Flow regimes are critical for determining physical and biological processes in rivers, and their classification and regionalization traditionally seeks to link patterns of flow to physiographic, climate and other information. There are many approaches to, and rationales for, catchment classification, with those focused on streamflow often seeking to relate a particular response characteristic to a physical property or climatic driver. Rationales include such topics as Prediction in Ungauged Basins (PUB), helping with experimental approaches, and providing guidance for model selection in poorly understood hydrological systems. While scale and time are important considerations for classification, the Annual Daily Hydrograph (ADH) is a first-order easily visualized integrated expression of catchment function, and over many years is a distinct hydrological signature. In this study, we use t-SNE, a state-of-the-art technique of dimensionality reduction, to classify 17110 ADHs for 304 reference catchments in mountainous Western North America. t-SNE is chosen over other conventional methods of dimensionality reduction (e.g. PCA) as it presents greater separability of ADHs, which are projected on a 2D map where the similarities are evaluated according to their map distance. We then utilize a Deep Learning encoder to upgrade the non-parametric t-SNE to a parametric approach, enhancing its capability to address ‘unseen’ samples. Results showed that t-SNE was an effective classifier as it successfully clustered ADHs of similar flow regimes on the 2D map. In addition, many compact clusters on the 2D map in the coastal Pacific Northwest suggest information redundancy in the local hydrometric network. The t-SNE map provides an intuitive way to visualize the similarity of high-dimensional data of ADHs, groups catchments with like characteristics, and avoids the reliance on subjective hydrometric indicators.
