Choosing the right tool for the job: comparing stream channel classification frameworks

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1 Department of Watershed Sciences, Utah State University, Logan, UT, USA
2 Department of Watershed Sciences and Ecology Center, Utah State University, Logan, UT, USA
3 National Oceanic and Atmospheric Administration, Seattle, WA, USA
4 Eco Logical Research, Providence, UT, USA
5 School of Environment, University of Auckland, Auckland, New Zealand
6 Department of Environmental Sciences, Macquarie University, Sydney, Australia
7 Pacific Spatial Solutions, Reston, VA, USA
8 Wildland Hydrology, Fort Collins, CO, USA
DOI
10.7287/peerj.preprints.885v1
Published
Accepted
Subject Areas
Aquaculture, Fisheries and Fish Science, Biogeography, Ecology, Ecosystem Science, Environmental Sciences
Keywords
Stream classification, Natural Channel Design, watershed monitoring, River Styles, John Day Watershed, Fluvial geomorphology, Watershed planning, Stream processes, Columbia River Basin
Copyright
© 2015 Kasprak et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Kasprak A, Hough-Snee N, Beechie T, Bouwes N, Brierley GJ, Camp R, Fryirs KA, Imaki H, Jensen ML, O'Brien G, Rosgen DL, Wheaton JM. 2015. Choosing the right tool for the job: comparing stream channel classification frameworks. PeerJ PrePrints 3:e885v1

Abstract

Stream classification provide a means to understand the diversity and distributions of channel and floodplains that occur across a landscape while drawing linkages between geomorphic form and process. Accordingly, stream classification is frequently employed as a watershed planning tool. In practice, a variety of frameworks are available to managers for classifying rivers, yet little information exists about how frameworks compare. Specifically, the data, time, and expertise required to implement a given classification, consistency of classification results, and the subsequent geomorphic interpretation between multiple frameworks have not been discussed following data-driven framework comparisons. Here we apply four classification methods within a watershed of high conservation interest in the U.S. Columbia River Basin. We compare the results of the River Styles Framework (RS), Natural Channel Classification (NCC), Rosgen Classification System (RCS), and channel form-based statistical classification. We find that the four frameworks generally classified reach types consistently. Where divergence in classified channel types occurs, differences can be attributed to the (a) spatial scale of input data used, (b) the requisite metrics and their order in completing a framework’s decision tree and/or (c) whether the framework attempted to classify current or historic channel form. We discuss the relative effort, disciplinary expertise required to complete each classification, noting that if a framework classifies current or pre-disturbance channel form, results can provide insight on watershed disturbance. By classifying a single watershed using multiple frameworks, we are able to identify trade-offs between frameworks, discussing how each framework mechanistically differs in grouping streams and their driving processes.

Author Comment

This article has been submitted to the American Geophysical Union's Water Resources Research as a research article.

Supplemental Information

Supplementary Materials for: Choosing the Right Tool for the Job: Comparing Stream Channel Classification Frameworks

DOI: 10.7287/peerj.preprints.885v1/supp-1