the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Oct 2025
Drought propagation in the Rhine River basin and its impact on navigation using LAERTES-EU regional climate model dataset
Abstract. Drought events have become more frequent in Europe over the past decades. The shipping and industrial sectors are severely affected by these events, e.g., due to significant reductions in water levels in the Rhine River and interruptions in the transport of goods. Hydrological droughts in the Rhine closely resemble extreme meteorological droughts identified using the Standardized Precipitation Evapotranspiration Index (SPEI) over both short and long periods. However, the possibility of determining low water level events using non-observed meteorological data, e.g., from large regional climate model datasets, and their implication on navigation has not yet been explored. The main objective of this study is to utilize the Large Ensemble of Regional Climate Model Simulations for Europe (LAERTES-EU) to search for extreme drought years, to assess their contribution to discharge, and to determine possible navigation restrictions on the waterways in the Rhine. We employed a methodology that evaluates the SPEI values to identify meteorological drought, which are then used to obtain discharge values by applying the hydrological model WRF-Hydro. The top 10 most extreme meteorological drought events in the LAERTES-EU dataset are considered in this study, assessing drought propagation by using the SPEI. These events displayed different degrees of severity in terms of duration and reduction of the streamflow as measured by the navigable threshold GlQ20 than the extreme drought observed in 2018. In the selected gauges, several LAERTES-EU events were ranked above 2018 when comparing them with the historical records in terms of mean discharge of the period June–November. These results imply that, even under today's climatic conditions, the streamflow values in the Rhine can be substantially worse than in 2018, generating costly economic and ecological consequences if mitigation measures are not implemented.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-3988', Anonymous Referee #1, 20 Oct 2025
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AC1: 'Reply on RC1', Andrea Campoverde, 13 Nov 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3988/egusphere-2025-3988-AC1-supplement.pdf
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AC1: 'Reply on RC1', Andrea Campoverde, 13 Nov 2025
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RC2: 'Comment on egusphere-2025-3988', Anonymous Referee #2, 02 May 2026
This study investigates the impacts of potential droughts on Rhine River navigation and industry using the drought identification index (SPEI) and regional climate model simulations (LAERTES-EU), and then translates these climate data into hydrological impacts using WRF-Hydro. By analyzing the top 10 modeled drought events, the results imply that several simulated droughts exceed the severity of the 2018 event in terms of duration and streamflow reduction, suggesting that even under current climate conditions, worse low-flow scenarios are possible with significant economic and ecological consequences. Overall, this study provides insights into the potential societal impacts of extreme droughts. However, I have several comments and suggestions as follows to improve the current manuscript.
1) While the Standardized Precipitation Evapotranspiration Index (SPEI) was proposed by previous studies, it would be better to present the basic technical details of this index in Section 2.3.
2) Lines 109 and 140: What is the correlation coefficient (Pearson, Spearman, or any other) used in those studies? How statistically significant is the correlation?
3) Section 2.4: The model performance in both the calibration and validation (if any) periods should be presented.
4) Line 211: How were the SPEI ranking values combined?
5) Figure 2: Several SPEI-12 values (Jan and Feb) for EV 6 events in sub-figure c) are missing? Also, use the same term “SPEI-3” or “SPEI3” in both the text and the figures.
6) Lines 366-367: “In Section 3.2, EV2 was not ranked as the most severe event because the analysis was an average streamflow value of a fixed period.” Does this imply that SPEI cannot capture the severity of drought? If so, how should the current form of SPEI be refined?
7) Figure 6: Please increase the value range for streamflow in Kaub.Minor Issue:
8) Line 16: “…to identify meteorological droughts,”
9) Line 20: To avoid confusion, it is suggested not to use “GlQ20” in the abstract.
10) It is not necessary to repeat the full term of SPEI after its first appearance in the manuscript, e.g., Lines 82 and 138.
11) Line 97: Change “12.000” to “12” ?
12) Figure 1: It is suggested to add a legend to present the meanings of the different lines and points, and add a north arrow to all the maps.
13) Lines 130-132: Keep the format of the references consistent.
14) Lines 145-150: Keep the font style of the variables consistent in both the text and the equations.
15) Lines 269-271: Please rephrase the text here.Citation: https://doi.org/10.5194/egusphere-2025-3988-RC2
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Evaluation of "Drought propagation in the Rhine River basin and its impact on navigation using the LAERTES-EU regional climate model dataset."
This study assessed hydrological droughts in the Rhine basin using model output simulations and a hydrological model. The purpose was to evaluate the ability of atmospheric drought indices to capture hydrological drought variability across the large Rhine basin. Although the topic of the proposal is of great scientific and applied management interest, I identify significant data and methodological issues. The focus of the project is not clearly defined, and there are substantial uncertainties in the experimental design—particularly regarding the hydrological modelling, which is poorly detailed, the use of model outputs, and their comparability with observations. Moreover, the manuscript lacks a meaningful discussion section. Further details on methodological uncertainties are provided below.
Line 145. The method used to calculate atmospheric evaporative demand is overly simplistic and uncertain. If the available data do not allow the application of the Penman–Monteith equation, the appropriate alternative would be the Hargreaves equation, which uses maximum and minimum temperature.
Lines 161–170. These formulations are unnecessary and confusing, as they merge the calculation of potential evaporation with the fitting of data to compute the SPEI.
Section 2. This section is confusing, and it is not possible to infer a logical structure in the methodological design. It remains unclear how the various elements described—simulations from GCMs and RCMs, the drought index, and hydrological modelling—are integrated into a coherent methodological framework aligned with the stated objectives. Overall, the hydrological modelling component is not sufficiently informative. Planning a hydrological model for such a large basin is an enormous task, subject to major uncertainties, and these challenges and methodological limitations are not adequately addressed in the manuscript’s methodology section.
A key issue is whether the assessment based on climate models accounts for the fact that these models do not reproduce the natural climate variability observed in reality. This must be considered when comparing observations with model simulations, as in Figures 2 and 3. The authors explain that LAERTES-EU is a model-generated dataset from decadal hindcasts (i.e., initialized with historical data), but this does not guarantee that model simulations reproduce the observed variability. This is an essential point that must be clarified, since if the model simulations do not match observed variability, they cannot be reliably used in this study, which aims to evaluate drought propagation based on real drought events. For example, forcing the hydrological model with data that do not reflect the observed variability may introduce substantial bias into the conclusions, as the most significant drought events are identified from observations.
Indeed, Table 3 clearly shows that the simulations using model-generated data overestimate the severity of drought events. However, it is not possible to determine whether this problem arises from the input data or from the inherent uncertainties associated with modelling such a large basin.