Updating the US Hydrologic Classification: An Approach to Clustering and Stratifying Ecohydrologic Data

Hydrologic classifications unveil the structure of relationships among groups of streams with differing stream flow and provide a foundation for drawing inferences about the principles that govern those relationships. Hydrologic classes provide a template to generalize hydrologic responses to disturbance and stratify research and management needs applicable to ecohydrology. We used a mixed-modelling approach to create hydrologic classifications for the continental US using three streamflow datasets, a reference dataset compiled under more strict traditional standards and two additional datasets under more relaxed assumptions. A variety of models were applied to each dataset and Bayes criteria were used to identify optimal models and numbers of clusters. Using only reference-quality gages, we classified 1715 stream gages into 12 classes across the US. By including more streamflow gages (n=2402 and 2618) of lesser reference quality in subsequent classifications, we observed minimal increases in dimensionality (i.e. multivariate space) at the expense of increasing uncertainty and outliers. Part of the utility of classification systems rests in their ability classify new objects and stratify data by common properties. We constructed separate random forest models to predict hydrologic class membership based on hydrologic indices or landscape variables. In addition, we provide an approach to assessing potential outliers due to hydrologic alteration based on class assignment. Departures from class membership due to disturbance take into account multiple hydrologic indices simultaneously; thus, classes can be used to determine if disturbed streams are functioning within the natural range of hydrologic variability. This article is protected by copyright. All rights reserved.