Classification of watersheds in the conterminous United States using shape-based time-series clustering and Random Forests

Abstract

Watershed classification is considered necessary for various purposes, including improving the transferability of streamflow information and allowing the generalization of hydrologic theories. In this paper, we proposed a classification method using shape-based time-series clustering techniques to define hydrologically homogeneous classes for a set of 638 gauged watersheds in the conterminous United States. We defined 15 distinct flow-regime classes based on standardized weekly-step mean annual hydrographs of the watersheds analyzed. Most classes showed regionality, though to various degrees. Classes in the Atlantic Coast region showed strong geographical contiguity indicating that spatial proximity may be an appropriate indicator of flow-regime similarity; but on the other hand, nearby watersheds may exhibit different intra-annual variabilities and watersheds far apart from one another may exhibit a similar flow regime. Such cases show that spatial proximity may not be used as a universal indicator of flow-regime similarity. To expand the flow-regime classification to ungauged watersheds, where no streamflow data are available, we proposed a physical-characteristic classification method using the Random Forests algorithm based on a set of physical-climatic attributes. Based on the Boruta feature-selection algorithm, attributes related to snowiness, precipitation seasonality, and green vegetation coverage stood out as the most relevant controls on regime-class memberships. By analyzing the within-class variability of the watershed attributes, we found that watersheds of the same flow-regime class may be very different in terms of individual attribute values indicating that flow-regime dis/similarity may not be equated to dis/similarity in individual watershed attributes; instead, it is the interplay among attributes that determines the streamflow behavior. Overall, methods proposed for the classification of gauged and ungauged watersheds are believed to be of value in a range of applications including selecting gauged donor watersheds for estimating historical streamflow records at ungauged watersheds.