The streamflow signatures and climate- and landscape attributes are compiled from the EStreams dataset and include long-term averages, as well as inter-annual variability around these averages (for derivation details and sources of the non-standard signatures and attributes, see Tables S1 and S2 in the Supplement). For variables that represent the intra-annual timing, the average timing signatures and attributes were calculated with circular statistics to account for the circularity of the year . Specifically, the normalised day of year, tnorm (ranging from 0 to 1) was mapped onto the unit circle using two dimensionless circular coordinates: 1tSA=sin⁡(2πtnorm)(spring–autumn axis),2tWS=cos⁡(2πtnorm)(winter–summer axis)

This decomposition ensures that dates near the year boundary (e.g., 31 December and 2 January ) are represented as adjacent points on the circle, allowing for a correct computation of mean timing. The circular mean coordinates (tSA‾,tWS‾) were then converted back to day of year using tdoy=(365.25/2π)×atan2(tSA‾,tWS‾), producing an unambiguous average day of the year. In this mapping, tWS=+1 corresponds approximately to 1 January and tWS=-1 to 1 July, while tSA=+1 corresponds approximately to 1 April and tSA=-1 to 1 October . For subsequent analysis with k-means clustering and Random Forest classification, the sine and cosine components (tSA‾,tWS‾) were used directly. However, throughout the manuscript, timing is for clarity mostly reported as the reconstructed day of year tdoy (noted as t). Further details on the circular-statistics approach for computing timing signatures and related attributes are provided in Sect. S1 in the Supplement.

In a deliberate decision to preserve and use as much information as possible to characterise the hydrological response, the k-means clustering and Random Forest classification analysis were based on a comprehensive set of signatures and attributes. Note that all signatures and attributes used in the study contain independent information. Signatures and attributes that are consistently and strongly correlated with multiple other signatures and attributes were discarded after a preliminary scan. Some of the signatures and attributes retained for the analysis contain some overlapping information, but overall the level of correlation was considered low enough to be acceptable (Fig. S1), in particular as k-means clustering and Random Forest classification are rather robust in the presence of correlated variables .

In the following sections, the hydrological signatures and climate- and landscape attributes are referred to with a capital letter followed by the variable name in subscript to clearly distinguish between the different categories, the following notation is used: hydrological signatures are denoted with an H, the climate attributes with C, the landscape attributes are categorised into vegetation and landcover denoted by V, soil with S, geology with a G, topography with a T, and the anthropogenic attributes with an A.

To identify hydrological similarity between our catchments in the form of multiple HRTs, k-means clustering, a technique frequently used in comparable studies, was applied . k-means clustering groups catchments by dividing the dataset into a predefined number of clusters (here: HRTs), each represented by a centroid. The algorithm iteratively assigns each catchment to the nearest centroid based on Euclidean distance, and then updates the centroid positions to minimise the within-cluster sum of squared distances. Because the number of clusters k must be specified beforehand, it is necessary to evaluate different choices of k to identify a solution that balances cluster compactness and interpretability. Two common evaluation metrics are the Elbow Method and the Silhouette Score. The Elbow Method assesses how the total within-cluster variance decreases with increasing k, with a breakpoint, i.e. an “elbow” indicating a point beyond which additional clusters do not provide substantial improvements. It is reported as inertia, which is the sum of the squared distances to the closest cluster centroid, for each catchment. The Silhouette Score, on the other hand, quantifies how similar each catchment is to its assigned cluster compared to other clusters, with higher values reflecting better-defined and more distinct clusters. The k-means clustering algorithm was run for a range of k from 2 to 20. The most suitable number of clusters and thus HRTs was determined using the Elbow Method and Silhouette Score metrics. To ensure that all hydrological signatures contributed equally to the clustering, the variables were normalised using min–max scaling to account for their different units and magnitudes. The k-means clustering resulted in a set of k hydrologically similar groups (HRTs), with each catchment assigned to one of these HRT classes (labelled 1 to k). These HRT class labels were subsequently used as the target variable in the Random Forest classification model.

The Random Forest classification model, implemented with the scikit-learn Python library, was used here with the climate- and landscape attributes as input features to predict the membership of each catchment to one of the classes of hydrological response types (HRT) (Sect. 4.2) in a supervised multiclass classification problem. The idea behind this is that each HRT class represents a unique combination of hydrological characteristics as defined by the hydrological signatures. By predicting catchment membership to the class of HRT from climate- and landscape attributes, it can then be analysed to which extent hydrological classes can be predicted based on landscape and climate, as well as which climate- and landscape attributes are more relevant for shaping hydrological response pattern. While Random Forest regression models have been successfully used to predict individual hydrological signatures e.g.,, predicting membership to a HRT class, representing a multi-dimensional composite of hydrological signatures, provides a complementary perspective that may help reveal the drivers of overall hydrological behaviour and its spatial variability.

