#ArticleAlert: A Comparison of Stream Channel Classification Frameworks @PLOSONE
While Alan Kasprak and I have blogged about this project before as a preprint, our long awaited manuscript, "The Blurred Line between Form and Process: A Comparison of Stream Channel Classification Frameworks," has finally gone into press over at PLOS ONE.
@AlanKasprak & I #etal Compared #Stream #Channel #Classification Frameworks in #OR https://t.co/dA0XWN9Ca7 @PLOSONE pic.twitter.com/96plptQvbp— Nate Hough-Snee (@NHoughSnee) March 16, 2016
It's a long story, but we began this project as a side project from Joe Wheaton, Gary Brierley, and Kirstie Fryirs' River Styles course in the fall of 2013. That's right, two-plus years, eleven reviews, numerous revisions, and roughly a year on the PeerJ pre-print server, and our comparison of four major approaches to classifying streams has been published in PLOS ONE. We undertook this paper to compare the Rosgen Classification System, Natural Channel Classes, stage one of River Styles, and statistical classification using CHaMP monitoring data. We compared reaches that are actively monitored within the Middle Fork John Day Watershed for their status as salmonid habitat.
First, everyone likes a nice, elegant naming convention for their stream channels. I know I do. You probably do too. Dave Montgomery and John Buffington certainly did, and there have been all sorts of attempts to classify, simplify, or otherwise discretize how much water and sediment move through stream networks over a given timeframe, and the ensuing patterns in channel form that follow.
Streams are complex systems, conduits of water and sediment that have all sorts of hydraulic feedbacks from landscape and valley setting that change sediment size, quantity, and frequency of mobilization to instream wood and riparian vegetation. There are also a lot of streams in the world. To simplify these complex systems, many people have undertaken classification with one goal: to communicate what channels look like.
The unspoken part of this is that classifications, while rudimentary, may give really neat, concise ideas of the processes that underlie channel form. Rates and quantities of water and sediment are expensive to measure, and with millions of miles of stream and river in this world, really, really hard to measure from a logistical perspective. In the modern era, we often model these things across stream networks, to estimate how much water and sediment move through a system. However, in many day-to-day settings like local, state, federal, and tribal agencies that manage rivers, the mission is to make a management decision based limited resources to collect data and often limited information on streams. This is where classification, widely criticized in academic settings, is a powerful tool for communicating what sets of stream channel forms exist within a given watershed, and perhaps even why these forms exist.
Classification reduces complex systems to their component pieces. Will a stream channel classification win you a Nobel Prize? Hell, as we found out, it won't even get you published in WRR! But what classifications do well is tell you something about the form of a channel. Aaannnnd in many cases, this form is linked to processes that are tied to landscape setting, watershed land use, flow regimes, and floodplain management.
So, Alan, myself and a football team of coauthors from around the world decided that it was time to compare two historically popular classification frameworks, a recently created classification of Pacific Northwest streams' historic condition, and a statistical clustering based on monitoring data. If management decisions are going to be made on form, we should at least document how consistent and comparable each framework is, right?
|Distinct groups of stream reaches with perfect agreement (0.0) between classifications were more common than anticipated.|
Grab a look at the whole article at PLOS ONE or over on Researchgate.
Alan has written a post over on his website as well.
Utah Public Radio's Andrew Durso has kindly contributed a new piece over at UPR.org.
Utah State Today's Mary-Ann Muffoletto kindly covered this for the USU research newsletter.
Phys.org also picked up the beat