Abstract. The behavior of every catchment is unique. Still, we need ways to classify them as this helps to improve hydrological theories. Usually catchments are classified along either their attributes classes (e.g. climate, topography) or their discharge characteristics, which is often captured in hydrological signatures. However, recent studies have shown that many hydrological signatures have a low predictability in space and therefore only dubious hydrological meaning. Therefore, this study uses hydrological signatures with the highest predictability in space to cluster 643 catchments from the continental United States (CAMELS (Catchment Attributes and MEteorology for Large-Sample Studies) dataset) into ten groups. We then evaluated the connection between catchment attributes with the hydrological signatures with quadratic regression, both in the overall CAMELS dataset and the ten clusters. In the overall dataset, aridity had the strongest connection to the hydrological signatures, especially in the eastern United States. However, the clusters in the western United States showed a more heterogeneous pattern with a larger influence of forest fraction, the mean elevation or the snow fraction. From this, we conclude that catchment behavior can be mainly attributed to climate in regions with homogenous topography. In regions with a heterogeneous topography, there is no clear pattern of the catchment behavior, as catchments show high spatial variability in their attributes. The classification of the CAMELS dataset with the hydrological signatures allows testing hydrological models in contrasting environments.
Clustering CAMELS using hydrological signatures with high spatial predictability