Updating the US hydrologic classification: an approach to clustering and stratifying ecohydrologic data Ryan A. McManamay,* Mark S. Bevelhimer and Shih-Chieh Kao Environmental Sciences Division, Oak Ridge National Lab, Oak Ridge, TN 37831, USA ABSTRACT Hydrologic classifications unveil the structure of relationships among groups of streams with differing streamflows 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 USA using three streamflow datasets, a reference dataset compiled under more strict traditional standards and two additional datasets compiled 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 gauges, we classified 1715 stream gauges into 12 classes across the USA. By including more streamflow gauges (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 to classify new objects and stratify data by common properties. We constructed separate random forest models to predict hydrologic class membership on the basis of 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. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. Supporting information may be found in the online version of this article. KEY WORDS environmental flow; streams; water policy; aquatic conservation; dams Received 5 November 2012; Revised 19 June 2013; Accepted 19 June 2013 INTRODUCTION Classifications depict our current state of knowledge about a subject area (Melles et al., 2012) and provide the structure and relationships within and among groups of objects (Sokal, 1974). These relationships provide a foundation for drawing inferences about the principles that govern relationships among different classes and how to interpret unclassified objects (Sokal, 1974). With regard to river systems, stream classifications and their use in management have a fairly long history (Horton, 1945; Strahler, 1957; Pennak, 1971; Rosgen, 1994). However, Melles et al. (2012) suggested that the advent of the ‘computer age’ dramatically enhanced opportunities for developing data-intensive classification systems across larger spatial scales and at higher resolutions (e.g. Bailey, 1983; Omernik, 1987; Omernik and Bailey, 1997; Snelder and Biggs, 2002; Snelder et al., 2007). Likewise, discussions regarding novel approaches, evaluation/testing, and appropriate scales for river classifications systems have continued to increase in recent years (Snelder and Biggs, 2002; Snelder et al., 2007; Leathwick et al., 2011; Melles et al., 2012; Olden et al., 2012). Hydrology varies extensively across continents and globally (Kennard et al., 2010b; Haines et al., 1988), yet streams display reoccurring patterns in the magnitude, duration, frequency, timing, and rate of change of flow events within regions (Acreman and Sinclair, 1986; Burn and Arnell, 1993; Poff et al., 1997). These repeatable patterns naturally predispose streams to hydrologic classification. One of the primary justifications for developing hydrologic classifications is to provide a means for developing environmental flow standards to support the preservation of freshwater biodiversity and ecosystem services (Arthington et al., 2006; Poff et al., 2010). For example, streams that behave similarly hydrologically should share similar patterns in ecology (Arthington et al., 2006) and respond similarly to a *Correspondence to: Ryan A. McManamay, Environmental Sciences Division, Oak Ridge National Lab, Oak Ridge, TN 37922, USA. E-mail: mcmanamayra@ornl.gov ECOHYDROLOGY Ecohydrol. (2013) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/eco.1410 Published 2013. This article is a U.S. Government work and is in the public domain in the USA.