the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Feb 2025
Multi-fidelity model assessment of climate change impacts on river water temperatures, thermal extremes and potential effects on cold water fish in Switzerland
Abstract. River water temperature is a key factor for water quality, aquatic life, and human use. Under climate change, inland water temperatures have increased and are expected to do so further, increasing the pressure on aquatic life and reducing the potential for human use. Here, future river water temperatures are projected for Switzerland based on a multi-fidelity modelling approach. We use 2 different, semi-empirical surface water temperature models, 22 coupled and downscaled general circulation- to regional climate models, future projections of river discharge from 4 hydrological models and 3 climate change scenarios (RCP2.6, 4.5, and 8.5). By grouping stream sections, catchments and spring-fed water courses under representative thermal regimes, and by employing hierarchical cluster-based thermal pattern recognition, an optimal model and model configuration was selected, model performance optimized and climate change impact assessment on river water temperatures improved.
Results show that, until the end of the 21st century, average river water temperatures in Switzerland will likely increase by 3.1±0.7 °C (or 0.36±0.1 °C per decade) under RCP8.5, while under RCP2.6 the temperature increase may remain at 0.9±0.3 °C (0.12±0.1 °C per decade). Under RCP8.5, temperatures of rivers classified as being in the Alpine thermal regime will increase the most, that is, by 3.5±0.5 °C, followed by rivers of the Downstream Lake regime, 3.4±0.5 °C.
A general decrease of river discharge in summer (-10 to -40 %) and increase in winter (+10 to +30 %), combined with a further increase in average near-surface air temperatures (0.5 °C per decade), bears the potential to not only result in overall warmer rivers, but also in prolonged periods of extreme summer river water temperatures. This dramatically increases the thermal stress potential for temperature sensitive aquatic species such as the brown trout in rivers where such periods occur already, but also rivers in where this previously was not a problem. By providing information of future water temperatures, the results of this study can guide managements climate mitigation efforts.
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Status: open (until 01 May 2025)
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RC1: 'Comment on egusphere-2024-3957', Anonymous Referee #1, 18 Mar 2025
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This is my review of "Multi-fidelity model assessment of climate change impacts on river water temperatures, thermal extremes and potential effects on cold water fish in Switzerland” by Love Raman Vinna et al., submitted to Hydrology and Earth System Sciences. In this paper, the authors combine an ensemble of climate models, hydrological models and two water temperature models with varying parameter settings per station to derive water temperature projections in Swiss rivers. In addition, they determined future changes in projected extremes, hysteresis effects and impacts on brown trout. I found the study interesting, with sound methodology. However, the manuscript would benefit from a clearer presentation to fully convey its strengths. I therefore suggest minor revisions, and I provide comments below to help improve these aspects.
Major comments:
While comprehensive and detailed, the data and methods section is somewhat difficult to follow due to its length and repetition. I recommend a thorough revision to improve its structure and conciseness, eliminating redundant wording. This will enhance readability and streamline the section. Below, I provide specific examples and suggestions to support this refinement.
The presentation of results could be refined to enhance clarity and readability. Figures 4, 6, and 7, along with Table 3, contain a wealth of information, but an additional or alternative figure presenting the data in a more aggregated way—such as by thermal regime—could help highlight key differences more effectively while still accounting for uncertainty. While the station-based results provide valuable detail, incorporating more synthesized figures or tables could make the main findings more accessible. Additionally, summarizing key insights more narratively, rather than listing numbers extensively, may improve the flow of the results section.
Specific comments:
Title: the “cold water fish” might suggest a more elaborate analysis on general fish species when only brown trout is considered in the analysis. Suggestion to rephrase/remove.
L 16-18, abstract: Provide more detailed “Alpine thermal regime” and “Downstream lake regime”.
The introduction could benefit from a better gap description, and background building up to this gap description, for example highlighting the gaps of current water temperature projections for Switzerland available in literature. Also, since part of the novelty of the study lies in the modelling approach employed, this gap can also be made more apparent.
Figure 1: Good overview figure, although it would benefit from a distinction between data and data sources and operations on that data in the flow chart. This is an open suggestion
L73-92: this is a very general explanation about the choice of models to use, I would suggest to move it to the intro
L126-138: can you give a little more extensive description of the CH2018 climate data? E.g. introduce that they are derived from the EURO-CORDEX regional climate modelling ensemble, with the number of RCMs driven by GCMs and future scenarios, as well as the horizontal resolution. These names return then in Table 1, allowing the reader to understand where they come from.
L130- ...It would help to also introduce the Hydro-CH2018 in a little bit more detail afterwards, with a subclause per hydrological model on its characteristics (semi-distributed, empirical etc). A suggestion is to structure the description of the different data sources with different subtitles
L151: I might have missed it, but how many monitoring stations are eventually used in the study? If these are the stations on Fig. 1 panel a, provide a reference to that fig (also for its other panels).
L167: DIS criterion: what is the threshold used to assume horizontal distance is “minimal”?
L168: how is the representativeness of the meteorological stations to upstream drainage area assessed?
Table 2: I would suggest to move this table to the appendix, as it is very extensive and does not add much to the results.
L179: “were already statistically downscaled”, please add details on how this is done. This could be part of the paragraph where the CH2018 scenarios are explained in more detail. Also, how big is the bias, and how much would it impact the results?
L222-228, suggestion to move this paragraph to after the model description.
L248-250: The fact that for each river monitoring station, the best water temperature model is employed is a key strength of the study, and should, to my opinion, be more pronounced throughout the study (eg intro describing the gap on this, abstract and earlier in the methods where the multi-fidelity is mentioned first).
L287-320: there is some repetition in this section, and parts are more difficult to follow, please consider condensing it retaining the same information
L332: could you give a more explicit explanation of a hysteresis example here, relevant for Swiss rivers?
L357-361: this is a good example of lines that could be shortened.
L368-372: if an extreme is defined by the deviation of the 90th percentile compared to the median of a certain 30 year period, why does this period need to be detrended? i.e., there is no certain time (beginning or end of period) where the analysis is carried out? Or am I missing something?
L396-400: If this info would be presented in a small table, it would be easier to grasp
Section 3.2 on hysteresis analysis. For a non-expert in hysteresis, I found the results difficult to interpret. To my opinion, it would be beneficial to provide some guiding sentences on how these results could be interpreted.
Section 3.3 and figure 6. It should be more clear from the start of the paragraph that the “extreme event severity index” is used, so the values do not represent absolute extremes, but deviations from the “normal” in the respective period. L477-479 indicate this, but it should be more up front in the paragraph and figure to avoid confusion for the reader.
L515-525: these are very general sentences which fit better in an introduction section than in a conclusion
L525: why are the water temperature models of “lower fidelity”?
L587-593: why is it important to study these hysteresis effects?
L605-614: I would move these lines to the results section, and provide more explanations on the differences between thermal regimes here in the discussion.
L634-652: same comment as above, these lines are more for the results section, which would make that section more digestible.
Textual comments
L93-94: “multi-fidelity modelling” and “from multiple different fidelity levels”, I would avoid such repetitions in the same sentence
L111-114: this is repetition of what is said above
Caption Table 1: “hydraulic models” or “hydrological models”?
L161: “Only stations...”, hydrological measuring stations are meant here, I suppose?
L170-172: “For situations ...”, You lost me in this sentence. Would it be possible to reformulate more clearly?
L374: section title: indicate that the thermal thresholds are for fish.
L421-422: reformulate “at for each station for with ...”
L667: suggestion to just name this section “5. Conclusions”
Citation: https://doi.org/10.5194/egusphere-2024-3957-RC1
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