in producing such a detailed data set in such a challenging environment. That is quite an achievement in itself. I similarly want to applaud the authors for the very well thought-through and instructive perceptual description of the hydrological functioning of the study catchment in section 3. Such in-depth process-contextualization is unfortunately more and more forgotten in a research environment that is increasingly relying on the automated exploitation of large data sets, leveraging the power machine learning.
To balance this enthusiastic assessment, I also would like to flag a few concerns that the authors may want to look into. More specifically, I was quite surprised by the model they use. No pun intended, but the authors sort of “re-invent the wheel” with their model. That is in principle ok. However, several choices along the modelling process lead to the situation that the resulting “wheel” has somewhat of a hexagonal shape. These rough edges make the model experiment a somewhat bumpy ride. Although some of the over-simplifications and limitations of the model are acknowledged in section 7, it remains unclear why the model was designed in such a way instead of making use of model architectures and parametrization schemes provided by many other existing tracer-aided models, that have similar or even lower number of parameters. Some examples include Birkel et al. (2010, 2020, etc), Benettin et al. (2015), Wang et al. (2023) and many, many more.
The most critical concerns are:
(1) The model does not account for evaporation in a way that goes beyond a constant, calibrated(?) and bulk initial abstraction. What this initial abstraction actually represents does not even seem clear for the authors (see l.525: “ […] evapotranspiration […] most likely included in the initial abstraction […]”).
Even in such high-elevation settings, evaporation is typically ~30% of annual precipitation and to a large part confined to the modelled summer months. As evaporation is controlled by the subtle interactions of energy and water supply, it does not sound reasonable to set it to a constant value, in particular as it will also affect the isotopic signature of the water volume that is in storage at any time.
(2) The model has a similarly over-simplified representation of snow dynamics. Although, I do understand that a detailed energy-balance-based snow module is beyond the scope of this analysis and most snow melt has already occurred before model periods start in July, I do not understand why the authors do not use a very simple degree-day model to represent snow melt. As far as I understand, the model now assumes that snowmelt occurs from a water storage Ssnow, that in fact is assumed to be completely liquid and independent of temperature. Despite the thermal momentum of the snowpack, some temperature dependence should nevertheless be assumed. In addition, I found it surprising that *all* the snowmelt bypasses the subsurface and directly feeds the stream. While I can understand the reasoning for some of the snowmelt, I strongly disagree that none of it reaches the subsurface. Further, it seems as if no elevation discretization for snow melt has been attempted. This may lead to additional uncertainties due to the temporally differential snow melt input (and snow storage) at different elevations.
(3) The distinction between Qd and Qsoil is rather vague and will only add unnecessary complexity to the model, actually having very little discriminatory power because for both the rainfall isotope composition is assumed. The fact that both represent very fast responding processes, I doubt that the available data allow for a clear and unambiguous distinction between the two.Other comments:
p.2l.40: it may be worth to be a bit more precise here. “Streamflow-only” calibration is not the actual problem. It has been shown in many studies that using signatures of streamflow, such as flow duration curves, autocorrelation functions, baseflow index and in particular runoff coefficients are powerful additional model calibration objectives. The runoff coefficient in particular allows to approximate closure of the energy balance as it forces the model towards reproducing evaporation fluxes that are consistent with observations, i.e. E/P = 1 – Q/P + dS/dt.
Would be good to emphasize that the real problem is the frequent one-dimensional calibration to exclusively *time series* of stream flow using, not even distinguishing high- and low flows and not making use of signatures.p.2,l.46: would be good to add the more recent work of Tong et al. (2021) here.
p.2,l.49: “water transport” is quite awkward. I know what you mean, but try please try to rephrase
p.2,l.50: “hydrological celerity” reads also a bit awkward. Please rephrase
p.2,l.51f: this is not logical. Should be the other way round. Stream flow does not propagate through the system. Instead, rainfall signals propagate through the system to generate stream flow. Please rephrase.
p.4,l.119: what is a “typical European alpine climate”? The hydro-climatic characteristics vary substantially between the Eastern and Western as well as between the Northern and Southern Alps. Even between individual adjacent valleys, the climate characteristics can considerably differ. Please rephrase.