As the choices made in the modelling process can substantially affect the results and interpretation thereof, a stepwise approach with many alternative model set-ups was applied here. In a first step, each model was tuned through iterative adjustment of hyperparameters in a Monte Carlo sampling strategy with 5000 realisations. Each realisation was evaluated based on its accuracy, i.e. the fraction of correctly classified catchments over the total sample (Acc = true positives/total sample) for both training and validation. From the set of all realisations, a solution was chosen that balances a good training (Acct≥0.6) with a robust validation performance Accval (ΔAcct-Accval≤0.1) to limit overfitting (for the retained solution's model parameter set see Sect. S7). In the second step, the retained solution was used to further train and evaluate the model using a 10-fold cross-validation, with 90 % of the data, i.e. catchments in a HRT, used for training and 10 % for testing in each fold. Due to the unequal number of catchments in the individual HRTs, the HRTs were weighted according to their respective number of catchments in the model. After that initial model set-up that predicted membership to 10 individual HRT classes, the same sequence of steps was repeated using 6, 7, 8 and 9 HRT classes, respectively, to analyse the sensitivity of the model and to assess how well the HRTs are defined.

To evaluate in a last step, the relative influences of climate and landscape on hydrological behaviour, five experiments were conducted in addition to the initial experiment (1-CL). In these experiments, the Random Forest classification model was trained and tested using only climate- or different landscape-attributes (Table ). In Experiment 2-C, exclusively the 47 climate attributes were used for model training and testing, while Experiment 3-VLC used the 11 vegetation and landcover attributes. As some of the vegetation signatures may directly reflect short-term climatic variability, Experiment 4-VLS used only the 7 static vegetation and landcover attributes. Experiment 5-SGT included the 21 signatures related to soil, geology and topography, and Experiment 6-A used the 5 anthropogenic attributes.

For the analysis of the climate- and landscape-controls on the HRTs in the six experiments, model performances are reported as averages over the cross-validation folds. Besides the accuracy Acc, model performance of each experiment was evaluated using receiver operating characteristic curves (ROC curves), the area-under-the-curve metric (AUC) and the confusion matrix for a more nuanced picture of the true positive, false positive and false negative rates of the classification. The ROC curve illustrates the model performance across varying classification thresholds by plotting the true positive rate against the false positive rate, demonstrating the relative trade-off between the correctly and incorrectly classified instances. The classification threshold determines how predicted probabilities are turned into class labels. For example, if the threshold is 0.5, a catchment with a predicted probability of ≥0.5 of belonging to class A, will be classified as A. Changing this threshold shifts the balance between correct and incorrect classifications, and the average of all the different true and false positive rates makes up the ROC curve. An overall robust and well-performing model approaches the upper left corner (high true positive rate RTP and low false positive rate RFP), reaching high AUC values, while poorer model performance with lower AUC is reflected by curves closer to the diagonal, which represents random guessing.

However, dealing with a multiclass prediction, the ROC curves and AUC are averaged across all HRT classes and folds, and therefore only provide partial information on the model performance. To assess the final classification outcomes, confusion matrices were used. These compare the true HRT class to the predicted HRT class, with correct predictions, i.e. true positive rates, for each HRT plotting along the diagonal. Each row represents the true HRT, and each column the predicted HRT. Off-diagonal rows therefore, indicate the false negative rates, while off-diagonal columns show the false positive rates. Together, this provides insights into which HRTs are more robustly predicted than others and which HRTs are more susceptible to misclassifications.

For each experiment, permutation feature importance scores from the associated Random Forest classification model were used to evaluate the relative contributions of the climate- and landscape attributes to the model performance. Feature importance reveals to which extent a feature (here: climate- or landscape attribute) influences the classification outcome. It is quantified for each feature as the decrease in predicted accuracy when this specific feature is removed from the model. To ensure that feature importance scores were robust, it was calculated 10 times for each of the folds. The reported feature importance is the average of the 10 calculations across all folds.

Preliminary k-means clustering runs with k ranging from 2 to 20 (representing predefined numbers of HRTs based on the available hydrological signatures) did not yield a clear or unambiguous optimal number of HRTs. While the Inertia metric of the Elbow Method indicates no apparent break but rather a smooth decline with an increasing number of HRTs (Fig. a), the Silhouette Score metric suggests a major break at k=4 and another minor break at 12 HRTs (Fig. b). The generally low values of the Silhouette Score, i.e. <0.25, further indicate that it is unlikely that the number of well-defined HRTs exceeds that range.

As a balanced choice to allow for some detail while not inflating the number of HRTs warranted by the data, the results are thus hereafter reported with a focus on 10 HRTs. However, to test the significance of the choice of HRTs numbers on the results, the results of k-means clustering with 6, 7, 8, and 9 HRTs were compared to the 10 HRTs, but no substantial difference was found between the different HRT numbers, see Fig. S3.

Overall, Fig.  suggests that there is little evidence that the set of 40 hydrological signatures used for this analysis contains sufficient information to robustly distinguish more than 10 different types of HRTs for the 7175 study catchments across Europe. The results of a Principal Component Analysis of the signatures further support this interpretation. Indeed, as shown in Fig. a, the first two principal components (PC) account for merely ∼38 % of the variance in the signatures, and at least 19 principal components are required to explain >90 % of the variance (Fig. c). This suggests that the signatures cannot be effectively compressed due to a low level of redundancy in their respective information contents and suggesting that signatures of individual catchments exhibit substantial scatter but not in a systematic enough manner. The latter implies that no detailed pattern of similarity that would go beyond ∼10 HRTs could here be identified based on k-means clustering. As a further consequence, the within-HRT variability of individual signatures can be substantial. Showing the distributions of several selected signatures, Fig.  illustrates that some signatures, such as the baseflow index HBFI (Fig. e), the Gini coefficient HGC (Fig. f) or the flashiness index HRBI (Fig. i), exhibit a clear contrast across the HRTs and limited within-HRT variability which can also be seen by the standard deviations of the signatures around the normalised means of the individual HRTs in Fig. . In contrast, many other signatures such as the days between highest and lowest flow HN(QH-QL) (Fig. j) or the autocorrelation coefficient HAC30 (Fig. g) are characterised by substantial within-HRT variability, lower across-HRT contrast and thus little discriminatory power which can be seen by the limited differences between the standard deviations of these signatures across the HRTs in Fig. . Note that the distributions of all hydrological signatures in the individual HRTs is provided in Fig. S2.

