the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Aug 2025
Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter
Abstract. Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1,735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability.
Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2025-2727', Rasmus Benestad, 02 Sep 2025
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AC1: 'Reply on CC1', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC1-supplement.pdf
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AC1: 'Reply on CC1', Guillaume Evin, 23 Jan 2026
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RC1: 'Comment on egusphere-2025-2727', Anonymous Referee #1, 06 Oct 2025
This study explored the sources of uncertainty in different components of the model chain and investigated their contributions to two climate indicators and three hydrological indicators. The variation in performance was evaluated based on different regional characteristics. I consider the motivation of this paper very good, especially in the context of using ensembles for climate projection. The structure of the paper is well organized, and the presentation is good as well. However, I have a few concerns about the calculation methods that need to be resolved before the paper can be accepted.
- What is the purpose of applying cubic splines to the projection and what are the effects on the trend analysis (Line 583)? What is the meaning of the smooth trend denoted as CRi(t)? Please elaborate on the calculation method. Additionally, is the smoothing suitable for precipitation and hydrological indicators (especially max1D)? The authors may need to showcase some results from this step.
- In the estimation of internal variability (Lines 593–600), why does the method first estimate Di(t) as the difference between the raw projection (Yi(t)) and CRi(t), rather than directly simulating variability from the raw projection over the target period? Does this step reduce or increase the internal variability? Based on the results, the internal variability is super large—could this be because the smoothing is not applicable?
- Please elaborate on the calculation of ESi(t), using one example (e.g., RCP, s). Why is a linear regression model applied, and how is it used (Line 608)?
- For Equation (A7), does this equation still work if incomplete or unbalanced ensembles are used? How are the effects of incomplete ensembles reflected in the results? Authors failed to explain this in detail since this is the second major question to be solved.
- What is the difference between IV, RV, and FV? Should they use the same definition but with different superscripts/subscripts?
- Does the selection of the time span length (i.e., 30 years in this study) affect the results, since a longer time span would likely lead to larger internal variability?
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC1 -
AC2: 'Reply on RC1', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC2-supplement.pdf
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RC2: 'Comment on egusphere-2025-2727', Anonymous Referee #2, 20 Dec 2025
The manuscript “Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter” by Evin et al. investigates the outcomes of several climate-to-hydrology modeling chains over the area of France. The perspective is to quantify the uncertainty introduced at any level of the chain by the various combinations of models that can be used at any level of the chain. This study is of great interest because it highlights the value and limitations of future hydrological projections, providing advice on which hydrological indicators can be predicted more robustly and where within the study area.
The work is a great effort of synthesis but leaves incomplete a fundamental point that I believe should be extended and integrated in the work. In the Discussion section (and recalled also in the Conclusions) the authors note that, in some cases, some models/combinations of models are discordant, then contributing significantly to the overall uncertainty. They suggest (page 26) to “look carefully at these deviating models and understand the reasons behind the atypical behavior”; then, if the model deviates for “wrong reasons (numerical artifacts, bugs, model inadequacy)” should be discarded, otherwise the analyst should investigate the reason of the dissimilarity further. In this case, the authors acknowledge the possibility that the model is superior to others. This point is extremely important, and the authors also acknowledge it at the end of page 26 and in the Conclusions section.
However, I would have expected a more thorough investigation of this point, i.e. a deeper analysis of “what to do” in these cases. Clearly, it is not possible to provide general instructions valid for any case, but the study provides a very good base to identify a few specific cases to develop in more detail. For instance, one could look at a specific catchment/climate regime and decide whether one model/model chain should be discarded or, on the contrary, should be preferred because more reliable in a specific context (see e.g. the cases reported in section 5.4.3). I understand that this is not an easy task, but it is necessary to make the paper much more than a descriptive product.
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC2 -
AC3: 'Reply on RC2', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC3-supplement.pdf
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AC3: 'Reply on RC2', Guillaume Evin, 23 Jan 2026
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RC3: 'Comment on egusphere-2025-2727', Prajwal Khanal, 08 Jan 2026
The manuscript titled Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter by Evin et al. is well written and presents a comprehensive and carefully structured discussion, along with clearly articulated limitations and conclusions. I particularly appreciated the depth and clarity of the discussion section. The proposed methodology provides a robust framework to quantify uncertainties arising from multiple sources, including GCMs, RCMs, HMs, and internal variability, across different hydrological regimes, which I found both insightful and highly relevant.