p.4,Fig.1: the symbols in the figure are not very well visible. Please adjust the color scheme. As in any good map, please add scale and north arrow
p.5,l.141: please be more precise here. Am I right to assume that these are volume-weighted bulk samples? Please add.
p.5,l.149: how was the snow sampled? Was a core over the entire depth taken? Or just one part of the snowpack? Please specify.
p.5,l.153: also here: are these bulk samples over that period? Volume-weighted? Or grab samples at each interval?
p.5,l.153: also here, grab or bulk samples? If the different samples (rain, snow, spring) are different type of samples, how was this accounted for in the model?
p.6,l.185f: I know what you mean, but it would be nevertheless nice for the reader if you described how this is visible and add some summary numbers here.
p.6,l.191: replace “lower” with “less”
p.8,Fig.4: please add (daily) rainfall and snowmelt volumes to the figures
p.8,l.216: ok, but please provide an estimate of the areal fraction of the catchment covered by a glacier
p.8,l.223-230: nice! But please add some specific summary numbers
p.9,l.253: how is a rain event defined?
p.9,l.255: “storage potential” is confusing here, as it may be interpreted as the maximum theoretical storage, which is in your notation Smax,soil. Please rephrase.p.10,l.266f: how are vH and vS determined? Are they calibration parameters?
p.11,section 4.2: please mention somewhere in the section that your model set-up implicitly assumes complete mixing in each storage component.
p.13, section 5: please add somewhere (1) a complete table or figure, showing all model parameters, and their prior as well as posterior distributions. Right now, it is completely unclear how many and which parameters the model has; and (2) a table or figure given a complete overview of the model performance metrics.
p.14,l.377: this is of course quite subject, but I would be careful calling a NSE = -0.07 “reasonably well”. Please rephrase.
p.16,Fig.8: this figure is nice, but difficult to read. Please adjust the colour schemes so that colours can actually be distinguished. In addition, consider showing the uncertainty intervals as semi-transparent shaded areas instead of showing all individual lines.
p.16,l.409: No. Only the snowmelt in the *modelled period* is a small contribution. Given that 40% of annual precipitation is falling as snow (as mention earlier in the manuscript), overall spring/summer snow melt *must* be higher, but it largely occurs before the model period. Thus, please rephrase.
p.18,l.437: of course, as there is almost no snow left in the modelling period…
References:
Benettin, P., Bailey, S. W., Campbell, J. L., Green, M. B., Rinaldo, A., Likens, G. E., ... & Botter, G. (2015). Linking water age and solute dynamics in streamflow at the Hubbard Brook Experimental Forest, NH, USA. Water Resources Research, 51(11), 9256-9272.
Birkel, C., Tetzlaff, D., Dunn, S. M., & Soulsby, C. (2010). Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate, upland catchments. Hydrological Processes: An International Journal, 24(3), 260-275.
Birkel, C., Duvert, C., Correa, A., Munksgaard, N. C., Maher, D. T., & Hutley, L. B. (2020). Tracer‐aided modeling in the low‐relief, wet‐dry tropics suggests water ages and DOC export are driven by seasonal wetlands and deep groundwater. Water Resources Research, 56(4), e2019WR026175.
Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., ... & Blöschl, G. (2021). The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model. Hydrology and Earth System Sciences, 25(3), 1389-1410.
Wang, S., Hrachowitz, M., Schoups, G., & Stumpp, C. (2023). Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models. Hydrology and Earth System Sciences, 27(16), 3083-3114.Citation: https://doi.org/10.5194/egusphere-2026-4249-RC2
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- 1
This manuscript combines discharge and stable water isotope observations with hydrological modelling to investigate rapid streamflow responses in a steep Alpine catchment. The authors collected a valuable dataset over two summer periods and integrated these observations into an isotope-enabled model to explore the contributions of different flow components. The results highlight the substantial contribution of groundwater to summer streamflow and demonstrate the potential of isotope observations to constrain modelled flow partitioning. The research questions and approach are well suited to the HESS readership, and the study offers a useful contribution to understanding streamflow generation in Alpine catchments.
However, I have several concerns about the prescribed isotope signatures, the calibration procedure, and the robustness of the inferred flow contributions. These issues affect how confidently the model results can support the process interpretations.