The most informative hydrological signatures, ranked by their cumulative variance across the first 13 PCs (∼80% of variance explained) as shown in Fig. b and d, included the Gini coefficient HGC as metric of flow seasonality (1st rank), the baseflow index HBFI (2nd) as metric of flow stability, the mean flow HQ (5th) as metric of overall flow magnitude but also descriptors of inter-annual variance, such as HV,Q5 as metric of variance of low-flow magnitude (3rd), and the variance of the Gini coefficient HV,GC, as metric of inter-annual differences in flow seasonality (7th). The picture that emerges illustrates that while a few signatures of inter-annual variability are among the top-ranked ones, the majority of them, i.e. 10 out of 15, ranks in the bottom half of all signatures. HRTs are thus dominantly defined by signatures of averages, in particular flow seasonality, stability and magnitude (Fig. d).

Building on these defining signatures, the 10 HRTs exhibit a wide range of hydrological behaviours, with varying degrees of distinctness (Fig. , Table ).

For example, HRTs 1 (stable, weak, winter regime), 7 (wet, strong, spring regime), 8 (wet, strong, late-winter regime), 9 (wet, strong, early-winter regime) and 10 (dry, intermittent, winter regime) are readily distinguishable (Fig. a–d) due to their stable, strong or extreme flow-regime characteristics. HRT 1 represents a highly stable regime with low mean flows, high baseflow (HBFI) contributions and the lowest flashiness index (HRBI) (Fig. a, e, i). In contrast, HRT 10 shows an extreme regime characterised by very steep flow duration curves (HFDC) and strong seasonality as illustrated by the highest Gini coefficients (HGC) (Fig. b, f), while HRTs 7–9 exhibit pronounced seasonal peaks and lows and highly consistent timing of high flows (Ht(QH)) (Fig. a, c, d). In contrast, less distinct HRTs, such as HRT 2 (transitional, low, multi-seasonal regime), show a wide spread in the timing of high and low flows (Ht(QH), Ht(QL)) and in the slopes of the flow duration curves (HFDC), whereas HRT 5 (transitional, moderate, winter regime) remains close to the overall average for most signatures (Figs. , ). However, the within-HRT variability is not uniform, each HRT shows limited variability in some signatures and substantial spread in others. For example, HRT 1 shows low variability in the baseflow (HBFI) and flashiness (HRBI) indices but high variability in autocorrelation(HAC30) and rising limb density (HRLD). Similarly, HRT 7 has tightly grouped flashiness (HRBI) and high-flow timing (Ht(QH)), but a wide spread in rising limb density (HRLD) and the days between high and low flows (HN(QH-QL)). Less distinct classes, such as HRT 2, show a similar variation in signature values, with relatively low variability in rising limb density (HRLD) but high variability in baseflow (HBFI) and flashiness (HRBI) indices and the timing of flows (Ht(QH), Ht(QL)) (Fig. c, d, e, i). These heterogeneous patterns illustrate why detailed descriptions of the HRTs beyond the major contrasts remain challenging.

Despite the difference in within-HRT hydrological signature variation, broad hydrological patterns emerge across the HRTs and form the basis for their classification into flow-regime types (Table ). Key characteristics, reflecting the dominant signatures from PCA analysis (Fig. d), include the magnitude of average discharge, the strength and shape of the seasonal cycle, and the predominant season of high flows. Using these criteria, the HRTs range from stable to transitional, wet, and dry regimes, and from weakly seasonal to strongly seasonal systems with winter-, spring-, summer-, or multi-seasonal peak-flow timing. The geographical distribution of the HRTs as shown in Fig. , reflects some of these large-scale hydrological differences but does not show consistent spatial clustering. Certain HRTs are geographically concentrated: for example, HRT 7 (wet, strong, spring regime) is largely confined to the Alps, northern Scandinavia and Iceland (Fig. h), while HRT 9 (wet, strong, early-winter regime) is mainly located in the UK and Ireland (Fig. j). Other well-defined classes, such as HRT 10 (dry, intermittent, winter regime) and HRT 8 (wet, strong, late-winter regime), are more widely distributed across Europe. Likewise, HRT 1 (stable, weak, winter regime) and HRT 6 (transitional, moderate, spring regime) span a broad latitudinal gradient from southern Spain to Finland, while HRT 3 (wet, moderate, summer regime) and HRT 4 (transitional, moderate, summer regime) are clustered in transitional regions between high and low elevations. Thus, spatial proximity may explain some, but far from all patterns in the geographical distribution of HRTs.