The discussion provides a comprehensive interpretation of the results and highlights key findings regarding the relative contributions of individual models to overall uncertainty. In particular, the finding such as that one or a small number of models can contribute disproportionately to the total uncertainty is both interesting and well-supported. A deeper investigation into the underlying reasons for inter-model differences or the identification of hydrological conditions under which certain models may perform better could form the basis of a separate study. Nevertheless, by focusing solely on model output (data alone), the present work opens up a wide range of future research directions.
Overall, the manuscript is scientifically sound, clearly presented, and well-motivated, and I wholeheartedly recommend it for publication.
Cheers,
Prajwal Khanal
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC3 -
AC4: 'Reply on RC3', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC4-supplement.pdf
-
AC4: 'Reply on RC3', Guillaume Evin, 23 Jan 2026
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2025-2727', Rasmus Benestad, 02 Sep 2025
I think this paper is very interesting and a welcome contribution. I also appreciate the opportunity to discuss some of of the points made herein.
One point raised is "Model uncertainty arises from model imperfections" which is important, but this paper neglects uncertainties connected with the downscaling approach because it fails to mention others then dynamical downscaling (aka regional climate models, abbreviated as 'RCMs'). There are also other ways of downscaling global climate models (GCMs) which are based on entirely different assumptions and come with different strengths and weaknesses. We expect them to produce similar results if thay all are skillful, independently of each other. Hence, if dynamical and empirical-statistical downscaling give similar outlooks, then the results can be considered as being more robust. Therefore, I recommend that the paper includes some discussion on empirical-statistical downscaling in order to get a more complete picture on uncertainties associated with modelling.
The need of bias-adjustment also introduces uncertainties. It's in a fashion similarto 'sweeping the problem under the carpet', but also it assumes that the present biases are similarto those in a changed climate.
There is at least one example of downscaling precipitation statsistics large multi-model CMIP ensembles that may be of relevance: https://doi.org/10.5194/hess-29-45-2025. However, this example focuses on downscaling daily precipitation statistics and may require an additional step using weather generators to produce time series needed as input for hydrological models. On the other hand, the downscaled precipitation statistics provides a rule-of-thum estimate for number of days per year with heavy rainfall. The method described in this paper will provide a basis for studying the connection between climate change and water-born diarrhoea outbreak in the EU-SPRING project (https://www.springsproject.eu/).
One motivation for downscaling statistical properties (e.g. parameters of statistical distributions) is that statistical properties often are easier to predict/quantify than individual outcomes.
In some cases, climate internal variability (IV) actually provides some useful information about inter-annual variability and the range of plausible outcomes. For example, downscaled results of large ensembles provide a band of plausible temperatures in https://doi.org/10.1073/pnas.2503806122 that can be compared with historical temperatures, and such an evaluation reveals whether the downscaled results match the observed inter-annual variability. The mean of the model spread can be be interpreted as the climate normal, whereas upper and lower limits represent hot and cold years. It is laso interesting to note that the ensemble spread in some cases is close to being normally distributed.
The statement "To our knowledge, the Explore2 MME is the largest ensemble of hydrological projections ever produced from regional climate experiments at the scale of a country" is probably true - see https://doi.org/10.5194/hess-29-45-2025 where MMEs were downscaled for SSP370, SSP126, SSP245, and SSP585, ech with ~30 ensemble members (there are also unpublished results (work in progress) with downscaling total annual precipitation of 200-300 ensembles for each SSP).
When it comes to evaluation, it is not clear if the results are evaluated involving the complete chain of models. I.e. is the downscaling combined with hydrological modelling of GCM historical runs able to reproduce observed trends and inter-annual variability? Also, are the RCMs able to repeoduce past variability and trends?