General comments
(1) The model assigns the mean winter snowpack isotope signature to snowmelt during the summer simulation period. The authors acknowledge that snowmelt can differ isotopically from the snowpack, but the justification for assuming a small effect during July–September is not sufficiently clear. The timing of the analysis alone does not establish that the winter mean represents the residual snowmelt entering the stream.
Could the authors explain whether any observations or citations support this assumption for the study catchment? If direct observations are unavailable, I suggest testing plausible alternative snowmelt isotope signatures and evaluating their effects on the estimated snowmelt and groundwater contributions.
In addition, snowmelt discharge is represented using a storage-discharge relationship. What evidence, beyond the discharge and isotope data used to constrain the model, supports the simulated timing and magnitude of summer melt? Please distinguish the evidence for residual snowmelt occurrence from the evidence supporting its estimated contribution.
(2) direct runoff and soil drainage are assigned the same summer-average rainfall isotope signature. It is unclear why this is appropriate for soil drainage, which could include water stored before the rainfall event and mixing within the soil. Please explain the physical basis for this assumption and provide supporting observations or references.
(3) The use of a constant rainfall isotope signature within each summer also needs further justification, particularly because the study focuses on rapid responses to individual rainfall events. How much variability is present in the available precipitation isotope observations, and how might neglecting this variability affect the inferred component contributions?
(4) I noticed that Section 7 already acknowledges limitations associated with spring representativeness and endmember variability. However, these limitations should also be connected to the reliability of the reported flow fractions. A focused sensitivity analysis would help establish whether the main conclusions remain consistent under plausible alternative isotope assignments.
(5) I found Section 4.3 difficult to follow. Please distinguish clearly between the observed variables used for calibration, the four likelihood components, and the performance metrics used to evaluate the simulations. My understanding is that calibration uses discharge and streamwater δ¹⁸O, each separated into low-flow and high-flow conditions. These are therefore four likelihood components derived from two observed variables.
The sequence of calibration and evaluation should also be clearer. Initial states are first estimated using both summers and then fixed before the reciprocal single-year calibrations. Consequently, observations from the evaluation year have already contributed to estimating its initial conditions. This does not invalidate the comparison, but it limits its independence. Please state this explicitly in Section 4.3 and distinguish evaluation conditional on fitted initial states from a fully independent validation.
(6) Please clarify which parameter sets were used to produce Section 5.3 and Figures 8 and 9. Figure 8 displays multiple simulations, whereas Figure 9 presents single percentages. How many parameter sets contribute to each figure? Do the percentages represent a selected simulation, an ensemble mean, or another summary?
(7) The uncertainty in the component contributions is central to interpreting these results. I suggest reporting intervals for the seasonal flow fractions and explaining how they were calculated. Please also distinguish uncertainty arising from sampled model parameters from uncertainty associated with the prescribed isotope signatures and model structure.
(8) The narrower range of contributions obtained with isotope calibration is useful evidence that the additional observations constrain the model. However, a narrower range under fixed assumptions does not necessarily demonstrate that the resulting partitioning is more accurate. This distinction should be reflected in both the results and discussion.
Specific comments
(1) In Section 3, please distinguish field observations from the hypotheses used to construct the perceptual model. This would help readers follow how the observations motivate the proposed flow components.
(2) In Figure 4, please explain the black points and connecting line in the isotope panels as streamwater observations in the caption.
(3) In Section 4.1, which model elements were developed for this study and which were adopted or adapted from previous formulations? Also please explain which processes are spatially distributed and which are lumped, including the role of the 25 subcatchments.
(4) In Section 4.3, can authors describe how the low-flow and high-flow periods were selected and provide enough information for this classification to be reproduced?
(5) Figures 8 and 9, The model contains five flow components, but these figures show four and three component categories.
(6) Figure 9, each percentage is calculated from total component volume divided by total streamflow volume, or from the mean of instantaneous fractions? These calculations can produce different results.
(7) In Section 5.3, the word “confirming” links simulated snowmelt to the observed daily discharge fluctuations. These fluctuations already helped motivate the snowmelt component in the perceptual model, so their use as independent confirmation should be qualified.