The initial hyperparameter tuning approach for model selection, based on 10 classes of HRTs and all 84 climate- and landscape attributes as input features of the Random Forest classification model to predict membership of catchments to individual HRTs, resulted in training accuracies Acct∼0.45–1 (Fig. S4a). In contrast, the validation accuracy Accval remains <0.70 for all realisations. These overall reductions of accuracies between calibration and validation indicate substantial model overfitting, and thus limited predictive power as Acct increases. As expected, validation accuracies increase for model set-ups based on a lower number of HRTs (k=6–9), as shown in Fig. S4b–e. However, with Accval not exceeding 0.7 for any of the set-ups, the improvements are very minor, while the overall pattern remains unchanged. The opposite, with partly substantial declines in predictive power, was observed with model set-ups using higher numbers of HRTs (not shown). To thus preserve both, at least moderate levels of detail in the distinction of HRTs as well as at least moderate levels of model predictive power, 10 HRTs were eventually used as a balanced choice for the subsequent analysis. More specifically, the hyperparameter set resulting in Acct=0.70 and Accval=0.60 for these initial runs (Fig. S4a) was selected for further analysis.

The first of the six full experiments, predicting membership of individual catchments to the 10 different HRTs based on the complete set of 84 climate- and landscape attributes, i.e. 1-CL, resulted in rather stable results across the 10 cross-validation folds, with Acct=0.68–0.69 and Acctest=0.58–0.60 (Figs. a and S5). A more nuanced analysis of the results reveals that, at the chosen classification threshold, the true positive rates, i.e. the fraction of catchments whose membership to a specific HRT is correctly predicted, reaches with RTP=0.57–0.61 comparable values across all 10 folds as illustrated by the individual dots in Fig. b. It also illustrates that the false positive rates, i.e. the fraction of catchments with a specific HRTs whose membership to other HRTs is incorrectly predicted, remains very low with RFP=0.04–0.05. These values describe the model's performance when turning predicted probabilities into hard class labels, and are also reflected in the confusion matrices (Fig. ). Independently of this threshold, the ROC analysis yields an AUC=0.93, indicating that the combined climate- and landscape attributes used in the 1-CL experiment have predictive power that is in any case substantially higher than a random guess, represented by the 1 : 1 line in Fig. b and the associated AUC=0.5.

A further stratification of the results exposes pronounced differences between the individual 10 HRTs. Membership to some HRTs, such as HRTs 7 (wet, strong, spring regime) and 9 (wet, strong, early-winter regime), is with Acctest of up to 0.89 and AUC of up to 0.99 very well predicted (Figs. a and S5). These well-predicted HRTs are generally well defined by several hydrological signatures that are distinct to those of other HRTs (Figs.  and S5). While HRT 7 is, for example, characterised by elevated flow magnitudes, in particular HQ and HQ5, as well as markedly different timing characteristics, including the latest half-flow day Ht(HFD) and the earliest low flows Ht(QL), HRT 9 equally stands out with high HQ, but also exhibits the highest HQ95, the largest flow variability HCV and the earliest half-flow day Ht(HFD) (Fig. S2). This is contrasted by membership to other HRTs that are less well predicted. This is in particular the case for HRT 5 (transitional, moderate, winter regime) with Acctest=0.34 (Fig. a) and AUC=0.83 (Fig. S6). As a transitional HRT, none of its hydrological signatures is clearly discernible from the other HRTs, making an effective discrimination difficult (Figs.  and S6).

From a spatial perspective, no clear pattern emerges in the correctly predicted HRTs (Fig. ). However, some regions show an overrepresentation of misclassifications, notably northern Spain, the Paris Basin, South-East England, and southern Poland (Fig. ). While HRT 10 (dry, intermittent, winter regime) is predicted fairly homogeneously across its domain (Fig. j), the misclassified catchments span nearly all other HRTs, with the largest share belonging to HRT 8 (wet, strong, late-winter regime). Other well-predicted classes include HRTs 7 (wet, strong, spring regime), 8, and 9 (wet, strong, early-winter regime), with HRTs 7 and 9 being the most spatially homogeneous. These HRTs occupy relatively compact hydro-climatic domains, in contrast to HRT 8, which is generally well predicted but has misclassifications distributed widely across Europe (Fig. g, h, i).

HRTs 2 (transitional, low, multi-seasonal regime), 5 (transitional, moderate, winter regime), and 6 (transitional, moderate, spring regime) display more spatially variable predictive performance (Fig. b, e, f). For HRT 2 and HRT 6, correctly classified catchments occur in dense clusters, whereas the misclassified ones are scattered across Europe. In contrast, both correct and incorrect predictions of HRT 5 are widespread. The most frequent confusions are HRT 2 being misclassified as HRT 7 (wet, strong, spring regime), HRT 5 as HRT 8 (wet, strong, late-winter regime), and HRT 6 as HRT 4 (transitional, moderate, summer regime), consistently across all experiments (Figs. b, e, f and S7–S11). HRT 5 and HRT 8 share similarities in timing (Fig. c, d), but otherwise show limited resemblance (Fig. ). HRT 6 and HRT 4 also overlap in the timing of their high and low flows (Fig. c, d) and exhibit similar seasonal patterns (Fig. a). In contrast, a comparison of signatures between HRT 2 and HRT 7 (Fig. ) reveals no striking similarities, suggesting that their mutual misclassification may result from similarities in climatic or landscape controls rather than hydrological behaviour alone.