Citation: https://doi.org/10.5194/egusphere-2025-2727-CC1 -
AC1: 'Reply on CC1', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC1-supplement.pdf
-
AC1: 'Reply on CC1', Guillaume Evin, 23 Jan 2026
-
RC1: 'Comment on egusphere-2025-2727', Anonymous Referee #1, 06 Oct 2025
This study explored the sources of uncertainty in different components of the model chain and investigated their contributions to two climate indicators and three hydrological indicators. The variation in performance was evaluated based on different regional characteristics. I consider the motivation of this paper very good, especially in the context of using ensembles for climate projection. The structure of the paper is well organized, and the presentation is good as well. However, I have a few concerns about the calculation methods that need to be resolved before the paper can be accepted.
- What is the purpose of applying cubic splines to the projection and what are the effects on the trend analysis (Line 583)? What is the meaning of the smooth trend denoted as CRi(t)? Please elaborate on the calculation method. Additionally, is the smoothing suitable for precipitation and hydrological indicators (especially max1D)? The authors may need to showcase some results from this step.
- In the estimation of internal variability (Lines 593–600), why does the method first estimate Di(t) as the difference between the raw projection (Yi(t)) and CRi(t), rather than directly simulating variability from the raw projection over the target period? Does this step reduce or increase the internal variability? Based on the results, the internal variability is super large—could this be because the smoothing is not applicable?
- Please elaborate on the calculation of ESi(t), using one example (e.g., RCP, s). Why is a linear regression model applied, and how is it used (Line 608)?
- For Equation (A7), does this equation still work if incomplete or unbalanced ensembles are used? How are the effects of incomplete ensembles reflected in the results? Authors failed to explain this in detail since this is the second major question to be solved.
- What is the difference between IV, RV, and FV? Should they use the same definition but with different superscripts/subscripts?
- Does the selection of the time span length (i.e., 30 years in this study) affect the results, since a longer time span would likely lead to larger internal variability?
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC1 -
AC2: 'Reply on RC1', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC2-supplement.pdf
-
RC2: 'Comment on egusphere-2025-2727', Anonymous Referee #2, 20 Dec 2025
The manuscript “Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter” by Evin et al. investigates the outcomes of several climate-to-hydrology modeling chains over the area of France. The perspective is to quantify the uncertainty introduced at any level of the chain by the various combinations of models that can be used at any level of the chain. This study is of great interest because it highlights the value and limitations of future hydrological projections, providing advice on which hydrological indicators can be predicted more robustly and where within the study area.
The work is a great effort of synthesis but leaves incomplete a fundamental point that I believe should be extended and integrated in the work. In the Discussion section (and recalled also in the Conclusions) the authors note that, in some cases, some models/combinations of models are discordant, then contributing significantly to the overall uncertainty. They suggest (page 26) to “look carefully at these deviating models and understand the reasons behind the atypical behavior”; then, if the model deviates for “wrong reasons (numerical artifacts, bugs, model inadequacy)” should be discarded, otherwise the analyst should investigate the reason of the dissimilarity further. In this case, the authors acknowledge the possibility that the model is superior to others. This point is extremely important, and the authors also acknowledge it at the end of page 26 and in the Conclusions section.
However, I would have expected a more thorough investigation of this point, i.e. a deeper analysis of “what to do” in these cases. Clearly, it is not possible to provide general instructions valid for any case, but the study provides a very good base to identify a few specific cases to develop in more detail. For instance, one could look at a specific catchment/climate regime and decide whether one model/model chain should be discarded or, on the contrary, should be preferred because more reliable in a specific context (see e.g. the cases reported in section 5.4.3). I understand that this is not an easy task, but it is necessary to make the paper much more than a descriptive product.
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC2 -
AC3: 'Reply on RC2', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Guillaume Evin, 23 Jan 2026
-
RC3: 'Comment on egusphere-2025-2727', Prajwal Khanal, 08 Jan 2026
The manuscript titled Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter by Evin et al. is well written and presents a comprehensive and carefully structured discussion, along with clearly articulated limitations and conclusions. I particularly appreciated the depth and clarity of the discussion section. The proposed methodology provides a robust framework to quantify uncertainties arising from multiple sources, including GCMs, RCMs, HMs, and internal variability, across different hydrological regimes, which I found both insightful and highly relevant.
