Conclusion

Here, we demonstrate the potential of t-SNE as an approach to compare similarity among hydrographs that is particularly useful for large data sets. By converting annual daily hydrographs to 2-D representation, their degree of similarity is indicated by distance on the map. t-SNE distance can be used as novel similarity metric, supplementing other comparative metrics such as Xcorr and Nash-Sutcliffe efficiency. In this application on reference watersheds in western North America, t-SNE outperformed PCA analysis for dimensional reduction, suggesting its potential in classifying and regionalizing streamflows. A deep learning encoder network was developed and trained to project new data onto existing maps to identify hydrological counterparts, overcoming previous challenges of the non-parametric t-SNE approach. While subjectivity in classification limits deep learning algorithms in some circumstances, we believe that t-SNE is underutilized in hydrological applications and that it has considerable potential for extremely large data sets.