The relative roles of the individual climate- and landscape attributes as drivers of streamflow behaviour were then analysed by comparing their feature importance (Fig. ). Overall, the seasonality of precipitation Cϕ(P) (1st rank), the seasonality of potential evaporation Cϕ(EP) (2nd) as well as the fraction of precipitation falling as snow CF(Snow) (3rd) emerge as the strongest predictors of membership to classes of HRTs and thus as most dominant controls on streamflow behaviour (Fig. a). Next to these three climate attributes, almost half of the top 20 ranking predictors are landscape attributes. Most notably, several descriptors of vegetation attributes such as the average NDVI VNDVI (4th), its inter-annual variance VV,NDVI (7th) or average tree cover fraction VF(TC) (8th) stand out, while the fraction of carbonate rocks GF(C) (11th) is the highest-ranking attribute from the soil, geology and topography group. In addition, the fraction of agricultural areas AF(Agri) (9th) appears as most relevant from the group of anthropogenic attributes.

In the second experiment 2-C, predicting membership to the 10 HRTs based exclusively on the reduced set of 47 climate attributes resulted in model performances Acct=0.66–0.67 and Acctest=0.56–0.60 that are not considerably different to the 1-CL experiment using the complete set of 84 climate- and landscape attributes (Figs. a and S5). Similarly, the ROC curve is with AUC=0.92 practically indiscernible from the one of the 1-CL experiment (Fig. b). Not only is the overall model performance of the 2-C experiment comparable to 1-CL, but also the model's skill to predict membership to the individual HRTs is largely equivalent between the two experiments with Acctest=0.32–0.89 for the individual HRTs but also preserving the hierarchy of which HRTs are more robustly predicted than others (Figs. b and S6). It can further be observed that the ranking of the feature importance of, in particular, the highest ranking climate attributes is equally preserved with precipitation Cϕ(P), Cϕ(EP), CF(Snow) as well as the phase shift between P and EP CΔϕ(P,EP) and the temperature range CR(T) also being the dominant climate controls in the 2-C experiment (Figs. b and S12a).

The prediction of membership to individual HRTs only based on the 11 vegetation and landcover attributes of this study in experiment 3-VLC resulted in Acct=0.59–0.60 and Acctest=0.45–0.51 (Figs. a and S6) and consequently a slightly reduced AUC=0.87–0.89 (Fig. b) across the 10 cross-validation folds. Despite these slightly lower overall accuracies, several individual HRTs could be predicted at least as well or even better with vegetation and landcover attributes here in 3-VLC than when also considering climate attributes in 1-CL and 2-C (Figs. c and S6). More specifically, it was found that HRTs 1 (stable, weak, winter regime), 6 (transitional, moderate, spring regime) and 7 (wet, strong, spring regime) are characterised by average Acctest=0.58, 0.46 and 0.90, respectively. They are thus all predicted within a margin of ±0.02 as compared to 2-C, which is not a significant difference (p>0.05). This is contrasted with other HRTs that could be less well predicted in the 3-VLC experiment, most notably HRT 4 (transitional, moderate, summer regime) (Acctest=0.36) and HRT 5 (transitional, moderate, winter regime) (Acctest=0.18). While the two top-ranking vegetation and landcover attributes in terms of feature importance, i.e. the inter-annual variance of NDVI VV,NDVI and the tree cover fraction VF(TC) (Fig. c), also appear among the top 3 vegetation and landcover controls in the 1-CL experiment (Fig. a), the overall coherence of the ranking compared to 1-CL is rather modest (Fig. S12b). The latter is reflected, amongst others, by the marked rise of the range of the Parde coefficient of NDVI VR(NDVIParde) from the lowest ranking in 1-CL to the 4th ranking vegetation and landcover attribute. As VR(NDVIParde) is a metric of seasonality of vegetation activity, it also reflects a direct imprint of seasonal climatic conditions on vegetation, and is thus, to some extent a proxy of Cϕ(P) and Cϕ(EP), the dominant climate attributes in 1-CL and 2-C.

This reasoning is further supported by the 4-VLS experiment, which uses only static vegetation and landcover attributes. The omission of any information on temporal dynamics, i.e. seasonal as well as inter-annual, in this experiment led to further reduced prediction performances, i.e. Acct=0.53–0.54, Acctest=0.41–0.46 and AUC=0.85–0.87 (Figs. a, b, d, and S6), with tree cover fraction VF(TC) remaining the most dominant static vegetation and landcover attributes (Fig. d). In contrast, basing the predictions exclusively on soil, geology and topography attributes in experiment 5-SGT, results in accuracies Acct=0.60–0.61 and Acctest=0.46–0.51 (Figs. a and S6) with associated AUC=0.86–0.88 (Fig. b). This suggests that these 21 static landscape attributes can predict membership to individual HRTs to the same degree as the vegetation and landcover attributes (3-VLC) and only slightly less accurately than when using climate attributes (1-CL, 2-C). At the level of individual HRTs, it was found that soil, geology and topography attributes are the strongest predictors on HRT 1 (Acctest=0.62) and HRT 3 (wet, moderate, summer regime) (Acctest=0.58), exceeding climate as well as vegetation and landcover as dominant controls (Fig. e). In addition, HRT 7 (wet, strong, spring regime) is predicted with Acctest=0.88 in 5-SGT, which is effectively equivalent to the first four experiments. Further, the overall ranking of feature importances is broadly consistent with 1-CL. The top-ranking soil, geology and topography attributes of the 1-CL experiment, i.e. fraction of carbonate rock GF(C), fraction of very low permeability rock GF(PermVL) and bedrock depth GBD, also appear among the stronger predictors here in 5-SGT (Figs. e and S12d). Notwithstanding this general level of stability in the hierarchy of controls, the by far most dominant soil, geology and topography attribute emerging as control from 5-SGT is the catchment elevation TElev. Comparable to the role of dynamic vegetation and landcover attributes in the 3-VLC experiment, TElev here reflects climatic gradients and thus correspondingly acts as proxy for climate attributes such as for the fraction of snow CF(Snow).

