the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Dec 2025
Climate and landscape jointly control Europe's hydrology
Abstract. The complex composition of hydrological systems, climates and landscapes makes it challenging to explain and predict hydrological streamflow response. Many previous large-sample studies, mostly focused on the United States, identified climate as the primary control, with landscape exerting only a minor role in shaping hydrological behaviour. Yet, a few other studies report contradicting results with landscape being a more dominant driver. In this study, we use an unprecedentedly large sample of more than 7000 catchments in Europe from the EStreams dataset to identify and map functionally similar catchments, together with their spatially variable climate and landscape controls. The wide spatial and temporal gradient of the study catchments was used to identify hydrological response types (HRTs) based on 40 hydrological streamflow signatures related to long-term averages and inter-annual variability of magnitude, timing, duration, frequency, and seasonality. Overall, 10 HRTs could be identified. Several HRTs are well defined and well distinguishable, largely due to catchments with strongly seasonal or more extreme behaviour. Other HRTs remain difficult to distinguish, as these catchments represent more transitional conditions with increasingly overlapping characteristics between HRTs. The underlying drivers of the HRTs were identified by using 84 climate- and landscape attributes to predict catchment membership to their respective HRT with a Random Forest classification model. Climate emerges as the dominant driver of hydrological behaviour at the continental scale. However, landscape was found, in 4 out of 10 HRTs, to be at least as strong or even stronger a control on the hydrological response. These results highlight that the complex, integrated nature of hydrological response remains challenging to disentangle, even with extensive datasets and advanced modelling approaches, and therefore, climate and landscape needs to be understood as joint drivers in a co-evolutionary perspective.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-6372', Anonymous Referee #1, 12 Feb 2026
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RC2: 'Comment on egusphere-2025-6372', Juraj Parajka, 15 Feb 2026
General comments
The study aims to classify the hydrological response of a large sample of European catchments (7175) using a wide range (40) of hydrological signatures, and to identify the roles of climate and landscape on the similarity/dissimilarity of catchment response. The results show that using a large sample of signatures and catchments does not, in itself, allow for the identification of more than 10 clusters, and many catchments still exhibit diverse hydrological responses that overlap across the clusters.
The study presents a very complex and comprehensive analysis that evaluates a very large sample of catchments and signatures. This is a significant advantage over previous studies, and it is worth publishing. Still, the complex approach and a large dataset also have limitations, particularly with respect to the clarity of presentation of the results. While the Data and methods sections generally well describe the details about the applied data and methods, the results section, in its current form, is very difficult to read, mainly because of the use of numerous abbreviations. In my opinion, the Discussion section is a key part, which can provide, for the readers, the discussion of the main findings, their link to previous knowledge/results and some synthesis about the limitations and uncertainties associated with the selection of the catchments and datasets used for estimation of the climate and landscape characteristics. In my opinion, this part can still be improved, and I have the following questions and requests, which can be discussed and presented in more detail:
- Would it be possible to provide (in the Supplement) the list of used catchments and their assignment to the clusters?
- The study analyses a very large number of catchments. Are all of them needed to evaluate the research questions? I wonder what the impact of (a) mixing smaller and larger catchments, (b) using nested catchments, and (c) using catchments with human impact in the analysis is? Is it not expected that, in larger catchments, the impacts of landscape and climate are mixed? To what extent is the finding about the small role of spatial proximity influenced by using nested catchments? Some of the signatures (such as flashiness) reflect the climate or landscape controls. Still, it can also reflect human impacts (such as reservoir operations), resulting in a mixed (noisy) clustering of catchments.
- Many regions are impacted by climate change, i.e., increasing air temperatures and changes in precipitation, which are associated with changes in hydrological response. What is the impact of such changes on the main findings?
Specific comments
- It might be worth mentioning that the Estreams meteorological characteristics are derived from the EOBS dataset. Because in some regions EOBS tend to underestimate precipitation, I’m curious about some details on how the long-term water balance is tested in the selected catchments (e.g., those that include glaciers or have a significant portion of snowfall).
- Q5 versus Q95. Usually it is used in an opposite way, such as the Q95 describes the low flows.
- The results of the Elbow method for finding the optimal number of clusters do not indicate that 10 clusters is the most optimal variant. I expect that using various methods to select the optimal number of clusters can bring different optimal cluster number. What is the idea for presenting two methods with different results?
- HRT 6 and 8 includes the largest number of catchments which are situated across different climate regions (e.g. as defined by Koppen classification). Please discuss the results for these two clusters in more detail. Does it mean that if they cross different climates that in these catchments the landscape is more important? What is the impact of nested and large catchments here?