The discussion provides a comprehensive interpretation of the results and highlights key findings regarding the relative contributions of individual models to overall uncertainty. In particular, the finding such as that one or a small number of models can contribute disproportionately to the total uncertainty is both interesting and well-supported. A deeper investigation into the underlying reasons for inter-model differences or the identification of hydrological conditions under which certain models may perform better could form the basis of a separate study. Nevertheless, by focusing solely on model output (data alone), the present work opens up a wide range of future research directions.
Overall, the manuscript is scientifically sound, clearly presented, and well-motivated, and I wholeheartedly recommend it for publication.
Cheers,
Prajwal Khanal
Citation: https://doi.org/10.5194/egusphere-2025-2727-RC3 -
AC4: 'Reply on RC3', Guillaume Evin, 23 Jan 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2727/egusphere-2025-2727-AC4-supplement.pdf
-
AC4: 'Reply on RC3', Guillaume Evin, 23 Jan 2026
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Benoit Hingray
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Yves Tramblay
Jean-Philippe Vidal
Jérémie Bonneau
François Colleoni
Joël Gailhard
Florence Habets
Frédéric Hendrickx
Louis Héraut
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Céline Monteil
Simon Munier
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Jean-Michel Soubeyroux
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Jean-Pierre Vergnes
Mathieu Vrac
Eric Sauquet
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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(18033 KB) - Metadata XML
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Supplement
(23090 KB) - BibTeX
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- Final revised paper
I think this paper is very interesting and a welcome contribution. I also appreciate the opportunity to discuss some of of the points made herein.
One point raised is "Model uncertainty arises from model imperfections" which is important, but this paper neglects uncertainties connected with the downscaling approach because it fails to mention others then dynamical downscaling (aka regional climate models, abbreviated as 'RCMs'). There are also other ways of downscaling global climate models (GCMs) which are based on entirely different assumptions and come with different strengths and weaknesses. We expect them to produce similar results if thay all are skillful, independently of each other. Hence, if dynamical and empirical-statistical downscaling give similar outlooks, then the results can be considered as being more robust. Therefore, I recommend that the paper includes some discussion on empirical-statistical downscaling in order to get a more complete picture on uncertainties associated with modelling.
The need of bias-adjustment also introduces uncertainties. It's in a fashion similarto 'sweeping the problem under the carpet', but also it assumes that the present biases are similarto those in a changed climate.
There is at least one example of downscaling precipitation statsistics large multi-model CMIP ensembles that may be of relevance: https://doi.org/10.5194/hess-29-45-2025. However, this example focuses on downscaling daily precipitation statistics and may require an additional step using weather generators to produce time series needed as input for hydrological models. On the other hand, the downscaled precipitation statistics provides a rule-of-thum estimate for number of days per year with heavy rainfall. The method described in this paper will provide a basis for studying the connection between climate change and water-born diarrhoea outbreak in the EU-SPRING project (https://www.springsproject.eu/).
One motivation for downscaling statistical properties (e.g. parameters of statistical distributions) is that statistical properties often are easier to predict/quantify than individual outcomes.
In some cases, climate internal variability (IV) actually provides some useful information about inter-annual variability and the range of plausible outcomes. For example, downscaled results of large ensembles provide a band of plausible temperatures in https://doi.org/10.1073/pnas.2503806122 that can be compared with historical temperatures, and such an evaluation reveals whether the downscaled results match the observed inter-annual variability. The mean of the model spread can be be interpreted as the climate normal, whereas upper and lower limits represent hot and cold years. It is laso interesting to note that the ensemble spread in some cases is close to being normally distributed.
The statement "To our knowledge, the Explore2 MME is the largest ensemble of hydrological projections ever produced from regional climate experiments at the scale of a country" is probably true - see https://doi.org/10.5194/hess-29-45-2025 where MMEs were downscaled for SSP370, SSP126, SSP245, and SSP585, ech with ~30 ensemble members (there are also unpublished results (work in progress) with downscaling total annual precipitation of 200-300 ensembles for each SSP).
When it comes to evaluation, it is not clear if the results are evaluated involving the complete chain of models. I.e. is the downscaling combined with hydrological modelling of GCM historical runs able to reproduce observed trends and inter-annual variability? Also, are the RCMs able to repeoduce past variability and trends?