In the final experiment 6-A, using only anthropogenic attributes as predictors of membership to HRTs, the lowest model performances among all six experiments were found with Acctest=0.27–0.34 and AUC=0.76–0.78 (Fig. ). Similarly, the membership to the individual HRTs was also much less well predicted (Fig. e). The most dominant control on HRT membership from this group of attributes was the fraction of agricultural areas AF(Agri) (Fig. e), which consistently also appeared as the highest ranking from this group of attributes in the 1-CL experiment (9th rank) (Fig. a).

Viewing these results in a broader context, we can spatially map the HRTs according to the attribute group that serves as their strongest predictor. In total, 4 out of the 10 HRTs were predicted equally well or better by landscape attributes than by climate attributes (Fig. ). Together, this amounts to 3226 catchments, which is 45 % of the total dataset. Soil, geology and topography attributes emerge as the dominant drivers of hydrological response in low to middle-elevation regions in central-eastern Europe, corresponding to HRTs 1 (stable, weak, winter regime) and 3 (wet, moderate, summer regime). Other catchments in the same geographic region, but belonging to HRT 6 (transitional, moderate, spring regime), are more strongly influenced by vegetation attributes. Vegetation also emerges as the strongest driver for catchments in HRT 7 (wet, strong, spring regime), in the higher elevation region of the Alps and at high-latitude regions.

With the aim to distinguish the rich, multi-dimensional intricacies of the hydrological response and to identify coherent groups of hydrological similarity across 7175 river catchments in Europe at a much higher level of detail than what is currently known , individual HRTs were defined by 40 signatures of averages and inter-annual variability of streamflow magnitudes, timing and dynamics. In contrast to the initial expectations and somewhat surprisingly given the amount of data underlying the analysis, the k-means clustering method used here did not provide sufficient evidence to distinguish more than 10 individual classes of HRTs. These individual HRTs effectively only capture rather broad systematic differences between different response types at the continental scale. As the study domain is limited to Europe, these patterns reflect the variability of climatic and landscape conditions present in the region and may differ in areas with other environmental gradients. Within-HRT variability of the individual signatures remains pronounced (Figs.  and S2) and comparable to previous studies e.g.,. Each HRT shows low variability for some signatures and high variability for others, indicating that the HRTs represent certain aspects of hydrological behaviour well, while remaining broad or diffuse in others.

For example, HRT 7 (wet, strong, spring regime) is one of the most distinct classes, yet it exhibits large variability in the 30 d autocorrelation (HAC30), rising limb density (HRLD), and days between highest and lowest flow (HN(QH-QL)) (Fig. g, h, j), while showing very little variability in the slope of the flow duration curve (HFDC) (Fig. b). HRT 10 (dry, intermittent, winter regime), another well-defined HRT, has, on the other hand, one of the largest variability in the slope of the flow duration curve (HFDC), but less variability in timing of low flow (Ht(QL)) and 30 d autocorrelation (HAC30). Similar variation in the hydrological signatures is also seen in the less distinct classes such as HRT 2 (transitional, low, multi-seasonal regime), which exhibit less variability in rising limb density (HRLD) and high variability for the baseflow index (HBFI) (Fig. e, h). These heterogeneous patterns of within-class variability illustrate that general descriptions of the hydrological response characteristics that go beyond the major contrasts between the 10 HRTs, remains challenging. The key hydrological signatures with the strongest contrasts and thus the highest discriminatory power between HRTs included the Gini coefficient HGC, the baseflow index HBFI, as well as HQ and HQ95 but also the half-flow day Ht(HFD) as measure of timing and the variances of the Gini coefficient HV,GC and flow HV,Q as measures of inter-annual variability (Figs. , d, and S2). The combined importance of metrics of intra-annual timing, dynamics, magnitude and inter-annual variability for the identification of HRTs underlines the multi-faceted nature of streamflow, which requires all these different aspects for a meaningful characterisation of (dis)similarity of the hydrological response. In line with these findings, it was found that several HRTs were well-defined and clearly distinct from others. This is in particular the case for HRTs 1 (stable, weak, winter regime) and 10, which represent the respective extremes of many of the hydrological signatures. While HRT 1 is characterised by a stable, groundwater-driven system with little seasonality and generally low magnitudes of flow, HRT 10 is a highly dynamic, intermittent regime with strong seasonal variations in flow. HRTs 6–9 are similarly well defined by contrasts in multiple signatures. Other HRTs are less clearly defined. For example, HRT 5 (transitional, moderate, winter regime) closely resembled the overall sample average across most signatures, making it challenging to interpret, a challenge also noted by . Attempting to solve this issue, we analysed the effect of reducing the number of HRTs from 10 to 6–9. However, this did not eliminate the less distinct groups but instead increased the within-cluster variability of the remaining HRTs (not shown). Illustrating the complexity of classification, this suggests that certain HRTs may represent transitional or mixed hydrological behaviours that cannot be systematically and clearly enough distinguished with the available data, as it is in these cases difficult to attribute cluster boundaries to a single or even a few dominant signatures. The challenges in classifying HRTs are similar to those reported by , who found that some clusters are difficult to discern because catchments share climatic conditions and lack between-cluster diversity, whereas in other cases, differences in climate do not translate into statistically distinct flow regimes. The overall spatial pattern of the 10 HRTs identified in our study is broadly consistent with , who identified 11 clusters of hydrological similarity across Europe. Some of the most distinct HRTs are similar, such as the high average flow, dynamic (i.e. low HBFI) spring flow regime clustering on the west coast of the UK and Ireland (corresponding to our HRT 9 (wet, strong, early-winter regime)), the summer high flow regime of the Alps and wide parts of Scandinavia (our HRT 7) or the drier, more extreme regime with frequently intermittent flow in the Mediterranean regions (our HRT 10). However, differences in catchment sample size and signature selection lead to differences in other HRTs and limit further direct comparison.