Citation: https://doi.org/10.5194/egusphere-2025-6372-RC2
Data sets
EStreams: An Integrated Dataset and Catalogue of Streamflow, Hydro-Climatic Variables and Landscape Descriptors for Europe (1.2) Thiago V. M. do Nascimento et al. https://doi.org/10.5281/zenodo.14778580
Model code and software
Code used in: Climate and landscape jointly control Europe's hydrology Julia M. Rudlang https://doi.org/10.5281/zenodo.17987885
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Summary Comment: The manuscript seeks to broadly analyze hydrologic signatures across Europe. The analysis sought to address the question of whether we can (1) group hydrological signature data into hydrological response types, and (2) can we predict hydrological response types from climate and landscape attributes? The analysis is of interest, but the Methods are difficult to follow.
General Comments:
Introduction – The Introduction largely poses climate and landscape as opposing and homogeneous contenders driving hydrological response. As climate acts on a landscape, for example, the rate that precipitation moves into the stream network will depend in part on topography, soil type and wetland/floodplain storage capacity, it would be helpful to acknowledge or frame these overarching hydrological drivers as more inter-related instead of opposed.
Introduction - Landscape controls can represent a large number of different variables and attributes, but few specific landscape attributes are mentioned, please elaborate on what specific landscape drivers have tended to emerge as important in past studies. This will help to justify the types of landscape variables included in the analysis.
Data - What percent of the gages are nested within another gage used in the analysis? How does nested gages influence the independence of these watersheds?
Data - As large dams can influence and bias discharge values, how were the presence of dams addressed within the selection of gages and watersheds?
Methods – Please add a study area section. A criticism raised in the Introduction is that many available large sample data sets only provide coarse spatial representations of hydrological, climatic and landscape contrasts. What is the spatial representation range across these gages? How does this compare, for example, to the range in US based studies?
Section 3.2 – What is the justification for performing k-means clustering on hydrologic signatures? Has this method been used previously to group signatures?
Section 3.2 – Clarify the inputs and outputs for this step, so the suite of hydrologic signature values for each catchment were put into the clustering, what was the HRT output that was then used as the dependent variable in the RF? Was it signature values that represent each HRT? Or the classification of each catchment into an HRT? This is really important to clarify.
Section 3.3. – Please justify why no variable selection process was used. While random forest techniques are generally insensitive to multicollinearity, the inclusion of highly correlated variables can deflate or bias variable importance values, complicating model interpretation and making it more challenging to identify the most predictive variables. Further, this also has potential implications for comparing the experiments, in Figure 7 the differences in results between models could be attributed to variable groups, or in part to the number of variables included in the model. In a regression analysis, typically an adjusted R2 would be calculated to account for differences in explanatory variable counts.
Line 225 – Why wasn’t a sub-section of independent watersheds withheld? Given the likelihood that at least some of the watersheds are actually nested, this raises concerns if the nesting biased the cross-validation results.
Comment – Spatial autocorrelation can bias model results but does not appear to be considered in the analysis, how was spatial autocorrelation considered, tested for or accounted for in the analysis?
Discussion – Discussion of errors, limitations and sources of uncertainty is quite limited, please add a section to the Discussion to more thoroughly address potential sources of error and uncertainty in your Methods and provide potential future directions for this research.
Technical Corrections
Introduction – Please define hydrological signatures and provide a few examples of how such signatures are useful, this helps broaden the analysis appeal.
Line 23 – Add a reference
Line 27 – change to “patterns”
Line 47 – remove the word “however”
Line 151 and 168 – What test was used to identify highly correlated variables? What threshold was used to eliminate these correlated variables? And how was the retained variable decided?
Data – Add the median watershed size
Table 1 – Either here or in section 3.1.1 add references for the hydrological signatures either individually or the previous publications from which this signature list was compiled.
Table 1 – Hd(Ql) – correct the description
Table 2 – In addition to providing sources in S2, please also cite the source of each variable in the Table.
Table 1 and 2 – Do these tables include highly correlated variables?
Table 2 – What are open areas? Consider using a different term here. Also, remove labeling of median LAI and NDVI as “Seasonality”
Table 2 – Why wasn’t floodplain data used? Is this highly correlated with the mean flat area fraction?
Lines 215-217 – One could also argue the opposite – that grouping watersheds into HRT classes from many different signatures, makes it more difficult to understand what each HRT represents, and therefore the relative importance of climate and landscape variables in predicting the more ambiguous HRT classes, is harder to interpret. I recommend toning down the criticism of predicting individual signatures here.
Line 220 – What hyperparameters were tuned?
Line 256 – What package was used to derive the Random Forest classifications? And what type of feature importance scores were used? (Gini or permutation?)
Lines 264-265 – Revise sentence for clarity
Line 276 – Add mention of using PCA to the Methods
Line 278 – I don’t think “entails” is the correct word here, change to “suggests” or something similar.
Line 449 – How much do you think this is because of the geography being limited to Europe?
Section 5.3 – I wonder if deriving HRTs for each of the original categories: magnitude frequency, duration, timing, seasonality would improve the clumping and facilitate an easier interpretation of what the HRTs represent.
Comment - Given the number of signatures, variables and HRT’s referred to only as numbers – the Results in particular are challenging to follow! Consider revising the names of the HRT’s from a random numbering system to something more meaningful to facilitate easier interpretation of the results.