A key feature of our results is that while a few HRTs are characterised by rather sharply defined geographic associations, such as the Alps and Scandinavia (HRT 7 (wet, strong, spring regime)) or the British Isles (HRT 9 (wet, strong, early-winter regime)), others extend over wide parts of Europe, some even spanning from southern Spain to northern Finland (e.g. HRT 6 (transitional, moderate, spring regime)). As a major implication, this demonstrates that spatial proximity, even across larger regional scales, is a poor predictor of streamflow behaviour. It also contrasts with earlier studies , where spatial proximity was reported to play a stronger role, and clusters tended to correspond to regions with similar climatic conditions or physical likeness. One potential reason is that those studies included climatic descriptors for clustering hydrological response types, whereas our classification approach was exclusively based on hydrological signatures.

In six individual experiments, membership of individual catchments to one of the 10 HRTs was predicted based on climate- and/or landscape attributes with the aim to identify the dominant drivers of streamflow behaviour. While this allowed to capture the major continental pattern, the overall prediction performances were rather modest, with accuracies not exceeding 0.69 for training and 0.59 for testing, respectively (Fig. ). These levels of performance are comparable to previous studies. For example, reported that 60 % of catchments in Europe were correctly classified in their approach. In contrast, and reported lower errors (∼10 %) for predictions of correct regime class, although based on much smaller samples of catchments (<850) in the United States.

At the level of the individual HRTs, the prediction accuracies varied across all experiments. HRT 7 was throughout the best predicted with Acctest=0.81–0.90, due to its distinct snowmelt-driven regime. HRTs 1 and 8–10 were also consistently well predicted with climate- and landscape attributes, mirroring the same HRTs that were most clearly defined by hydrological signatures. In contrast, HRT 5 is the worst-predicted cluster in all the experiments and at the same time the least well-defined cluster in terms of contrasts in hydrological signatures. Similar difficulties were seen for HRTs 2–4 and 6, which appear to be transitional classes, with mixing features of other HRTs. Confusion matrices for the experiments further revealed systematic misclassification among these less distinct HRTs (Fig. ). This is consistent with earlier work suggesting that weak between-HRT diversity complicates the predictions , making it challenging to capture the essence of what is driving those clusters, at least at a continental scale. On the other hand, these HRTs have catchments distributed across multiple climate regions (from southern Spain to northern Nordics), indicating that similar hydrological response patterns can emerge under differing climatic conditions. This does not necessarily imply that landscape characteristics are more important than climate but rather suggests that similar hydrological behaviour can arise from different combinations of climatic and landscape controls. This is consistent with the concept of equifinality and uniqueness of place , where different processes or conditions can lead to comparable hydrological responses. Our Random Forest results support this interpretation, as both climate and landscape variables contribute to predictive skill across experiments. It could be reasoned that reducing the number of total HRTs would also limit the choice of the model, resulting in these “bland” catchments being lumped together into a more distinguishable and thus better predictable group. However, this was not the case, as in all experiments run with a reduced number of 6–9 HRTs, the less distinct clusters were preserved and the prediction accuracies were not substantially increased (Fig. S4), highlighting the inherent ambiguity in these HRTs.

The overall prediction accuracy Acctest=0.55–0.61 (Fig. a) of experiment 2-C based on climate attributes underlines the important role of climate as driver of streamflow behaviour. However, the experiments based on landscape attributes resulted in only slightly lower accuracies with Acctest<0.52. In addition, it was also found that landscape attributes can predict membership to HRTs at least as good or even better than climate attributes for 4 out of 10 HRTs (Fig. ), with a total of 3226 catchments (45 %). In low- to mid-elevation regions of central-east Europe, soil, geology and topography related attributes are the most dominant controls of the hydrological response (HRTs 1, 3). Similarly, vegetation attributes emerge as strong controls in these regions (HRT 6), but notably also in the higher elevation region of the Alps and at higher latitudes (HRT 7). The importance of landscape as driver of the hydrological response is further exacerbated by the ranked importances of the attributes in experiment 1-CL (Fig. a). While climate attributes rank the highest, around half of the top 20 predictors are landscape attributes. In contrast to several studies that conclude that climate controls the hydrological response to a large extent with only limited landscape influence , our results provide broad empirical evidence supporting other previous studies that argue that landscape exerts a stronger influence than often assumed, in particular in specific regions .

In light of our results, it can be argued that the frequently adopted climate vs. landscape perspective may lead to an overly simplistic perception of the system. This is for example illustrated by the dominant attributes that emerge in the landscape-only experiments 3-VLC (Fig. c) and 5-SGT (Fig. e). In 3-VLC, metrics of both inter-annual variability (VV,NDVI) and seasonality (VR(NDVIParde)) were in the top-ranking attributes. Both are direct imprints of climatic conditions on the landscape over rather short time scales. Similarly, the fraction of tree cover (VF(TC)) as well as the elevation (TElev) as further key attributes in 3-VLC and 5-SGT, respectively, reflect aggregate effects of different temperature regimes across elevational gradients, although correlation with individual climate attributes such as the fraction of snow CF(Snow) is very limited (R2=0.09). These examples illustrate that landscape attributes are to a certain extent shaped by climate, thus also containing information on climatic conditions. It is therefore argued that the climate vs. landscape dichotomy is false and should be replaced by a climate and landscape perspective that explicitly accounts for the interactions in and co-evolution of the overall system e.g.,.

The spatial distribution and quality of the underlying data represent a source of uncertainty in this study. Streamflow observations are unevenly distributed across Europe, with a high density of gauged catchments in central Europe and lower coverage in the eastern and southern regions . This imbalance may lead to an overrepresentation of hydrological characteristics of well-monitored regions, potentially limiting the representation of more data-scarce areas. In addition, the meteorological forcing is derived from the E-OBS dataset, whose accuracy similarly depends on station density and varies spatially. In regions with lower station density, particularly in parts of southern and eastern Europe, precipitation may be underestimated . This may influence the derived climatic attributes and, consequently, their relationship with hydrological response. Despite the limitation in station density and uncertainty in the meteorological data, the dataset is considered sufficiently robust for identifying large-scale patterns across Europe .

It should be noted that there exists potential multicollinearity among the hydrological signatures and climate and landscape attributes. As the aim of the study was to retain a broad set of signatures and attributes, no extensive variable selection was applied to minimise redundancy. Instead, a Pearson correlation-based pre-filtering step was used to remove highly correlated variables (|r|>0.75). This resulted in 50 % of the hydrological signatures and climate and landscape attributes exhibiting r<0.12 (equivalent to explaining ∼1 % of the variance) and only 2.5 % of the attributes being correlated with r>0.6 (explaining 36 % of the variance; see Fig. S1). Some correlation remains, and to explicitly test the robustness of the model interpretation, the consistency of the feature importance ranking of the climate and landscape attributes across experiments was evaluated (Fig. S12). The most influential variables show broadly consistent rankings across model configurations, suggesting that the main conclusions are not sensitive to multicollinearity effects. Nevertheless, correlated predictors may still affect the attribution of importance and comparisons between experiments with different predictor sets.

The presence of nested catchments may introduce dependencies that influence the cross-validation in the Random Forest experiments. To assess the impact of nested catchments on the results, the analysis was repeated with a subset of non-nested catchments (n=4137). The results show that both the clustering into hydrological response types and the Random Forest classification performance remain largely unchanged (Figs. S15, S16 and S17), with only a small decrease in testing accuracy (1 %–5 % across experiments, Fig. S17). These results indicate that, while nesting introduces some dependence, its influence on the overall clustering and classification results is sufficiently limited for the purpose of this analysis.

There are also methodological limitations related to the use of k-means clustering to group catchments into hydrological response type groups (HRTs) based on 40 hydrological signatures. k-means relies on rigid cluster boundaries and assigns equal weight to all signatures, which may not reflect the actual complexity of hydrological systems. This limitation is reflected in the weak elbow (Fig. ) and consistently low Silhouette Scores across the range of cluster numbers, indicating limited separability in the data. The PCA results also show that a relatively large number of principal components are required to explain most of the variance, suggesting that the signatures do not form clearly distinct groups in feature space. This may partly reflect the system complexity, where hydrological behaviour varies continuously and overlaps in multidimensional space, rather than forming discrete groups .

Several directions for future research can be derived from the above reflections. First, further work should aim to disentangle the relative roles of landscape by analysing subsets of catchments within more homogeneous climatic regions. Second, the present analysis is based on long-term aggregated statistics and does not explicitly account for temporal dynamics beyond inter-annual variances. Future studies should investigate changes in hydrological response over time, for example, by analysing different climatic periods or assessing the impacts of climate change. Third, alternative clustering approaches that allow for more flexible or overlapping group structures (e.g. fuzzy or hierarchical clustering) could be explored to better capture the continuous nature of hydrological variability. Finally, continued development of spatially explicit modelling and validation approaches may help to better account for spatial autocorrelation in large-sample hydrological studies.

Overall, this study demonstrates that even the use of an unprecedented range of data does not allow to systematically characterise and robustly identify more than 10 broad types of hydrological response classes across Europe, and to narrow down the within-class variability. The question of what constitutes the hydrological response and (dis)similarity therein thus remains largely unresolved. Similarly, the modest accuracies to predict membership to the 10 HRTs based on a wide spectrum of climate- and landscape attributes illustrates that the question what drives the hydrological response remains equally unresolved. In other words, a satisfactory and generalisable answer to the question “Why do we in a specific catchment observe a specific flow magnitude at a specific moment, and why is it different to the flow in other catchments at other moments?” is still lacking. Ultimately, the results of this study underline that available data are still insufficient to describe the complexity of the hydrological system, highlighting the need for considerably more data with higher spatial and temporal resolution and observational detail. This further suggests that Beven's “uniqueness of place” is a larger obstacle to generalisation in hydrology than previously thought and that this postulate is more relevant than ever.